User Centred Networked Health Care: Proceedings of MIE 2011

USER CENTRED NETWORKED HEALTH CARE Studies in Health Technology and Informatics This book series was started in 1990 ...

Author: A. Moen | S.K. Andersen | J. Aarts | P. Hurlen

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USER CENTRED NETWORKED HEALTH CARE

Studies in Health Technology and Informatics This book series was started in 1990 to promote research conducted under the auspices of the EC programmes’ Advanced Informatics in Medicine (AIM) and Biomedical and Health Research (BHR) bioengineering branch. A driving aspect of international health informatics is that telecommunication technology, rehabilitative technology, intelligent home technology and many other components are moving together and form one integrated world of information and communication media. The complete series has been accepted in Medline. Volumes from 2005 onwards are available online. Series Editors: Dr. O. Bodenreider, Dr. J.P. Christensen, Prof. G. de Moor, Prof. A. Famili, Dr. U. Fors, Prof. A. Hasman, Prof. E.J.S. Hovenga, Prof. L. Hunter, Dr. I. Iakovidis, Dr. Z. Kolitsi, Mr. O. Le Dour, Dr. A. Lymberis, Prof. J. Mantas, Prof. M.A. Musen, Prof. P.F. Niederer, Prof. A. Pedotti, Prof. O. Rienhoff, Prof. F.H. Roger France, Dr. N. Rossing, Prof. N. Saranummi, Dr. E.R. Siegel, Prof. T. Solomonides and Dr. P. Wilson

Volume 169 Recently published in this series Vol. 168. D.P. Hansen, A.J. Maeder and L.K. Schaper (Eds.), Health Informatics: The Transformative Power of Innovation – Selected Papers from the 19th Australian National Health Informatics Conference (HIC 2011) Vol. 167. B.K. Wiederhold, S. Bouchard and G. Riva (Eds.), Annual Review of Cybertherapy and Telemedicine 2011 – Advanced Technologies in Behavioral, Social and Neurosciences Vol. 166. V. Koutkias, J. Niès, S. Jensen, N. Maglaveras and R. Beuscart (Eds.), Patient Safety Informatics – Adverse Drug Events, Human Factors and IT Tools for Patient Medication Safety Vol. 165. L. Stoicu-Tivadar, B. Blobel, T. Marčun and A. Orel (Eds.), e-Health Across Borders Without Boundaries – E-salus trans confinia sine finibus – Proceedings of the EFMI Special Topic Conference, 14–15 April 2011, Laško, Slovenia Vol. 164. E.M. Borycki, J.A. Bartle-Clar, M.S. Househ, C.E. Kuziemsky and E.G. Schraa (Eds.), International Perspectives in Health Informatics Vol. 163. J.D. Westwood, S.W. Westwood, L. Felländer-Tsai, R.S. Haluck, H.M. Hoffman, R.A. Robb, S. Senger and K.G. Vosburgh (Eds.), Medicine Meets Virtual Reality 18 – NextMed Vol. 162. E. Wingender (Ed.), Biological Petri Nets Vol. 161. A.C. Smith and A.J. Maeder (Eds.), Global Telehealth – Selected Papers from Global Telehealth 2010 (GT2010) – 15th International Conference of the International Society for Telemedicine and eHealth and 1st National Conference of the Australasian Telehealth Society ISSN 0926-9630 (print) ISSN 1879-8365 (online)

Userr Centtred Network ked Health H C Care Proceed dings of MIE M 2011

Edited by y

A Anne Moeen Universsity of Oslo, Norway

Stig Kjær K And dersen Aalborg University, Denmark D

J Aartss Jos Erasm mus Universitty, Rotterdam m, The Netheerlands

and

Peetter Hurllen A Akershus University Hosp pital, Norwa ay

Amstterdam • Berrlin • Tokyo • Washington, DC

© 2011 European Federation for Medical Informatics. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 978-1-60750-805-2 (print) ISBN 978-1-60750-806-9 (online) Library of Congress Control Number: 2011934890 Publisher IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: [email protected] Distributor in the USA and Canada IOS Press, Inc. 4502 Rachael Manor Drive Fairfax, VA 22032 USA fax: +1 703 323 3668 e-mail: [email protected]

LEGAL NOTICE The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS

User Centred Networked Health Care A. Moen et al. (Eds.) IOS Press, 2011 © 2011 European Federation for Medical Informatics. All rights reserved.

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Preface This volume of Studies in Technology and Health Informatics contains the proceedings of MIE2011, the 23rd Conference of the European Federation of Medical Informatics. MIE2011 is hosted by Forum for Databehandling i Helsesektoren (FDH)1 in collaboration with the European Federation of Medical Informatics (EFMI). MIE2011 builds on the traditions of 22 preceding MIEs, starting in Cambridge (1978), and more recently in Geneva (2005), Maastricht (2006), Gothenburg (2008) and Sarajevo (2009). The special theme for MIE2011 is “User centered networked health care”, highlighting design for and experiences by health professionals and patients working and living in ICT enabled environments. This ties into the Scandinavian tradition of active user involvement in all aspects of design and implementation of complex workplace technology. MIE2011 will highlight the broad range of health informatics research and innovations at regional, national, and international levels. Health care is transforming into a networked activity where strict boundaries between health care facilities and the home are vanishing. Patients demand and require continuity of care. Health care providers become team players sharing decision-making responsibilities with colleagues and patients. This poses interesting challenges for health informatics, where critical appraisal of strategies for user involvement, deployment and sustainable use of information systems and new forms of patient-provider collaboration are needed. Ideas presented as “meaningful use” open additional perspectives and exciting opportunities to ensure that the users, understood broadly as health providers, patients, their families or consumers at large, would be offered solutions according to their needs. Such trends and developments are recognized across the contributions at MIE2011. Related to the specific theme, let us highlight specifically: •

•

•

•

1

User-centeredness is discussed in terms of usability and usefulness for the health provider, but also in terms of citizen orientation, empowerment and opportunities for patient-provider collaboration or self-care enabled by web applications. Networked health care may be realized by an integrated, operational EHR in collaborative environments where appropriate information is available at the point of need. Continued development of standards and terminologies is complemented with search strategies to make sense of free text entries in several languages. Coordination and collaboration in an institution or across levels of care is another line of development towards patient oriented health care where current developments discussions of interoperability, standards and search strategies in new ways. Such achievements also calls for discussions of privacy and security, as well as careful evaluation to systematize and share the experiences and gains. The changing environments of care reflect the changing division of labor and calls for more permeable boundaries between community health, primary care, and specialized care to support unfolding patient trajectories of care. In this

Norwegian Society for Medical Informatics

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picture, integration and additional perspectives in health informatics, like social care informatics, open opportunities to support mobility of patients and providers in new and innovative ways. MIE2011 received approximately 500 submissions for consideration. Selection has been a major challenge for the Scientific Program Committee. The majority of the submissions received three reviews, which were accompanied by suggestions and advice for possible improvements of the contribution. We are indebted to the over 220 colleagues who volunteered time and energy to serve as reviewers. To honor their contribution they are all listed in this book. The Conference Program and the Proceedings offers a selection of oral presentations submitted as full papers or short communications, as well as workshops, panels and posters. Many of these activities are sponsored by the EFMI Working Groups. It is encouraging for the development of health informatics that many of the submissions are by young researchers. We are pleased that MIE2011 is an arena where they choose to share their ideas and findings with peers. Furthermore, MIE2011 will host an application oriented track “partnerships in innovation” where EFMI institutional members and corporate affiliates participate actively. Most topics presented in this MIE2011 proceedings are interdisciplinary in nature and may interest a variety of stakeholders: nurses, physicians and allied health providers, health IT specialists, informaticians, engineers, academics and representatives from industry and consultancy. This European conference gathers participants from most parts of the world, reflected by the nationalities of the more than 1150 contributing authors representing Europe, Asia, Africa as well as South and North America. We hope you will enjoy the program of keynotes, presentations of accepted papers, workshops, panels and posters and participate in exchange of ideas and experiences. The proceedings is an integral part of MIE2011. The printed version of the MIE2011 proceedings includes the PubMed indexed, full papers accepted for presentation. The MIE2011 CD includes the printed proceedings (i.e., the full papers), short communications, posters, workshops, panels as well as demonstrations and the “partnership in innovation” synopses. To facilitate wider access to the material presented during MIE2011, the proceedings with the full papers will be available from IOS Press online book platform. The additional material on the CD will be handled as an EFMI publication. We are grateful to the colleagues who agreed to serve as members of the SPC core: Drs. Truls Østbye, Elske Ammenwerth, Ronald Cornet, Rolf Engebrecht, Sabine Koch, Silvana Quaglini, Pieter Toussaint, and Rune Fensli. Dr. Alexander Horsch chaired the subcommittee for workshop and panel selection. Dr. Sabine Koch chairs the award committee for the Peter L. Reichertz Prize that will be awarded to the best paper by a young scientist, Dr. Robert Cornet chairs the committee selecting recipient of the Rolf Hansen Prize for the best paper on clinical information system and Dr. Elske Ammenwerth chairs the committee selecting recipient of the prize for the best poster. Acknowledgement: The editorial team is grateful to Ms. Shazia Mushtaq for her careful and extensive work editing the submissions and preparing the proceedings.

Oslo, Aalborg, Rotterdam, June 2011 Anne Moen, Stig Kjær Andersen, Jos Aarts, Petter Hurlen (editors)

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Reviewers for MIE2011 The following persons contributed to the selection of papers: Aarts, Jos Abidi, Syed Sibte Raza Adams, Samantha Ahmadian, Leila Alonso, Albert Alpay, Laurence Alsafadi, Yasser Ammenwerth, Elske Andersen, Stig Kjær Anguita, Alberto Atalag, Koray Aydin, Serap Balkanyi, Laszlo Bamidis, Panagiotis Bastos, Laudelino Ben Said, Mohamed Bernad, Elena Bertelsen, Pernille Beuscart-Zephir, Marie-Catherine Bichel-Findlay, Jen Bodenreider, Olivier Borycki, Elizabeth Bouamrane, Matt-Mouley Boye, Niels Bratan, Tanja Breil, Bernhard Breu, Ruth Burgert, Oliver Bygholm, Ann Bø, Marte Rime Capozzi, Davide Ceusters, Werner Cheshire, Paul Chronaki, Catherine Chute, Christopher Cimino, James Cornet, Ronald Costa, Carlos Courteille, Olivier Creswick, Nerida Cummings, Elizabeth

Daskalakis, Stylianos Day, Karen de Bruijn, Berry de Clercq, Etienne de Keizer, Nicolette de la Calle, Guillermo de Lusignan, Simon Dexheimer, Judith Dias, Andre Dinesen, Birthe Eccher, Claudio Effken, Judith Eisenstein, Eric Elberg, Pia Faxvaag, Arild Fernandez-Breis, Jesualdo Tomas Ferrazzi, Fulvia Fioriglio, Gianluigi Focsa, Mircea Forkert, Nils Daniel Fox, Scott Fritz, Fleur Garde, Sebastian Georg, Gersende Georgiou, Andrew Giacomini, Mauro Gibaud, bernard Giuliani, Francesco Gong, Yang Grabar, Natalia Grandison, Tyrone György, Surján Hackl, Werner O Hägglund, Maria Hains, Isla Hanmer, Lyn Hartvigsen, Gunnar Hartz, Tobias Haskell, Robert Hasman, Arie Heimly, Vigdis

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Heinzl, Harald Héja, Gergely Horsch, Alexander Hsu, William Huang, Chun-His Hurlen, Petter Huser, Vojtech Häyrinen, Kristiina Hörbst, Alexander Ingenerf, Josef James, Andrew Jao, Chiang Joubert, Michel Juhola, Martti Kalra, Dipak Karanikolas, Nikitas Karopka, Thomas Kastania, Anastasia Katt, Basel Kaufman, David Kim, G Kindler, Hauke Koch, Sabine Kohl, Christian Kondoh, Hiroshi Korpela, Mikko Koutkias, Vassilis Kristiansen, Lill Kurzynski, Marek König, Sergio Layzell, Brian Leonardi, Giorgio Lopez, Diego Lovis, Christian Lungeanu, Diana Lærum, Hallvard Magrabi, Farah Mantas, John Mayorov, Oleg Mazzoleni, M.Cristina Menke, James Mensah, Edward Merabti, Tayeb Mihalas, George Moehr, Jochen Moen, Anne Mohd Yusof, Maryati Mohyuddin, Mohyuddin Montani, Stefania

Mulvenna, Maurice Murray, Peter Musgrove, Marcela Muttitt, Sarah Mykkänen, Juha Møller, Marcel Neumuth, Thomas Neveol, Aurelie Niggemann, Joerg Nilsson, Gunnar Nishibori, Masahiro O’Connor, Martin Oemig, Frank Oliveira, Jose Luis Orman, Häkan Otero, Paula Ozkaynak, Mustafa Panzarasa, Silvia Parry, Dave Pasche, Emilie Pearl, Adrian Peek, Niels Pelayo, Sylvia Peleg, Mor Petrovecki, Mladen Portet, Francois Power, Michael Prinz, Michael Protti, Denis Punys, Vytenis Quaglini, Silvana Quantin, Catherine Rasmussen, Anne Razavi, Amir-Reza Reichert, Assa Richards, Janise Rigby, Michael Roderer, Nancy Rodrigues, Jean Marie Rognoni, Carla Rosenbeck, Kirstine Rubrichi, Stefania Röhrig, Rainer Saboor, Samrend Sacchi, Lucia Saka, Osman Santos, Raquel Sara, Antony Saxena, Kshitij

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Schabetsberger, Thomas Schmidt, Rainer Scholl, Jeremiah Scott, Philip Sedlmayr, Martin Seroussi, Brigitte Shahsavar, Nosrat Shifrin, Michael Showell, Chris Sintchenko, Vitali Smalheiser, Neil Smith, Catherine Spyns, Peter Staemmler, Martin Stausberg, Jørgen Stenzhorn, Holger Stern, Milton Stoicu-Tivadar, Lacramioara Subramaniam, Kailash Supek, Selma Svatek, Vojtech Takeda, Hiroshi

Takian, Amir Teodoro, Douglas Thiel, Rainer Toussaint, Pieter Toyoda, Shuichi Tucker, Allan Tudor, Mrs Anca Tusch, Guenter Vagnoni, Matthew Valdez, Rupa van Engen-Verheul, Mariette Warren, Jim Weber, Patrick Westbrook, Johanna Wolf, Klaus-Hendrik Zai, Adrian Zhang, Songmao Zrimec, Tatjana Zvarova, Jana Østbye, Truls Øyri, Karl

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Contents Preface Anne Moen, Stig Kjær Andersen, Jos Aarts and Petter Hurlen Reviewers for MIE2011

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Citizen-Centred e-Health A Unified Approach for Social-Medical Discovery Haggai Roitman, Yossi Mesika, Yevgenia Tsimerman and Sivan Yogev Information Provision for Adolescents with Cancer Anna Shillabeer Electronic Symptom Reporting by Patients: A Literature Review Monika A. Johansen, Eva Henriksen, Gro Berntsen and Alexander Horsch Increasing Physical Activity Through Health-Enabling Technologies: The Project “Being Strong Without Violence” Corinna Scharnweber, Wolfram Ludwig, Michael Marschollek, Wolfgang Pein, Peter Schack, Reiner Schubert and Reinhold Haux Review of Mobile Terminal-Based Tools for Diabetes Diet Management Eunji Lee, Naoe Tatara, Eirik Årsand and Gunnar Hartvigsen Interaction Between COPD Patients and Healthcare Professionals in a Cross-Sector Tele-Rehabilitation Programme Birthe Dinesen, Stig Kjaer Andersen, Ole Hejlesen and Egon Toft Enhancing Self-Efficacy for Self-Management in People with Cystic Fibrosis Elizabeth Cummings, Jenny Hauser, Helen Cameron-Tucker, Petya Fitzpatrick, Melanie Jessup, E. Haydn Walters, David Reid and Paul Turner Evaluation of a Hyperlinked Consumer Health Dictionary for Reading EHR Notes Laura Slaughter, Karl Øyri and Erik Fosse A Pilot Assessment of Why Patients Choose Not to Participate in Self-Monitoring Oral Anticoagulant Therapy Morten Algy Bonderup, Stine Veje Hangaard, Pernille Heyckendorff Lilholt, Mette Dencker Johansen and Ole K. Hejlesen Mobile Peer Support in Diabetes Taridzo Chomutare, Eirik Årsand and Gunnar Hartvigsen Evolution of Health Web Certification Through the HONcode Experience Célia Boyer, Vincent Baujard and Antoine Geissbuhler Personal Health Data: Patient Consent in Information Age Dragana Martinovic, Victor Ralevich and Milan Petkovic Emotions and Personal Health Information Management: Some Implications for Design Enrico Maria Piras and Alberto Zanutto Socio-Technical Challenges in Designing a Web-Based Communication Platform Miria Grisot, Maja van der Velden and Polyxeni Vassilakopoulou

3 8 13

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Results of the 10th HON Survey on Health and Medical Internet Use Natalia Pletneva, Sarah Cruchet, Maria-Ana Simonet, Maki Kajiwara and Célia Boyer Social Connectedness Through ICT and the Influence on Wellbeing: The Case of the CareRabbit Sanne R. Blom, Magda M. Boere-Boonekamp and Robert A. Stegwee Technological Choices for Mobile Clinical Applications Frederic Ehrler, David Issom and Christian Lovis Modified Rand Method to Derive Quality Indicators: A Case Study in Cardiac Rehabilitation Mariëtte van Engen-Verheul, Hareld Kemps, Roderik Kraaijenhagen, Nicolette de Keizer and Niels Peek A Cloud-Based Semantic Wiki for User Training in Healthcare Process Management D. Papakonstantinou, M. Poulymenopoulou, F. Malamateniou and G. Vassilacopoulos Reference Architecture of Application Services for Personal Wellbeing Information Management Mika Tuomainen and Juha Mykkänen Development of a Web-Based Decision Support System for Insulin Self-Titration A.C.R. Simon, F. Holleman, J.B. Hoekstra, P.A. de Clercq, B.A. Lemkes, J. Hermanides and N. Peek TreC – A REST-Based Regional PHR Claudio Eccher, Enrico Maria Piras and Marco Stenico

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Decision Support, Knowledge Management, Guidelines Next Generation Neonatal Health Informatics with Artemis Carolyn McGregor, Christina Catley, Andrew James and James Padbury Limitations in Physicians’ Knowledge when Assessing Dementia Diseases – An Evaluation Study of a Decision-Support System Helena Lindgren A Generic System for Critiquing Physicians’ Prescriptions: Usability, Satisfaction and Lessons Learnt Jean-Baptiste Lamy, Vahid Ebrahiminia, Brigitte Seroussi, Jacques Bouaud, Christian Simon, Madeleine Favre, Hector Falcoff and Alain Venot An OCL-Compliant GELLO Engine Jing Mei, Haifeng Liu, Guotong Xie, Shengping Liu and Baoyao Zhou Improvement of Inter-Services Communication Through a CDSS Dedicated to Myocardial Perfusion Scintigraphy Julie Nies, Gersende Georg, Marc Faraggi, Isabelle Colombet and Pierre Durieux Prognostic Data-Driven Clinical Decision Support – Formulation and Implications Ruty Rinott, Boaz Carmeli, Carmel Kent, Daphna Landau, Yonatan Maman, Yoav Rubin and Noam Slonim

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Knowledge-Based Surveillance for Preventing Postoperative Surgical Site Infection Arash Shaban-Nejad, Gregory W. Rose, Anya Okhmatovskaia, Alexandre Riazanov, Christopher J.O. Baker, Robyn Tamblyn, Alan J. Forster and David L. Buckeridge Factors Known to Influence Acceptance of Clinical Decision Support Systems E. Kilsdonk, L.W.P. Peute, S.L. Knijnenburg and M.W.M. Jaspers Cross-Frontier Information Provision in the ALIAS European Project Frédérique Laforest, Atisha Garin-Michaud, Thierry Durand, Emmanuel Eyraud and Edouard Barthuet Event-Driven Architecture for Health Event Detection from Multiple Sources Kerstin Denecke, Göran Kirchner, Peter Dolog, Pavel Smrz, Jens Linge, Gerhard Backfried and Johannes Dreesman Towards an Interoperable Information Infrastructure Providing Decision Support for Genomic Medicine Matthias Samwald, Holger Stenzhorn, Michel Dumontier, M. Scott Marshall, Joanne Luciano and Klaus-Peter Adlassnig Identifying Patients for Clinical Trials Using Fuzzy Ternary Logic Expressions on HL7 Messages Raphael W. Majeed and Rainer Röhrig Towards a Metadata Registry for Evaluating Augmented Medical Interventions Anne-Sophie Silvent, Alexandre Moreau-Gaudry and Philippe Cinquin A Comparison of Internal Versus External Risk-Adjustment for Monitoring Clinical Outcomes Antonie Koetsier, Nicolette de Keizer and Niels Peek Interoperability Driven Integration of Biomedical Data Sources Douglas Teodoro, Rémy Choquet, Daniel Schober, Giovanni Mels, Emilie Pasche, Patrick Ruch and Christian Lovis Creating Knowledge Archive in the Internet Medical Consultant for Decision Support at the Point of Care Draško Nakić and Suzana Loškovska Architecture of a Decision Support System to Improve Clinicians’ Interpretation of Abnormal Liver Function Tests Raphaël Chevrier, David Jaques and Christian Lovis

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Education – Professional Development Push and Pull Models to Manage Patient Consent and Licensing of Multimedia Resources in Digital Repositories for Case-Based Reasoning Andrzej A. Kononowicz, Nabil Zary, David Davies, Jörn Heid, Luke Woodham and Inga Hege Next Steps in Evaluation and Evidence – from Generic to Context-Related Michael Rigby, Jytte Brender, Marie-Catherine Beuscart-Zephir, Hannele Hyppönen, Pirkko Nykänen, Jan Talmon, Nicolette de Keizer and Elske Ammenwerth Virtual Ward Round Michael Storck and Frank Ückert

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Professional Development of Health Informatics in Northern Ireland Paul McCullagh, Gerry McAllister, Paul Hanna, Dewar Finlay and Paul Comac How Important is Theory in Health Informatics? A Survey of UK Academics Philip Scott, James Briggs, Jeremy Wyatt and Andrew Georgiou Better Quality in Healthcare Through Gamified Simulation Based Skill Training Application Weronika Tancredi, Mikael Wintell and Lars Lindsköld Implementation of a Web-Based Interactive Virtual Patient Case Stimulation as a Training and Assessment Tool for Medical Students A. Oliven, R. Nave, D. Gilad and A. Barch Online CME Usage Patterns M. Cristina Mazzoleni, Carla Rognoni, Enrico Finozzi, Ines Giorgi, Marco Pagani and Marcello Imbriani How Do Nursing Students Perceive the Notion of EHR? An Empirical Investigation Parisis Gallos, Stelios Daskalakis, Maria Katharaki, Joseph Liaskos and John Mantas Recording and Podcasting of Lectures for Students of Medical School Pierre Brunet, Marc Cuggia and Pierre Le Beux

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Electronic Health Record, Workflow, Intra- and Interorganizational Collaboration Developing an Electronic Health Record for Intractable Diseases in Japan Eizen Kimura, Shinji Kobayashi, Yasuhiro Kanatani, Ken Ishihara, Tsuneyo Mimori, Ryousuke Takahashi, Tsutomu Chiba and Hiroyuki Yoshihara Three Key Concerns for a Successful EPR Deployment and Usage Rebecka Janols, Bengt Göransson and Bengt Sandblad Implementation of an Open Source Provider and Organization Registry Service Markus Birkle, Benjamin Schneider, Tobias Beck, Thomas Deuster, Markus Fischer, Florian Flatow, Robert Heinrich, Christian Kapp, Jasmin Riemer, Michael Simon and Björn Bergh Implementation and Experimentation of TEDIS: An Information System Dedicated to Patients with Pervasive Developmental Disorders Mohamed Ben Said, Laurence Robel, Erwan Vion, Antoine El Ghazali, Bernard Golse, Jean Philippe Jais and Paul Landais Traceability of Patient Records Usage: Barriers and Opportunities for Improving User Interface Design and Data Management Ricardo Cruz-Correia, Luís Lapão and Pedro Pereira Rodrigues Important Ingredients for Health Adaptive Information Systems Yalini Senathirajah and Suzanne Bakken Everyday Ethical Dilemmas Arising With Electronic Record Use in Primary Care Ellen Balka and Marianne Tolar The Shift in Workarounds Upon Implementation of Computerized Physician Order Entry Heleen van der Sijs, Irene Rootjes and Jos Aarts

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Task Analysis and Interoperable Application Services for Service Event Management Juha Mykkänen, Hannu Virkanen, Pirkko Kortekangas, Saara Savolainen and Timo Itälä Organs Transplantation – How to Improve the Process? Viriato Ferraz, Gerardo Oliveira, Pedro Vieira-Marques and Ricardo Cruz-Correia A Reference Architecture for Integrated EHR in Colombia Edgar de la Cruz, Diego M. Lopez, Gustavo Uribe, Carolina Gonzalez and Bernd Blobel Integration Services to Enable Regional Shared Electronic Health Records Ilídio C. Oliveira and João P.S. Cunha Towards Smart Environments Using Smart Objects Martin Sedlmayr, Hans-Ulrich Prokosch and Ulli Münch Interoperability in Hospital Information Systems: A Return-On-Investment Study Comparing CPOE with and Without Laboratory Integration Rodolphe Meyer and Christian Lovis Building the Technical Infrastructure to Support a Study on Drug Safety in a General Hospital Melanie Kirchner, Thomas Bürkle, Andrius Patapovas, Anja Mathews, Reinhold Sojer, Fabian Müller, Harald Dormann, Renke Maas and Hans-Ulrich Prokosch Implementing Change in a Diverse and Politicized Landscape Espen Skorve Characteristics of German Hospitals Adopting Health IT Systems – Results from an Empirical Study Jan-David Liebe, Nicole Egbert, Andreas Frey and Ursula Hübner Nursing Information System: A Relevant Substitute of the Paper Nursing Record Margreet B. Michel-Verkerke GP Connector: A Tool to Enable Access for General Practitioners to a Standards-Based Personal and Electronic Health Record in the Rhine-Neckar Region Oliver Heinze, Holger Schmuhl and Björn Bergh Proposal of an End-To-End Emergency Medical System Samir El-Masri and Basema Saddik The General Practitioner in the Giant’s Web Vigdis Heimly When Information Sharing is not Enough Berit Brattheim, Arild Faxvaag and Pieter Toussaint Information and Communication Needs of Healthcare Workers in the Perioperative Domain Børge Lillebo, Andreas Seim and Arild Faxvaag Clinical Situations and Information Needs of Physicians During Treatment of Diabetes Mellitus Patients: A Triangulation Study Gudrun Hübner-Bloder, Georg Duftschmid, Michael Kohler, Christoph Rinner, Samrend Saboor and Elske Ammenwerth

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A Constructivist Approach? Using Formative Evaluation to Inform the Electronic Prescription Service Implementation in Primary Care, England Jasmine Harvey, Anthony Avery, Justin Waring, Ralph Hibberd and Nicholas Barber Can Cloud Computing Benefit Health Services? – A SWOT Analysis Mu-Hsing Kuo, Andre Kushniruk and Elizabeth Borycki

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Evaluation Medical Providers’ Dental Information Needs: A Baseline Survey Amit Acharya, Andrea Mahnke, Po-Huang Chyou, Carla Rottscheit and Justin B. Starren What Makes an Information System More Preferable for Clinicians? A Qualitative Comparison of Two Systems Habibollah Pirnejad, Zahra Niazkhani, Jos Aarts and Roland Bal Does PACS Facilitate Work Practice Innovation in the Intensive Care Unit? Isla M. Hains, Nerida Creswick and Johanna I. Westbrook Innovation in Intensive Care Nursing Work Practices with PACS Nerida Creswick, Isla M. Hains and Johanna I. Westbrook Evaluation of Telephone Triage and Advice Services: A Systematic Review on Methods, Metrics and Results Sara Carrasqueiro, Mónica Oliveira and Pedro Encarnação Human Factors Based Recommendations for the Design of Medication Related Clinical Decision Support Systems (CDSS) Sylvia Pelayo, Romaric Marcilly, Stéphanie Bernonville, Nicolas Leroy and Marie-Catherine Beuscart-Zephir Making a Web Based Ulcer Record Work by Aligning Architecture, Legislation and Users – A Formative Evaluation Study Anne G. Ekeland, Eva Skipenes, Beate Nyheim and Ellen K. Christiansen Assessing the Role of a Site Visit in Adopting Activity Driven Methods Irmeli Luukkonen, Kaija Saranto and Mikko Korpela A Multi-Method Study of Factors Associated with Hospital Information System Success in South Africa Lyn A. Hanmer, Sedick Isaacs and J. Dewald Roode Assessing Biocomputational Modelling in Transforming Clinical Guidelines for Osteoporosis Management Rainer Thiel, Marco Viceconti and Karl Stroetmann Technical Data Evaluation of a Palliative Care Web-Based Documentation System Tobias Hartz, René Brüntrup and Frank Ückert

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Imaging and Biosignals Extracting Gait Parameters from Raw Electronic Walkway Data André Dias, Lukas Gorzelniak, Angela Döring, Gunnar Hartvigsen and Alexander Horsch Safe Storage and Multi-Modal Search for Medical Images Jukka Kommeri, Marko Niinimäki and Henning Müller

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Respiration Tracking Using the Wii Remote Game-Controller J. Guirao Aguilar, J.G. Bellika, L. Fernandez Luque and V. Traver Salcedo A Nomenclature for the Analysis of Continuous Sensor and Other Data in the Context of Health-Enabling Technologies Matthias Gietzelt, Klaus-Hendrik Wolf and Reinhold Haux Image-Based Classification of Parkinsonian Syndromes Using T2’-Atlases Nils Daniel Forkert, Alexander Schmidt-Richberg, Brigitte Holst, Alexander Münchau, Heinz Handels and Kai Boelmans Cell Edge Detection in JPEG2000 Wavelet Domain – Analysis on Sigmoid Function Edge Model Vytenis Punys and Ramunas Maknickas

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Information Modeling, Storage and Retrieval Using Multimodal Mining to Drive Clinical Guidelines Development Emilie Pasche, Julien Gobeill, Douglas Teodoro, Dina Vishnyakova, Arnaud Gaudinat, Patrick Ruch and Christian Lovis Defining and Reconstructing Clinical Processes Based on IHE and BPMN 2.0 Melanie Strasser, Franz Pfeifer, Emmanuel Helm, Andreas Schuler and Josef Altmann Facilitating Access to Laboratory Guidelines by Modeling Their Contents and Designing a Computerized User Interface Mobin Yasini, Catherine Duclos, Jean-Baptiste Lamy and Alain Venot Evaluation of Multi-Terminology Super-Concepts for Information Retrieval Nicolas Griffon, Lina F. Soualmia, Aurélie Névéol, Philippe Massari, Benoit Thirion, Badisse Dahamna and Stefan J. Darmoni Framework Model and Principles for Trusted Information Sharing in Pervasive Health Pekka Ruotsalainen, Bernd Blobel, Pirkko Nykänen, Antto Seppälä and Hannu Sorvari Populating the i2b2 Database with Heterogeneous EMR Data: A Semantic Network Approach Sebastian Mate, Thomas Bürkle, Felix Köpcke, Bernhard Breil, Bernd Wullich, Martin Dugas, Hans-Ulrich Prokosch and Thomas Ganslandt A Novel Way of Standardized and Automized Retrieval of Timing Information Along Clinical Pathways Eva Gattnar, Okan Ekinci, Vesselin Detschew Computing the Compliance of Physician Drug Orders with Guidelines Using an OWL2 Reasoner and Standard Drug Resources Joseph Noussa Yao, Brigitte Séroussi and Jacques Bouaud Automatic Definition of the Oncologic EHR Data Elements from NCIT in OWL Marc Cuggia, Annabel Bourdé, Bruno Turlin, Sebastien Vincendeau, Valerie Bertaud, Catherine Bohec and Régis Duvauferrier Developing a Model for the Adequate Description of Electronic Communication in Hospitals Samrend Saboor and Elske Ammenwerth

477

482

487 492

497

502

507

512 517

522

xviii

Contextualization in Automatic Extraction of Drugs from Hospital Patient Records Svetla Boytcheva, Dimitar Tcharaktchiev and Galia Angelova Revisiting the Area Under the ROC Berry de Bruijn Service Delivery for e-Health Applications Martin Staemmler A KPI Framework for Process-Based Benchmarking of Hospital Information Systems Franziska Jahn and Alfred Winter

527 532 537

542

Natural Language Processing, Data Mining Medical Knowledge Evolution Query Constraining Aspects Ann-Marie Eklund Optimal Asymmetrical SVM Using Pattern Search. A Health Care Application Gilles Cohen and Rodolphe Meyer Factuality Levels of Diagnoses in Swedish Clinical Text Sumithra Velupillai, Hercules Dalianis and Maria Kvist Network Analysis of Possible Anaphylaxis Cases Reported to the US Vaccine Adverse Event Reporting System after H1N1 Influenza Vaccine Taxiarchis Botsis and Robert Ball Using Pharmacogenetics Knowledge to Increase Accuracy of Alerts for Adverse Drug Events Yossi Mesika, Byung Chul Lee, Yevgenia Tsimerman, Haggai Roitman and Heon Kyu Park Schizophrenia Prediction with the Adaboost Algorithm Jan Hrdlicka and Jiri Klema Applying One-vs-One and One-vs-All Classifiers in k-Nearest Neighbour Method and Support Vector Machines to an Otoneurological Multi-Class Problem Kirsi Varpa, Henry Joutsijoki, Kati Iltanen and Martti Juhola Roogle: An Information Retrieval Engine for Clinical Data Warehouse Marc Cuggia, Nicolas Garcelon, Boris Campillo-Gimenez, Thomas Bernicot, Jean-François Laurent, Etienne Garin, André Happe and Régis Duvauferrier Truecasing Clinical Narratives Markus Kreuzthaler and Stefan Schulz Checking Coding Completeness by Mining Discharge Summaries Stefan Schulz, Thorsten Seddig, Susanne Hanser, Albrecht Zaiβ and Philipp Daumke

549 554 559

564

569

574

579 584

589 594

Privacy and Security Healthcare Professionals’ Experiences with EHR-System Access Control Mechanisms Arild Faxvaag, Trond S. Johansen, Vigdis Heimly, Line Melby and Anders Grimsmo

601

xix

Personal Health Information on Display: Balancing Needs, Usability and Legislative Requirements Erlend Andreas Gjære, Inger Anne Tøndel, Maria B. Line, Herbjørn Andresen and Pieter Toussaint Watermarking – A New Way to Bring Evidence in Case of Telemedicine Litigation Gouenou Coatrieux, Catherine Quantin, François-André Allaert, Bertrand Auverlot and Christian Roux Sharing Sensitive Personal Health Information Through Facebook: The Unintended Consequences Mowafa Househ End-to-End Security for Personal Telehealth Paul Koster, Muhammad Asim and Milan Petkovic

606

611

616 621

Public Health, Catastrophes, Outbreaks The Epidemiologic Surveillance of Dengue-Fever in French Guiana: When Achievements Trigger Higher Goals Claude Flamand, Philippe Quenel, Vanessa Ardillon, Luisiane Carvalho, Sandra Bringay and Maguelonne Teisseire Prescribing History to Identify Candidates for Chronic Condition Medication Adherence Promotion Jim Warren, Debra Warren, Hong Yul Yang, Thusitha Mabotuwana, John Kennelly, Tim Kenealy and Jeff Harrison Challenges for Signal Generation from Medical Social Media Data Johannes Dreesman and Kerstin Denecke Providing Trust and Interoperability to Federate Distributed Biobanks Martin Lablans, Sebastian Bartholomäus and Frank Ückert Web 2.0 in Healthcare: State-of-the-Art in the German Health Insurance Landscape Mirko Kuehne, Nadine Blinn, Christoph Rosenkranz and Markus Nuettgens Improving the Transparency of Health Information Found on the Internet Through the Honcode: A Comparative Study Sabine Laversin, Vincent Baujard, Arnaud Gaudinat, Maria-Ana Simonet and Célia Boyer

629

634

639 644

649

654

Telemedicine and Mobile Health Data Privacy Preservation in Telemedicine: The PAIRSE Project Ebrahim Nageba, Bruno Defude, Franck Morvan, Chirine Ghedira and Jocelyne Fayn Relevance and Usability of a Computerized Patient Simulator for Continuous Medical Education of Isolated Care Professionals in Sub-Saharan Africa Georges Bediang, Cheick Oumar Bagayoko, Marc-André Raetzo and Antoine Geissbuhler Applications of Medical Intelligence in Remote Monitoring István Vassányi, György Kozmann, András Bánhalmi, Balázs Végső, István Kósa, Tibor Dulai, Zsolt Tarjányi, Gergely Tuboly, Péter Cserti and Balázs Pintér

661

666

671

xx

Virtual TeleRehab: A Case Study Lena Pareto, Britt Johansson, Sally Zeller, Katharina S. Sunnerhagen, Martin Rydmark and Jurgen Broeren Patient Empowerment by Increasing Information Accessibility in a Telecare System Vasile Topac and Vasile Stoicu-Tivadar

676

681

Terminology, Ontologies and Standardization A Standard Based Approach for Biomedical Knowledge Representation Ariel Farkash, Hani Neuvirth, Yaara Goldschmidt, Costanza Conti, Federica Rizzi, Stefano Bianchi, Erika Salvi, Daniele Cusi and Amnon Shabo Ontology-Based Framework for Electronic Health Records Interoperability Carolina González, Bernd G.M.E. Blobel and Diego M. López Ontology-Based Knowledge Management for Personalized Adverse Drug Events Detection Feng Cao, Xingzhi Sun, Xiaoyuan Wang, Bo Li, Jing Li and Yue Pan A Formal Analysis of HL7 Version 2.x Frank Oemig and Bernd Blobel Simplifying HL7 Version 3 Messages Robert Worden and Philip Scott Creating an Ontology Driven Rules Base for an Expert System for Medical Diagnosis Valérie Bertaud Gounot, Valéry Donfack, Jérémy Lasbleiz, Annabel Bourde and Régis Duvauferrier A Methodology and Supply Chain Management Inspired Reference Ontology for Modeling Healthcare Teams Craig E. Kuziemsky and Sara Yazdi Supporting openEHR Java Desktop Application Developers Hajar Kashfi and Olof Torgersson Large Scale Healthcare Data Integration and Analysis Using the Semantic Web John Timm, Sondra Renly and Ariel Farkash ACGT: Advancing Clinico-Genomic Trials on Cancer – Four Years of Experience Luis Martin, Alberto Anguita, Norbert Graf, Manolis Tsiknakis, Mathias Brochhausen, Stefan Rüping, Anca Bucur, Stelios Sfakianakis, Thierry Sengstag, Francesca Buffa and Holger Stenzhorn Architectural Approach for Providing Relations in Biomedical Terminologies and Ontologies Mathias Brochhausen and Bernd Blobel Integration of Classifications and Terminologies in Metadata Registries Based on ISO/IEC 11179 Sylvie Mn Ngouongo and Jürgen Stausberg Development of a New International Classification of Health Interventions Based on an Ontology Framework Béatrice Trombert Paviot, Richard Madden, Lori Moskal, Albrecht Zaiss, Cédric Bousquet, Anand Kumar, Pierre Lewalle and Jean Marie Rodrigues

689

694

699 704 709

714

719 724 729

734

739

744

749

xxi

The Revision of the Korean Classifications of Health Interventions Based on the Proposed ICHI Semantic Model and Lessons Learned Boyoung Jung, Chaeyoung Jung, Jean Marie Rodrigues, Cédric Bousquet, Anand Kumar, Pierre Lewalle, Béatrice Trombert Paviot, Hoonshik Yang and Sukil Kim Web-Based Collaboration for Terminology Application: ICNP C-Space Claudia C. Bartz and Derek Hoy Mapping Medical Records of Gastrectomy Patients to SNOMED CT Eun-Young So and Hyeoun-Ae Park Terminology for the Description of the Diagnostic Studies in the Field of EBM Natalia Grabar, Ludovic Trinquart and Isabelle Colombet Representing Knowledge, Data and Concepts for EHRS Using DCM William Goossen Ontology-Based Automatic Generation of Computerized Cognitive Exercises Giorgio Leonardi, Silvia Panzarasa and Silvana Quaglini Creating a Magnetic Resonance Imaging Ontology Jérémy Lasbleiz, Hervé Saint-Jalmes, Régis Duvauferrier and Anita Burgun Validation of the openEHR Archetype Library by Using OWL Reasoning Marcos Menárguez-Tortosa and Jesualdo Tomás Fernández-Breis Grouping Pharmacovigilance Terms with Semantic Distance Marie Dupuch, Magnus Lerch, Anne Jamet, Marie-Christine Jaulent, Reinhard Fescharek and Natalia Grabar The Archetype-Enabled EHR System ZK-ARCHE – Integrating the ISO/EN 13606 Standard and IHE XDS Profile Michael Kohler, Christoph Rinner, Gudrun Hübner-Bloder, Samrend Saboor, Elske Ammenwerth and Georg Duftschmid Using a Logical Information Model-Driven Design Process in Healthcare Yu Chye Cheong, Linda Bird, Nwe Ni Tun and Colleen Brooks SNOMED CT Implementation: Implications of Choosing Clinical Findings or Observable Entities Anne Randorff Rasmussen and Kirstine Rosenbeck What is the Coverage of SNOMED CT® on Scientific Medical Corpora? Dimitrios Kokkinakis Assisting the Translation of the CORE Subset of SNOMED CT into French Hocine Abdoune, Tayeb Merabti, Stéfan J. Darmoni and Michel Joubert Recording Associated Disorders Using SNOMED CT Ronald Cornet and Nicolette F. de Keizer SNOMED CT’s RF2: Is the Future Bright? Werner Ceusters Serious Adverse Event Reporting in a Medical Device Information System Fabrizio Pecoraro and Daniela Luzi Metadata – An International Standard for Clinical Knowledge Resources Gunnar O. Klein Comparing Existing National and International Classification Systems of Surgical Procedures with the CEN/ISO 1828 Ontology Framework Standard Jean M. Rodrigues, Ann Casey, Cédric Bousquet, Anand Kumar, Pierre Lewalle and Béatrice Trombert Paviot

754

759 764 769 774 779 784

789 794

799

804

809 814 819 824 829 834 839

844

xxii

Model Driven Development of Clinical Information Sytems Using openEHR Koray Atalag, Hong Yul Yang, Ewan Tempero and Jim Warren

849

Translational Research A Metadata-Based Patient Register for Cooperative Clinical Research: A Case Study in Acute Myeloid Leukemia Anja S. Fischer and Ulrich Mansmann De-Identifying an EHR Database – Anonymity, Correctness and Readability of the Medical Record Kostas Pantazos, Soren Lauesen and Soren Lippert Service Oriented Data Integration for a Biomedical Research Network Matthias Ganzinger, Tino Noack, Sven Diederichs, Thomas Longerich and Petra Knaup Single Source Information Systems Can Improve Data Completeness in Clinical Studies: An Example from Nuclear Medicine Susanne Herzberg and Martin Dugas Reporting Qualitative Research in Health Informatics: REQ–HI Recommendations Zahra Niazkhani, Habibollah Pirnejad, Jos Aarts, Samantha Adams and Roland Bal Cell Seeding of Tissue Engineering Scaffolds Studied by Monte Carlo Simulations Andreea Robu, Adrian Neagu and Lacramioara Stoicu-Tivadar The ONCO-I2b2 Project: Integrating Biobank Information and Clinical Data to Support Translational Research in Oncology Daniele Segagni, Valentina Tibollo, Arianna Dagliati, Leonardo Perinati, Alberto Zambelli, Silvia Priori and Riccardo Bellazzi IT Infrastructure Components to Support Clinical Care and Translational Research Projects in a Comprehensive Cancer Center Hans-Ulrich Prokosch, Markus Ries, Alexander Beyer, Martin Schwenk, Christof Seggewies, Felix Köpcke, Sebastian Mate, Marcus Martin, Barbara Bärthlein, Matthias W. Beckmann, Michael Stürzle, Roland Croner, Bernd Wullich, Thomas Ganslandt and Thomas Bürkle Using a Robotic Arm to Assess the Variability of Motion Sensors Lukas Gorzelniak, André Dias, Hubert Soyer, Alois Knoll and Alexander Horsch The Single Source Architecture x4T to Connect Medical Documentation and Clinical Research Philipp Dziuballe, Christian Forster, Bernhard Breil, Volker Thiemann, Fleur Fritz, Jens Lechtenbörger, Gottfried Vossen and Martin Dugas Information Technology Solutions to Support Translational Research on Inherited Cardiomyopathies Riccardo Bellazzi, Cristiana Larizza,, Matteo Gabetta, Giuseppe Milani, Mauro Bucalo, Francesca Mulas, Angelo Nuzzo, Valentina Favalli and Eloisa Arbustini

857

862 867

872

877

882

887

892

897

902

907

xxiii

Usability, HCI, Cognitive Issues Emerging Approaches to Usability Evaluation of Health Information Systems: Towards In-Situ Analysis of Complex Healthcare Systems and Environments Andre W. Kushniruk, Elizabeth M. Borycki, Shigeki Kuwata and Joseph Kannry Contextualization of Automatic Alerts During Electronic Prescription: Researchers’ and Users’ Opinions on Useful Context Factors Elske Ammenwerth, Werner O. Hackl, Daniel Riedmann and Martin Jung Reducing Clinicians’ Cognitive Workload by System Redesign; A Pre-Post Think Aloud Usability Study L.W.P. Peute, N.F. de Keizer, E.P.A. van der Zwan and M.W.M. Jaspers Impact of Alert Specifications on Clinicians’ Adherence M.M. Langemeijer, L.W. Peute and M.W.M. Jaspers Medication Decision-Making on Hospital Ward-Rounds Melissa Baysari, Johanna Westbrook and Richard Day A Qualitative Analysis of Prescription Activity and Alert Usage in a Computerized Physician Order Entry System Rolf Wipfli, Mireille Betrancourt, Alberto Guardia and Christian Lovis Combining Usability Testing with Eye-Tracking Technology: Evaluation of a Visualization Support for Antibiotic Use in Intensive Care Aboozar Eghdam, Johanna Forsman, Magnus Falkenhav, Mats Lind and Sabine Koch Design of a Mobile, Safety-Critical In-Patient Glucose Management System Bernhard Höll, Stephan Spat, Johannes Plank, Lukas Schaupp, Katharina Neubauer, Peter Beck, Franco Chiarugi, Vasilis Kontogiannis, Thomas R. Pieber and Andreas Holzinger Facilitating the Iterative Design of Informatics Tools to Advance the Science of Autism David R. Kaufman, Patrick Cronin, Leon Rozenblit, David Voccola, Amanda Horton, Alisabeth Shine and Stephen B. Johnson Evaluation of Computer Usage in Healthcare Among Private Practitioners of NCT Delhi P. Ganeshkumar, Arun Kumar Sharma and O.P. Rajoura Contextual Inquiry Method for User-Centred Clinical IT System Design Johanna Viitanen A Method to Measure the Reduction of CO2 Emissions in E-Health Applications Paola Di Giacomo and Peter Håkansson

915

920

925 930 935

940

945

950

955

960 965

970

EFMI Invited Session: Health Informatics Research Management Medical Informatic Research Management in Academia – The Danish Setting Stig Kjær Andersen Research Management in Healthcare Informatics – Experiences from Norway Arild Faxvaag, Pieter Toussaint and Trond S. Johansen Research Management: The case of RN4CAST Dimitrios Zikos and John Mantas

977 980 985

xxiv

eMeasures: A Standard Format for Health Quality Measures Catherine Chronaki, Charles Jaffe and Bob Dolin Clinical Information Systems: Cornerstone for an Efficient Hospital Management Christian Lovis Patient Centered Integrated Clinical Resource Management Jacob Hofdijk Subject Index Author Index

989

992 996 1001 1009

Citizen-Centred e-Health


User Centred Networked Health Care A. Moen et al. (Eds.) IOS Press, 2011 © 2011 European Federation for Medical Informatics. All rights reserved. doi:10.3233/978-1-60750-806-9-3

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A Unified Approach for Social-Medical Discovery Haggai ROITMAN1, Yossi MESIKA, Yevgenia TSIMERMAN, Sivan YOGEV IBM Research, Haifa 31095, Israel

Abstract. In this paper we describe a novel social-medical discovery solution, based on an idea of social and medical data unification. Built on foundations of exploratory search technologies, the proposed discovery solution is better tailored for the social-medical discovery task. We then describe its implementation within the IBM Medics system and discuss a sample usecase which demonstrates several new social-medical discovery opportunities. Keywords. social-medical discovery, entity-relationship graph, IBM Medics

1. Introduction In recent years, social-media (web 2.0) has become one of the main driving forces on the web. Unlike traditional semantic-web technologies, which mainly focus on efficient interoperable data exchange among computers, social media technologies focus on online collaboration and knowledge sharing among people. Nowadays, the healthcare domain exhibits a similar shift towards the adaptation of social web technologies [1,2]. New Health 2.0 services now empower patients to take more active part in managing their health wellbeing [1,3], and offer patients new set of tools for sharing personal social and medical data, and sharing experiences or expertise about various healthrelated topics through social collaboration between patients, physicians, and various healthcare service providers [3,4]. In this line of services, online services such as Google Health and Microsoft Health-Vault now allow patients to share their personal health records (PHR); this compared to traditional EMR systems that prohibit patients from accessing their own medical records. Depending on patients’ privacy preferences, PHR data may be publicly (or partially) shared, offering new discovery opportunities. For example, personalized medical content recommendations may be delivered to patients based on their PHR data [5]. Furthermore, several online social-medical community services such as Patients-Like-Me and Cure-Together allow patients to discover other patients who share similar medical characteristics, such as similar disorders or symptoms. For example, by joining to a medical community on Patients-Like-Me, patients may get additional medical (and even mental) support, which leverage the community’s power to discover new possible treatment plans, clinical trials, expert physicians, etc [4]. Finally, online services such as Med-Help and Drugs.com provide access to rich medical knowledge gathered from various medical knowledge resources (e.g., HCLS 1

Corresponding Author. Haggai Roitman, IBM Research, Haifa 31095, Israel; email: [email protected].

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H. Roitman et al. / A Unified Approach for Social-Medical Discovery

Linked Open Drug Data (LODD)). Such resources can be used by users who seek drugrelated information, wish to find expert advice, or find evidence for various health related topics. Despite the increasing amounts of social data fused together with rich medical data, there still remains a great challenge of how to fully utilize this new combination for purposes of efficient social-medical discovery. Existing social media discovery solutions use relatively simple data models that record relationships between people and their associations with unstructured (text) documents [6]. Therefore, existing social data models are not well suited for handling medical data, which is usually structured in its nature, semantically rich (e.g., defined over some medical terminologies such as SNOMED-CT, UMLS, ICD-10, etc), standard-based (e.g., HL7 RIM), etc. On the other hand, existing social-medical solutions utilize only simplified data models and provide limited discovery capabilities that merely exploit the social-medical dataspace. For example, social community services such as Patients-Like-Me currently provide very simple query interfaces for exploring their social-medical community data, spanning from simple keyword search to very limited category-based search over several medical facets such as symptoms or demographic data. As another example, personalized medical recommendation systems that utilize PHR data commonly ignore social data. Furthermore, patients, and even more expert users such as physicians, usually find it hard to explore data that they are not familiar with its structure, terminology, query language, etc [7]; hence, a more exploratory solution is desired which can gradually guide patients within the social-medical dataspace. Such data exploration should be backed up with as much evidence as possible, yet very intuitive even for non-expert users such as patients usually are. Aiming at fulfilling the gaps, in this paper we describe a novel social-medical discovery solution, based on an idea of social and medical data unification. Built on foundations of exploratory search technologies, the proposed discovery solution is better tailored for the social-medical discovery task. In the rest of this paper we describe our solution, its fundamentals and discovery capabilities. We then describe its implementation within the IBM Medics patient empowerment system.

2. Methods We now present a novel model for social-medical discovery. Built on foundations of conceptual modeling, social data and medical data are fused together using a uniform representation in the form of a rich entity-relationship (ER) data graph. In turn, social discovery can be augmented with medical discovery and vice-versa. This allows to explore new facts about social and medical entities through various paths within the ER graph. For example, we may discover similar patients not only based on direct patient similarity, but also based on their relationships with other similar social or medical entities, e.g., similar medications, allergies, family bonds, treating physicians, etc. Social and medical facts known to exist are modeled by entities and their relationships. Such facts can be gathered by observing and collecting data from various data sources, such as the ones that were mentioned in Section 1. Each fact is accompanied with an evidence link which traces its source origin, e.g., a fact about an adverse drug reaction between two drugs may be linked to its FDA alert page or knowledge from DrugBank. Social entities include among others, patients, physicians, or even “virtual entities" such as various health service providers (e.g., hospitals).


5

Medical entities include among others, medications, allergies, immunizations, symptoms, genetic variations, etc. Each entity may have a rich set of attributes describing its properties. For example, a patient is represented as a single entity in the graph together with its socio-demographic attributes such as gender, age, location, etc. As another example, each medication is represented as an entity with attributes such as its generic or brand name (code), substance name, etc. Both social and medical entities may have relationships with other social or medical entities. For example, a patient entity may be related to some consumed medication; a medication entity may be related to some drug-interacting medication entity; a patient entity may be related to his treating physician entity, etc. Using such a discovery model allows to support various types of exploratorydriven queries over the social-medical data graph. This includes rich keyword-based queries that can also be mixed with more structured query predicates, allowing to express very complex information needs. For example, users can submit a query like “Hemophilia AND Patient.age:[40 TO *)” to discover all patients whose age is above 40 and are related to Hemophilia related topics. Furthermore, the discovery model supports rich faceted-search and data lineage capabilities that allow interactive exploration of the social-medical dataspace. For that, we implemented an extended faceted-search model that enables to index and retrieve both text and structured data formats and supports an OLAP-like complex faceted search over rich entityrelationship data. Using the faceted-search user interface, users may start their search based on some initial information need. Search result includes a list of social or medical entities or both, uniformly ranked by their relevance to the user’s query. Each entity is further accompanied with relationship links that allow the user to flexibly explore the sub-graph induced from that entity. In addition, facets about various entities in the result set (e.g., patient age or gender distributions) further allow the user to quickly filter out entities according to facets of interest and explore the graph projected by those facets. Such social-medical data exploration may be highly useful for patients who wish to explore possible treatment plans by following the medication links of some patient returned as result to their query, or for physicians and researchers who wish to discover new interesting patterns in the social-medical dataspace. We discuss more example usages in the next section.

3. Results We have implemented the proposed social-medical discovery solution within the IBM Medics system. IBM Medics is a novel clinical decision support system (CDSS) developed in collaboration between three IBM labs and the GIL hospital in Korea. IBM Medics empowers the patients and helps to increase patient safety by assisting patients and their medical providers with daily medical decision-making. One of the main services in IBM Medics is the social-medical discovery (SMD) service. SMD serves various queries, submitted by patients, physicians, and researchers, which explore its social-medical dataspace. IBM Medics social-medical dataspace is formed by integrating social and medical data stored within IBM Medics sub-systems together with data it gathers from public social-medical sources.

6


Figure 1. IBM Medics social-medical discovery (SMD) user interface.

Figure 2. Example social-medical sub-graph for the query “Hemophilia".

Figure 1 depicts SMD’s main user interface with an example discovery usecase. In this example, two patients were returned as a result to an initial query “Hemophilia" submitted by the user, who later on followed the “Related patients" link to discover relevant patients. We can also observe that SMD provides several facets related to those patients, and for each patient, the user may further follow several relationship links to explore that patient’s social-medical sub-graph.


7

Finally, to illustrate additional discovery options, Figure 2 further depicts a possible social-medical sub-graph that may be explored by a patient user searching for Hemophilia-related information. By following the links to Hemophilia-related patients (e.g., based on information gathered from Patients-Like-Me or Google Health), a patient searcher can discover new possible treatments, e.g., other medications consumed by those patients or physicians who treat those patients, and whom the patient may contact for her own benefit. Furthermore, the searcher may discover Hemophilia-related symptoms (e.g., Hematuria) gathered from WebMD.com, or discover related genetic variations gathered from PubMed, etc. Using the searcher’s own medical profile, she can also discover possible lineage paths between her genetic profile (e.g., from 23AndMe) and Hemophilia. The patient can also detect whether a new medication, which she just discovered through some related patient, has a potential interaction (e.g., based on Drug-Bank gathered knowledge) with any of the medications she currently consumes.

4. Conclusions Though many Health 2.0 services have already emerged, there is still a strong requirement for discovery solutions that are better tailored to this domain. In this paper, we suggested a novel unified social-medical discovery solution, implemented within the IBM Medics patient empowerment system, which serves as a major step towards this goal and brings new discovery opportunities for the Health 2.0 domain. As future work, we wish to leverage the new discovery model and develop new online patientsimilarity search methods that fully utilize the power of social-medical discovery. In addition, using user studies, we plan to perform usability analysis among IBM Medics users, and examine other potential useful discovery use-cases. Finally, we now work on a new privacy model that is better tailored for the social-medical discovery domain, allowing patients to have a more fine granular (PHR-section level) control on the social-medical data they share and discovered by others.

References [1] [2] [3] [4] [5] [6] [7]

Eysenbach G. Medicine 2.0: social networking, collaboration, participation, apomediation, and openness. J Med Internet Res, 10(3):e22+, 2008. Hughes B, Joshi I, Wareham J. Health 2.0 and medicine 2.0: Tensions and controversies in the field. Journal of Medical Internet Research, 10(3):e23+, August 2008. Brubaker JR, Bren D, Lustig C, Hayes GR. Patientslikeme: Empowerment and representation in a patient-centered social network. Workshop on Research in Healthcare, CSCW, 2010. Wicks P, Massagli M, Frost J, Brownstein C, Okun S, Vaughan T, Bradley R, Heywood J. Sharing health data for better outcomes on PatientsLikeMe. J Med Internet Res, 12(2), June 2010. Roitman H, Messika Y, Tsimerman Y, Maman Y. Increasing patient safety using explanation-driven personalized content recommendation. IHI, November 2010. Carrington PJ, Scott J, Wasserman S. Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences). Cambridge University Press, February 2005. Cline RJW. Consumer health information seeking on the Internet: the state of the art. Health Education Research, 16(6):671–692, 2001.

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Information Provision for Adolescents with Cancer Anna SHILLABEERa1 Senior Consultant, IT Advisory. Ernst and Young, Adelaide, Australia.

a

Abstract. Recent research has provided a detailed insight into what information cancer patients as a generic group require and we now understand that this requirement changes during the disease episode. This paper will focus on the information needs of adolescent cancer patients as little research has been done in this area and unlike every other group of cancer patients very little improvement in information provision and clinical outcomes for this small but important group of people has occurred over the past 20 years. Adolescents have specialised needs and have for too long been grouped either with young children or adults. This paper describes our current knowledge regarding their special needs and outlines future directions to facilitate equality in information provision for this group. Keywords. Information provision, adolescent, cancer.

1. Introduction Researchers have made significant advances in our understanding of what information patients require when they are diagnosed with a life changing illness such as cancer. We also understand that this requirement changes during the disease episode and that while some patients require access to all possible sources of information others require very little and are happy to allow their treating physician full control over the management of their condition and decisions regarding treatment options. Less knowledge has been published regarding the specific needs of young adults with a life changing illness such as cancer and this represents a significant omission and an area deserving attention due to the impact of such a diagnosis during the formative years and the deep psychosocial impact of cancer on a young adults feeling of connection with their peers and the wider world in which they live. This paper will focus on the information needs of young cancer patients for a number of reasons; there is little work focussing on the specific needs of this group; young people are often afforded a lower status than adults and excluded from decision making processes [1, 2, 16]; the cancer episode is prolonged and hence longitudinal tracking to determine the impact of various solutions can be monitored and; teens are already concerned about changes to their bodies and thought processes and a diagnosis such as cancer can exacerbate this and have a deep and long lasting impact on their mental and physical wellbeing far beyond the effect of the illness [2]. Specifically targetted information provision is vital to ensuring they are able to take a mature role in their illness and can overcome feelings of isolation and loss of control and can still 1

Corresponding author

A. Shillabeer / Information Provision for Adolescents With Cancer

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identify themselves as an individual not a ‘freak of nature’ [1, 4]. This paper will provide an overview of recent research in the area of information provision for adolescents and will outline future directions to facilitate the provision of a personalised information management focus for all healthcare providers.

2. Impact of Information on the Patient Experience There has been work published around the world that outlines a number of positive impacts on the adult patient experience that can be directly related to the quantity and quality of information presented. The following are commonly stated: • Development of a closer and more trusting relationship with the primary physician and other healthcare workers [9] • Improved participation in decision making [8, 9, 10]. • Reduced fear/higher compliance with treatments and investigations [10]. • Increased ability to cope and develop long term strategies [1]. • Overall patient empowerment [13]. Work focusing on children and adolescents show that these groups have similar outcomes resulting from the provision of high quality, well timed and appropriately targetted information [1]. The difference is highlighted when comparing the mode of delivery. While most adults are satisfied with hospital leaflets and talking to their physician [10] teens use information as a means of connecting with their physician and prefer a more interactive mode [9]. A significant number do not feel that hospital produced information is appriate for them as it either uses complex medical terminology that they do not understand and hence alienates them further from the discussion processes, or, it is written for children and is too simplistic to answer their needs [9]. There are a number of reasons for the observed dichotemy: • For both research and clinical purposes patients are most often divided into two groups; children under approximately 10-16 years and adults who encompass all others [for example 14, 3]. It is not unusual for adolescents to represent less than 5% of a research cohort and for the group to have an average age of 60 or above [11]. This leads to the needs of adolescents being included in the results for adults as they are outliers so their outcomes may become obfuscated and unreported and not considered suitably significant for further research investment. • Cancer treatment for adolescents almost always occurs in either a children’s or adult’s hospital and is not specifically designed for the needs of young people. The information is therefore tailored for the primary patient body and again teens do not form a large enough single group to warrant investment in new information provision. • There are few who specialise in working with adolescents in the cancer field and hence the body of expert knowledge is limited. It can therefore be difficult to gather, understand and incorporate appropriate input. Whilst there are some moves towards addressing these issues including the provision of an expert in adolescent oncology in the U.K. [5] and a new initiative in Adelaide, Australia by the State Government to develop an adolescent treatment facility as part of the new Royal Adelaide Hospital development [7], these initiatives are often focussed on treatments and environments for adolescents, which are vitally

10


important considerations but there is little suggestion that information provision will be a core factor in the plan. The issues documented here are therefore likely to persist. The continued inappropriateness of information provision in the adolescent cancer environment will contribute to the teen’s feelings of isolation and will reduce their ability to relate to their healthcare professionals and participate in decision making. This has been stated earlier to have a measurable impact on the outcomes for adolescents with cancer and potentially contributes to the alarming statistic that there has been little improvement in cancer outcomes for adolescents in the past 20 years despite up to 50% improvements for all other groups [3, 6]. The primary message from this work is that an adolescent cancer patient is an isolated generic entity with unique characteristics and should not be absorbed into the requirements for information provision of other groups [15]. It is also evident that clinical outcomes and overall feelings of wellbeing can be directly affected by the information a patient receives [8, 9, 10], thus providing a significant motivation for researchers and for interest and investment by healthcare providers. It is suggested that not only will patients recover faster and be more compliant and active in their treatment but this could create a flow on effect in terms of financial savings to the organisation through shorter treatment plans, reduced side effects and earlier interventions.

3. Information Media If we accept that the adolescent cancer patient group is worthy of separate consideration we must then determine their specific needs and understand how we might best serve those needs. Whilst there is little evidence of any significant research in this niche area there has been some foundational work done on the health information needs of adolescents in general. That research provides a clear indication of what is required in terms of the preferred sources of information and modes of delivery. Whilst generalised to the provision of healthcare information to all adolescents, there are significant parallels with information provision needs in adolescents with cancer that suggest this research is likely applicable to this context albeit not conclusively tested. 3.1. Information Sources •

•

Health professionals are seen as the primary source of information, especially in a cancer diagnosis, but the quality of interactions and hence information gathering from this source is directly affected by the quality of the relationship & terminology used. This critical relationship is however complicated by its association with the appropriateness of information received thus potentially presenting a spiralling information gap if not done well [1, 2, 8, 16]. Two of the most crucial factors in an adolescent’s ability to form a relationship with their doctor are the need for privacy and confidentiality and the difficulty in developing trust at the same pace as the need for information and it is suggested that only 30% of doctors in general have taken the time to actively address these issues [16]. On a psychosocial level parents and friends are vital in maintaining consistency in a patient’s life and reduce the potential to become isolated or consumed by the cancer diagnosis [6]. This group has been a traditional


•

• •

11

source of information for adolescents and many know of no other way of gaining new information. However, many adolescents express problems in confiding to friends and parents, especially regarding matters of sexual and mental health, both of which are significant areas of concern during a cancer episode [17]. Fellow patients are almost universally stated to be a highly important source of information although they must be matched by age, interests and diagnosis [1,8]. It can be a great source of reassurance when a patient further through the treatment protocol is enjoying life and can talk about what to expect and how to manage new experiences. Unfortunately doctors do not always support the patient receiving unqualified, anecdotal information and this presents a barrier to non traditional forms of information provision and suggests a need for professional mediation for all sources [10]. Printed materials are readily available but as discussed earlier are often generic and do not adequately meet the needs of adolescent patients who prefer a more conversational information gathering process [1, 3]. For many an online medium is seen as less confronting and judgemental than a clinical space, confidential through anonymity, more freely available in terms of time and location and not constrained by the formality of clinical language. These are seen as core requirements of a successful information medium for this group [16, 17, 18]. There are a small number of organisations that provide a digital presence including Canteen in Australia [3], the Teenage Cancer Trust in the U.K. [6] and the kidshealth website in the U.S. [12] but these do not aim to provide qualified clinical information and have more of an emotional support and fundraising focus. One dedicated online health information source for adolescents, the Teenage Health Freak website, was reported to have had over 52,000 hits per day between 2000 and 2007 [17] but does not address cancer. With the knowledge that utilisation of some non traditional information sources has increased by almost 50% between 2008 and 2009 alone [18] and 75% of adolescents have used the Internet to source health information [17], digital media should be considered core if information provision is to meet needs of young cancer patients. Whilst not all adolescents have access to this medium, for most the need for auxiliary active, ongoing, incremental information gathering could be easily satisfied [8, 17].

The minimum requirements of any information provision medium for adolescents are, privacy and confidentiality, access at any time and place, teen specifc langauage, anonymity, no parental consent, non judgemental qualified advice and low cost [16, 17]. All of these criteria can be met through the utilisation of online technologies including email, blogs, websites and social networking. Whilst it is important for adolescents to communicate with people ‘just like them’, it is important to incorporate medical professionals to ensure high quality advice is given and hence mediated sites, which have been shown to attract high traffic, should be the primary focus[17].

4. Future Directions The overview of research presented in this paper has demonstrated that even after many years of work in information provision for adolescents with cancer, few improvements

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have been realised. Whilst clinical and environmental changes can take years, modifications in the presentation of information could be relatively fast and enable an immediate impact to be felt. Given the importance of information on the generalised experience of patients as described herein it should be seen as a moral obligation to apply a similar research focus for cancer patients and allow young people access to at least the health information quality we as adults receive. Future work should focus on confirming the specific information needs and modes of delivery for adolescents with cancer and actively engage them through empowerment in the development of a targetted information model. The result should be a flexible system using familiar digital and other technologies that will enable adolescents with cancer to actively participate in gathering information at a time and in a form that best suits their individual needs. The focus for this work shall therefore be on testing the suitability of a range of digital media options that better reflect the aesthetic and information provision preferences of young people without changing the consistency of qualified information content provided to all patients.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11]

[12] [13] [14] [15] [16] [17] [18] [19]

Beresford B, Sloper P. The information needs of chronically ill or physically disabled children and adolescents, Social Policy Research Unit, University of York, York. (1999) Rolinson JS. Health information for the teenage years. Information Research. 3(3) (1998). CanTeen. http://www.canteen.org.au/ Dec 2010. James’ Story. http://www.canteen.org.au/default.asp?articleid=2511&menuid=86 Jan 2011 Teenage Cancer Trust. http://www.teenagecancertrust.org/what-we-do/health-professionals/professor/ Bleyer A. The adolescent and young adult gap in cancer care and outcomes. Current Problems in Pediatric and Adolescent Heathcare. Volume 35, Issue 5 (2003), 182-217. Adelaide Now. Http://adelaidenow.com.au/news/in-depth/big-shoes-to-fill-as-lance-departs. Jan 2011. Ankem K. Types of Information Needs Among Cancer Patients. LIBRES 15.2: (2005). Better Together: Scotland’s Patient Experience Programme: Building on Children and Young People’s Experiences. (2009). http://www.scotland.gov.uk/Publications/2009/06/12150703/2 Dec 2010. Adler J, Paelecke-Habermann Y, Jahn P, Landenberger M, Leplow B, Vordermark D. Patient information in radiation oncology: A cross-sectional pilot study using the EORTC QLQ-INFO26 module. Radiation Oncology. 4:40, (2009) Isenring E, Cross G, Kellett E, Koczwara B, Daniels L. Nutritional status and information needs of medical oncology patients receiving treatment at an Australian public hospital. Nutrition and Cancer. 62(2) (2009) 220-228 Kidshealth. http://kidshealth.org/teen/ diseases_conditions/cancer/deal_with_cancer.html Jan 2011 Vordermark D. Patient Information and Decision Aids in Oncology: Need for Communication Between Patients and Physicians. Journal of Clinical Oncology. 28(29). (2010). Oakley C, Powell S. Cancer Directorate Patient Involvement Work 2005/2006. NHS. England 2006 http://www.stgeorges.nhs.uk/docs/about/EHR/CancerPPISummary300806.pdf Jan 2011 Albitron K, Bleyer WA. The Management Of Cancer In The Older Adolescent. European Journal of Cancer. Volume 39, Issue 18 (2003), 2584-2599. McPherson A. Adolescents in primary care. BMJ. Volume 330, February (2005), 465 – 467. Harvey K, Churchill D, Crawford P, Brown B, Mullany L, Macfarlane A, McPherson A. Health Communication and adolescents: what do their emails tell us? Family Practice. June (2008) 304 - 311. Lenhart A, Ling R, Campbell S, Purcell K. Teens and Mobile Phones. (2010). Http://pewinternet.org/reports/2010/Teens-and-Mobile-Phones.aspx. April 2011. Christie D. Adolescent development. BMJ. Volume 330, February (2005), 301 – 304.


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Electronic Symptom Reporting by Patients: a Literature Review Monika A. JOHANSENa,1, Eva HENRIKSENa, Gro BERNTSENa, Alexander HORSCHb,c a Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway b Research group Telemedicine, Department of Clinical Medicine, University of Tromsø, Tromsø, Norway c Technische Universität München, München, Germany

Abstract. A literature review has been conducted to gain an overview of which technologies and patient groups have previously been employed in scientific studies with regard to patients reporting symptoms electronically. This paper presents preliminary results from the review, based on the abstracts from relevant publications. The Medline database search identified 974 publications. Of these, 235 (24%) met the inclusion/exclusion criteria. The number of studies has increased heavily over the past two decades. A lot of the studies are small with regard to sample size, but we see that the number of studies increase over time. Cancer and lung diseases are the largest diagnosis groups. Cancer symptom reporting seems to take place inside the healthcare institutions, while lung disease and musculoskeletal disease reporting mainly take place at home via Internet. Keywords. electronic symptom reporting, physician-patient relations, consumer participation, data collection, review

1. Introduction The traditional patient and provider roles are in change. A new approach is arriving, focusing on patient-provider information technology partnership to promote more patient-centred healthcare [1] and personal health information management systems [2]. Consequently, there is a need to build new and better computerized tools to support the patient as an active partner in healthcare, while at the same time take into consideration the challenges and constraints patients and providers have to deal with. Healthcare providers often find it demanding to determine the patient’s main problem or concern [3]. The way patients present their problems, the sequence, importance and severity of symptoms influence their professional interpretation [4]. Likewise, studies of consultation interviews show that physicians elicit only around 50% of the medical information considered important [5]. The facts that patients have increasing difficulties with correctly remembering symptom levels beyond the past several days [6], and that older patients do not report most of their symptoms to health professionals [7] are worsening this situation. In contrast, we find that people report a higher number of and/or more serious symptoms when using computer-mediated communication 1

Corresponding Author: Monika A. Johansen, E-mail: [email protected]. Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, N-9038 Tromsø, Norway.

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M.A. Johansen et al. / Electronic Symptom Reporting by Patients: A Literature Review

compared to face-to-face encounters or in phone talks [8], p. 28-29. This supports the viability of patient-centric symptom reporting tools to report and grade symptoms electronically in pre-clinical and clinical settings, and, if possible, at the time when the symptoms are present. However, this is a new and unexplored area and there is a need to assemble the knowledge that already exists. The main purpose of this study is, based on review of abstracts, to establish an overview of clinical settings such tools might be useful for and technologies that have previously been examined in scientific studies. This knowledge will be valuable for everybody planning to conduct research in the field and for the development of future symptom reporting tools.

2. Methods 2.1. Inclusion and Exclusion Criteria The inclusion criteria were: I1) Original studies; I2) Patients or parents reporting symptoms or health information electronically, either to healthcare personnel, or to an organization/institution, or a public system that processes and/or interprets the data for healthcare purposes and provides feedback. The focus is on systems that can be established within the healthcare system, including e-diaries and personal health records accessible by health providers; I3) The information reported must represent patient symptoms at present or within the last few days. The exclusion criteria were: E1) Retrospective questionnaire, prevalence surveys, screening, and test of medicines; E2) All electronic communication that requires patient and healthcare personnel to be present at the same time, as for instance video conference; E3) Automatic biometric measurements, since these are defined as reporting of signs, not symptoms. 2.2. Search and Assessment Strategy The Medline database was searched, limited to publications from 1990 to 1st September 2010, human medicine, in English language. The search was built up around four search files (What – Who – Why – How), with the logical function OR within the files, and AND between the files. Already known eligible publications were reviewed to identify possible MeSH terms and relevant search words. The What-file included 22 search terms for symptoms and synonyms. The Who-file searched for “patient*” and “parent*” plus 18 relevant MeSH terms. The Why-file included 35 search terms for “self-report*”, “pre-report*”, and synonyms. Finally, the How-file included 38 search terms, for the possible technology involved. The search strategies were pilot tested and modified several times to ensure that they identified eligible publications. The first and second author reviewed and rated independently all abstracts as “potentially relevant” or “not relevant”, and subsequently merged their results. In all cases where the reviewers had disagreed in the perceived eligibility of the publication, the two reviewers discussed the abstracts to reach consensus. Finally, the second author reviewed the entire abstracts a second time to extract more specific information characterizing these publications. We used the diagnosis categories of the International Classification of Primary Care (ICPC), as a basis for classification of clinical conditions. Where we found more than five papers within a category, main and subgroup figures are presented explicitly.


15

Figure 1. Number of included publications over the years

3. Results 3.1. Literature Search Results The search in Medline identified 1006 references, including 32 duplicates. Of the referenced 974 articles, 235 met all inclusion and exclusion criteria. Initially we had agreed on inclusion for 190 and exclusion for 628 (total 818) papers, while for 156 papers agreement was reached only after a consensus discussion (45 inclusion, 111 exclusion). Considering the publication year of these 235 articles, the number of papers increased heavily over the past two decades (Figure 1). Authors from the United States published 151 papers, UK 24, Norway 9, Australia 8, Germany 7 and The Netherlands 6. The remaining 30 papers were spread on 16 countries, with four or less from each. Fifty-six of the abstracts did not report how many patients were involved. The other 179 ranged from five to 10999 recruited patients, in average 235 (median 77), in total 42038 patients. Thirty of the studies involved 20 or less patients, 109 involved 100 or less, and only six involved more than 1000. The average number of patients involved increased from 72.5 for the studies conducted during 1990-1999, to 93 for those in 2000-2004, and further to 281 for 2005 to Sept. 2010. The exact number of healthcare providers involved is in general not reported in the abstracts. 3.2. Technologies and Clinical Settings The systems employed in the studies have been categorized within three different scenarios: 1) An inside scenario, where the patient is present inside the healthcare institution, using a local computer, stand-alone or connected to the network, to input symptoms prior to the encounter. The most typical technology in use is the tablet-PC with touch-screen. 2) An outside stationary scenario, using a computer and the Internet at a location distant from the healthcare provider, usually at home, to report symptoms. Applications used are mainly e-diary or more general web applications, and in some cases e-mail. 3) An outside mobile scenario using a handheld device with mobile communication technologies to report symptoms. This includes “ordinary” mobile phones, smartphones and PDAs, where the use ranges from simple text messages (SMS) to advanced applications.

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Table 1. Number of articles related to technology and conditions categorized by use of ICPC. Subgroup count is included in main group. Patient may be Inside healthcare institution, or Outside using stationary PC or mobile solution. Technology/condition not evident in abstract are placed in the “Unspec.” column/row. Inside Outside (Stationary) Outside (Mobile) Unspec. Diseases, Clinical Conditions Total Touch Web e-mail e-diary unsp. SMS Appl. others/mix Cancer 50 16 4 1 2 10 17 Lung diseases 48 7 9 6 6 2 6 12 - Asthma 36 6 5 6 5 2 5 7 - COPD 7 1 2 1 1 2 Cardiovascular Psychiatry Diabetes Musculoskeletal - Rheumatologic

17 18 12 15 9

Gastrointestinal Neurological HIV/AIDS Unspec./others Total

8 8 6 53 235

5

1

5 3 1

1 3 1

1 1

4 3 2 1

2 5 38

6 28

1 3

5 21

2 2 1 1

3

1 2 2

2 3 1 3 17

5

12 36

10 6 7 3 2 4 4 3 21 87

While the scenarios are overlapping only when observing one patient over time, the technologies in use are largely overlapping. Smartphones can, for instance, be used for more or less the same applications as stationary computers. Table 1 presents the scenarios and the most common technologies involved, related to the diseases or conditions where symptoms are reported electronically. Cancer is not a separate category in ICPC, but did represent a large and distinct body of literature and is therefore presented separately. In addition to including abstracts where it was unclear which medical condition they belong to, the “Unspec./others” row include the A-group of ICPC (common/unspecified), four papers dealing with lifestyle services, and four dealing with follow-up procedures after surgical interventions. For the specified scenarios, cancer and lung diseases are clearly the largest groups. The largest part of the cancer symptom reporting seems to take place inside healthcare institutions. Lung disease and musculoskeletal disease reporting mainly take place at home via Internet. For the other disease categories the unspecified group is too large to indicate any trends. Web, e-diary and more advanced mobile applications are more used than e-mail and SMS, technologies where the user interface has limited functionality.

4. Discussion and Conclusion Over the last two decades, the number of studies on electronic symptom reporting has increased heavily. This may indicate the start of a new paradigm. However, most studies are small in terms of sample size (median 77), and many are best characterized as feasibilities studies. This number has increased over time, though, which may reflect a general improvement of study quality. Cancer and lung diseases are the largest diagnosis groups. Cancer symptom reporting seems to take place inside the healthcare institutions, while lung disease and musculoskeletal disease reporting mainly take place at home via Internet.


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Tablet-PC with touch-screen applications, advanced smartphone and PDA applications, web and e-diary represent the main technologies examined in these studies. Electronic symptom reporting appears to be used in situations where it is challenging to identify all aspects of a situation in a short clinical encounter, in other words in diseases that are complex to diagnose, in long-term diseases, and in taboo and sensitive situations. We also find examples of electronic symptom reporting for the opposite, the more easy cases, where the electronic symptom reporting might substitute a face-toface consultation. Examples here are follow-ups after surgery. It seems to be easier to create a meaningful electronic dialogue when the system is focused on a specified diagnosis or clinical problem as opposed to an open approach where the patient’s health problem is unspecified or unknown when the symptom reporting starts. A lot of the studies focus more on technologies than on health effects, and most of the studies seem to be underpowered to document clinical outcomes or specific benefits for patients or healthcare personnel, as revealed by a former review of new technologies [9]. On the other hand, electronic symptom reporting empowers the patient as an active partner in healthcare. Patients support the electronic reporting of symptoms to their doctor before each encounter [10] and they believe it will improve the level of care and effectiveness during the encounter [11]. Further, electronic symptom reporting has been demonstrated to reveal patients preferences and information needs prior to the consultation [12]. This is an early report of a larger and more thorough review. The next step is the extension of the search to more databases and the full-text review of all relevant papers, in order to reveal more information regarding usefulness and to classify types of health outcomes. The preliminary results presented in this paper are considered highly encouraging for these ongoing efforts.

References Kaplan B, Brennan PF. Consumer informatics supporting patients as co-producers of quality. J Am Med Inform Assoc, (2001);8(4): 309-316. [2] Randeree E, Whetstone M. Personal health records: Patients in control. In Wilson EV, Editor, PatientCentered E-Health. IGI Global: Hershey, PA. (2009); p. 47-59. [3] Haugli L, Finset A. Lege-pasient-forholdet ved funksjonelle lidelser. Article only in Norwegian, Tidsskrift for Den norske legeforening, (2002);122(11): 1123-1125. [4] Johansen MA. Data from focus-group interview with 5 GPs, Norwegian Centre for Integrated Care and Telemedicine, University Hospital North Norway HF: Tromsø. (2009). [5] Roter DL, Hall JA. Physician's interviewing styles and medical information obtained from patients. J Gen Intern Med, (1987);2(5): 325-329. [6] Broderick JE, et al. The accuracy of pain and fatigue items across different reporting periods. Pain, (2008);139(1): 146-157. [7] Brody EM, Kleban MH. Physical and mental health symptoms of older people: who do they tell? J Am Geriatr Soc, (1981); 29(10): 442-449. [8] Johnsen JAK, Gammon D. Connecting with Ourselves and Others Online: Psychological Aspects of Online Health Communication. In Wilson EV, Editor. Patient-Centered E-Health, IGI Global: Hershey, PA(2009) [9] Roine R, Ohinmaa A, Hailey D. Assessing telemedicine: a systematic review of the literature. CMAJ, (2001);165(6):765-771. [10] Sciamanna CN, Diaz J, Myne P. Patient attitudes toward using computers to improve health services delivery. BMC Health Serv Res, (2002);2(1): 19. [11] Benoit A, et al. Using electronic questionnaires to collect patient reported history. AMIA Annu Symp Proc, (2007); p. 871. [12] Buzaglo JS, et al. An Internet method to assess cancer patient information needs and enhance doctorpatient communication: a pilot study. J Cancer Educ, (2007); 22(4): 233-240. [1]

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Increasing Physical Activity through Health-Enabling Technologies: the Project “Being Strong Without Violence” Corinna SCHARNWEBER1,a, Wolfram LUDWIGa, Michael MARSCHOLLEKa, Wolfgang PEINb, Peter SCHACKc, Reiner SCHUBERTd, Reinhold HAUXa a Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig Institute of Technology and Hannover Medical School, Germany b General Education Secondary School Sophienstreet, and c Elementary and General Education Secondary School Pestalozzistreet; Braunschweig, Germany d Braunschweig Health Planning Department, Germany

Abstract. Due to the increasing prevalence of adiposity in children numerous schools are introducing prevention programmes. Among these is “Gewaltlos Starksein” (“Being strong without violence”), a project of Hauptschule Sophienstraße Braunschweig, Germany (a general education secondary school for grades 5-10). This study aims to discover possible increases in activity through “Gewaltlos Starksein” where health–enabling technologies play a major role. A prospective intervention study with a span of 1.5 years was designed to measure this increase in activity. Partners in this study were Hauptschule Sophienstraße as the intervention group and Grund- und Hauptschule Pestalozzistraße as control group. Data collection was performed using a multi-sensor device, and questionnaires. Confirmatory data analysis of average metabolic equivalent (METs) yielded no significant results. Exploratory analysis showed interesting results, especially concerning the number of steps during leisure time. Descriptive analysis of questionnaires showed that all children enjoy physical activity. There were differences in sports team participation, open-air games and club affiliation. The study could not prove that the intervention “Gewaltlos Starksein” improves physical activity in children. However, the increased leisure activity step count indicates that “Gewaltlos Starksein” has positive effects on children’s behaviour. This should be investigated in a further study in cooperation with psychologists. Keywords. Health-Enabling Technologies, Evaluation Study, Activity, Children

1. Introduction The prevalence of overweight and adiposity in children and adolescents has increased steadily over the last 25 years. [1, 2]. Overweight and adiposity during infancy can lead to the manifestation of numerous diseases that can reach into adulthood [3, 4, 5]. It could be shown that there is a disparity between energy input and energy consumption in overweight children and adolescents [6]. Balancing this disparity requires measures that motivate children and adolescents to increase physical activities and to change 1

Corresponding Author: Peter L. Reichertz Institute for Medical Informatics, University of BraunschweigInstitute of Technology, Germany, Muehlenpfordtstr. 23, 38106 Braunschweig, Germany; E-mail: [email protected].

C. Scharnweber et al. / Increasing Physical Activity Through Health-Enabling Technologies

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their eating habits. In order to support and increase physical activity in children and adolescents in the “westliches Ringgebiet”, a quarter of Braunschweig, the action alliance "Das Westliche Ringgebiet - ein Stadtteil in Bewegung Steh auf...Mach mit... Lauf los!!!“ was initiated on March 1, 2009 [7]. The objective of this alliance is to convey knowledge and competence regarding health and activity issues to families and children. In order to realize this objective a cooperation was initiated that includes 22 sub-projects for a sustainable development of physical activities [7]. One of these sub-projects is the intervention “Gewaltlos Starksein” (“Being strong without violence”) at Hauptschule Sophienstraße (a general education school for grades 5-10). The introduction of additional mandatory athletic and charitable (“We help others” shopping for seniors) working groups intends to positively influence pupils’ activity patterns, their confidence and self-assurance [8]. Accompanying the intervention “Gewaltlos Starksein”, the study at hand presents an evaluation with an emphasis on changes in the intensity and forms of everyday activities in children aged 10 to 14 years at Hauptschule Sophienstraße. Within the prospective intervention study heath-enabling technologies [9, 10] play a major role. Consequently, this evaluation study investigates the efficiency and effectiveness of the intervention “Gewaltlos Starksein” in relation to children’s activity patterns.

2. Method For the evaluation of the intervention „Gewaltlos Starksein”, the study was designed as a prospective non-randomized comparative intervention study. The evaluation investigates and compares activity patterns of children in Hauptschule (HS) Sophienstraße (intervention group) and Grund- und Hauptschule (GHS) Pestalozzistraße (control group). All study subjects were enrolled in their respective school on August, 6, 2009. The study has a time-span of 18 months (from August 18, 2009 until January 31, 2011). Within this timeframe, five measurement campaigns with duration of two weeks each are performed (see Table 1). The campaigns are performed with 32 children aged between 10 and 14 years. In the run-up to the study, the children’s parents were informed about course and content of the study through an information letter and an informative meeting. Since participation in the study is not mandatory was not compulsory for the children, the informative meeting was followed by a registration phase until August 14, 2009. At this date, a total of 50 children had submitted letters of agreement for participation in the study. The final participants were then drawn by lot, wherein 16 children from each school were selected. The group size of 16 was chosen because of the minimum class size of 14 children plus 2 more to cope with participant drop outs. The 16 children from HS Sophienstraße participated in the intervention in addition to their normal classes. The 16 children from GHS Pestalzzistraße form the control group. The evaluation is conceived as a sensor-based study and is performed in parallel in both schools. Thus, each of the 32 children participated in all five measurement campaigns. Each measurement phase starts with a self-assessment of activity levels by the participants. The standardized questionnaire “MoMo Activity questionnaire for adolescents from 6 to 17 years“ [11] is used for this assessment. Following the assessment, parameters for the configuration of the SenseWear Pro 2 CE sensor used in the study are collected. This sensor wristband is then to be worn for one week. The wristband may only be removed for activities such as bathing, showering or swimming

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in order to avoid contact with water. On the seventh day of the measurement campaign the sensors are collected, read out and the data storage is cleared. On day eight, the process is repeated for another week of measurements. Afterwards, the participants receive a report of the collected data. This includes a one-on-one interview where the children are informed about the relevance and impact of the data collected by their sensor. At the end of the interview each child receives an easily understandable and comprehendible print-out for their personal records. Table 1. Time-span and measurement campaigns for the study

3. Result An analysis of the two sensor parameters metabolic equivalent (METs) and step count shows if the intervention changes the children’s activity patterns. Awareness and commitment to physical activities are clarified through analysis of the “MoMo Activity questionnaire“ (cf. [11]).

4. MET – Metabolic Equivalent A confirmatory data analysis is used to determine changes in METs over the course of the study. From the beginning of the study, the null hypothesis H0 is assumed: there is no change in METs over the duration of the study between intervention group and control group. Throughout the course of the study, the null hypothesis is intended to be replaced by the alternative hypothesis H1, which states the existence of a change in METs between intervention group and control group over the duration of the study. To be able to discard hypothesis H0 a Mann-Whitney U test was performed. For the study, the following values were derived: n1=9, n2=9, U=30, α=0,05, Uα=18. Using these values for formula 1 we arrive at:

The condition in formula 1 is not fulfilled, thus hypothesis H0 cannot be discarded. Within this study there is no significant change in METs between intervention and control group.

5. Leisure Time Step Count The exploratory data analysis of the collected sensor data yielded interesting results especially regarding the step count during leisure time. This analysis assesses possible positive effects of the intervention on the children’s behaviour during leisure time. „Leisure Time Steps“ are steps that the children made outside of school phases, i.e. in the afternoon or on weekends. This analysis uses the number of steps counted by the


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sensors within the measurement phases. Increases or decreases in the number of steps between two measurement campaigns are determined by computing the delta (Δ) of the step count. Sorted by the magnitude of the changes it can be determined if the step count of the children within the intervention group can be increased. The analysis of changes in the leisure time step count showed that out of 16 children in the intervention group 31% had a positive and 25% had a negative change in the number of leisure time steps. Within the control group, out of 14 children 14% had a positive and 50% had a negative change in the number of leisure time steps. Over the course of the study, a number of participants left the study and in some cases medical conditions resulted in unusable datasets. Within the intervention group, these unusable datasets accounted for 44% of all sets, compared to 36% within the control group. A mean value analysis of leisure time steps for measurement campaigns M1 to M5 produced the mean values shown in figure 1. Within the intervention group the step count increased slightly by 1733 and within the control group it decreased by 15267.

Figure 1. Mean values of „Leisure Time Steps“ for measurement campaigns M1 to M5

This allows us to form the hypothesis that “Gewaltlos Starksein” has a positive effect on the children’s leisure time activities.

6. MoMo - Activity Questionnaire The MoMo Activity questionnaire for adolescents from 6 to 17 years is a selfassessment of children’s activities and their attitude towards physical activity and sports. In order to assess the changes in the children’s self-assessment and their attitude towards activity and sports, the questionnaire is used before each measurement campaign. The questionnaire encompasses 51 questions and statements that have to be answered and assessed by the children. A descriptive analysis of the questionnaire has shown that all children show great interest in sports and physical activity. Differences became visible for questions about participation in teams sports, open-air games and club affiliations.

7. Discussion The concept of this evaluation study is based on the measurement of activity using sensors. Thus, the evaluation of the intervention is focused solely on changes in children’s physical activity. Psychological changes through “Gewaltlos Starksein” could not be proven. For this reason, a separate evaluation by experts is advised. The design of this study is aligned with other national and international studies on adiposity prevention for children and adolescents. In particular, the studies CyberMarathon [12], CHILT - Children´s Health Interventional Trial [13], IDEFICS European project on diet- and lifestyle-related disorders in Children [14] and Planet

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Health - An Interdisciplinary Curriculum for Teaching Middle School Nutrition and Physical Activity [15] were used as input for the study design. The selection of intervention and control groups was not randomized. The intervention group was determined through the project specification „Gewaltlos Starksein“ [8], and the control group was selected according to pre-determined parameters such as type of school, geographic area, class size, and school organisation. With the exception of the postponement of measurement campaign M5 until November 2010, the study could be performed as planned. The study has shown that the intervention “Gewaltlos Starksein” has no significant influence on changes in children’s activity patterns. Concerning the exploratory analysis of sensor parameters, especially the number of leisure time steps has proven to be of interest. Although no increase could be demonstrated, the step count within the intervention could be kept constant while the step count within the control group decreased. This allows the conclusion that the intervention has a positive effect on the participating children. However, to prove this conclusion will require a separate study in cooperation with psychologists.

References [1]

[2]

[3] [4] [5] [6] [7]

[8]

[9] [10] [11] [12] [13] [14]

[15]

Kurth BM, Schaffrath Rosario A: Prevalence of overweight and adiposity in children and adolescents result of the children’s' and adolescents' health survey (KiGGS) [in German]. Bundesgesundheitsblatt Gesundheitsforschung - Gesundheitsschutz 2007;50:736–743 Kronmeyer-Hausschild K, Wabitsch M. Current views on prevalence and epidemiology of overweight and adiposity in children and adolescents in Germany [in German]. [Internet] Working Group on Adiposity in Children and Adolescents of the German Society of Pediatrics and Adolescent Medicine. [cited 2011 Jan 30]. Available from: www.a-g-a.de Ebbeling C, Pawlaw D, Ludwig D. Childhood obesity: public-health crisis, common sense cure. Lancet. 2002;360:473-482. Reinehr T. Complications of adiposity in infancy and adolescence [in German]. [Internet]. 2005 [cited 2011 Jan 30]. Available from: www.a-g-a.de Burke V. Obesity in childhood and cardiovascular risk. Clin Exp Pharmacol Physiol 2006;Sep; 33(9):831-7. Warschburger P, Petermann F, Fromme C. Adiposity: Training with children and adolescents [in German]. 2. Au. Beltz Psychology Publisher Union; 2005. ISBN-10: 3621274898. Rake H, Schubert R. Das Westliche Ringgebiet - ein Stadtteil in Bewegung Steh auf...Mach mit...Lauf los!!! Application for implementation of the action alliance Healthy Lifestyles and Living Environments to the Federal Ministry of Health [in German]; 2009. Pein W. Being Strong without Violence - Health and Social Competence through Confidence and Selfassertion [in German]. [Internet]. 2009 [cited 2011 Jan 30]. Available from: http://www.bs4u.net/wegweiser/index.cfm?fuseaction=portal.page&page=15799 Bardram J E. Pervasive Healthcare as a Scientific Discipline. Methods Inf Med. 2008; 47(3):178-185 Koch S, Marschollek M, Wolf K H, Plischke M, Haux R. On Health-enabling and Ambient-assistive Technologies. Methods Inf Med. 2009; 48(1):29-37 Bös K, Worth A, Heel J, et al. Test manual of the motor activity module for the infancy and adolescence health survey of the Robert Koch Institute [in German]. Haltung und Bewegung. 2004; 24. Plischke M, Marschollek M, Wolf KH, Haux R, Tegtbur U. CyberMarathon - increasing physical activity using health-enabling technologies. Stud Health Technol Inform. 2008;136:449-54. Graf C. CHILT - Children´s Health InterventionaL Trial. [Internet]. 2008 [cited 2011 Jan 30]. Available from: http://www.chilt.de Ahrens W. IDEFICS - Identification and prevention of Dietary- and lifestyleinduced health EFfects In Children and infantS. [Internet]. 2006 [cited 2011 Jan 30]. Available from: http://www.ideficsstudy.eu/Idefics/index Carter J, Wiecha J, Peterson K, Gortmaker SL. Planet Health - An Interdisziplinäre Curriculum for Teaching Middle School Nutrition and Physical Activity. Human Kinetics; 2001.


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Review of Mobile Terminal-Based Tools for Diabetes Diet Management Eunji LEEa,1, Naoe TATARAa,b, Eirik ÅRSAND b,a, Gunnar HARTVIGSENa,b a Department of Computer Science, University of Tromsø, Tromsø, Norway b Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway

Abstract. Changing dietary habits is one of the most challenging tasks of diabetes self-management. Mobile terminals are increasingly used as platforms for tools to support diet management and health promotion. We present literature describing mobile terminal-based support tools for management of diabetes focused on diet. We also propose a summary of key success factors for designing such tools and discuss recommendations for future research. Keywords. Diabetes, Nutrition, Diet, Self-help, Self-management, Mobile phone

1. Introduction Medical recommendations in both Type 1 and Type 2 diabetes management involve nutrition, physical activity, and medications if necessary. Of these three elements, patients regard following nutrition recommendations as especially challenging, partly due to their lack of knowledge, understanding or skills concerning diet management. Mobile terminals are considered to have high potential as a platform for supporting tools for people with diabetes, due to their portability and emerging technologies embedded [1]. For such tools to be useful for diet management, they should be designed so that users can easily and quickly find necessary information and eventually achieve healthy dietary habits [2]. In this paper, we present findings from reviewing academic literature that describes mobile-terminal-based tools supporting diet management in diabetes. The aim is to improve knowledge about how a tool for diabetes diet management should be designed to promote health.

2. Methods PubMed, ACM digital library and IEEE Xplore were searched for relevant literature using the following combination of keywords: {(food OR nutrition OR diet) AND (cell phone OR mobile phone OR personal digital assistant (PDA) OR handheld)}. After removal of duplicates, only the papers including the keyword, ‘Diabetes’ were selected. The search was conducted in September 2010. 1

Corresponding author: Eunji Lee, Department of Computer Science, University of Tromsø, 9037 Tromsø, Norway; E-mail: [email protected]

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E. Lee et al. / Review of Mobile Terminal-Based Tools for Diabetes Diet Management

Following exclusion criteria were applied: (i) papers not written in English; (ii) papers of which full text was not available; and (iii) review articles. Finally, the relevance of each publication was examined by reading the abstract and the whole text if needed. The following data were extracted from the final selected papers: study design, type of mobile terminal used, targeted population, main purpose of the tool used or developed, significant features of the tool regarding diet management, and the findings for each study.

3. Results After removal of duplicates, 27 papers were found, of which five met the exclusion criteria. Based on the abstracts, 16 papers were selected as relevant to diet/nutrition. One of these focused on insulin therapy and another was found irrelevant to diabetes, leaving 14 papers for inclusion in this review. 3.1. Study Design, Terminal Type, and Target Population Ten papers [3-12] describe design and development of management tools for people with diabetes. Of these, seven [3,5-10] describe results from evaluation of tools by potential users regarding usability, feasibility and general acceptance; two [11,12] report results from technical evaluation of tools; the last paper [4] describes the design and development of a tool from a technical perspective. Three of the papers [7,9,11] state that the design requirements were obtained by involving people with diabetes as potential users. Evaluations by potential users are conducted through field testing, namely evaluation by use of a tool in the users’ real-life setting for a certain period [3,5,7-9], and through laboratory testing [6,10]. Clinical outcomes such as HbA1c were also examined in four studies [3,5,13,14]. In three studies described by the four other papers [13-16], the effectiveness, acceptance and feasibility of commercially available tools based on mobile terminals were investigated in the context of clinical intervention. Six studies [3,6,7,9,11,12] involved mobile phones as the terminal; the others involved PDAs. Windows Mobile-based phones with a touch-sensitive screen were mostly used [6,7,9,11]. The commercially available applications were all PDA-based. The year of publication and of each study indicates a clear shift from PDAs to Smartphone-type mobile phones. Six studies described in seven papers [7,8,10,13-16] target people with Type 2 diabetes, and two studies [3,11] target young people with Type 1 diabetes. The others do not specify the target population, but one study [9] limited participation to people aged over 18. 3.2. The Purpose of the Tool and Special Features In six studies [3,4,7,10,11,13], a tool was used or developed for overall diabetes management with recording of blood glucose values, physical activities and other relevant data in addition to food intake. In the seven studies described in the eight other papers, a tool dedicated to dietary management was used or developed. Several tools are designed for use as a part of telemedicine intervention, where health care professionals support patients remotely by viewing and analyzing the stored data [3,6,10,11]. The tools described in four studies [4,5,13-16] give patients nutrition


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information for a selected food item and/or results of automatic analysis of recorded foods in terms of nutrients and calorie intake; some provide feedback based on the patient’s personal information, such as calorie balance or nutrition balance over meals [4,15,16]. One tool focuses on the glycaemic index (GI) of food items, showing a GI value with an indicator, low, medium or high, for assisting in food choices [14]. Recording of food or drink items uses various methods. The most common is to identify items from a database [4,5,8,10,13-16]. Not all the papers specify the number of items in the database, but one includes more than 4300 items [15,16] whereas another includes 423 items [8]. Portion size can be adjusted in some of these tools [8,10,15,16], and two tools present photographs of food or drink items that can be used as a reference [8,10]. Other methods of recording include free text input [11] and photographing using a camera on a mobile phone [6,12]. The tool described in paper [12] is designed to recognize a food item by semi-automatic analysis of the photo together with contextual information. Meal types, such as breakfast, lunch, or dinner, are also used as data for recording [4,8,10,14-16], and time for meal intake can also be recorded on two tools [8,11]. The tool used in two of the studies [6,7] has only six buttons for the user to select a meal, snack, or drink with high or low carbohydrate content, enabling simple and quick recording in only a few operations. After data entry, this tool shows cumulative totals of foods or drinks recorded by category together with feedback according to personal goals, and smileys when goals are achieved [7]. One study [9] involves tools designed and developed purely for educational purposes utilizing three types of games incorporating several education theories and customizable functions so that patients can play and learn about diet management. 3.3. Summary of Findings In four of the studies [3,5,13,14] where clinical outcomes are evaluated, it is observed that HbA1c decreased among the participants in the intervention group who completed the study. However, in the study described in [5], decrease in HbA1c is only observed among the group of participants whose history of having diabetes is shorter than the other group. In the study described in [7], the participants improved their nutrition habits, especially intake of vegetables and fruits. In most of the identified studies, the tools used are generally well accepted by participants in terms of ease of use [5,6,10,13-15], usefulness, problem-solving capabilities, learning and motivational effects in dietary management [5-7,9,13-15], and feasibility for patient interventions due to high accuracy and reliability of recorded data [8,15]. It is noteworthy that no drop-outs from the studies due to difficulties in using the tools are reported in the selected papers. However, in the studies described in [9,15], considerable time was devoted to instructions for use, and the 12 elderly participants without experience in using PDAs or with problems in motor skills remained in the study, but gave up on using the PDA [15]. In some studies, consequences such as drop-outs from the study, decrease in use, low use, or negative opinions of the tools were observed – partly due to burdensome or tiresome daily registration [7,13-15], apparent improvement in glycaemic control [13], or saturation of effects on diet management [7], or misunderstanding, underestimating importance of self-management or treatment regimens, or limited understanding [15]. Despite the generally positive opinions of the tools, some difficulties in behaviour change are reported in terms of nutrition habit [5] and adherence in self-monitoring of diet [16]. Sevick et al. found that adherence to diet self-monitoring is not associated

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with sociodemographic characteristics, but rather with the level of adherence in the early phase of intervention [16]. Concerning tool features, customization or modification based on personal data or users’ skills is considered important and beneficial [6,9,14]. Timely, automatic and personalized feedback should be incorporated in a motivating and easily interpretable manner [6,13-15]. A database showing nutrient and calorie content is considered powerful if it contains enough variety and numbers of food and drink items that are familiar to users [6,14]. Simple categorization for recording nutrition habits is well accepted and appreciated for routine use [6], but some participants consider such categorization too coarse [7]. Photographs of food and drink items are considered useful, especially if they include a scale or familiar cutlery as a reference of size, for adjusting portion sizes [10]. Photographing food and drink items for recording and later consultation is considered practical for occasional use, but not for routine use [6]. Educational games are considered most suited for the young population and for shortterm use. Thus, the easiness and the ability to quickly launch and complete functions are important [9].

4. Discussion The identified publications show that mobile terminal-based tools have been generally well accepted and shown to be effective for diet management or glycaemic control to a certain degree. For successful diet management, people with diabetes need a good understanding of their diet regimen. In order to make a diet management tool feasible and useful, it should enable recording of food intake in an easy, but accurate enough manner. It should also provide immediate analytical feedback based on personal data in an easily interpretable way, preferably with other data about and exercise so that patients can reflect on their total behaviour. The tool should also include educational materials, with a database of food and drink items familiar to patients. For accurate recording of food quantities, visual reference such as photographs taken using a familiar object to indicate size is considered useful. From this review, key features to achieve both ease of use and accuracy in recording could not be clearly identified because of the mixed feedback from the participants, the time and effort required for user instructions, and the study designs, which do not compare the different tools in some of the studies. Food recognition by photographing may have a high impact when the technology enables reliable identification. Another challenge is how to design a tool that supports adherence in self-monitoring over a substantial period – long enough for achieving healthy effects. It might not be necessary for a tool to be used permanently, if use of the tool leads to better diet management, but often it needs to be used at least periodically for maintaining awareness of the importance of a healthy dietary regimen. As described in [6], simple and quick registration with immediate feedback would be suitable for routine use, but at the same time a tool should be designed so that it will not be tiresome or boring. Key features that encourage a wide variety of patients to be continuously engaged in using a tool should be investigated in future research, borrowing knowledge from the field of persuasive technology, human computer interaction, and psychology. The market for advanced mobile phones, e.g. smartphones, is growing rapidly and a great number of mobile applications are available on the market today. Further


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research is required to examine such applications to identify key features for design of effective and useful support tools for diet management for people with diabetes – and other disease cases that will benefit from diet management. Acknowledgements: This work was supported by the Centre for Research-based Innovation, Tromsø Telemedicine Laboratory (TTL), Norwegian Research Council Grant No. 174934.

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[2] [3] [4]

[5] [6] [7] [8]

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Tatara N, et al. A Review of Mobile Terminal-Based Applications for Self-Management of Patients with Diabetes. In: eHealth, Telemedicine, and Social Medicine, 2009. eTELEMED '09. International Conference on. 2009. p. 166-175. Nagelkerk J, et al. Perceived barriers and effective strategies to diabetes self-management. Journal of Advanced Nursing. 2006;54(2):151-158. Farmer A, et al. A real-time, mobile phone-based telemedicine system to support young adults with type 1 diabetes. Inform Prim Care. 2005;13(3):171-177. Kyung-Soon Park, et al. PDA based Point-of-care Personal Diabetes Management System. In: Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. 2005. p. 3749-3752. Tsang MW, et al. Improvement in diabetes control with a monitoring system based on a hand-held, touch-screen electronic diary. J Telemed Telecare. 2001 Feb 1;7(1):47-50. Årsand E, et al. Designing mobile dietary management support technologies for people with diabetes. J Telemed Telecare. 2008 Oct 1;14(7):329-332. Årsand E, et al. Mobile phone-based self-management tools for type 2 diabetes: the few touch application. J Diabetes Sci Technol. 2010 Mar;4(2):328-336. Fukuo W, et al. Development of a Hand-Held Personal Digital Assistant-Based Food Diary with Food Photographs for Japanese Subjects. Journal of the American Dietetic Association. 2009 Jul;109(7):1232-1236. DeShazo J, et al. Designing and remotely testing mobile diabetes video games. J Telemed Telecare. 2010 Aug 2;:jtt.2010.091012. Tani S, et al. Development of a Health Management Support System for Patients with Diabetes Mellitus at Home. J Med Syst. 2009 1;34(3):223-228. Mougiakakou S, et al. Mobile technology to empower people with Diabetes Mellitus: Design and development of a mobile application. In: Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on. 2009. p. 1-4. Shroff G, et al. Wearable context-aware food recognition for calorie monitoring. In: Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on. 2008. p. 119-120. Forjuoh SN, et al. Improving diabetes self-care with a PDA in ambulatory care. Telemed J E Health. 2008 Apr;14(3):273-279. Ma Y, et al. PDA-assisted low glycemic index dietary intervention for type II diabetes: a pilot study. Eur J Clin Nutr. 2006 May 17;60(10):1235-1243. Sevick MA, et al. Design, feasibility, and acceptability of an intervention using personal digital assistant-based self-monitoring in managing type 2 diabetes. Contemp Clin Trials. 2008 May;29(3):396-409. Sevick MA, et al. Factors associated with probability of personal digital assistant-based dietary selfmonitoring in those with type 2 diabetes. J Behav Med. 2010 3;33(4):315-325.

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Interaction Between COPD Patients and Healthcare Professionals in a Cross-Sector Tele-Rehabilitation Programme Birthe DINESENa,1 , Stig Kjaer ANDERSEN a, Ole HEJLESEN a, Egon TOFT a a Department of Health Science and Technology, Aalborg University, Denmark

Abstract. This paper explores how technology affects the interaction between chronic obstructive pulmonary disease (COPD) patients and healthcare professionals in a cross-sector tele-rehabilitation programme. The qualitative analysis has shown that a community of rehabilitation can be created despite the presence of long-distance technology. In the tele-rehabilitation programme, the interaction between the COPD patients at home and the healthcare professionals at the clinic has evolved with dialogue as the basis for mutual learning processes and new relationships. Managed properly, rehabilitation at a distance can be both effective and satisfying. Keywords. Tele-rehabilitation, COPD patients, healthcare professionals, wireless technology, preventive integrated care

1. Introduction In 2005, three million people died of chronic obstructive pulmonary disease (COPD), equivalent to 5% of all deaths globally that year [1]. Patients with severe and very severe COPD have a readmission rate of 63% during a mean follow-up of 1.1 year, with physical inactivity as the most significant predictor for readmission [2]. Those COPD patients with serious symptoms experience significant limitations in their everyday life, and the effect of medical treatment is limited. Many COPD patients must live with a reduced level of function, inactivity, frustration and social isolation. The issue, then, is to develop the most effective means of delivering and coordinating multidisciplinary care for COPD patients [3]. Reviews of non-telecommunicationsbased disease management programs for patients with COPD show these programs to be heterogeneous in terms of interventions, outcome measures and study design [4,5]. There is a need for more research on disease management programs for COPD patients that cross-cuts both primary and secondary care [6,7,8]. In the research and innovation project, called “Telehomecare, chronic patients and the integrated healthcare system” (the TELEKAT project), we have taken up the challenge of combining rehabilitation activities and use of new technology in order to develop a cross-sector telerehabilitation programme for COPD patients. The patients in focus are those with severe or very severe COPD. The aim of this paper is to explore how technology 1

Corresponding Author: Birthe Dinesen, Assistant Professor, Aalborg University, Department of Health Science and Technology, Fredrik Bajers Vej 7 D1, DK-9220 Aalborg, Denmark, E-mail: [email protected], tel. +45 20515944

B. Dinesen et al. / Interaction Between COPD Patients and Healthcare Professionals

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affects the interaction between COPD patients and healthcare professionals in a telerehabilitation programme. Through user-driven innovation, the TELEKAT project has focused on developing a programme of tele-rehabilitation that can be carried out in the patient’s own home in collaboration with various healthcare professionals. Rehabilitation, instead of being carried out at a clinic, can thus become a part of the patient’s everyday life in the home environment. A telehealth monitor box is installed in the patient’s home for four months. Based on wireless technology the telehealth monitor can collect and transmit data about the patient’s blood pressure, pulse, weight, oxygen level and lung function to a web-based portal or to the patient’s electronic health care record. Healthcare professionals, e.g., general practitioners (GP), district nurses, nurses, doctors and physiotherapists at the health care centre or hospital, can assess the patient’s data, monitor the patient’s disease and training inputs and provide advice to the patient. Patients and relatives can also view the data on the web portal and decide with whom they want to share their data (see figure 1).

Figure 1. The TELEKAT programme for telerehabilitation

2. Theoretical Framework This study is based on the notion of “communities of practice”, as inspired by the learning theorist Etienne Wenger [9]. Wenger defines “communities of practice” as groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly. Wenger sees learning as a social practice centering around knowledge-sharing. Learning process is thus more than an individual cognitive process. Learning takes place in interaction with others, with whom one has a common interest. Hence, one becomes a part of a social learning process. Through the communities of practice, the participants realize that they gain more knowledge and understanding for the common interest. Over time and in sustained interaction, the participants develop a shared practice and repertoire of resources: they exchange experiences, stories, tools, and ways of addressing recurring problems. Participants will be involved in a set of relationships over time.

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3. Methods The case study method [10] is chosen as the overall research strategy for this study and serves as an explorative and in-depth study. A randomised study (n=111) has also been conducted. One group of COPD patients, called “intervention group” (n=57), received home monitoring using tele-rehabilitation technology. A second, control group (n=48) of patients followed the traditional rehabilitation programme. Clinical and economic data from the randomised study is not reported in this paper. Data collection techniques included: documentary materials, participant-observation inspired by Delamont [11] (total hours: 163 hours), qualitative interviews inspired by Kvale [12] with healthcare professionals, of which there were 6 GPs, 6 nurses and doctors at hospital, 6 nurses at the healthcare centre and 8 district nurses. Of the 57 COPD patients in the intervention group, 22 were interviewed three times while doing home monitoring. All the transcribed interviews were coded with Nvivo 8.0 software and analyzed and identified in steps inspired by Kvale (2009). The research process was carried out in dialogue with research colleagues. In order to optimize generalization of case studies, reference literature [13] recommends analytical generalization. In the TELEKAT project, analytical generalization has been applied by using a theoretical framework and a triangulation of data collection. Ethical approval was obtained from the local Ethics Committees.

4. Findings The findings of how technology influences the interaction between COPD patients and healthcare professionals in a telerehabilitation programme are presented in terms of themes and examples in table 1. Table 1. Themes of how technology influences the interaction between COPD patients and healthcare professionals Themes Mutual learning process “Community of rehabilitation” From authority to dialogue

Technology as network creator Technology as a pedagogical tool Cared for and feeling secure

Examples Healthcare professionals state that they learn more about COPD patients and rehabilitation in their everyday life.COPD patients state that they were able to integrate and maintain changes of lifestyle in their everyday life. Healthcare professionals and COPD patients have developed a joint commitment and perception of telerehabilitation. Dialogue between hospital and patient (and family) breaches the healthcare professionals’ knowledge monopoly. Patients express the view that they have developed dialogue with the healthcare professionals on a more equal basis. The design of the web portal makes it possible for the healthcare professionals, e.g., doctor at hospital, patients’ GP and the patients to be able to access the same data. Measured values that were accessible and visualised through graphics provide the patients with an overview of the development of their own symptoms. The COPD patients state that they feel cared for and secure in their interaction with the healthcare professionals.

Table 2 presents the baseline characteristics of the participants in the randomized study.

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Table 2. Characteristics of interviewed COPD patients at baseline. The values are shown are the mean or median. Variable Number Age (years) FEV1 (liters) Weight (kg) BMI (kg/m2) Oxygen saturation (on air) Blood pressure Pulse MRC dyspnoea score

Telerehabilitation Group (n= 57) Male 23 69,6 1,10 79,61 25,74 93,33 137/79 77 3,5

Female 34 67,2 0,75 67,53 25,31 93,63 136/82 85 3,64

Control Group (n= 48) Male 22 70,6 1,16 79,56 26,8 94,11 136/80 80 3,6

Female 26 59,9 0,74 60,67 22,76 94,42 132/77 80 4,00

5. Discussion Based on a qualitative analysis, the interaction between COPD patients and healthcare professionals in the tele-rehabilitation programme can be characterized in terms of Wenger’s “community of rehabilitation”, linking COPD patients and healthcare professionals across sectors (see table 1). The characteristics of the interviewed COPD patients in the tele-rehabilitation group are representative compared to the control group (see table 2) in the TELEKAT study. The COPD patients expressed the view that their relationships with the healthcare professionals had developed from one of being subordinated to professional authority to a relationship of dialogue where the focus was on mutual learning. Observations showed that COPD patients tended to become more active as they participated in the programme. The rehabilitation process thus became a learning process. It was more than an individual cognitive process centered on the patient, since the learning was also distributed amongst healthcare professionals, the family and network of the COPD patients. Observations showed that the telerehabilitation programme created a bridge between the healthcare professionals’ domain and the patients’ home domain. Moreover, the programme also challenged the traditional authority relationships and ways of interacting between professional healthcare practitioners and patients. This change in interaction between healthcare professionals and patients from an authoritarian relationship to a more egalitarian relationship based on dialogue between the parties has been seen in another study on home hospitalisation [7]. Equal dialogue not only enhances patients’ personal capacities to handle the consequences of living with the limitations of severe COPD. It also enhances the ability of healthcare professionals to provide treatment and to help patients regain some of their lost potential for staying active and avoiding rehospitalisation. Healthcare professionals and patients expressed the view that the design and function of the web portal promoted networking between the parties. Observations and the qualitative analysis showed that being able to see the measured and visualized values on the screen motivated the patients to involve themselves more deeply in their rehabilitation activities, giving them a better understanding of their own disease. This tendency has been seen in studies of home monitoring of other chronic diseases [14]. In the TELEKAT study, however, the patients articulated the view that they felt well cared for and were secure in the knowledge that the healthcare professionals were there for them “at the end of

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the line”. This made them feel more secure in carrying out the rehabilitation activities in their homes, despite the fact that no one was there to supervise them on the spot.

6. Conclusion The qualitative analysis has shown that a community of rehabilitation can be created despite the presence of long-distance technology. In the tele-rehabilitation programme, the interaction between the COPD patients at home and the healthcare professionals at the clinic has evolved with dialogue as the basis for mutual learning processes and new relationships. Managed properly, rehabilitation at a distance can be both effective and satisfying. Acknowledgements. The TELEKAT project is funded by the Program for User-driven Innovation, the Danish Enterprise and Construction Authority, Center for Healthcare Technology, Aalborg University, and by various clinical and industrial partners in Denmark. For further details, see www.telekat.eu.

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[6] [7] [8]

[9] [10] [11] [12] [13] [14]

World Health Organization, Chronic obstructive pulmonary disease (COPD). Fact sheet N°315; 2009. Available at: http://www.who.int/mediacentre/factsheets/fs315/en/ Accessed 8th December 2010. Garcia-Aymerich J, Farrero E, Felez MA, Izquierdo J, Marrades RM, Anto JM. Risk factors of readmissions to hospital for a COPD exacerbation: a prospective study. Thorax. 2003; 58:100-105. Niesink A, Trappenburg JCA, Weert-van Oene GH, Lammers JWJ, Verheij TJM, Schrijvers AJP. Systematic review of the effects of chronic disease management on quality-of-life in people with chronic obstructive pulmonary disease. Respiratory Medicine. 2007; 101: 2233-2239. Lemmens KMM, Nieboer AP, Huijsman R. A systematic review of integrated care use of disease management interventions in astma and COPD. Respiratory Medicne. 2009; 103: 670-691. Peytremann-Bridevaux I, Staeger P, Brideaux PO, Ghali WA, Burn B. Effectiveness of Chronic Obstructive Pulmonary Disease- Management Programs: Systematic Review and Metaanalysis. American Journal of Medicine. 2008; 121 (5): 433-443. Paré G, Jaana M, Sicotte C. Systematic Review of Home Telemonitoring for Chronic Diseases: The Evidence Base. 2007; Journal of the American Medical Informatics Association.2007;14(3): 269-277. Dinesen B. Implementation of Telehomecare Technology – impact on chronically ill patients, healthcare professionals and the healthcare system. Aalborg, Aalborg University; 2007. Polisena J, Tran K, Cimon K, Hutton B, McGill S, Palmer K, Scott RE. Home telehealth for chronic obstructive pulmonary disease: a systematic review and meta-analysis. Journal of Telemedicine and Telecare.2010; 16 (3):120-127. Wenger E. Communities of practice. Learning, meaning, and identity. Cambridge, University Press; 1998. Yin R. Case Study Research Design and Methods. London, Sage Publications Inc; 2009. Delamont S. Ethnography and participant observation. In: Seale C, Gobo G, Gubrium JF, Silverman D, editors. Qualitative Research Practice. London: Sage Publications; 2007. pp. 205–17. Kvale S. Brinkmann S. Interviews: Learning the Craft of Qualitative Research Interviewing. Los Angeles, SAGE Publications; 2009. Flyvberg B. Five misunderstandings about case-study research. In: Seale C, Gobo G, Gubrium JF, Silverman D, editors. Qualitative Research Practice. London: Sage Publications; 2007. pp. 390-404. Dinesen B, Andersen PER. Qualitative evaluation of a diabetes advisory system, DiasNet. Journal of Telemedicine and Telecare. 2006; 12(2):71-74.


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Enhancing Self-Efficacy for SelfManagement in People with Cystic Fibrosis Elizabeth CUMMINGSa, Jenny HAUSERb, Helen CAMERON-TUCKERC, Petya FITZPATRICKc, Melanie JESSUPd, E Haydn WALTERSc, David REIDb,c , Paul TURNERa a eHealth Services Research Group, University of Tasmania b Tasmanian Adult Cystic Fibrosis Unit, Royal Hobart Hospital, Tasmania c Menzies Research Institute, University of Tasmania d Griffith University, Queensland, Australia

Abstract: This paper reports on a research trial designed to evaluate the benefits of a health mentoring programme supported with a web and mobile phone based self-monitoring application for enhancing self-efficacy for self-management skills and quality of life for people with CF. This randomised, single-blind controlled trial evaluated two strategies designed to improve self-management behaviour and quality of life. Task-specific self-efficacy was fostered through mentorship and self-monitoring via a mobile phone application. Trial participants were randomised into one of three groups: Control, Mentor-only and Mentor plus mobile phone. Analysis and discussion focus on the experiences of participants through a methodology utilising descriptive statistics and semi-structured interviews. The results highlight the challenges of stimulating self-management behaviours particularly in adolescents and in the evaluation of the role of mobile applications in supporting them. Keywords: chronic disease, self-management, information technology, m-health

1. Introduction Managing and maintaining health care support for the chronically ill poses numerous challenges for conventional models of health care delivery [1]. These challenges are particularly evident where the chronically ill are primarily children or young adults, as in the case of cystic fibrosis (CF) [2]. In response, new models of care have emerged including some that aim to support more patient involvement through mentoring and self-management [3]. Some evidence suggests that these types of interventions can be as effective as the introduction of new medications [4], although it is acknowledged that there are limitations to the techniques that have so far been utilised to evaluate interventions of this type [5, 6]. At the same time, there has been an increasing diffusion of web based and mobile information and communication technologies (ICTs) to assist in improving care delivery [7]. These systems have strong potential for supporting home based medical care [8] and there are also a number of studies reporting positive outcomes achieved in patients with chronic illness through encouraging self–management supported by technology [9]. It is however, evident that these types of interventions are also highly complex and require sophistication in the approaches utilised to implement them [10] and to evaluate their impacts [11, 12].

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E. Cummings et al. / Enhancing Self-Efficacy for Self-Management in People with Cystic Fibrosis

This paper reports on a research trial designed to evaluate the benefits of a health mentoring programme supported with a web and mobile phone based self-monitoring application for enhancing self-efficacy for self-management skills and quality of life for people with CF. The paper focuses on the mobile phone application, its usage and participants’ perception of its value in assisting them to self-manage.

2. Methods This randomised, single-blind controlled trial evaluated two strategies designed to improve self-management behaviours and quality of life in adolescents and adults with CF. Task-specific self-efficacy was fostered through mentorship and self-monitoring via a mobile phone and web-based application. Participants were recruited from within the CF community across Tasmania. All potential participants received a letter outlining the study and requesting volunteers. Respondents willing to participate then attended their regular CF clinic to formally consent and to allow baseline measurements and randomisation to be conducted. A total of nineteen participants were recruited through the paediatric and adult CF clinics. The study was approved by the Tasmanian statewide ethics committee (H0008370) and all participants and their parents or guardians (if aged less than 18 years) provided written informed consent. Trial participation eligibility criteria were as follows: Inclusion: formal diagnosis of Cystic Fibrosis (genotype or positive sweat test); able to provide informed consent; and landline telephone (to allow for mentoring). Exclusion: diagnosis of other active lung disease; awaiting organ transplantation; or severe lung disease (FEV1 Felt U, Bister M, Strassnig M, Wagner U. Refusing the information paradigm: informed consent, medical research, and patient participation. Health: An Interdisciplinary Journal. 2009: 13, 87-106.

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Evaluation of a Hyperlinked Consumer Health Dictionary for Reading EHR Notes Laura SLAUGHTERa,b,1, Karl ØYRI a, Erik FOSSE a The Intervention Centre, Oslo University Hospital (OUH) and Dept. of Clinical Medicine, Oslo, Norway b Dept. of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway a

Abstract. In this paper, we report on a pilot study conducted to test the usefulness and understandability of definitions in a Consumer Health Dictionary (IVS-CHD). Our two main goals for this study were to evaluate functionality of the dictionary when embedded in electronic health records (EHR) and determine the methodology for our larger-scale project to iteratively develop the IVS-CHD. The hyperlinked IVS-CHD was made available to thoracic surgery patients reading their own EHR. We asked patients to rate definitions on two 5-level Likert items measuring perceived usefulness and understandability. We also captured the terms that patients wanted defined, but that were not included in the IVS-CHD. Preliminary results indicate the types of problems that must be avoided when creating definitions, for example, that patients prefer detailed explanations that include medical outcomes, and that do not use "unfamiliar" terms they must also look up. We also have gained insight into the types of terms that patients want defined from their EHR notes, especially certain abbreviations. Patients further commented on the experience of reading EHR notes directly from the same system used by healthcare personnel and the help strategy of linking the contents to a hyperlinked dictionary. Keywords. Consumer Health Information, Dictionary, Electronic Health Records

1. Introduction Health records are internal working documentation used by healthcare professionals, and are also official legal documents. Until recently, patients' ability to understand and use the contents of these records has not been a huge concern. Yet now, with more countries passing legislation giving patients legal right to access their records and increasing availability of personal health records systems, many researchers are working on ways to help patients understand their health record content [1-3]. The Intervention Centre (IVS), a multi-disciplinary research centre at Oslo University Hospital (Rikshospitalet) in Oslo, Norway, has developed a consumer health dictionary (IVS-CHD), which is accessible through hyperlinks and embedded in the electronic health records (EHR) used in the hospital. Patients reading their records see, for example, their surgical notes with hyperlinked terms throughout the text. When mousing over a hyperlinked term, definitions are displayed in a pop-up box. At the bottom of the pop-up box, other links can be found that take the reader to further 1

Corresponding Author: Laura Slaughter

L. Slaughter et al. / Evaluation of a Hyperlinked Consumer Health Dictionary

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information, opening a new web browser window with the contents. The IVS-CHD resources include the patient version of a Norwegian catalog of pharmaceuticals with the detailed drug descriptions [4] and an encyclopedia of medical information that was written for patients [5]. The encyclopedia contains textual information in addition to diagrams, Flash programs, videos, and animations. The third source was the Norwegian Medical Dictionary (Norsk Medisinsk Ordbok) published by Kunnskapsforlaget [6]. Our primary task is the evaluation of IVS-CHD definitions for use by patients and the general usefulness of the embedded consumer health dictionary tool. We report on our study that is seen as a preparation for further work to iteratively improve the patient-friendly definitions of medical terms. The concerns we address are: • Is our consumer health dictionary seen as a useful explanatory tool for patients that will help them understand their own record content? • What makes a good definition for patients? Patients are not one heterogeneous group. How do we write good definitions for everyone? • When patients read their records, what do they really want help with? Which words do they want to look up and why?

2. Methodology We evaluated the IVS-CHD using patients from the thoracic surgery department at Oslo University Hospital (Rikshospitalet). These patients are referred to the hospital from all regions in Norway and may live in either an urban or rural area in the country. Through our interactions with these patients, we iteratively developed the methodology that will be used in the larger-scale research project. 2.1. Participants The five participants in this study were outpatients at the thoracic surgery unit. All the patients were male and between the ages of 58-68, having diagnostic codes related to cardiac transplant, carotid artery stenosis, or myocardial infarction. Their occupational backgrounds were diverse. They all came for tests and preparatory work in advance of an upcoming scheduled surgery. Patients were asked to participate as they became available, over a two-week period in December of 2010. Patients had to fulfill the selection criteria before they were asked to participate: (1) the patient must have had at least one prior surgery at the hospital so that there would be a previous history of notes for the patient to read, (2) the patient must be a native speaker of Norwegian, (3) the patient must be able to read/write in Norwegian, and (4) the patient must have normal cognitive functions (i.e. no stroke patients or known cognitive impairments). 2.2. The IVS-CHD Consumer Health Dictionary As stated above, the definitions displayed when patients mouseover EHR text come from several sources, which are merged in the IVS-CHD. Surgeons affiliated with IVS wrote some of the definitions. They were only instructed to create a definition that would be understandable to patients. We cannot be certain of the rules used for forming definitions in the drug handbook [4] or the encyclopedia (NEL) [5]. For the Norwegian medical dictionary [6], the editor has written that “definitions should not contain words

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that cannot be found elsewhere in the dictionary, and they should be built up hierarchically so that the concept group the word belongs to (medicine, disease, muscle clip, etc.) is the first thing explained, and only after that the specifics for the word." [7] Definitions in the Norwegian medical dictionary [6] are often preceded by explanatory synonyms. 2.3. Procedures The total time allocated for each patient was 45 minutes, and this was the maximum possible due to constraints such as staffing time and convenience for the patients. The patients were tested in a private room with two researchers and a nurse present. They used a laptop to read their own records directly within the hospital’s EHR. All patients were explained the purpose of the study and signed a consent form prior to completing study tasks. Task 1, Rate Definitions: The patient selects a part of the record from the doctor’s notes, nursing notes, surgical notes, or discharge summary- the entire record is available so the patient chooses what is of interest to them. All terms in the EHR text having definitions in the IVS-CHD are displayed using standard blue hyperlinks. When a patient clicks on a hyperlink, we automatically record that the term has been accessed. After reading a definition, the patient then rates the definition on two 5-level Likert items. They are: 1) the usefulness of the definition is not useful/useful, and 2) understanding the definition is difficult/easy. In addition to the rating, the patient’s comments about the definition are recorded. There can be more than one definition for a term available (since there are combined sources, e.g. one definition written by surgeons and one from the Norwegian medical dictionary). The patient must give ratings and comment on each definition. Task 2, Complete Brief Questionnaire: The patient answers the following questions: (1) Do they wish to read their EHR notes: on paper, on screen, or no preference? (2) What can be done to make the records easier to read? (3) How can the EHR be improved to make it easier for patients to understand? Task 3, Underline Difficult Medical Terms: Patients read a printed copy of the discharge summary from their last thoracic surgical procedure at the hospital. Terms are not underlined as they were on-screen using the IVS-CHD functionality. The patients are then asked to read and underline for themselves the terms that they feel are necessary to have defined. We do this in order to find out what terms need to be defined that are not yet in the dictionary.

3. Results There were 5 patients participating and together they rated a total of 25 definitions. We were able to capture some aspects of the types of definitions thoracic surgery patients prefer to have and terms that they need defined, though the small sample-size is a limitation of the current study.

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3.1. Definitions The definitions written by the published medical dictionaries did not fair any better than those written by the surgeons from our hospital. Problems and desiderata are described in Table 1. Table 1. Lessons Learned From Problematic Definitions. Defining Medical Terms for Thoracic Surgery Patients: Lessons Learned (1) Do not use unfamiliar medical terms within the definition that also need to be looked up. e.g. vertebral artery* - an artery that arises from the subclavian artery supplying the brain with blood (translation of: arteria vertebralis - arterie som avgår fra arteria subclavia og forsyner hjernen med blod) *written by surgeons (2) Make sure the definition is complete. Definitions need to fit in the context of the patient’s situation and must therefore include the necessary information for understanding what happened during a procedure. e.g. thoractomy[6] - surgical opening of the chest (translation of: torakotomi - kirurgisk åpning av brystkassen) should contain additional information about approach: sternotomy, posterolateral, and anterolateral. (3) Avoid single word definitions.e.g. dilation[5] - expansion (translation of: dilatasjon utvidelse) (4) Avoid circular definitions and definitions that are based on the same term but in a different grammatical form; instead go straight to the needed clarification. e.g. palpatory[6] - has to do with palpation (translation of: palpatorisk - som har å gjøre med palpasjon) (5) When possible, write definitions that explain effects. Explanation is crucial to patients who prefer outcome information. e.g. A definition rated highly by a patient: TIA[5] - "transient ischemic attack" transient decreased blood flow to part of the brain with transient loss of body or mental functions, the condition clears within 24 hours (translation of: TIA - "transitorisk iskemisk atakk", forbigående nedsatt blodstrøm til en del av hjernen og med forbigående tap av kropps- eller mentalfunksjoner, tilstanden normaliserer seg i løpet av 24 timer) e.g. A definition given a low rating by a patient: TIA[6] - transient ischemic attack, transient bouts of oxygen deprivation in parts of the brain (translation of: TIA transitorisk ischemisk attakk, forbigående anfall av oksygenmangel i deler av hjernen.)

3.2. Terms to Define Below in Table 2, we present an example list of terms that patients accessed in the IVSCHD while reading their notes related to thoracic surgery, and also those terms in the records they want included in the dictionary in the future. Table 2. Examples of Terms Patients Want in the IVS-CHD to Help With Understanding Their EHR Notes Examples of Terms Accessed in IVS-CHD opiates (opiater) carotid stenosis (carotisstenoser) abdominal aortic aneurism (abdominalt aorta aneurisme) doppler (shortened version of doppler heart monitor) BT (abbreviation of blood pressure in Norwegian) SPO2 (abbreviation of oxygen saturation level) intercurrent (interkurrente) cardiopulmonary (kardiopulmonale) central obesity (sentral adipositas) abdomen (abdomen)

Terms to Include in the IVS-CHD poststenotic (post stenotisk) coartation (coorctasjon- misspelling coartation)

of

Patients want to be able to see expanded versions of all the shortened expressions and abbreviations in their EHR. The IVS-CHD currently has limited ability to identify abbreviations and acronyms. It identifies acronyms with several meanings, but does not have context-sensitivity built-in and therefore displays all possible meanings to the patient. The thoracic surgery patients did not seem disturbed by this and were able to identify the correct definition themselves.

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3.3. General Comments and Usefulness of the IVS-CHD for Reading EHR Records We recorded the comments concerning reading of EHR surgical notes and the use of the IVS-CHD. Overall, patients regarded the IVS-CHD positively, and thought it would be useful for themselves as well as for non-specialist healthcare personnel. One of the patients said he was expecting a "translation" and would prefer to receive a different patient-oriented version of his discharge summary. Another clearly voiced that "I’m not interested in learning", meaning that he did not want to learn the anatomy, procedure, etc. connected to his own surgical procedure. It was the outcome and future treatment plans that he wanted. Another said that he wanted the definitions to be personalized with examples from his own records. Lastly, one of the patients stressed that terms having the potential to be misunderstood should be defined in the IVS-CHD, such as "negative" test result. This patient said, "at first I thought it meant that the test result was bad", but then was relieved to know that having a negative result is actually a very good thing. This statement confirms a finding in Keselman et al. [2]

4. Discussion Difficulties that patients experience with medical terminology have been studied extensively. The primary tools that have been developed to help alleviate these problems are consumer health vocabularies (CHV) [e.g. 8,9]. CHV's can be used within information systems in a variety of ways, but they are primarily intended to automatically replace "unfriendly" professional terms with terms that are considered more appropriate for patients, thereby changing the text to a simpler version [1]. Our approach is to provide easily accessible definitions to patients reading their EHR rather than "translating" the text to a patient-friendly version. This is similar to SciReader [10], which is another tool to read medical content with instantaneous definitions. In this pilot study, we have taken a step forward to evaluate this type of proposed aid to understanding. Understanding what terms patients want defined and how to write useful consumer-oriented definitions is a problem to be addressed. Future studies will focus on patients' needs for dictionary resources versus translated versions of EHR content.

References Leroy, G. Eryilmaz, E. Laroya, B.T. Health information text characteristics, AMIA Annu Proc. (2006), 479-83. [2] Keselman, A. Slaughter, L. Smith, C.A. Kim, H. Divita, G. Browne, et.al. Towards consumer-friendly PHRs: patients´ experience with reviewing their health records. AMIA Annu. Proc. (2007), 399-403 [3] Deléger, L. and Zweigenbaum, P. Paraphrase acquisition from comparable medical corpora of specialized and lay texts. AMIA Annu. Proc (2008), 146-150. [4] Felleskatalogen, http://www.felleskatalogen.no/ [5] Norsk Electronisk Legehåndbok (NEL), http://legehandboka.no/ [6] Nylenna, M. (ed.), Medisinsk ordbok 7th ed., Kunnskapsforlaget, Oslo, Norway, 2009. [7] Nylenna, M. Hvordan lages en medisinsk ordbok?, Tidsskr Nor Legeforen 22 (2009), 2401-2400. [8] Messai, R. Simonet, M. Bricon-Souf, N. Mousseau, M. Characterizing consumer health terminology in the breast cancer field. Stud. Health Technol Inform (2010), 160 (Pt2), 991-4. [9] Hong, Y. Ehlers, K. Gilles, R. Patrick, T. Zhang, J. A usability study of patient-friendly terminology in an EMR system. Stud. Health Technol Inform (2010), 160 (Pt1), 136-140. [10] Gradie, P.R. Litster, M. Thomas, R. Vyas, J. Schiller. M.R. SciReader enables reading of medical content with instantaneous definitions. BMC Medical Informatics and Decision Making (2011), 11(4). [1]


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A Pilot Assessment of Why Patients Choose Not to Participate in SelfMonitoring Oral Anticoagulant Therapy Morten Algy BONDERUP a, Stine Veje HANGAARD a, Pernille Heyckendorff LILHOLTa , Mette Dencker JOHANSENa Ole K HEJLESENa b 1 a Department of Health Science and Technology, Aalborg University, Denmark b Department of Health and Nursing Science, University of Agder, Norway

Abstract. Patients suffering from heart diseases often face lifelong oral anticoagulant therapy. Traditionally, the patient’s general practitioner takes care of the treatment. An alternative management scheme is a self-monitoring setup where the patient monitors and manages the oral treatment himself.. Despite international evidence of reduced thrombosis risk and death rate among patients enrolled in selfmonitoring, a majority of eligible patients deselect this opportunity. Little is about the causes if this. This study is a pilot assessment of why patients, located in the North Denmark Region, choose not to participate. The study is based on qualitative interviews with two nurses working in a medical practice and two patients participating in conventional anticoagulant therapy. The results of this study seem to suggest that at least some patients feel a lack of information to base their decision regarding self-monitoring or conventional management on and that the knowledge among the health personnel at the medical clinics should be increased. Keywords. anticoagulant therapy, self-monitoring, self-management, INR

1. Introduction Patients suffering from heart diseases often face lifelong oral anticoagulant therapy (OAT) in the form of vitamin K-antagonists like warfarin because of their increased risk of thrombosis [1]. Traditionally, the OAT has been clinic-based as the patient’s general practitioner (GP) monitors treatment effect measured by the International Normalization Ratio (INR) of prothrombin time and adjusts the oral vitamin Kantagonist dosage to maintain INR within the therapeutic range. In recent years, a self-monitoring OAT alternative has been introduced where the patient performs the monitoring using a portable INR measuring device and adjusts the dosage according to an dosing algorithm scheme [1]; a setup inspired by the setup used in diabetes. The self-monitoring system is widely used in Germany [2] and Switzerland [3]. International studies have shown that self-monitoring setups are associated with a reduction in the risk of thrombosis and death [1] and results in higher self-efficacy and improved treatment-related quality of life [4]. 1

Corresponding Author: Ole K Hejlesen, Dept of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7 D1, DK-9220 Aalborg, Denmark; E-mail: [email protected].

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M.A. Bonderup et al. / A Pilot Assessment of Why Patients Choose Not to Participate

Nevertheless there are many patients who deselect participation in selfmanagement OAT [3]. In Denmark more than 80.000 patients receive OAT, but only 7% of them perform self-monitoring [5]. Considering the obvious benefits for the individual patient associated with self-monitoring OAT, the low rate of attendance to this treatment appear paradoxical, but little is known about patients’ motives for deselecting self-monitoring OAT. The present paper presents a pilot study assessing why some of the patients located in The North Denmark Region choose not to participate in self-monitoring OAT.

2. Methods 2.1 Design We designed a qualitative interview study with health care professionals (doctors or nurses) responsible for clinic-based OAT and patients on clinic-based OAT who had deselected self-monitoring OAT. 2.2 Respondents The Thrombosis Centre of Aalborg Hospital (a specialized hospital unit responsible for patients on self-monitoring OAT and patients with severe coagulation disorders) selected a GP clinic to recruit patients and health care professional respondents from. The participating GP health care professionals were selected to match the following criteria: 1) employment on a GP clinic in the North Denmark Region, 2) employment as a doctor or a nurse responsible for patients in clinic-based OAT at the GP clinic, and 3) previous referral of patients for self-monitoring OAT in the Thrombosis Centre, Aalborg Hospital. The GP health care professionals selected patients from their clinic to participate in our study. The participating patients were selected to match the following criteria: 1) age ≥ 18 years, 2) clinic-based OAT, 3) eligible for and presented with the opportunity of self-monitoring OAT 4) deselection of self-monitoring OAT within three months. All participants received oral and written information about the study and gave their consent before the interview. All participants were informed that participating was voluntary and that they could withdraw their consent at any time. 2.3 Interviews The health care professionals were interviewed in a focus group interview whereas the patients were interviewed individually. All interviews were semi-structured. The health care professional focus group interview took place in the GP clinic. The patient interviews were intended to take place in the patients’ homes, but for practicality reasons one was conducted via telephone instead. The interviews were based on an interview guide. Topics covered by the health care professional focus group interview were referral criteria for self-monitoring OAT; patient information regarding self-monitoring OAT; patients’ reasons for deselecting self-monitoring OAT; and suggestions for self-monitoring OAT setup and information improvement to enhance selection. Topics covered by the patient individual interviews


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were indication of OAT; information regarding self-monitoring OAT; reasons for deselecting self-monitoring OAT; and suggestions for self-monitoring OAT setup and information improvement to enhance selection. The interview guide was available to the health care professionals before the interview but this was not the case in the patient interviews. One researcher (PHL) conducted all interviews supported by researchers MAB and SVH. All interviews were sound recorded and later transcribed and the transcripts were reviewed for flaws by one other researcher than the one who did the transcription. 2.4 Data Analysis Emergent themes were identified using meaning condensation in the transcripts and a coding frame was formed. The coding frame was applied to the transcripts. The dominant themes relevant for the aim of the current paper were found through an iterative process of transcript coding, analysis of coded text, and discussions between the authors. In this analysis of coded text, the main steps were self-understanding, critical common sense-understanding and theoretical understanding. Transcription, coding, and theme identification was done using the open source OpenCode qualitative data analysis program (version 2.1, Department of Public Health and Clinical Medicine, Umeå University, Sweden). None of the investigators had any financial interests in the study.

3. Results We interviewed two female nurses from one GP clinic and two patients treated in the same GP clinic (one female, 74 years old; and the other male, 66 years old). 3.1. Health Care Professional Interviews We identified four main themes in the health care professional focus group interview: assumptions regarding patients’ attitudes to the inconvenience of going to the GP clinic for OAT management; assumptions regarding patients’ views on security and safety in clinic-based vs. self-monitoring OAT; guidelines for patients eligible for referral to the Thrombosis Center, Aalborg Hospital; and knowledge about self-monitoring OAT as directed by the Thrombosis Center, Aalborg Hospital. The nurses presumed that patients consider it convenient to have the doctor or nurse manage the treatment as visits to the clinic for OAT management are combined with visits for other reasons and thus induce no additional visits. The nurses also presumed that patients feel confident and safe being in clinic-based OAT. The nurses expressed their lack of knowledge about criteria for patients to be eligible for selfmonitoring OAT and they agreed that they use their own judgment which may be different and not always consistent. The nurses did not seem to offer the selfmonitoring OAT to patients, who are fulfilling well established objective criteria for self-monitoring. They also expressed their lack of knowledge about the Thrombosis Centre in Aalborg and about self-monitoring OAT in general.

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3.2. Patient Interviews We identified four main themes in the patient interviews: experience of seriousness and relevance of information; feeling of security and safety; views of responsibilities of patients and health care system; and experienced and anticipated ability and convenience related to each of the two possible OAT regimens. The patients expressed that they had received only limited information about selfmonitoring when they were offered self-monitoring at the clinic and especially they did not note any serious encouraging information about possible benefits for them or specific information about practical issues like measurement frequency and reimbursement/funding of equipment. They did not consider the self-monitoring OAT a serious, relevant offer, and therefore, they felt that they had an insecure basis for choosing self-monitoring OAT as an alternative to clinic-based OAT. Both patients were satisfied with their current clinic-based OAT management and they did not consider it inconvenient to have to come to the clinic, because the clinic was located close to their homes and they had frequent visits to the clinic for other reasons than OAT. One of the patients expressed her distinct feeling of security and safety due to the personal contact with the nurse or the doctor during the OAT management visits and though she expressed her confidence of currently being able to perform self-monitoring OAT she was afraid that her physical and cognitive ability would prevent her from continuing self-monitoring OAT for more than a year. One of the patients also expressed the view that the task of OAT management is a responsibility of the health care system and not something that can be put on a single individual.

4. Discussion The results of the interviews showed that there may be several causes of patients’ deselection of self-monitoring OAT despite obvious advantages of the setup over the clinic-based OAT setup [1, 4]. One major reason for deselection of self-monitoring OAT suggested by the nurses and confirmed by the patients is the convenience as experienced by the patients of a clinic-based OAT, as no additional visits for OAT management is induced and where the geographical proximity of the clinic allows for frequent visits without inconvenience. This is also reported by Fritschi and co-workers to be a reason for discontinuing self-monitoring OAT among patients first started on self-monitoring OAT but switching to clinic-based OAT [3]. The nurses’ lack of knowledge about who to refer to the treatment and which benefits can be expected and how the self-monitoring setup is organized by the Thrombosis Center may be reflected in the patients’ experiences of vague information and no clear recommendation of self-monitoring OAT over clinic-based OAT. Lack of clear criteria from which to refer the patient is confirmed to a major obstacle for selfmonitoring OAT as reported in a Cochrane meta-analysis [1], but criteria have been published and widely accepted [6]. Such criteria and a detailed description of the procedure should be made available for, and used by, the employees at the GP clinics. It should contain guidelines for which patients should be offered self-monitoring, when and how self-monitoring is offered, and which information is to be given when selfmonitoring is offered.


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Assuming that the patients’ apparent lack of knowledge is a result of the observed lack of knowledge at the GP clinic, patients’ lack of knowledge can be addressed by increasing the knowledge level of the GP nurses and by offering detailed referral guidelines to the GP clinic employees. Clearly, the data material in our pilot study is not large enough to justify any conclusions. However, if the results indicated in our pilot study are confirmed in larger studies, the initiatives mentioned above are clearly indicated to increase the number of patients who are referred to self-monitoring. Increasing and supporting health care professionals’ knowledge about organization and eligibility of patients to selfmonitoring OAT seem to be major steps. In addition to the need for larger studies of why patients choose not to participate in self-monitoring oral anticoagulant therapy, there might also be a need for broader studies of the technology “anticoagulant therapy”. Such studies may not only be used to improve the technology, but may also contribute with valuable information, which may be relevant when trying to decrease the number of patients who, despite being qualified for self-monitoring oral anticoagulant therapy, choose not to participate.

References [1] [2]

[3] [4] [5] [6]

Garcia-Alamino JM, Ward AM, Alonso-Coello P, et al. Self-monitoring and self-management of oral anticoagulation, Cochrane Database Syst. Rev. (2010). Sawicki PT. A structured teaching and self-management program for patients receiving oral anticoagulation: a randomized controlled trial. Working Group for the Study of Patient SelfManagement of Oral Anticoagulation, JAMA. 281 (1999), 145-150. Fritschi, J. Raddatz-Muller, P. Schmid, P. Wuillemin WA. Patient self-management of long-term oral anticoagulation in Switzerland, Swiss Med. Wkly. 137 (2007), 252-258. McCahon D, Murray ET, Murray K, Holder RL, Fitzmaurice DA. Does self-management of oral anticoagulation therapy improve quality of life and anxiety? Fam. Pract. 28 (2011), 134-140. Jensen B. /Hjerteforeningen, Banebrydende medicin undervejs. (2009) Downloaded from http://www.hjerteforeningen.dk/index.php?pageid=334&newsid=79 in December 2010. Fitzmaurice DA, Gardiner C, Kitchen S, Mackie I, Murray ET, Machin SJ. British Society of Haematology Taskforce for Haemostasis and Thrombosis, An evidence-based review and guidelines for patient self-testing and management of oral anticoagulation, Br. J. Haematol. 131 (2005), 156-165.

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Mobile Peer Support in Diabetes a

Taridzo CHOMUTARE a,b,1 , Eirik ÅRSAND a,b , Gunnar HARTVIGSEN a,b Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Norway b Department of Computer Science, University of Tromsø, Norway

Abstract. As in other domains, there has been unprecedented growth in diabetesrelated social media in the past decade. Although there is not yet enough evidence for the clinical benefits of patient-to-patient dialogue using emergent social media, patient empowerment through easier access to information has been proven to foster healthy lifestyles, and to delay or even prevent progression of secondary illnesses. In the design of diabetes-related social media, we need access to personal health data for modelling the core disease-related characteristics of the user. We discuss design aspects of mobile peer support, including acquisition of personal health data, and design artefacts for a healthcare recommender system. We also explore mentoring models as a tool for managing the transient relationships among peers with diabetes. Intermediate results suggest acquiring health data for modelling patients’ health status is feasible for implementing a personalized and mobile peer-support system. Keywords. Social media, personalization, mHealth, diabetes self-management

1. Introduction To improve self-management for a large population with lifestyle-related diseases such as diabetes, it is ideal that users can always access health information [1] in a ubiquitous manner, quickly and easily. Mobile phones are now highly pervasive and becoming more powerful with various new technologies, increasing their potential as universal devices for chronic disease self-management. However, the information and support services must be personalized and tailored to avoid cognitive overload, and to enhance user experience. Several techniques have been developed for filtering and personalizing Internet information. It has been shown repeatedly that user models can be used in recommender systems [9] to personalize web information, as in popular shopping websites. We are increasingly getting used to seeing recommender systems in use, for example, in e-business applications such as online shops (e.g. Amazon) and entertainment systems (e.g. Pandora Internet Radio). There are many trade-offs that need to be made, for instance regarding performance versus precision, and recommendations versus predictions (which are more demanding). Knowing that even straightforward matching of genre and topic sometimes yields unexpected recommendations, we must be particularly aware of trust and privacy issues in the context of health-related systems. One criticism of much of the literature is that too 1

Corresponding author: Taridzo Chomutare, Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, 9038 Tromsø, Norway; E-mail: [email protected]

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little attention has been paid to the practicalities of user profile management regarding privacy concerns in healthcare, especially given integration with social media [10]. Recent research confirms results from earlier studies indicating that users’ disclosure of personal data depended on how sensitive they perceived the data to be, and how much they trusted or stood to benefit from disclosure and use of the system [6]. This paper discusses the design of mobile peer support in a self-help system for people with diabetes. Two main issues are addressed. The first issue relates to improvement of the user profile management model from the European Telecommunications Standards Institute (ETSI) (technical specification, TS 102 747), through consideration of the health status use case. This concerted effort to model core disease-oriented properties of the user represents a paradigm shift. This new paradigm emphasizes relevance of recommendations to the health status of the patient and not necessarily recommendations that reflect the user’s conscious interests. Secondly, the ideas for fostering social engagement with family and friends put forward by Morris et al. [11] are here extended to establishing temporary, conditional friendships with strangers. New ideas about methods for exploring ad hoc social networks involving short-term relationships with peers are investigated. Mentoring models [5] are explored as potentially useful tools for motivating and sustaining participation for both the patient with a specific health challenge (protégé) and her/his mentor. The “Few Touch Application” (FTA) [3], originally developed as a self-help system for Type 2 diabetes, forms the background for the presented work.

2. Methods The presented system is designed to create a dynamic user model that can reason about a lifelong patient, and is used in personalizing health information and interaction with peers, inspired by the “Patients-Like-Me” [12] concept. The research methods for designing the FTA platform have been multidisciplinary, with an engineering approach in the design of the mobile application, user profile and recommender frameworks. Focus group meetings were used for facilitating participatory design methods; brainstorming, paper prototyping, interviews, questionnaires, and usability testing. A scheduled clinical trial will provide evidence of the impact that peer interactions in social networking websites have on clinical outcomes. The FTA platform was designed to collect the following personal information: • personal aims for food habits and physical activity [3] • blood glucose values using a wireless glucometer system [14] • dietary habits information [4] • physical activity using both a step counter [2] and manual registration • weight monitoring using a wireless weight scale (planned FTA add-on) This information forms part of the critical health indicators for both Type 1 and Type 2 diabetes, and comprises more or less mandatory parameters that users are monitoring on a daily basis. The health data are modelled and encapsulated in the user profile, which is applied to Internet social media content using a recommender system.

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3. Results 3.1. User Profile Management and Peer Support The first task involved in managing a user profile is often creating the profile, followed by making updates. User profiles have traditionally contained information about the user (alias, age, gender, language, etc.), preferences (layout, navigation, etc.), usage behaviour (clicked links, user-created tags, content rating, etc.), and recently social data (information about friends, their usage behaviour, folksonomy, etc.) and context of use (at work/home, driving/shopping, weather, in a meeting, etc.), and presence (available for chat, away, do not disturb, etc.) information. This study adds validated diabetes health data (blood glucose, HbA1c, weight, etc.). The data is a fragment of the health status, the new dimension in managing user profiles for healthcare use. These diabetesrelated data are pivotal in construction of recommendations regarding relevant peers and communities. 3.2. The Mobility Aspect and Recommender Systems A salient aspect to consider when designing mobile applications is the rapid changes in the context of use. Traditional recommender systems do not consider the complex contexts of the user environment. Context modelling techniques are still immature and their use in recommender systems is still relatively undeveloped. In this work, we consider a few high-level contexts, mentioned in the preceding section. 3.3. Healthcare Recommender System Figure 1 illustrates the design of the healthcare recommender system, where the recommender engine is based on a hybrid algorithm, comprising both collaborative filtering and content-based approaches. In the figure, the context of use is related to the user model and fed to the recommender engine, where this knowledge about the user is mapped onto Internet social media content and user profiles. The output is personalized information, presented as recommendations of vital content and predictions about potentially interesting peers or communities.

Figure 1. Recommender framework for the mobile peer-support system.


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The presentation of recommendations regarding potentially relevant content is based on aggregated ratings by the community. To increase the usefulness of recommended content, users are requested to rate and tag content, and these new ratings are fed back to the recommender system using learning algorithms. The discreet and easy rating mechanism suitable for small screens uses the Facebook style of “Like”. Quality assuring user-generated content and processing it into actionable knowledge is vital. Generally, current health-related social media is not proactive about quality assuring user contributions. One drawback of much of the media, albeit in the spirit of patient safety, is the reliance on manual moderation of user-generated content, with only a few options for automation tools. As user-generated content increases, manual moderation will become increasingly impractical, but natural language processing research is a promising alternative.

4. Discussion Defining a sufficiently comprehensive representation of a patient is not an easy task because many variables affect the person’s total health status. Innovative representations must be extensible and be able to abstract all the relevant health aspects. Intermediate results confirm feasibility of acquiring and modelling diabetes-related health data – as input to a mobile peer-support system. One important outcome emerging from this work is that automatically acquiring health data significantly reduces threats to data validity. Automatic data acquisition using sensors overcomes two challenges faced in healthcare social media. The first challenge is that of data validity, where users register symptoms, medications or outcomes manually [7; 12]. Some healthcare social networks allow users to manually register and see each other’s health data, but the value of such data is degraded by legitimate questions regarding its validity. The other challenge that is potentially addressed by automatic data acquisition is that of motivating users to manually provide their health data frequently over substantial periods of time. Using recommender systems is one of the more practical ways of implementing intelligent web applications. In this work, decoupling the health status and context of use from the user model has scaling advantages. The respective modelling complexities are transparent to the management of the core profile. These design artefacts for applying diabetes data to recommender systems can be generalized to chronic health information systems, making the artefacts appealing and relevant to a wider audience. Recent researchers have gone as far as proposing full integration of recommender systems with Personal Health Records (PHR) [8; 13]. This approach is promising, but is still largely immature, and suffers from inherent information redundancy and severe security risks. The concepts explored in this work for managing pseudonymised, transient and conditional relationships are, however, rather challenging. Mentoring models [5] are promising as implements for managing such relationships. Mentoring relationships are informal and allow a user to mentor or be mentored by another user who has demonstrated consistent control over a particular health aspect such as weight management. Mentoring relationships may be suitable for maintaining high morale in the community and for sustaining the motivation to succeed in a specific health issue. Further work is needed for elaborating social [11] and psychology theories to design algorithms for managing relationship dynamics and motivation.

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5. Conclusion Coping with the substantial demands for lifestyle changes among diabetes patients requires the right information and sound motivational tools. Given that patients possess appropriate tools for cooperation, sharing everyday experiences with similar-profiled patients may be more effective for enhancing self-management and increasing selfefficacy than relying on generic information found in books and the Internet. Although this paper was not designed specifically to evaluate factors related to relevance or usefulness of peer recommendations, the new diabetes-related dimensions that are addressed add to a growing body of literature on social media and its application aspects in the healthcare domain. The authors foresee great opportunities using mobile phones as means for peer support in enhancing the quality of life of people with diabetes and other chronic diseases. Acknowledgements. This work was supported in part by the Research Programme for Telemedicine, Helse Nord RHF, Norway, and Centre for Research-based Innovation, Tromsø Telemedicine Lab. (TTL), Norwegian Research Council Grant No. 174934.

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Alpay, L. Verhoef, J. Toussaint, P. and Zwetsloot-Schonk, B. What makes an "informed patient"? The impact of contextualization on the search for health information on the Internet, Stud Health Technol Inform 124 (2006), 913-919. Arsand, E. Olsen, O.A. Varmedal, R. Mortensen, W. and Hartvigsen, G. A system for monitoring physical activity data among people with type 2 diabetes, Stud Health Technol Inform 136 (2008), 113118. Arsand, E. Tatara, N. Ostengen, G. and Hartvigsen, G. Mobile phone-based self-management tools for type 2 diabetes: the few touch application, J Diabetes Sci Technol 4 (2010), 328-336. Arsand, E. Tufano, J.T. Ralston, J.D. and Hjortdahl, P. Designing mobile dietary management support technologies for people with diabetes, J Telemed Telecare 14 (2008), 329-332. Buell, C.Models of Mentoring in Communication, Communication Education 53 (2004), 1. Cunningham, S.J. Masoodian, M. and Adams, A. Privacy issues for online personal photograph collections, J. Theor. Appl. Electron. Commer. Res. 5 (2010), 26-40. Domingo, M.C.Managing Healthcare through Social Networks, Computer 43 (2010), 20-25. Hammer, S. Kim J. and Andre, E.MED-StyleR: METABO diabetes-lifestyle recommender, in: Proceedings of the fourth ACM conference on Recommender sys, Barcelona, Spain, 2010, pp. 285-288. Heitmann, B. Kim, J.G. Passant, A. Hayes, C. and Kim, H.-G. An architecture for privacy-enabled user profile portability on the web of data, in: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, ACM, Barcelona, Spain, 2010, pp. 16-23. Kristoffersen, S. Privacy Management in a Mobile Setting, in: Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, IEEE Computer Society, 2009. Morris, M.E. Markopoulos, P. De Ruyter, B. Mackay, W. Lundell, J. Dishongh, T. and Needham, B. Fostering Social Engagement and Self-Efficacy in Later Life: Studies with Ubiquitous Computing, in: Awareness Systems, Springer London, 2009, pp. 335-349. Wicks, P. Massagli, M. Frost, J. Brownstein, C. Okun, S. Vaughan, T. Bradley, R. and Heywood, J. Sharing health data for better outcomes on PatientsLikeMe, J Med Internet Res 12 (2010), e19. Wiesner, M. and Pfeifer, D. Adapting recommender systems to the requirements of personal health record systems, in: Proceedings of the 1st ACM International Health Informatics Symposium, ACM, Arlington, Virginia, USA, 2010, pp. 410-414. Årsand, E. Andersson, N. Hartvigsen, G. No-Touch Wireless Transfer of Blood Glucose Sensor Data., COGnitive systems with Interactive Sensors (2007).


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Evolution of Health Web certification through the HONcode experience Célia BOYERa, Vincent BAUJARD a, Antoine GEISSBUHLER a,b a Health On the Net Foundation b Service Cyber Santé et Télémédecine, Hôpitaux Universitaires de Genève Geneva Switzerland

Abstract. Today, the Web is a media with increasing pervasiveness around the world. Its use is constantly growing and the medical field is no exception. With this large amount of information, the problem is no longer about finding information but assessing the credibility of the publishers as well as the relevance and accuracy of the documents retrieved from the web. This problem is particularly relevant in the medical area which has a direct impact on the wellbeing of citizens and in the Web 2.0 context where information publishing is easier than ever. To address the quality of the medical Internet, the HONcode certification proposed by the Health On the Net Foundation (HON) is certainly the most successful initiative. The aims of this paper are to present certification activity through the HONcode experience and to show that certification is more complex than a simple code of conduct. Therefore, we first present the HONcode, its application and its current evolutions. Following that, we give some quantitative results and describe how the final user can access the certified information. Keywords. HONcode certification, Trustworthiness, Transparency, Health Web, Internet

1. Introduction In recent years the ease of publishing on the Internet has been further increased with the advent of the Web 2.0 phenomenon. Thus, despite the wealth of content available, the question is not just about finding information but also whether the information provided is credible. The problem is particularly acute in the medical information domain which has a direct impact on the health of public [1]. In response to the lack of transparency of the health information, many theoretical and practical initiatives have marked the short history of the Web. The most significant trends that have been applied to the Web on the quality of information (medical or not) are: the selection of webpages (e.g. Yahoo), self-regulation (e.g. Discern[2]), the popularity of webpages (e.g. Page Rank[3]), the certification of websites (e.g. URAC[4], HONcode[5]), education of the user (e.g. OMNI[6]) and the collaboration of users.

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C. Boyer et al. / Evolution of Health Web Certification Through the HONcode Experience

2. Material and Methods Initiated in 1995, the implementation of the HONcode (see Table 1) [7] (third party certification) began in 1996, Discern (self-evaluation) in 1998, WebMedica in 1998 (certification only for Spanish), Hi-Ethics (third party certification) in 2000, eHealth Code of Ethics (self-evaluation) in 2001, URAC in 2001 (very detailed but expensive), European Guidelines in 2002 (Eq. HONcode principles of the HON which participated in the development) and AFGIS in 2003 (dedicated to German sites). While some initiatives have disappeared or others do not have many candidates, the HONcode has been translated into 35 languages, had over 7400 sites certified by the end of 2010 in 102 countries and had been selected in 2007 by France to be the official certification body of French health websites. HONcode certification [7] is a voluntary act on the part of the site applicant; the first step is submitting the application form on the HON website. A pre-assessment is proposed to the webmaster to identify the missing principles. Once the certification request is submitted, HON experts evaluate the website. Each ethical principle which is not being complied by and should be added to the content of the webpages is indicated. Once the changes have been made, a unique seal of certification is issued. All HONcode sites are certified for 1 year and are reviewed annually. If a website no longer respects the HONcode, the webmaster receives a warning and if required changes are not made, the site may lose its certification. In addition if a user considers that a web site does not respect one or more of the HONcode principles while displaying the HONcode seal, he/she can report the violation using the complaint system accessible via the HONcode certificate linked to this website. The complaint is treated within 2 weeks by members of the HONcode team. If it is justified, the webmaster of the site is asked to bring modifications. As you can see, the certification process is interactive and provides a constructive contact between HON and the webmaster. Indeed, the aim is to bring up sites to a certain level of transparency. In keeping with this aim, some additions have been made to address the peculiarities of Web 2.0 [7]. The collaborative platform in addition to the current guidelines should respect as well the ones added specific to the Web 2.0. In view of the dynamics of the Web, the certification is in continuous expansion. Initiatives based on algorithms of criteria recognition, based on rules or by automatic learning were presented to give an indication of ethics to the health Web pages. While the model of supervised learning Aphinyanaphongs [8] is based on static examples of good and bad pages and therefore dependent on fields, the HON approach is more generic since it is based on the model of the HONcode [9]. This last approach offer good results with 78% of accuracy over all principles, and its integration in HON daily activity is in progress. The text retrieval was also used in creation of WRAPIN [10], a tool helping to determine the reliability of documents by checking the ideas contained against established benchmarks, and eventually enabling users to determine the relevance of a given document from a page of search results. Currently the visual information retrieval is being developed what is especially important for the doctors working with the images as radiologists. The retrieval of images can be done in two ways. The first one is done via simple text-based queries associated to an image. The second one is performed via requests based on matching exact database fields. The development and use of the second one for medical field is the subject of further research. One of running researches in the field is the 4-year EU project “KHRESMOI” [11] started in September of 2010, which aims to create a biomedical search engine targeted


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to the needs of lay populations, medical doctors and specifically radiologists. The KHRESMOI will archive effective automated extraction from biomedical documents, including improvements using crowd sourcing and active learning, and automated estimation of the level of trust and target user literacy, automated analysis and indexing for medical images in 2D, 3D and 4D, link unstructured and semi-structured information extracted from texts and images to structured information in knowledge databases, support cross-language search and create the adaptive user interface to assist in formulating the queries. The sources of information retrieval are: books, journals, web sites, images, and semantic data. It will also utilize the language resources to allow the translation and make the results available for a whole EU population. The expected impact on target users is fast availability of required trustworthy information.

Figure 1. Dynamic HONcode logo following the current status of the HONcode certification process Table 1 Presentation of the HONcode Principle (summarized) 1. Authoritative: indicate the qualifications of the authors 2. Complementarity: information should support, not replace, the doctor-patient relationship, the mission and the audience are explicated. 3. Privacy: Respect the privacy and confidentiality of personal data submitted to the site by the visitor 4. Attribution: Cite the source(s) of published information, date and medical and health pages 5. Justifiability: Site must back up claims relating to benefits and performance 6. Transparency: Accessible presentation, accurate email contact 7. Financial disclosure: Identify funding sources 8. Advertising policy: Clearly distinguish advertising from editorial content

3. Results of the Certification and Access to the Final User Currently the database represents more than 10 million pages indexed in Google. 52% of the certified sites are in English and about 11% in French, followed by sites in Spanish, Italian, German and Portuguese. For each evaluated site, the following information is collected: 1/ the HONcode principles respected, 2/ text extracted corresponding to the 8 principles, 3/ URLs of these text extracts, 4/ MeSH terms keyword [12] selected from the site and 5 / more general site label. In early 1996, a simple seal was introduced, allowing users to identify a certified site from a noncertified. However, the HONcode seal quickly became an additional safeguard for the Internet by requiring the sites to link the seal to the unique HONcode certificate on the HON site. The idea is to limit misuse of the HONcode seal, as the final verification is done on the HON site. The new basic principle is that custody by HON ultimately enables control of the display of the HONcode seal depending on the status of web site certification (the unique image generated for a given site is hosted at HON web site, Figure 1). Google is the search engine most used by the Internet; it can become the perfect tool for the promotion and awareness of the quality of medical information on the Internet when a user installs the HON Toolbar. HON Toolbar [13] is the most

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integrated way to access HONcode certified sites. It is composed of 3 features that are 1/ Identification of the HONcode membership in real time while browsing the Web. 2/ The search tool, HONcodeHunt, exclusively dedicated to certified HONcode sites accessible from the search bar of the browser. 3/ The emphasis of certified sites in popular search tools such as Google, Yahoo, MedlinePlus and Wikipedia. 13 years after the HONcode implementation, HON has looked for a way to evaluate the impact of the certification. To measure this impact a comparative and longitudinal study has been conducted in 2008 in collaboration with the French National Health Authority (HAS). The first study mentioned compared the compliance of 6 to 8 months certified websites (A) with the HONcode to the compliance of non certified French health websites that never asked for a certification and were taken as a Control sample (B). The second one compared the compliance to the eight HONcode principles of health websites before (T0) and 6-8 months after the certification (A). A second analysis was made to observe the website conformity to HONcode Principles 1, 4, 5 and 8 (see Table 1). Certified websites were ordered by their publisher’s type, to allow the building of a comparable sample of Control websites. The use of various sources allowed the decrease of distortions in the studies of non certified Control websites. 0.6% of health websites not asking for HONcode certification (control group B) does respect the eight HONcode ethical standards vs. 89% of certified websites (A). Regarding the principles 1, 4, 5 and 8, 1.2% of B respect these principles vs. 92% for A.

4. Conclusion and Perspectives We aim to show the many facets of the HONcode through its history, its evolution, implementation and use. During the past 15 years, HON has sought to promote the trustworthy medical information on the Web on a global scale. To meet the quantitative requirements of the Web, human expertise is assisted by many automated systems for a systematic, reliable and faster evaluation of websites. It is very important to expand distribution channels to reach as many potential users. Thus the realization of collaborations, to share our information, our philosophy and our vision, with major players such as the National Library of Medicine (USA) or Google is essential. The approach led by the HON is comprehensive and covers more than 35 languages around the world. At the same time, HON aims responding to local needs, the variety of languages, cultural differences and different regulations. The creation of local branches in different parts of the world, such as those initiated in Africa, Italy and Spain, should enable us to think locally and act globally to improve the quality of medical information on the Internet. France is the pioneer in quality eHealth by legislating on the issue of quality of health sites. A similar approach in other European countries will be welcomed to continue promoting the quality of medical information on the Internet for the benefit of Internet users.

References [1]

Fox, S. (2007) E-patients with a Disability or Chronic Disease. Pew Internet & American Life Project, 2007.

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Charnock, D. The DISCERN Handbook. 1998. Radcliffe Medical Press. Borges, .H. M. Cervi, P.T. Álvarez de Arcaya, G. Guardado, R. Rabaza, J. Sosa, Rate of compliance with the HON code of conduct versus number of inbound links as quality markers of pediatric web sites, in: Proceedings of the Sixth World Congress on the Internet in Medicine, Udine, Italy, 29 November— 2 December 2001. URAC: http://www.urac.org/MMandQualityChasm.asp, Nov 2008. Boyer, C. Baujard V. and Scherrer, J.R. HONcode: a standard to improve the quality of medical/health information on the internet and HON’s 5th survey on the use of internet for medical and health purposes. In 6th Internet World Congress for Biomedical Sciences (INABIS 2000), 1999. OMNI: omni.ac.uk, Dec 2008. HONcode Guidelines: http://www.hon.ch/HONcode/Guidelines/guidelines.html, May 2010. Aphinyanaphongs, Y. Aliferis, C. Text categorization models for identifying unproven cancer treatments on the web. Stud Health Technol Inform, 2007. Gaudinat, A. Grabar, N. Boyer, C. Automatic retrieval of web pages with standards of ethics and trustworthiness within a medical portal: What a name page tells us - 11th Conference on Artificial Intelligence in Medicine (AIME 07) - 07-11 July 2007 Amsterdam, The Netherlands. Joubert, M. Gaudinat, A. Boyer, C. Geissbuhler, A.. Fieschi, M. HON Foundation Council Members. WRAPIN : a Tool for Patient Empowerment within EHR. Stud Health Technol Inform. 2007;129:14751. Hanbury, A. Boyer, C. Gschwandtner, M. Müller, H. KHRESMOI: towards a multi-lingual search and access system for biomedical information, Medetel 2011 National Library of Medicine, Bethesda, Maryland. Medical Subject Headings, 2001. http://www.nlm.nih.gov/mesh/meshhome.html. HON Toolbar: http://www.hon.ch/HONcode/Plugin/Plugins.html

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Personal Health Data: Patient Consent in Information Age Dragana MARTINOVICa,1, Victor RALEVICHb, Milan PETKOVICc a University of Windsor, Windsor, Ontario, Canada b Sheridan Institute, Oakville, Ontario, Canada c Technology University, Eindhoven, Netherlands

Abstract. In this paper we report on findings related to treatment of patient consent in various circumstances and geographic domains; explore transfer of health data between custodians and geo-political entities; and emphasize importance of educating general public about issues related to handling health data. A specific set of questions about consent/legislation and related issues in the Canada, the USA and the EU are addressed in an attempt to answer them systematically. This comparison identifies similarities and differences along a set of dimensions. Keywords. Patient consent, data transfer, HER.

1. Introduction Both the literature and the everyday experiences confirm that the Internet has affected the practices in healthcare. Especially chronic patients and those who have recently been confronted with a health crisis are keen on using the Internet to search for information about treatments, risks, alternative cures, medical institutions, healthy life style, and other. Recent statistics indicate that 75%-80% of the Internet users in the US seek out the health-related information [1]. Patients across the world see health information technology as beneficial for scheduling visits, communicating with doctors, receiving results of diagnostic tests, and sending the results from home monitoring instruments to doctors’ offices by email [2]. Among Canadian physicians, the use of handheld personal computing devices to check prescriptions, track patients, check dosages or use decision supporting tools is on the rise [3]. Wireless access and mobile devices, with their portability, immediacy of service, and convenience, may provide for the ubiquitous and participatory healthcare. Mobile phones, may be used for tracking of infectious diseases, health education and promotion, sending warnings and alerts (e.g., to remind patients to take medications or book appointments), and for obtaining educated opinions from medical experts [4]. The recent European Commission reports [5], inform that 66% of European physicians use computers for consultations; among general practices, 80% electronically store administrative patient data, 92% electronically store medical data on diagnoses and medication, while 35% electronically store radiological images. In 1

Corresponding Author: Dragana Martinovic, Faculty of Education, University of Windsor, 401 Sunset Ave., Windsor, Ontario, N9B 3P4, Canada; E-mail: [email protected]

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the Netherlands, for example, 71% of physicians provide “e-prescribing.” However, it is of concern that with this increased reliance on computer applications and digitalization of health records, it becomes simpler to collect, store, and search electronic health data, thereby endangering patient’s privacy. In Canada, both patients and physicians are unaware that personal identifiable prescription data, travel from pharmacy computers via commercial networks to pharmaceutical companies ([6], [7]). Another issue arises from the increase in health services provided to non-residents. In this domain, the US is the biggest net exporter of medical services (supplier of medical services to non-residents travelling for medical reasons), while the residents of Canada acquire abroad over $300 million of medical services. While the health-related travel is on the rise, there are not enough data on security threats of such trade.

2. Method Methodologically, this paper gives a comparative overview of patient consent approaches in three geographic areas: Canada, the US and the EU; and rises concerns about technological solutions when patient health data cross legislative borders. In order to introduce a framework of privacy protection of health-related sensitive personal data, it is necessary to first specify: what is individually identifiable personal health information, and how can personal health-related information be protected? What is individually identifiable personal health information? Usually interpreted in very broad terms, individually identifiable personal information includes any demographic, contact, behavioral, and performance data in any media format [8]. Health data of any individual are most often accompanied with other types of data, i.e., demographics, historical data, family records, etc. Therefore, in cases when health data are compromised, the privacy of other personal information is also affected. How can personal health-related information be protected? This is primarily done through a rigorous legislative process. Because health data need to be comprehensive and easily related to other types of personal data, it is not feasible to protect privacy of the individual just by reducing the amount of stored sensitive information to the necessary minimum, and raising awareness of the threat of identity theft and other compromising actions. Errors and security breaches take their toll too. In 2009, the Privacy Commissioner of Ontario, Canada, issued her sixth order under PHIPA after records containing personal health information were found scattered on an Ottawa street outside a medical centre [9]. Recent security breach in Ontario involved the loss of a memory key “containing the health information of almost 84,000 patients who attended H1N1 flu vaccination clinics in the Durham Region” [10]. This prompted the Ontario Information and Privacy Commissioner to ask that health sector removes any personal health information from mobile devices, unless it is encrypted [11]. While the governments take such cases seriously, the question remains how to protect personal health information when it is in transfer between parties under different legislation, between healthcare institutions of different kind, or between countries? What is the status of patient consent in the information age?

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3. Result: E-Health Legislation and Related Technologies− Patient Consent In e-health legislation of Canada, the US and the EU, the expectation of the individual as well as his/her consent for use of personal health records are taken into account. In most cases, the consent determines access to personal health information necessary for consultations and transfers for consulting clinicians, referring clinicians, transferring facilities, receiving facilities and consumers. The patient consent requirement is further derived from different regulations such as the US Health Information and Portability Accountability Act [12], the Directive 95/46/EC of the European Parliament and of the Council, and Ontario Personal Health Information Protection Act [13]. According to these Acts, an individual exercises different levels of control over the collection, access, use and disclosure of his/her health information. This means that in some cases, the patient consent must be obtained before his/her health information may be accessed, used or shared. 3.1. Summary of Main Points Related to Patient Consent Based on Three Privacy Rules 3.1.1. HIPAA (US) • • •

The patient written consent must be obtained by the covered entities for the use and disclosure of the identifiable health information for the purpose other than they are permitted by the privacy rule. Individual has the right to request restriction on the use and disclosure of personal health information, and to request communication to be confidential. Each covered entity must provide a notice of its privacy practices to the patient.

3.1.2. Directive 95/46/EC (EU) • •

The consent is any freely given specific and informed indication of data subject’s wishes by which s/he agrees with processing of personal data. The EU Directive prescribes that the processing of personal data must be carried out with the consent of the data subject or be necessary under other conditions.

3.1.3. PHIPA (Ontario, Canada) • • •

Consent to the collection, use or disclosure of personal health information about an individual may be express or implied. The individual may withdraw the consent/has right to access own health records. Personal health information may change the custodian if the individual is informed before transferring his/her records or, if that is not reasonably possible, as soon as possible after transferring the records.

4. Discussion: Rigidity of the System and Various Threats As an attempt to make a seamless paradigm shift, many concepts in a networked environment are designed to mimic old ways of doing things. For example, there is a trend to replace the paper-based patient consent forms with their exact electronic versions (e.g. IHE Basic Patient Privacy Consent profile). In that way, consent forms remain static, natural language-based, and standard (as opposed to being customized).


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It can be concluded that in such case, digitization of health records, including the consent forms, does not take full advantage of the technological medium. In Canada, for instance, the process of digitization of consent forms is in its initial stage as electronic consent forms have to be printed and mailed or submitted in-person to the healthcare provider. Alternatively, the patient is presented with the consent form at the time of registration or consultation in a healthcare institution. However the Canada Health Infoway is announcing introduction of EHR where consent will be part of the record and it will be interpreted differently in different jurisdictions based on provincial health-related legislation. That is because each province may have specific consent policies, although they are mostly opt-out systems with various degrees of granularity [14]. European countries also use different consent models. For example, the Netherlands has an opt-out system, France, opt-in, while some countries want to support more detailed patient consent that can specify exceptions. There are many benefits to digitizing health information; for example, the process is made more efficient (as it allows for fast data storage and retrieval), convenient, costeffective, and the consumer preference could be immediately translated into machine readable polices (e.g. XACML policy), which provides fine grained access control to consumer’s health information [15]. As such, technology needs to support different consent models in a structured document as specified in the Privacy Consent Directive and the CDA R2 implementation guide for consent directive specifications of HL-7. However, besides the stated benefits, many problems intrinsic to privacy and security of electronic data management emerged. The ease with which patients’ sensitive health information are accessible in EHR systems, has raised concerns about the breach of data confidentiality and patient privacy, especially in cases when: • Health information is used outside the professional medical domain (e.g., wellness/personal health services, etc.); • Personal medical information is being used in a manner that acts against the interest of the individual (for example discrimination due to health status); • Health information is accessed wrongfully; • Personal medical information is used for commercial purposes; and, in general, if • Health information gets into wrong hands [16]. Other concerns are related to reliability of electronic data [17], lack of awareness in general public about the risks they encounter with electronic health data transfers, and increased danger of identity theft. This is particularly critical when sensitive information has to cross the border. Therefore, despite technical advances, there are social and cultural issues that limit and make problematic the use, management, and handling of personal health data across and within geographical borders.

5. Conclusions Here we argue that better understanding of issues around management of personal health data is necessary, patient consent should be properly addressed in all cases, and awareness of different technological and legal approaches in dealing with privacy preferences of patients should be raised. To achieve these goals, we embarked to develop a map of existing e-health legislation in Canada, the US, and the EU, and related standards, as well as a comparative model for obtaining patient consent electronically within and across borders, with recorded bottlenecks in both cases. This

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model should incorporate the existing mechanisms to identify and authenticate the person giving the consent, as well as to verify that consent has been given. Such comparative model should identify technical needs with respect to transferability from one health service to another, from one geographic domain to another; and develop recommendations that would be based on the ‘privacy by design’ [18] concept. This concept emphasizes that privacy cannot be assured solely by compliance with regulatory frameworks, but must be embedded in organizations’ operational designs. The international approach to this analysis is essential because in the domain of transfer of health data across the electronic networks (like the Internet), there is a knowledge gap across all sectors and lack of synchronization between geographic domains (e.g., the EU and North America). The empirical and comparative approach would bridge this gap.

References [1] [2] [3] [4] [5] [6] [7]

[8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

The Pew Internet & American Life Project, www.pewinternet.org HarrisInteractive (2007). http://www.harrisinteractive.com/news/newsletters/healthnews/ HI_HealthCareNews2007Vol7_Iss03.pdf Adatia, F., & Bedard, P.L. (2003). “Palm reading": Handheld software for physicians. Canadian Medical Association Journal (CMAJ), 168(6), 727-734. Morris, K. (2009). Mobile phones connecting efforts to tackle infectious disease. The Lancet Infectious Diseases, 9(5), 274. European Commission (2008) Report. http://europa.eu, ref.=IP/08/641 OPC of Canada (2009). http://www.priv.gc.ca/cf-dc/2010/ 2010_001_0323_e.cfm Zoutman, D.E., Ford, B.D., & Bassili, A.R. (2004). The confidentiality of patient and physician information in pharmacy prescription records: Commentary. Canadian Medical Association Journal (CMAJ), 170(5), 815-816. Martinovic, D., & Ralevich, V. (2007). Privacy issues in educational systems. Int. J. Internet Technology and Secured Transactions, 1(1/2), 132-150. IPC (2009). http://www.ipc.on.ca/site_documents/ar-09-PHIPA-e.pdf News Release (December 24, 2009). http://www.ipc.on.ca/english/About-Us/Whats-New/Whats-NewSummary/?id=132 IPC (2010). http://www.ipc.on.ca/english/Privacy/Stop-Think-Protect/ HIPAA Privacy Rule. http://www.hhs.gov/ocr/privacy/hipaa/ understanding/summary/index.html PHIPPA. http://www.e-laws.gov.on.ca/html/statutes/english/ elaws_statutes_04p03_e.htm#BK25 Canada Health Infoway (2007). White Paper. http://www2.infoway-inforoute.ca/Documents/ Information%20Governance%20Paper%20Final_20070328_EN.pdf Mwangi, E.W. (2008). Patient consent policies in XACML. Unpublished Master’s Thesis. Technical University of Eindhoven. Rooney, T., & Aitken, J. (2002). Consent and electronic health records. http://www.health.gov.au/ internet/hconnect/publishing.nsf/content/e250bd83358d3a56ca257128007b7ec9/$file/cons_dp.pdf Petkovic, M. (2009). Remote patient monitoring: Information reliability challenges, 9th Telsiks International Conference, IEEE Press, 295-301. Cavoukian, A. (2009a). Privacy by design. http://www.privacybydesign.ca/publications.htm#toc


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Emotions and Personal Health Information Management: some Implications for Design Enrico Maria PIRASa,1, Alberto ZANUTTO b e-Health unit; Fondazione Bruno Kessler, Italy b Facoltà di Sociologia, Università di Trento, Italy a

Abstract. This work reflects on the translation of a paper-based information system into an electronic one, taking account of the emotional dimension of material artifacts. A qualitative analysis carried out through semi-structured interviews enabled us to describe laypeople’s healthcare practices, and specifically the use of “ pediatric booklets”, which are paper health diaries designed to provide parents with a repository of the most relevant clinical data about their children. Our analysis reveals that parents’ use of the booklet does not depend only on the clinical relevance of the information contained in it. Its success rather depends on practices that reshape the booklet’s original meaning. In particular, parents use booklets as containers for other clinical records, and they consider them more as objects of affection and symbols of their caring for their children than as clinical tools with instrumental value in themselves. In the discussion we consider the risks of dematerializing health information tools by underestimating the relevance of the emotional side.2 Keywords. Personal Health Record, emotions, healthcare practices, booklets, insitu interviews, affordance

1. Introduction The inclusion of patients in monitoring and care processes is considered one of the most urgent needs of Western healthcare systems. The active role of laypeople in this field is perceived as a step towards more democratic and participatory forms of illness management [1], as a way to cope with a general shift from treatment and cure to management and care [2], and as a strategy to reduce the growing costs of healthcare. In this regard, providing laypeople with information and communication technologies (ICTs) is regarded as the best way to deliver more efficient forms of care. This perspective is reflected by the fortune of new labels such as computer health informatics [3] or personal health information management [4] and also by the growing body of literature on Personal Health Records (PHR), these being patient-

1 Corresponding Author: Enrico Maria Piras, c/o e-Health unit, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy. Telephone: 00390461314126, E-mail: [email protected]. 2 The present article is an entirely collaborative effort by two authors. If, however, for academic reasons individual responsibility is to be assigned, Enrico Maria Piras wrote Introduction and Results, Alberto Zanutto wrote Methods and Discussion.

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E.M. Piras and A. Zanutto / Emotions and Personal Health Information Management

controlled ICTs that enable laypeople to be a part of a digital environment where information flows seamlessly among a network of caregivers [5]. This scenario presents laypeople as able and willing to manage ICTs just as healthcare providers usually do in their work practices. This, however, is more wishful thinking than an easily achievable result because the pathway that leads from paperbased systems to electronic ones is tortuous, full of pitfalls, and rarely produces the results expected. While some studies have been conducted to understand how people manage paper-based health information in their homes [6; 7], to date little attention has been paid to the emotional implications of substituting paper-based systems with electronic ones. This paper reports qualitative empirical research and reflects on the translation of a paper-based health information system into a service available via the Internet. In particular, it considers the interplay between the emotional and functional dimensions through observation of the practices used to manage health documents in the household, focusing especially on patient booklets.

2. Methods This study is a part of a broader project of research and innovation aimed at prototyping and testing a regional PHR, this being a personally-controlled health information system designed to allow citizens to access, manage, share, and supplement clinical data. We analyzed laypeople’s current practices of document management in order to support the system’s technical development (here by “practice” we mean the relatively stable and socially recognized ways in which heterogeneity is ordered into a coherent set [8]). In particular, we focused on the management of “pediatric booklets” – paper records provided by the local healthcare authority to help parents keep track of the medical histories of their children – so as to explore the challenges of developing web-based versions of these documents. A first overall sample of 32 households, chosen on theoretical bases, was subjected to empirical study. Then, for the purpose of the detailed examination reported here, we selected 16 of them which were still managing a pediatric booklet. Of these, 10 out of the 16 informants interviewed were female, while seven of them had only one child, seven had two, and the last two had three children. In regard to the occupations of the interviewees, we had 5 teachers, 6 office workers, 1 professional, 3 independent professionals, and 1 PhD student. The interviews took place in the homes of the informants. This gave the researchers access to the pediatric booklets and enabled direct observation of the ways in which their management intersected with other dimensions of health information management and the activities of everyday life. The analyses was carried out by means of a grounded theory [9].

3. Results The analysis of the interviews revealed that home health record-keeping is a very complex activity aimed at enabling laypeople to provide healthcare personnel with the information that they need. The classification of documents into distinct thematic areas is invisible work [10] required of the patient by the healthcare system in order to save time for healthcare personnel and to grant the patients themselves their doctors’ full attention [7]. Here we focus on some specific practices related to the use of booklets,


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and pediatric booklets in particular. A pediatric booklet is a paper file (20x15 cm.) consisting of about 70 pages. Few of these pages give information to the parents, and even fewer allow the parents to take notes on their children. The other pages are supposed to be filled out by the pediatrician or any doctor that attends to the child, so as to provide an updated clinical history of the latter until s/he turns 14. While the parents are asked to keep the booklet and bring it with them to any encounter with doctors, it is primarily a clinical tool. The cases analyzed exhibited some shared features in how this paper-based information system was used. Firstly, the more the child grew, the fewer data were recorded in the booklet, proof that it is mostly the first phase of development that is of concern to pediatricians. Secondly, most of the parents stated that pediatricians filled out the booklet only occasionally, explaining that they preferred to use their own information systems (electronic or paper-based), and that the contingencies of the examination did not always let them work on the booklet. Thirdly, the pediatric booklet appears to have been of little or no use for the other doctors to which it was shown (e.g. in accident and emergency). Finally, none of the interviewees reported using the booklet to write information about the child’s health, claiming that they were instead only its keeper, and considered it as belonging to the pediatrician. Despite this discontinuous and fragmented use, all the parents kept the pediatric booklet constantly to hand and took it with them for any contact with health facilities, and whenever they travelled with their children (e.g. holidays). This seeming paradox is explained by the fact that, invariably, the book was used by parents as the place in which to keep all other health records. During the interviews we observed pediatric booklets filled with discharge letters, prescriptions, vaccination certificates, and any other diagnostic reports produced by medical facilities, with the exception of voluminous documentation such as radiological images. It was not rare to find booklets containing printed web pages to show to the doctor, leaflets picked up in a pharmacy, business cards of doctors, and, more in general, any other sort of paper document relating to the child's health. Another finding was that when the children turned 14, the booklet was not shown to the general practitioner to be evaluated. Rather, it was kept at home in the same spaces where the parents retained cherished objects concerning their children (toys, first communion favours, school reports) in order to preserve them and eventually give them to their sons and daughters when they left home. For the early years, the booklet was usually regarded as precious because the parents and pediatrician both paid close attention to the healthy development of the children. In these years, therefore, parents accumulated a number of health records concerning both routine contacts (e.g. vaccinations) as well as sporadic and exceptional ones (e.g. emergency room, hospitalization) with health care facilities. At this stage of the child's life, parents took the booklet with them to encounters with doctors, so that it became the easiest place to store documents arising from the meeting. After a few such interactions, the book became almost “naturally” the depository of health documentation. Overall, the paper-based booklet was a tool with limited clinical utility in the strict sense but of great importance in terms of the reassurance that it gave to parents. For the latter, the book represented the history of the child’s development punctuated by ‘minor incidents’. Because of the way in which the booklet was structured, in fact, it was not suited to accompanying a child with a complex pathological condition; and in fact, when such a condition occurred, the booklet was abandoned and specific files or folders were used.

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4. Discussion and Implication for System Design The analysis of the practices described above suggests that use of the pediatric booklet by the parents turn it into a new artifact: a booklet-container-symbol used not only for practical purposes but also to testify to parents’ own care-giving role. Initially, it is used by pediatricians to monitor various parameters associated with growth. Thereafter, the material affordances [11] of the booklet are exploited by the parents to appropriate it. The appropriation – the process of adaptation and adoption of the artifact [12] – depends on the materiality of the other objects with which it is put in connection by the users, such as clinical documents produced by healthcare facilities. In other words, the widespread use of the booklet can be interpreted as concerned both with the intrinsic characteristics of the object (e.g. forms to be filled out) and its possession of the same materiality as the other objects, so that it constitutes an “ecology of use” with them. The practices centred on the booklet also suggest that its widespread use also deals with its re-symbolization, so that it becomes not only a clinical tool but also the symbol of the parents’ care and attention for their children. This process culminates with the final loss of all relevance as a medical instrument (when children turn 14) and its new collocation alongside other objects of affection, a clear sign that it now has a purely emotional and symbolic value. The study of the practices related to this object raise substantial concerns about its possible translation into a digital information system. Although the information contained in its pages could be easily engineered, this would not guarantee a systematic use “substitutive” of the one observed. Achieving this result would require providing the users with a “system” (physical and functional) able to act as a frame of reference for all health information in regard to the child. The electronic system should maintain the features of a “booklet-container” of other records and of a “booklet-affective symbol”, in the absence of which it would risk becoming an impoverished artifact. These two dimensions remind us of two distinct challenges for the design of electronic systems for health care. The first requires a response in terms of digitization, access and standardization of health information, bearing in mind the importance of their integration. Although this is a primary objective at the current stage of development of information systems, it is far from being reached. Even more complex is the second challenge: that of responding to the loss of the emotional dimensions due to the practices of using physical objects and replacing them with electronic systems. Responding simultaneously to both these challenges does not seem straightforward, therefore. Health document management combines both retrieval of clinical records and their cataloguing in the booklet, but also the latter’s use as a container of service information (e.g. a specialist’s visiting card); a request to be made to the doctor (e.g. a note on a new therapy); documents useful for obtaining health services (e.g. prescriptions, heath insurance cards). The emotions that parents associate with the paper-based booklet derive precisely from these continuous forms of use, which should, albeit in different forms, be “jointly” reproducible in an electronic device. Currently, the design of healthcare information systems concentrates largely on the development of interoperable tools that afford easy access to information, for example with the support of multi-platform systems. This choice, however, is based on the idea that people give priority to “information.” Our analysis instead suggests that the affective value of the pediatric booklet as a “material object” resides in its ability to convey meanings and feelings generated by the large number of social practices in


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which the tool is used. For this reason, the design of systems like this one should incorporate these “emotional experiences of use.” This would seem to explain, for instance, why some objects with scant interoperabiliity but which are “technologically dedicated” seem better suited to emphasising the affective dimension that surrounds the use of any device. These “dedicated” objects safeguard a certain “materiality” of the original use. Study of the paper booklet confirms its limited clinical relevance but, at the same time, its importance in affectively reassuring parents as a comprehensive device “dedicated” to the health practices performed for their children. Also its engineered version must therefore be able, on the one hand, to safeguard the functionalities that make it a support for storytelling about the healthcare experience in which the children’s medical reports, analyses, and growth curves are collected, and on the other, to accompany digital and otherwise [13] memories relative to other aspects of childhood and preadolescence such as school, sport and hobbies. The research confirms that the paper-based booklet is, in this sense, a valuable tool in the construction of the parents’ identity as attentive to their children. This aspect should be set as the basis of the designers’ work. Acknowledgements: The study presented in this paper is a part of a broader project of research and innovation project aimed at prototyping and testing a regional PHR, TreC. The TreC project (Cartella Clinica del Cittadino – Citizen’s Clinical Record) is funded by the Autonomous Province of Trento (Italy) and managed by Fondazione Bruno Kessler.

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[6]

[7]

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Porter, R. The greatest benefit to mankind: A medical history of humanity from antiquity to the present, Harper Collins, London, 1997. Gerhardt, U. Ideas about illness: An intellectual and political history of medical sociology, Macmillan, London, 1989. Eysenbach, G. Consumer health informatics, British Medical Journal, 320 (2000), 1713-1716 Moen, A. Personal health information management, in P. W. Jones and J. Teevan (eds.), Personal Information Management, University of Washington Press, Seattle, 2007, 221-234. Tang, P. C. Ash, J. S. Bates, D. W. Overhage, J. M. Sands, D. Z. Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption, Journal of American Medical Informatics Association, 13 (2006), 121-126. Moen, A. Brennan, P. F. Health@Home: The Work of Health Information Management in the Household (HIMH): Implications for Consumer Health Informatics (CHI) Innovations, Journal of the American Medical Informatics Association, 12 (2005), 648-656. Piras, E. M. Zanutto, A. Prescriptions, x-rays and grocery lists. Designing a Personal Health Record to support (the invisible work of) health information management in the household, Computer Supported Cooperative Work, 19 (2010), 585-613. Gherardi, S. Organizational Knowledge: The Texture of Workplace Learning, Blackwell, Oxford, 2006. Glaser, B. G. Strauss, A. The discovery of the grounded theory: strategies for qualitative research, Aldine Publishing Company, Chicago, 1967. Star, S. L. Strauss, A. Layers of silence, arenas of voice: The ecology of visible and invisible work, Computer Supported Cooperative Work, 8 (1999), 9-30. Gibson, J. J. The ecological approach to visual perception, Houghton Mifflin, Boston, 1979. Dourish, P. The appropriation of interactive technologies: Some lesson from placeless documents, Computer Supported Cooperative Work, 12 (2003), 465-490. Stevens, M. M. Abowd, G. D. Truong, K. N. Vollmer, F. Getting into the Living Memory Box: Family archives & holistic design, Personal and Ubiquitous Computing, 7 (2003), 210–216.

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Socio-Technical Challenges in Designing a Web-Based Communication Platform Miria GRISOTa,1 Maja VAN DER VELDENa, Polyxeni VASSILAKOPOULOUb a University of Oslo, Department of Informatics, b National Technical University of Athens, 15780, GREECE

Abstract. This paper takes a socio-technical perspective to analyze the ongoing practices of making an eHealth infrastructure, namely a web-based communication platform, which aims to improve healthcare delivery in Norway. The platform is planned to support interaction between patients and healthcare providers, patient access to personal health information, and dissemination of health knowledge to the public. The analysis is based on the ‘scales of infrastructure’ concept found in Information Systems research, which shows the complexity of the design, development and implementation process across three scales of activities for achieving durability: institutionalization, organizing work, and technology enactment. The case analysis brings the non-linearity of the ongoing practices to the foreground, enabling a more in-depth understanding of the relationship between technology design and infrastructural work. Keywords. co-construction, eHealth, flexibility, durability, scales of infrastructure

1. Introduction Recently there has been an increased focus on the development of web-based eHealth solutions for on-line patient-provider communication. In Scandinavia, examples of such technology are the national Danish portal sundhed.dk, the national Swedish portal 1177.se, and the hospital-based minTRSSIDe portal at Sunnaas Hospital in Norway. The main purpose of these web-based solutions is to offer patients health information of high quality, a secure communication channel with health providers, and on-line access to a variety of services: booking of exams and visits, prescription renewal, direct access to one’s own medical record. The underlying vision is directed towards fostering patient empowerment by making patients more informed and proactive. However, health organizations face significant challenges in providing effective eHealth services. Challenges are related, for instance, to developing solutions that comply with privacy and security regulations [1], defining successful strategies for patient enrollment [2], and facing structural barriers [3]. Responses to these challenges shape design, development and implementation strategies of eHealth solutions. In this paper we are concerned with how decisions taken during the design, development and implementation process affect the durability of web-based eHealth solutions, in our specific case a patient portal.

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Corresponding author: Miria Grisot, Postboks 1080, Blindern, 0316 Oslo, NORWAY – e-mail: {miriag,majava}@ifi.uio.no

M. Grisot et al. / Socio-Technical Challenges in Designing a Web-Based Communication Platform

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We understand durability in a socio-technical perspective [4][5]: we argue durability is not only a matter of monitoring system performance and utilization over time, but it is the critical process of co-constructing long-term use where participants are involved in a complex web of institutional, technical and organizational practices. In order to analyze the complexity of these socio-technical activities we use the concept of scales of infrastructure [6]. This concept has been developed to make sense of the everyday practices of participants involved in developing e-infrastructures. Scales of infrastructure analytically differentiate participants’ actions with a specific focus on the temporal dimension, the “long term” [7]. The three scales are specified as: institutionalizing, organizing work, and enacting technology. Institutionalizing indicates actions aiming to achieve institutional persistence and permanence; organizing work indicates actions of articulating project work as it complexifies over time; enacting technology indicates the everyday actions of making technology work in practice by both developers and users. We take this lens to develop a socio-technical analysis of the activities illustrated by our case study, and contribute to understand the complexity of processes of designing, developing, and implementing a durable patientprovider web-based communication platform. The paper is structured as follows: first the case description and methodological approach are presented, then the case is analyzed according to the three different scales of infrastructure. Finally, we bring in the discussion the theme of socio-technical flexibility and conclude by specifying our preliminary (as the study is still ongoing) contribution to current medical informatics literature on web-based platforms for patient-provider communication.

2. Method and Case Description: MyHealthRecord The case reported in this paper is based on an ongoing (at the time of writing) study on the design, development, and implementation of MyHealthRecord (from now on MHR). MHR is a patient portal developed since 2005 by the IT department of a major Norwegian hospital and specifically tailored to the needs of selected patient groups and clinical units. MHR is designed to be a highly adaptable, configurable and scalable platform (selected functionalities and content are available to specific groups), and a secure, private, and trusted environment for communication between patients and health professionals. The research is designed as a case study [8] with focus on the shaping of MHR as technological object along social, technical and organizational dimensions. The research design was planned in order to regularly perform data collection over a oneyear period (September 2010-2011) following the main activities in the MHR project. The empirical material generates from qualitative data gathering: interviews with the project management as the primary method, review of documents and presentations, and observation of workshops with the users, as the secondary methods. All interviews were recorded and fully transcribed. We adopted an interpretive approach for the analysis of the data [9][10] going through transcripts, notes and documents in order to identify relevant themes. Relevance was determined by the use of the analytical concept of ‘scales of infrastructure’ in its three dimensions of practice (institutionalizing, organizing work, enacting technologies). The three scales were used as a sensitizing concept guiding our interpretation, revealing the complexity of coexisting practices, and serving as basis to discuss the relevance of a flexible approach to durable platforms.

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3. Results Our analysis of the case focuses on how the participants’ practices are directed towards constructing a solution for long-term use. At the same time, the analysis brings to the forefront how the concern for durability translates in practices related to designing and developing a solution that is socio-technically flexible: technically and organizationally scalable and extremely adaptable to users needs. The analysis is organized according to the three scales of: institutionalizing, organizing work, and enacting technology respectively. 3.1. Practices of Institutionalizing A critical aspect part of the work of the participants in the MHR project is their reflection and definition of the role of MHR, and how this relates to the on-going discussion in the Norwegian health policy scenario on patients’ active use of Internet, their right to have access to medical records, and the need to develop a national patient portal. This discussion is partially driven by the positive experiences reported from neighbouring countries, Denmark and Sweden. MHR has originally been developed with the idea to offer a portal solution for online access to patient records. One of the managers says: “Access to record was definitely part of MHR from the beginning and one of the very first sketches we did showed the record access. Not only access but also possibility to control others’ access to your record”. MHR is also based on the idea that record access is not enough. The same manager continues: “And it was also from the beginning thought not as just another door into the hospital where to get some information, but it should be a meeting point where also the hospital personnel should meet half ground, and the patient should be able to set the premises to decide how this meeting takes place”. Setting such vision for the platform is instrumental for its longevity: a new personal and secure communication channel between patients and health providers is the basis for improving existing services as well as developing new ones over time. Moreover, strategically patient representatives and patient associations have been involved in designing services together with clinical personnel. Directing it even more towards delivering a longlasting solution, MHR is envisioned as a portal for “a life time”. The same manager states: “it should adapt to different users, users’ needs and ideally also throughout a life time and taking into account that a person is not sick most of his life, so when one is not sick MHR, should be about health maintenance and prevention, more that disease and treatments”. Thus in practice, MHR strategically locates itself within the health policy debate, but proposes in addition to offer a platform that will support patient-health provider communication stretching both in time (a life time) and in space (independently of how many providers are involved in the delivery of care). This ambition translates into presenting MHR concretely as ‘record access’, but also more visionary as interaction tool, which is patient-centred, supports transparency (in relation to access to data), accountability, and continuity of care. 3.2. Practices of Organizing Work Another important ‘scale’ for the MHR infrastructure activities is related to the internal organization of the project as such. The project organization of MHR has been


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arranged to ‘survive’ in the context of the many other IT related initiatives of the Norwegian healthcare system. One of the managers explains: “We did organize this as independently as possible from everything else; we wanted the whole process to be influenced by other processes as little as possible. And that meant doing this ‘guerilla’ tactic: few people involved and designing the system as independent from other systems as possible. Because that is what we see with other projects, if you have a project going on for over three years the environment you work in is going to change drastically in three years, like merging with other hospitals or new management”. Organizing work with this “guerrilla tactic” allows the platform to be flexible and responsive. The team is able to swiftly respond to evolving needs without having to go through cumbersome management procedures and without compromising key MHR characteristics to accommodate other projects’ requirements. This type of work organization addresses the adaptability problem, the aim to “assure that the emerging system will remain adaptable at ‘the edge of chaos’ while it grows” [11]. 3.3. Practices of Enacting Technology A third scale concerns the everyday practices of making technology work. This scale focuses on how project participants work with the users during design, development and implementation in order to stay as close to actual work practices as possible: MHR needs to be configured to fit existing work practices. At the same time, users involved in MHR adoption use MHR activities as an occasion for reorganizing and rethinking through their own routines, forms, and information practices: they are required to actively participate in the tailoring process. Discussing users’ involvement, a manager says: “It is so difficult to attain involvement of clinical departments (…). For each clinical department we need at least one, preferably more, champion! Champions that really want to do it and think it is a splendid idea. Champions that can talk to their patients and to their colleagues and tell them to go for it. We are not in a position (and we should not be) to push this directly to the patients”. The commitment required on the part of the users is a critical factor of the long-term use of the solution. The way participation and commitment is constructed in practice is by promoting both short-term and long-term benefits from MHR-use. Short-term benefits are for instance given by the opportunity to digitize simple paper-based procedures, as the requirement of certain patients to fill out questionnaires before coming to visits. Longterm benefits are related, for instance, to the secondary use of data in the long term. Furthermore, we also see how MHR develops out of user requirements in a very specific and gradual way. Both the technology and the practices of infrastructuring coevolve and become gradually more complex over time.

4. Discussion The three scales of infrastructure, which we presented in our empirical case, make sense of the infrastructural work in the process of designing, developing, and implementing MHR. Project participants co-construct MHR by enacting different practices at the same time. The use of the “scales” concept for analyzing our case study enables us to base our understanding on a co-construction approach rather that linear models of interests and events as proposed in the literature [12], and to identify the concurrency of different concerns that trigger different coexisting practices. A further

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finding emerging from our data, which we understand as an emerging from the coconstruction process, is the centrality of flexibility. First, in the institutionalizing scale we see how MHR project management relates the new platform to the evolving Norwegian health policy by keeping a flexible image and identity and articulating its merits and impact in relation to broader objectives. Secondly, the “work organizing scale” helps reveal how the project itself is put together and kept going as participants reconcile independence with interdependency, local contingencies with universal aspirations and everyday task coordination with visionary work. This is achieved by “guerrilla tactics” aiming again for flexibility and thus allowing responsiveness and dedication. Finally, the “enacting technology scale” exposes the way user enrollment and commitment is constructed in practice by promoting both short-term and long-term benefits from MHR-use, but also how a flexible technical design renders MHR adaptable and configurable to the various situations of use. Within this more complex co-construction view we get a more in depth understanding of the role of flexibility for the long-term use (durability) of the system. This ‘project-wide’ flexibility is enabled by the ongoing co-shaping of technology design and infrastructural work making possible to carry through despite priority shifts, project contingencies and unanticipated requests. Acknowledgements; This work was supported by NFR Verdikt projects n. 176856, n. 193172, and FMO project n. EL0086.

References Kluge EHW. Secure e-health: managing risks to patient health data, International Journal of Medical Informatics 76 (2007), 402–406. [2] Kaplan B, Brennan PF. Consumer informatics supporting patients as co-producers of quality, J Am Med Inform Assoc. 8 (2001), 309–316. [3] Kaye R, Ehud K, Shalev V, Idar D, Chinitz D. Barriers and success factors in health information technology: A practitioner's perspective, Journal of Management & Marketing in Healthcare 3 (2010) 163-175. [4] Berg M. Patient care information systems and health care work: a socio-technical approach, International Journal of Medical Informatics 55 (1999), 87-101. [5] Arts J, Callen J, Coiera E, Westbrook J. Information technology in health care: Socio-technical approaches, International Journal of Medical Informatics 79 (2010), 389-390. [6] Ribes D, Finholt TA. The Long Now of Technology Infrastructure: Articulating Tensions in Development, Journal of the Association for Information Systems 10 (2009), 375-398. [7] Edwards P. Infrastructure and modernity: force, time, and social organization in the history of sociotechnical systems, in Misa TJ, Brey P, Feenberg A, eds. Modernity and Technology, MIT Press, Cambridge MA, 2003. [8] Yin R. Case Study Research Design and Methods, Sage Publications, Thousand Oaks CA, 2003. [9] Klein HK, Myers MD. A set of principles for conducting and evaluating interpretive field studies in information systems, MIS Quarterly, 23 (1999), 67-94. [10] Walsham G. Doing Interpretive Research, European Journal of Information Systems, 15 (2006), 320330. [11] Hanseth O, Lyytinen K. Design theory for dynamic complexity in information infrastructures: the case of building internet, Journal of Information Technology, 25 (2010), 1-19. [12] Wakefield DS, Mehr D, Keplinger L, et al. Issues and questions to consider in implementing secure electronic patient-provider web portal communications systems, International Journal of Medical Informatics 79 (2010), 469-477.

[1]


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Results of the 10th HON Survey on Health and Medical Internet Use Natalia PLETNEVAa, Sarah CRUCHETa, Maria-Ana SIMONETa, Maki KAJIWARAa, Célia BOYERa a Health on the Net Foundation, Geneva, Switzerland

Abstract. The Internet is increasingly being used as a means to search and communicate health information. As the mission of Health on the Net Foundation (HON) is to guide healthcare consumers and professionals to trustworthy online information, we have been interested in seeing the trend of the attitudes towards Internet use for health purposes since 1996. This article presents the results of the 10th HON survey conducted in July-August 2010 (in English and French). It was hosted on the HON site with links from Facebook and Twitter and from HONcode certified web sites. There were 524 participants coming mainly from France (28%), the UK (18%) and the USA (18%). 65% of participants represented the “general public”, while the remaining 35% were professionals. Information quality remains the main barrier users encounter while looking for health information online; at the same time, 79% believe they critically assess online content. Both patients and physicians consider the Internet to be helpful in facilitating their communication during consultations, although professionals are more sceptic than the general public. These results justify the continuing efforts of HON to raise public awareness regarding online health information and the ethical, quality and transparency issues, and to educate and guide users towards trustworthy health information. Keywords Survey, Health information, Internet usage, Internet

1. Introduction Since its inception, the Internet has been used for health purposes, and the trend is growing steadily. In 2009 in the USA, 61% of the population looked for health or medical information online [1]. Other US source states the percentage has increased from 27% to 76% from 1998 to 2010 [2]. Both users’ scepticism and the demand for high quality information are growing. In the USA, among those looking for health information online, the number of people dissatisfied with their search results (from 6% to 9% in the last five years) or with the reliability of information (from 5% to 8% in last five years) has been increasing [2]. The Internet influences the doctor-patient relationship. Doctors remain the most significant source of information for patients. In France, in 2010, patients preferred asking doctors rather than the Internet (89% vs. 64%) [3]. The international study (2008) revealed that 88% turn to their physicians to validate online information, but the same number (88%) turn to other sources to validate information from their doctors [4]. As the mission of the Health on the Net Foundation (HON) is to guide the growing community of healthcare consumers and providers on the World Wide Web to sound, trustworthy medical information and expertise, we have been interested in seeing the

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N. Pletneva et al. / Results of the 10th HON Survey on Health and Medical Internet Use

trend in the attitude towards Internet use for health purposes since 1996. In this article, the results of the 10th survey are presented.

2. Method HON surveys use non-probabilistic sampling and cannot ensure that participants are representative of the entire medical and health information-user community on the Internet. However, taking into account the Internet use experience of participants, we believe they represent the most empowered and actively engaged part of the global Internet population seeking health information. The survey was hosted on the HON web site in English and French between July and August 2010. The survey was open to anyone accessing the HON web page or its Facebook and Twitter accounts. It was also promoted through HONcode-certified web sites. The participants included general public (including patients) and healthcare professionals. The survey consisted of five parts, four parts were identical, and one part had two versions for each group [5]. The 2005 survey had the same structure and questions similar to 2010 survey [6]. Some questions required an answer on a “-4”-“+4” scale. For such questions we summed up the results into 3 groups: “disagree”/“rarely” (-4, -3, -2), “neither agree/nor disagree/rarely” (-1, 0, +1) and “agree”/ “often” (+2, +3, +4). If two out of three groups of results were distributed equally (i.e. disagree 12%, neither 43% and agree 45%), we used “would rather agree (12% disagree)”, and vice versa. We mentioned the difference between the 2005 and 2010 results only where the difference was more than 10%.

3. Results and Discussion 3.1. Who is Searching and for Whom? When, Where and What is Being Searched? Over 500 people participated in the survey (524). 65% filled the questionnaire in English and 35% in French. 65% were individuals, patients, patients’ associations’ members (later referred to as “citizens”/”patients”) and 35% were health and medical professionals (later referred to as “professionals”, “doctors”). Overall, respondents from 60 countries around the world filled the HON questionnaire, most participants coming from France (28%), the USA (18%) and the UK (18%). Compared with the 2005 version, there were more female participants (65% vs. 50% in 2005) which is echoed with other studies [7] [8]. Most of the participants were aged 20-59, the most active group being those aged 30-39 (30%). In the US, most online health information seekers are aged 18-49 [8]. For those aged between 33 and 44, getting health information is the primary Internet activity [9]. Apparently, the geographical coverage of the studies and the different methodology used to collect answers explain the difference, however, generally the tendency is the same. On average, the respondents had been using the Internet for 7 years or more (79%) (44% in 2005). 96% of users spend time checking and writing emails and 93% browsing the web. 60% read newsletter and take part in online communities (28% in 2005) and 51% participate in online communities (23% in 2005). This shows the growing popularity of web 2.0 services. The Internet is being used to retrieve information, but also to communicate with peers [10].


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In 79% of cases a web search is the starting point to clarify medical information obtained from physicians, the Internet etc. The frequency of search engines use has increased from 86% in 2005 to 94% in 2010. Secondly, web sites about specific health topics were listed (73%), and thirdly there were links from health web sites (66%). The importance of web sites suggested by a healthcare provider increased from 31% to 43%. Specialised search tools such as HONselect have lost popularity (29% in 2010 vs. 52% in 2005). The majority of users (61%) visit two to five web sites and 25% visit up to 10. 44% of users search for health information more than three times a week, 25% do it two to three times. We found no correlation between time spent searching health information and consultation time with a healthcare provider. Of all health information web sources, the most popular are medical journals or publishers (85%), hospitals (77%), universities and governmental agencies (76%) and non-commercial medical organizations (74%). Over the last 5 years the importance of hospitals as a source of online health information has increased from 60% to 77%. Respondents mostly search disease description (69%) and medical literature (62%). Other topics include: clinical trials (28%), patient community (24%), alternative medicine (22%), support groups (19%), weight loss (17%) and others (26%). Regarding medications, citizens mostly search for drug side effects (60%), safety (54%) and efficacy (52%). Over the last five years there were fewer searches on drug interaction (from 59% to 47%). Generic drugs and information regarding herbal or alternative treatments are frequently searched by 37% of citizens. Patients who participated in the survey rarely buy prescription (only 10% declared they did) or OCT (12%) drugs via the Internet. 3.2. Difficulties of Online Health Search We have asked participants about the difficulties they face when searching for online health information. For each barrier a scale of -4 to +4 was proposed. Access to reliable medical information was considered important by English(96%) and French- (76%) speaking respondents, however its quality remains the main barrier users encounter while looking for health information online (80%). Inadequate tools and applications, lack of time and support were considered less important. Internet training is not considered as an obstacle anymore by 47% of respondents (in 2005 this was still an obstacle for most participants, whereas for 34% of them it was not a barrier). The following factors are considered among the most valuable for improving the quality of online health information and services: • Trustworthiness/credibility – 96% • Accuracy and availability of information – 95% • Ease of finding information/Navigation – 93%. Information transfer rate (74%), privacy (73%), accessibility in terms of language and physical impairment (69%), and scientific complexity of information (59%) play a less important role. Commercialisation/advertising and sponsorship are not considered as quality-enhancing factors (from 31 to 42% in 5 years), neither are spam (44%) and Pay-to-view/Pay-for-use information or services (42%). Most citizens (78%) prefer to have the option of seeking complex medical information, especially the French-speaking ones (91%). 57% consider consumer web sites to be often superficial.

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What domains do users trust? Not surprisingly, .edu (70%), .gov (69%) and .org (65%) domains remain the most credible The.com domain was considered neither credible nor non-credible by 52% of respondents. National domains have gained more trust among French-speaking participants (64%) compared with English-speaking ones (19%). This may be potentially dangerous because .fr domains can be used by fictitious organizations or ones that are not based in France, and this can mislead users considering the .fr domains to be as trustworthy as .gov for example. Most respondents think quality should be ensured by associations representing non-profits organizations, both international (72%) and national (71%), and NGOs (69%). Over the last 5 years, the importance of NGOs has increased significantly from 46% to 69%. 79% believe they critically assess online health information and 83% state that they verify whether the web site is trustworthy or not by checking the source of information (88%), motivation (68%), URL (commercial or not) (66%) and, the sources of funding (55%). However, only 13% of users think their family and friends verify the trustworthiness of web sites, while most of them remain undecided. 49% state they are not anxious when conducting a web search, and 75% do not consider themselves to be cyberchondriatic. The majority (74%) of respondents said they were aware that the ranking of search results could be manipulated by commercial interests. The HONcode seal was the most recognized trust mark among participants of the survey (50%). There was however a significant difference between English-speaking and French-speaking respondents regarding the popularity of the HONcode. 41% of English-speaking participants knew the HONcode seal along with Good House Keeping (36%) whereas 67% of French-speaking participants knew it because of the the collaboration with the French National Authority for Health. 76% think that hospital web sites should always be certified. 66% also consider it appropriate for physicians’ web sites and 46% - for web sites selling software. 3.3. Doctor-Patient Relationships, Perspectives from Both Sides Both citizens and professionals were asked whether they discuss the Internet search results with their doctor. 53% of citizens declared that they did. As for professionals, 62% said they engaged in such communication (75% of English-speaking and 47% of French-speaking). We could not reach a certainty on certain questions. Both professionals and patients rather agree that it increases adherence to a physician's advice (22% and 11% disagree respectively) and instructions on taking prescribed pharmaceuticals (12% and 15% disagree respectively). The most controversial issues turned out to be (1) whether discussing online health information fosters patient mistrust and (2) whether it encourages patients to challenge a physician's authority. With regard to the first issue patients rather think it does not (17% think it does) whereas physicians rather think it does (21% think is does not). Regarding the second issue, patients remain undecided whereas 14% of doctors think it does not. Comparing all these findings with the ones of 2005 we see that both doctors and patients have become more critical by 2010. 80% of citizens keep thinking that a healthcare provider should suggest trustworthy sources of online health information. 72% of professionals agree it would be helpful for them to provide patients with such information (in 2005, only 59%). Most physicians would use a trustworthy online service that allows them to suggest web sites to their patients, especially if it is free for the patient (87%). However, so far 78% of patients say healthcare providers have never given them such information.


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4. Conclusions The survey findings demonstrate that the target audience is becoming more critical and less satisfied with the quality of online health information. Their worries have solid bases as there is a huge amount of misleading information online. Most respondents recognise this problem and believe they critically assess online health information. Although more than 500 answers do not represent all points of view, we believe that the growing scepticism on the part of physicians and patients justifies continuous efforts from HON and webmasters to increase public awareness of quality issues. First, we need to create more awareness among Internet users of reliable tools for “healthy” online surfing. Secondly we have to educate both the general public and health professionals. In the same direction, the UK Nuffield Council on Bioethics urges physicians to guide patients searching for health information on the Internet [11]. Medical students and practicing doctors should have such courses as a part of their curriculum. We believe that a similar course should be created for Internet users and adjusted to their background. And thirdly, patients and doctors need a communication tool which would be easy to use, save time during consultations, decrease professionals’ workload, and ensure access to trustworthy information on the web.

References [1]

Fox S., Jones S. The Social life of health information. Pew Research Center's Internet & American Life Project. [Online] June 2009. [Cited: 15 December 2010.] http://www.pewinternet.org/~/ media//Files/Reports/2009/PIP_Health_2009.pdf [2] H., Taylor. HI-Harris-Poll-Cyberchondrics. Harris Interactive. [Online] 04 August 2010. [Cited: 01 December 2010.] http://www.harrisinteractive.com/vault/HI-Harris-Poll-Cyberchondriacs-2010-0804.pdf. [3] Vers une meilleure intégration d’Internet à la relation médecins-patients. Conseil National de l'Orde des Medcins. [Online] 06 May 2010. [Cited: 23 November 2010.] http://www.conseil-national.medecin.fr/ article/vers-une-meilleure-integration-d%E2%80%99internet-la-relation-medecins-patients-982. [4] Health Engagement Barometer. Edelman. [Online] 2009. [Cited: 22 November 2010.] http://static.edelman.com/wwwedelman/healthengagement/docs/Edel_HealthBarometer_R13c.pdf. [5] Health On the Net Foundation. Survey 2010: Evolution of Internet use for health purposes. Health On the Net Foundation. [Online] August 2010. [Cited: 05 September 2010.] http://services.hon.ch/cgibin/Survey/Survey2010/quest_oct.pl. [6] Analysis of 9th HON Survey of Health and Medical Internet Users Winder 2004-2005. Health On the Net Foundation. [Online] 2005. [Cited: 23 November 2010.] http://www.hon.ch/Survey/Survey2005/ res.html. [7] Health information seeking: a review of measures and methods. Anker AE, Reinhart AM, Feeley TH. 3, March 2011, Patient Educ Couns, Vol. 82, pp. 346-54. [8] S, Fox. Health Topics. Pew Internet & American Life Project. [Online] 1 February 2011. [Cited: 16 April 2011.] http://www.pewinternet.org/~/media//Files/Reports/2011/PIP_HealthTopics.pdf. [9] Jones S., Fox S. Generations Online in 2009. Pew Internet & American Life Project. [Online] 28 January 2009. [Cited: 5 April 2011.] http://www.pewinternet.org/~/media//Files/Reports/2009/ PIP_Generations_2009.pdf. [10] S., Fox. Peer-to-peer healthcare. Pew Internet & American Life Project. [Online] 28 February 2011. [Cited: 15 April 2011.] http://www.pewinternet.org/~/media//Files/Reports/2011/ Pew_P2PHealthcare_2011.pdf. [11] Nuffield Council on Bioethics. Medical profiling and online medicine: the ethics of 'personalised healthcare' in a consumer age. [Online] October 2010. [Cited: 14 December 2010.]

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Social connectedness through ICT and the influence on wellbeing : the case of the CareRabbit Sanne R. BLOM, Magda M. BOERE-BOONEKAMP, Robert A. STEGWEE1 Department of Health Technology and Services Research, University of Twente, The Netherlands

Abstract. The CareRabbit has been introduced as a technological innovation in the care for children, enabling family and friends to stay in touch while the child is hospitalized. This study addresses influence of this innovation on the wellbeing of the children, and uses the validated KINDL questionnaire, eliciting information from children and parents at the end of hospitalization. A baseline and an experimental measurement are compared. The children in the CareRabbit group scored slightly higher on the KINDL questionnaire than children in the control group. For young children (age 4-7) the difference was large. Initial findings indicate that CareRabbit has a positive influence on wellbeing, although sample size and measured differences limit the support for this conclusion. The measured difference suggests that CareRabbit may be more valuable for younger children. Keywords: Innovation, Information and Communication Technology, Evaluation, Wellbeing

1. Introduction The CareRabbit (ZorgKonijn) is an e-health device that can be used to play messages (e.g. text, mp3) sent to it through the Internet. The device2 itself, depicted in figure 1 is a 23 cm high white rabbit with rotating ears and lights in its belly.

Figure 1. The CareRabbit e-health device.

The device is deployed in children’s departments in hospitals. Its aim is to make children feel comfortable and make their stay more pleasant, by keeping in touch with 1 2

Corresponding author. The device is called a Nabaztag, marketed independently by Mindscape France.

S.R. Blom et al. / Social Connectedness Through ICT and the Influence on Wellbeing

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friends and family. One of the questions in developing the CareRabbit further was formulated by IBM as follows: What is the value of the CareRabbit for its users and how can it best be used in hospitals? This paper addresses the value of the CareRabbit in terms of the wellbeing of the hospitalized children. Rather than measuring the perception of children and their parents regarding the technology used, we decided to employ a validated instrument to actually measure the wellbeing of the child. The e-health device fits in a ‘Family-centred care’ (FCC) approach of hospitals. FCC means that during a hospital admission, care is planned by the health staff around the whole family, not just the individual child, with key-concepts like ‘partnership in caring’ and ‘encouragement of family-to-family/peer support’. The aim of this approach is to minimize the impact of the child’s admission on all the family members and the child’s emotional trauma and to assist in recovery [1, 2]. Several studies show that social connectedness is important for a person’s wellbeing and health. Sadlo [3] states: “the experience of social connectedness makes a more important contribution to an individual’s subjective wellbeing, than the mode of communication”. Especially family members and friends can give us a feeling of belonging, understanding and being cared for. Having a social network for supports can buffer against stress, develop social skills [4], and leads to higher levels of life satisfaction and self-esteem [5]. Even the frequency of the contact with family and friends has been positively related to wellbeing [6]. Research shows that people use technology-based modes of communications as a supplement to their face-to-face communication, and not as a replacement [3]. The CareRabbit will most likely be used to supplement hospital visits. This research was part of a larger project directed at the implementation of the CareRabbit in Dutch hospitals, in itself a complex, multi disciplinary problem with a practice-oriented design. The project’s aim is to develop a business model for this specific innovation in order to get insight in the relevant factors of influence and implementing an e-health innovation in healthcare. Crucial part of this business model is the value of the CareRabbit services to the children and their family. This paper addresses the value in terms of the wellbeing of the children only.

2. Methods The research is carried out as part of the pilot studies with the CareRabbit in paediatric departments in two different hospitals. The hospitals that participated were the Martini Hospital in Groningen and the MST in Enschede. The methods that are used are: • Desk research on the influence of connectedness through an electronic device on the wellbeing of people and to describe the target group • Perform a baseline measurement on children’s wellbeing with children that are in the hospital, but haven’t used the CareRabbit. • Perform a measurement on children’s wellbeing with children that used the CareRabbit for at least two days With this information we will get a fair indication of the effect of the CareRabbit and the responses of the children. However, the validity of the outcomes is limited, since it is not a randomized trial in controlled circumstances. Wellbeing is measured with the KINDL questionnaire (www.kindl.org), a validated list consisting of 24 questions [7]. KINDL was chosen because it can be used with children with the age of four years or older, it has a limited amount of questions in six categories (Physical wellbeing, Emotional wellbeing, Self-esteem, Family, Friends,

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and School), and it is available in Dutch. For each category of children’s questionnaires (4-7, 8-11, 12-16), a matching questionnaire exists for parents to indicate perception on their child’s wellbeing. The questionnaire for the youngest children (4-7) is easier to fill in, even though help from an adult is desirable. The scores of KINDL can be normalized to a 0-100 point scale to make outcomes comparable. Missing values (mainly in the category School, since it was a holiday) are not included and scores on negatively formulated questions (“double negative”) are reversed. At each hospital, start-up sessions were organized with childcare workers, head of the department, IT staff and nurses. The project, pilot studies, and research were explained; CareRabbits were tested; and instructions were given on how to use the device and website. The control phase was executed first, for two months in both hospitals: first the researcher handed out the questionnaires, but eventually the childcare workers gave KINDL to children in the hospital eligible for participation. After the control phase was completed, the CareRabbit phase started: childcare workers handed out the devices, instructed parents and handed out questionnaires. Children that filled out one of the questionnaires got a a small mascot or “gelukspoppetje” and a card to thank them for their participation and wish them health and luck.

3. Results During the pilots 27 children used the CareRabbit and 32 children participated in the control group (27 of their parents participated). Of the CareRabbit group 12 parents and 11 children (34%) filled out a questionnaire. At the MST 23 children used a CareRabbit, at the Martini Hospital 4 children used one. Table 1 shows the background characteristics of each group of participants. Table 1: Distribution on background characteristics of the CareRabbit group and the control group Parents 4-7

Age

Children 8-16

4-7

8-11

12-16

CareRabbit

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

#Participants

6

6

21

21

6

6

15

11

6

15

#Questionnaires

4

6

8

21

4

6

6

11

2

15

Boys

2

3

1

3

2

3

1

6

0

7

Girls

2

3

7

18

2

3

5

5

2

8

Average age

4,3

6,0

10,8

11,7

4,3

6,0

9,3

10,2

15,5

13,9

# brothers/sisters

-

-

-

-

1,3

1,7

1,7

1,3

1,0

2,5

Earlier admissions = 0

1

1

2

11

1

3

4

7

0

8

Earlier admissions = 1

1

4

1

4

1

1

0

2

0

2

Earlier admission > 1

2

1

5

6

2

2

2

2

2

5

Based on the information of the childcare workers, three children asked to participate in the control group refused. Four children offered a CareRabbit declined, because they found their current facilities sufficient; one boy of 15 said he considered himself too old for the CareRabbit. However, two girls age 15 and 16 used the CareRabbit at the MST and were enthusiastic about it. This means that depending on the specific child, the CareRabbit may be of value for children older than 14. The childcare workers explained that the low amount of completed questionnaires was caused by not handing out the questionnaires and sometimes because (young)


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children were too excited about returning home, and therefore couldn’t concentrate on a questionnaire. The control period and CareRabbit period were approximately the same (10 weeks), however the CareRabbit period took place during the summer holidays, when most planned hospital admissions are postponed, and fewer children being admitted since the paediatric department is closed. The average age of the control group was slightly higher and more boys participated than in the CareRabbit group. This is consistent with perceived age of the target group of the CareRabbit. Childcare workers told that boys older than 12 were often not offered the CareRabbit or did not want to use it. Moreover, more children of the control group were admitted to the hospital before: this might indicate that more children of the control group have a longterm illness or serious condition. From here on, only the results from the questionnaires are analyzed, which for the CareRabbit group yields N = 12. The results of our measurements shows, among others, that the difference between the CareRabbit group and the control group on their normalized scores for the KINDL is 2,5 points (66,8 versus 64,3) indicating that the CareRabbit group scores are slightly higher on wellbeing then the control group, but this difference is not significant. For the youngest children (age 4-7) the difference is larger: 12,3 points for parents and 17,5 points for the children. The difference for the parents’ and children’s scores together is 14,52; this difference is significant with 97% certainty (t = 2,87, df = 7). For the children separately the difference is 17,6 points with 90% certainty; for parents the difference of 12,3 is 70% certain. This might indicate that the CareRabbit has a positive influence on wellbeing for younger children. However, since the circumstances are not controlled, other factors might have an influence too. The differences on the separate categories are minimal, except on School, where the CareRabbit group scores 0,4 points higher, and on Family, where the control group scores 0,3 points higher. These differences are not significant.

4. Discussion and conclusion The number of questionnaires filled out in the CareRabbit group is low (n = 12), and it is difficult to generalize the conclusions based on this information. Besides that, other factors might have been of influence as well (e.g. personal circumstances) since both measurements were not done within the same group (i.e. control measurement and CareRabbit measurement with the same person), but between groups. However, all children and parents that actually used the CareRabbit valued it. The children of the CareRabbit group scored slightly higher on the KINDL questionnaire than the children of the control group (66,8 versus 64,3). However, this difference is not significant, but it might indicate that the CareRabbit has an influence on wellbeing. For young children (age 4-7) the difference was larger (14,9), therefore suggesting that the CareRabbit may be more valuable for younger children. Not much information is known on children that did not want to use the CareRabbit. As far as known three children (boys, older then 14) refused the CareRabbit, but it is not clear what their precise motivation was or what they would have thought of it when they had tried it. Furthermore, it is not known what children that were not offered a CareRabbit by the childcare workers (for example, because they expected that it would not be appreciated by older children >16) would think of it. In general, the approach of the childcare workers might influence the results, as they handed out most CareRabbits and questionnaires and their attitude is bound to be

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different (e.g. enthusiastic, opinion on whether it was valuable for boys or older children, way of supplying). However, it is probably impossible to neutralize this effect in this type of research projects. Almost all questionnaires were filled out at the end of the stay. Some of the questionnaires of the control group were filled out one or two days earlier, but never on the day of admission itself. A future improvement on this research would be to give both the control group and the experimental group a questionnaire at admission and at discharge. In this way, the effect of the hospital stay itself and their improved health on their wellbeing would be excluded and the effect of the CareRabbit can be measured more thoroughly. Future research directions regarding the CareRabbit should include a follow-up study, taking into account this improved research design, with a larger number of participants. In addition, first steps have been set toward the elaboration of a business model, which takes into account the demonstrated value to the stakeholders. Based on the experience and feedback during the pilot study, the applicability to other patient groups in healthcare, such as elderly people, might also be investigated to discover whether the use of ICT in an alternative and inviting shape and form can improve their social connectedness and wellbeing. Given that this study was not limited in its approach but limited by practical issues, the small sample size has not refrained us from seeking publication at this stage. The results are positive and also show statistical significance. Furthermore all stakeholders (most importantly, the children, parents, and hospital staff) valued the CareRabbit. Therefore the study can be seen as a pilot study with positive results and thus invites researchers to do further research on social connectedness through the use of ICT. From a theoretical point of view, we have shown the applicability of a validated clinical instrument (KINDL) to assess the health and wellness outcome of the use of an e-health device. In combination with more traditional information systems approaches to assess the value of e-health applications, such as perceived usefulness and ease of use, this provides a richer methodological basis for future e-health research. Acknowledgements: The CareRabbit is a corporate social responsibility project within IBM The Netherlands, and we thank IBM The Netherlands and Juriën Taams for support of this research.

References [1] [2] [3] [4] [5] [6] [7]

Kuhlthau KA, Bloom S, Van Cleave J, et al. Evidence for family-centered care for children with special health care needs: a systematic review, Academic Pediatrics 11 (2011), 136-43 Mikkelsen G, Frederiksen K. Family-centred care of children in hospital - a concept analysis. Journal of Advanced Nursing 67 (2011) 1152-1162. Sadlo M. Effects of Communication Mode on Connectedness and Subjective Well-Being. Thesis, Australian Centre of Quality of Life, 2005. Cohen S, Sherrod DR, Clark MS. Social skills and the stress-protective role of social support, Journal of Personality and Social Psychology 50 (1986), 963-973. Takahashi K, Tamura J, Tokoro M. Patterns of social relationships and psychological well-being among the elderly, International Journal of Behavioural Development 21 (1997), 417-430. Nezlek JB, Richardson DS, Green LR, Schatten-Jones EC. Psychological wellbeing and day-to-day social interaction among older adults, Personal Relationships 9 (2002), 57-71. Raat H, E.Verrips, U.Ravens-Sieberer, J.M.Landgraf, Essink-Bot ML. Paediatric health profile measures: Does it make a difference? The example of the KINDL and CHQ-CF87, Quality of Life Research 11 (2002), 647.


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Technological Choices for Mobile Clinical Applications a

Frederic EHRLER a,1, David ISSOMa, Christian LOVIS a University Hospitals of Geneva, Division of Medical Information Sciences

Abstract. The rise of cheaper and more powerful mobile devices make them a new and attractive platform for clinical applications. The interaction paradigm and portability of the device facilitates bedside human-machine interactions. The better accessibility to information and decision-support anywhere in the hospital improves the efficiency and the safety of care processes. In this study, we attempt to find out what are the most appropriate Operating System (OS) and Software Development Kit (SDK) to support the development of clinical applications on mobile devices. The Android platform is a Linux-based, open source platform that has many advantages. Two main SDKs are available on this platform: the native Android and the Adobe Flex SDK. Both of them have interesting features, but the latter has been preferred due its portability at comparable performance and ease of development. Keywords: EPR, Android, Mobile Health

1. Introduction Providing care providers real-time, mobile and easy collaborative interactions with the hospital’s information system is an important challenge. It is a critical element to improve the efficiency and the safety of care processes [1]. Until recently, these interactions have been limited by devices and interaction models [2]. The new mobile devices represent an important step towards a solution. The development of clinical applications on these devices is not a usual problem of moving an application to a new operating system because of two elements: the pervasive presence of these devices and the disruptive new interaction paradigm introduced by multi-touch screens. Providing mobile services to physicians requires wise technological choices regarding the platform and the development environment [3]. In the following sections, we first introduce the context in which we started our development research. Then, we present the selection criteria employed to evaluate the candidate technologies. After that, we describe the application we developed to assess the functionality of the candidate SDKs. Finally, we present the advantages and drawbacks of the OSs and SDKs, which we assessed for our development, and what technology we chose to adopt at the end.

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Corresponding Author: Frederic Ehrler, University Hospitals of Geneva, Division of Medical Information Sciences, Rue Gabrielle-Perret-Gentil 4, CH-1211 Geneva 14, Switzerland; Email: [email protected].

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F. Ehrler et al. / Technological Choices for Mobile Clinical Applications

1.1. Background The Geneva University Hospitals (HUG) is a consortium federating the public hospitals in the Canton of Geneva, Switzerland. It provides primary, secondary, tertiary and outpatient care for the whole region with 45,000 inpatients and 850,000 outpatient visits a year [4]. The Clinical Information System (CIS) of the HUG is mostly an inhouse developed system. It is a service oriented and component-based architecture with a message-based middleware. It is written in Java with J2EE and open frameworks. All exchanges are in SOAP or HTTP/XML [5] [6]. All components building blocks of the CIS, including the ones discussed in this paper are built in such a way that they comply as much as possible with standards, such as IHE (Integrating the Healthcare Enterprise) profiles, so that they are not dependent of any local legacy system. This includes technical, semantically and human-machine interfaces, such as using a terminology server for the language of the interfaces.

2. Method In order to define the most appropriate technology to develop mobile clinical applications, we defined several criteria organized in three axes: • Hardware: market trends, cost, performance and user acceptance of the mobile devices. Strength of the mobile platform with regards to security, reliability, and privacy. • Human: availability of competent developers on the labor market and existence of a developer community. • Software: complexity of the development environment, cost, user friendliness and reusability of existing and new developments. It is important to take into account the price of the physical devices supporting the OS. Indeed, when each care provider of the hospital is equipped with a mobile device, a small difference on price becomes really significant. The performance, including power autonomy of the device, is obviously central. Indeed, the good course of the healing process often relies on the real-time access to the relevant information. The information must obviously remain secured as it concerns the private life of the patient. In addition, we have to consider how quickly developers can master the environment and how easily the work already done inside the CIS can be adapted to the new tools. The choice of widely used languages, such as ActionScript or Java, would definitely facilitate the adoption and development as numerous developers are already familiar with these languages. The existence of a professional development environment, the existence of open source projects in this field, and a sufficient developer community, which has already addressed the most obvious questions, also facilitate the developments. In order to evaluate the features and the ease of development with the different SDKs, we defined a prototype mobile application, sort of test use case, aiming to simplify the care process. With the help of this application, health professionals simply enter the information concerning the patient during the visit instead of recording all the information on laptops. The application is composed of a succession of screens where the user selects the unit, the room, and finally the patient being currently examined. On the last screen, the care provider can enter the vital signs of the selected patient.


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Figure 1. Communication between mobile applications and existing CIS

2.1. Communication Architecture Regarding the architecture, it was mandatory to think a model that would not create a dependency with any legacy system. Thus, we defined a gateway server providing a centralized access for the mobile application to any required information to or from the CIS. Thus, integrating any mobile application would only require integrating this bridge. It also clearly separates the services that are available remotely from the ones proposed as usual Web services. The gateway server is responsible for formatting the data properly before sending it to the appropriate application on the device. Once the mobile device receives the data, its embedded software is responsible to display the data through its interface and allows the interaction with the user. Figure 1 shows the link between our mobile application and the current CIS. The services of the existing CIS are externalized through a component named CIS gateway. When a mobile application requires data from the CIS, it communicates with the mobile gateway that transmits the request to the CIS gateway. The service directory is then queried to identify the appropriate service where to retrieve the required information. The information then returns through the same channel. All data transiting through the channel is formatted in XML.

3. Results 3.1. Choice of the OS The choice of the OS is challenging. There are numerous OS for mobile devices on the market, some of them with marginal shares. In order to simplify the work, it was decided to address only the four that are currently seen as major player, as per the Table 1 next page. The Apple iPhone is an interesting product as it is widely spread among users [7]. Unfortunately, the development policy of Apple is very restrictive. In addition, the development environment is unique to the OS, thus requiring very specific and devoted skills and education for the development team. Finally, there is a very limited choice of devices, as only the devices provided by Apple are available on the market.

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Table 1. Comparison of the principal existing OSs to develop on mobile devices (Market shares of Western Europe, November 2010) OS Developer Language Market shares

iOS Apple Objective-C 46.4%

Symbian Nokia C++ 21.77%

Android Google Java+XML 15.65%

RIM Blackberry Java 10.16%

Choosing between Android, Symbian and RIM was trickier. They all possess a significant share on the market, rely on well established language and possess efficient development environment. However, only Android offers all together a huge choice of devices, ranging from very small Smartphone to large tablets, a widespread development environment, a large open source community, and a very transparent development policy. 3.2. Choice of the SDK One would think it is straightforward to adopt the Android SDK to develop on the Android platform. However, it is worth taking into consideration Adobe, a major actor of the IT world that offers development tools for mobile devices running Android. Adobe provides a SDK named Adobe Flex that has the valuable advantage to generate programs that can be supported by several platforms without any change. We made a quick survey (Table 2) of Adobe Flex and Android SDK characteristics to clarify their benefits and limitations. Some restrictions related to the Flex Hero SDK have been identified. As this SDK is an additional layer over the native SDK, there can be a loss of functionalities. Fortunately, the Flex SDK can handle the main functions required to interact with the mobile device, such as positioning, multi-touch, inclination, etc... The only identified limitation is the impossibility to create Android widgets, but this is not required for our application purpose. The additional layer of the Flex SDK can also induce a reduction of performance. However, we did not observe in our tests and did not found objective and serious studies confirming or infirming this fact. Regarding the Integrated Development Environment (IDE), the two languages possess a dedicated tool that helps developers generate accurate code. For Android SDK, the Eclipse IDE is perfectly adapted as the code is standard Java language. With the addition of a plug-in, the Eclipse IDE can manage the installed SDK, the documentation, and some drivers to connect the mobile device to the computer. The plug-in offers automated compilation as well an emulator. It allows testing the application locally instead of loading it into the mobile device. For the Flex SDK, a new version of their development environment, Flex Builder, has been released recently by Adobe to program mobile applications. This IDE based on Eclipse offers programming facility to code in ActionScript and MXML. Like with the Android SDK, there is an emulator that facilitates the development significantly. Table 2. Comparison of principal existing SDKs to develop on Android platform Features Version IDE Language Execution platform

Flex Hero SDK Flex 4.5 Hero Flex builder Burrito ActionScript 3 + MXML Adobe Compatible

Android SDK Froyo 2.2 Eclipse Java+XML Android


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3.3. Comparing Platforms In order to improve our comparison, we developed our sample application on the two platforms. On the Figure 2, it can be seen that there are no strong differences in the human-machine interaction experience between the two interfaces. Both can display and manipulate lists, radio buttons and text inputs and other graphical component.

Figure 2. Android SDK and Adobe Flex screens to enter vital signs of the patient.

4. Conclusion Our constraints, needs and projects, led us to prefer the android OS due its compatibility with the largest number of devices and its open source policy. The selection of the SDK was more difficult as both the Android SDK and the Flex SDK met most needs in terms of features for the development of a mobile application on Android OS. The Flex SDK was finally chosen based on its portability to other platforms at comparable performance and ease of development.

References [1] [2] [3] [4] [5] [6] [7]

Prgomet M, Georgiou A, Westbrook JI. The Impact of Mobile Handheld Technology on Hospital Physicians' Work Practices and Patient Care: A Systematic Review. JAMIA 16(2009), 792-801. Kubben P. Neurosurgical apps for iPhone, iPod Touch, iPad and Android. Surg Neurol Int 22(2010), 89. Fischer S, et al. Handheld Computing in Medicine. JAMIA (2003), 139–149. Tschopp M, et al. Computer-based physician order entry: implementation of clinical pathways. Studies in health technology and informatics (2009), 673-7. Borst F, et al. Happy birthday DIOGENE: a hospital information system born 20 years ago. International Journal of Medical Informatics 54(1999), 157-167. Geissbuhler A, et al. Experience with an XML/HTTP-based federative approach to develop a hospitalwide clinical information system. Stud Health Technol Inform 84(2001), 735-9. Payne D, Godlee F. The BMJ is on the iPad. BMJ 19(2011).

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Modified Rand Method to Derive Quality Indicators: a Case Study in Cardiac Rehabilitation Mariëtte VAN ENGEN-VERHEULa,1, Hareld KEMPSa,b, Roderik KRAAIJENHAGENc, Nicolette DE KEIZERa, Niels PEEKa a Dept. of Medical Informatics, University of Amsterdam, Amsterdam, The Netherlands b Dept. of Cardiology, Máxima Medical Centre, Veldhoven, The Netherlands c NDDO Institute for Prevention and Early Diagnostics, Amsterdam, The Netherlands

Abstract. Quality indicators (QIs) are increasingly used to summarize quality of care and to give professionals’ performance feedback. We have previously developed a continuous multifaceted guideline implementation strategy that integrates computerized decision support with feedback on QIs and benchmarking. This paper focuses on development of QIs, and presents results of a case study in the field of cardiac rehabilitation. We present a modified Rand method that combines results from a literature search and guideline review with knowledge of an expert and patient panel in an extensive rating and consensus procedure. All sources contributed to the final set of 18 QIs for cardiac rehabilitation. Keywords. Quality Indicators, Health Care; Cardiac Rehabilitation

1. Introduction Improving quality and outcomes of care is a central theme in current health care policy. Clinical practice guidelines are considered essential instruments to improve the quality of care as their potential benefits are improved patient outcomes, reduced practice variation, and reduced costs. Despite wide promulgation however, professionals’ often do not follow guideline recommendations. A frequently used classification of barriers to guideline implementation is a division into individual (‘internal’) and environmental (‘external’) barriers [1]. Internal barriers relate professional’s knowledge and attitude towards guidelines. To improve these, computerized decision support (CDS) is known to be effective because it can provide guideline-based recommendations at the time and place where clinical decisions are made [2]. However, medicine is largely practiced as part of a team and embedded within complex organizations. Professionals may also encounter external barriers which hamper their ability to execute guidelines. They stem from environmental factors related to the team, organisation or health system they work in. It is therefore important to apply an implementation strategy with supplementary components directed at both internal and external barriers [1].

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Corresponding author: M.M. van Engen-Verheul, Dept. of Medical Informatics, University of Amsterdam, PO Box 22700, 1100 DD Amsterdam, The Netherlands; E-mail: [email protected].

M. van Engen-Verheul et al. / Modified Rand Method to Derive Quality Indicators

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Feedback on health care performance and outcomes has been shown to be an effective quality improvement method to overcome external barriers and can be used in addition to CDS [3]. It prompts professionals to change their behaviour if they receive feedback that their practice does not meet benchmark values (e.g., national target values or average performance within a peer group). Feedback reports contain results on quality indicators (QIs), i.e. quantitative measures to monitor and evaluate the quality of particular health care processes that affect patient outcomes [3]. QIs help professionals and their managers to identify suboptimal care and opportunity to improve quality and outcomes of care. Several methods exist for developing QIs, each with strengths and limitations. The first goal of this paper is to present a comprehensive method, which combines strengths from multiple methods to develop a QI set. The second goal is to apply our method and present a QI set developed during a case study in the field of cardiac rehabilitation (CR).

2. Methods To develop a QI set, a procedure developed by the Rand Corporation [4] is often used. Like other QI development methods this procedure combines scientific evidence and expert opinion using a consensus technique. Preliminary QIs extracted from the literature are anonymously rated by an expert panel. In a next round the panel meets to discuss, rerate and gain consensus. Criticisms of the Rand procedure include the lack of transparency in applying the definition of appropriate care, and weak reliability of the rating and consensus procedures. Also the lack of patient involvement and the fact that clinical practice guidelines are not consulted are mentioned [5]. To overcome these criticisms, we have modified the Rand procedure with successful elements of rating and consensus procedures from other QI development methods. First we defined appropriate care based on specific judgement criteria from the Organisation for Economic Co-operation and Development (OECD) [6]. Secondly we increased the reliability of the rating procedure by using a 5-point Likert scale for each criterion, as is often used in the Delphi technique [7]. Thirdly we structured the consensus procedure during the discussion meeting of the expert panel by applying the Nominal Group Technique (NGT) [8]. Finally we extended the number of consulted sources for QIs, adding a patient panel and review clinical practice guidelines in CR. Case study – We applied our modified Rand method to the field of CR. CR is a multidisciplinary therapy to support heart patients recover from a cardiac incident or intervention, and aims to improve their overall physical, mental and social functioning. Consistent with international guidelines, the Dutch guidelines for CR state that patients should be offered an individualized rehabilitation programme based on a needs assessment procedure. The guidelines mention all items which need to be collected during this procedure. An EPR with CDS facilities, based on the guidelines, was developed to overcome internal barriers and evaluated in a cluster randomized trial. It was shown that CDS considerably improved guideline adherence. However, the trial also revealed persisting barriers for implementation of the guidelines at organisational levels [9]. To overcome also these external barriers we developed a multifaceted guideline implementation strategy, which expands our CDS intervention with a benchmark-feedback loop including feedback reports on QIs [10].

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3. Results The modified Rand method (see Figure 1) consists of consultation of four sources (experts, patients, literature and guidelines) to collect information QIs. This is translated into a draft QI set which is rated on paper by the expert panel. Finally the expert panel meets to discuss and gain consensus on the final QIs. The steps in Figure 1 will be described in more detail now, followed by their application in the case study. Expert and patient panel – A questionnaire about quality characteristics is sent to consult both an expert panel and a patient panel. The expert panel should include professionals from all disciplines involved in the field of interest. They are asked to mention characteristics of excellent care service and what they would need to know about another clinic to assess their quality. The patient panel is asked to describe positive and negative experiences during their treatment. From the answers provided by experts and patients, quality characteristics of the health services are abstracted. Literature Search – Search terms concerning the field of interest (e.g., CR), are combined with MeSH terms and keywords referring to quality assurance, process and outcome assessment or quality indicators. From all included articles QIs and outcome measures related to high quality of the health service are abstracted. Review of Guidelines – The prevailing guidelines in the field of interest are reviewed to identify procedural and structural properties of high quality health services. Guidelines do not often describe a desirable level of outcomes of care but they do mention minimum procedures, standards and facilities that services should include. Case study: We invited 40 Dutch experts to our expert panel of whom 38 agreed to participate. The experts included professionals from all disciplines involved in CR (cardiologists, rehabilitation and sport physicians, company doctors, nurse practitioners, physiotherapists, psychologists, social workers, dieticians, and CR managers). Also we asked 30 patients of four CR clinics to take place in the patient panel of whom 15 participated. Overall, 92 different quality characteristics of CR were mentioned. The PubMed search identified 314 articles in which 15 QIs and 24 different outcome measures of CR services were mentioned. Most frequently used outcome measures related to exercise therapy and quality of life. Few outcome measures related to patient satisfaction and professional performance. Furthermore, the CR guidelines in the Netherlands were reviewed, from which we extracted 34 procedural quality characteristics and three structural properties of CR services. Translation of Results – The results of the four sources are translated into a draft QI set using the OECD framework on QIs [6]. This framework describes how concepts of health care should be measured by grouping them into dimensions and formulate them according criteria of importance, scientific soundness and feasibility. Rating on Paper – The draft QI set is presented to the expert panel. They rate all QIs on a Likert scale from 1 (total disagreement) to 5 (total agreement) based on three criteria: (i) The QI has a clear relationship with one or more patient outcomes; (ii) The QI can be a departure point for improvement actions; (iii) Information regarding the QI is easy to record [6]. For each QI the mean score per criterion, the standard response levels of individual experts. The rated QI set is ranked and shortened by mean score.


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Figure 1. Overview of the modified Rand method and the results of our case study (in Italics).

Case study: Based on the four sources we assembled a draft set of 81 quality QIs for CR. The draft set was structured into four clusters reflecting the chronological phases of CR (referral, needs assessment, evaluation, and follow-up) and one cluster concerning organization of care. In each cluster the QIs were classified as relating to either process, structure, or outcomes of care. Twenty-two experts rated the draft QI set. The highest ranked QI (patient’s lifestyle is assessed during needs assessment for CR) had a mean overall score of 4.47. The lowest ranked QI (CR patients improve their cognitive functioning) had a score of 2.94. Group Discussion – The NGT is used to lead the expert panel towards consensus through rounds of debate, discussion and an anonymous voting process [8]. Input for the discussion is the ranked QI set, the experts discuss the set and select the final QIs. Case study: We presented the QIs with their ranks, structured into clusters, to the expert panel. The panel voted for the QIs they preferred in an anonymous voting procedure. The results were shown on a screen and discussed. After hearing different opinions, the panel voted again in the light of the discussion to gain consensus. The final QI set and their original sources are presented in Table 1.

4. Discussion In the current study we modified the Rand method to develop QIs for measuring and reporting on quality of care. In our method results from a literature and guideline search are combined with the knowledge of an expert and patient panel in an extensive rating and consensus procedure. We applied our method to the field of CR, where the final QIs set showed that the four sources are complementary. We believe that using all sources results in a well-founded QI set covering all aspects of the health service of interest. Notably, the expert panel mentioned only few QIs related to outcomes of care. Furthermore, many QIs mentioned by the patient panel did not make it to the final QI set because they were opinion-based (e.g., friendly treatment). Our experience with the multidisciplinary expert panel during the group discussion was positive. Because of the early involvement and the reflection of all disciplines in CR, the panel showed great commitment to the QI development process. We believe this will ease implementation and acceptability of the final QI set in daily practice. However, actual benefits (quality improvement) and costs (registration time) can only be assessed afterwards.

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Table 1. Final QI set for CR (E= Expert panel, P= Patient panel, L= Literature and G= Guidelines). Nr

Type

Quality indicator

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Outcome Outcome Outcome Outcome Outcome Proces Proces Proces Proces Proces Proces Proces Proces Structure Structure Structure Structure Structure

Patients improve their exercise capacity during rehabilitation Patients improvement their quality of life during rehabilitation Amount of time needed to start resumption of work Patients quit smoking Patients meet the physical activity norms Average time between hoispital discharge and start of rehabilitation Complete data collection during needs assessment for rehabilitation Patients are offered a rehabilitation programme tailored to their needs Patients finish their rehabilitation programme Rehabilitation goals are evaluated afterwards Cardiovascular risk profile is evaluated afterwards Patients receive a discharge letter Cardiologists receive a report after the rehabilitation Rehab professionals work with a multidisciplinary patient record Specialized education for patients with chronic heart failure Long-term patient outcomes are assessed Clinics perform internal evaluations and quality improvement Patients participate in patient satisfaction research

Source E P L x x x x x x x x x x x x x x x x x x x x x x x x x

G x x x x x x x x x

x x x x

x x

To improve the data collection needed to report on QIs, the QI database should ideally be linked to an already existing data collection system such as an EPR. The next step in our research project is to implement the QI set in all clinics that already use an EPR for CR with CDS functionalities. During a multicenter randomized clinical trial the clinics will also receive feedback on the developed QI set in combination with educational meetings to overcome both internal and external barriers for guideline implementation. We expect that our modified Rand method to develop QIs can also be applied in other medical domains to further improve quality and outcomes of care. Acknowledgements. The authors would like to thank the Committee for Cardiovascular Prevention and Rehabilitation of the Netherlands Society of Cardiology and the National Multidisciplinary Assembly on Cardiac Rehabilitation for their contribution to the development of QIs for CR.

References [1] Cabana MD, Rand CS, Powe NR et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999;282:1458-65. [2] Garg AX, Adhikari NK, McDonald H et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293:1223-38. [3] Jamtvedt G, Young JM, Kristoffersen DT et al. Audit and feedback: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2006;(2):CD000259. [4] Brook RH, Chassin MR, Fink A et al. A method for the detailed assessment of the appropriateness of medical technologies. Int J Technol Assess Health Care 1986;2:53-63. [5] Hicks N. Some observations on attempts to measure appropriateness of care. BMJ 1994;309(6956):730-3. [6] Kelley E, Hurst J. Health care quality indicators project; Conceptual framework paper. OECD; 2006. [7] Moscovice I, Armstrong P, Shortell S et al. Health services research for decision-makers: the use of the Delphi technique to determine health priorities. J Health Polit Policy Law 1977;2:388-410. [8] Dunham RB. NGT: a users' guide. University of Wisconsin School of Business; 1998. [9] Goud R, de Keizer NF, ter Riet G et al. Effect of guideline based CDS on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. BMJ 2009;338:1440-9. [10] Van Engen-Verheul M, de Keizer N, Hellemans I et al. Design of a continuous multifaceted guidelineimplementation strategy based on CDS. Stud Health Technol Inform 2010;160:836-40


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A Cloud-Based Semantic Wiki for User Training in Healthcare Process Management D. PAPAKONSTANTINOUa1, M. POULYMENOPOULOUa, F. MALAMATENIOUa, and G. VASSILACOPOULOSa a Department of Digital Systems, University of Piraeus, Piraeus 185 34, Greece

Abstract. Successful healthcare process design requires active participation of users who are familiar with the cooperative and collaborative nature of healthcare delivery, expressed in terms of healthcare processes. Hence, a reusable, flexible, agile and adaptable training material is needed with the objective to enable users instill their knowledge and expertise in healthcare process management and (re)configuration activities. To this end, social software, such as a wiki, could be used as it supports cooperation and collaboration anytime, anywhere and combined with semantic web technology that enables structuring pieces of information for easy retrieval, reuse and exchange between different systems and tools. In this paper a semantic wiki is presented as a means for developing training material for healthcare providers regarding healthcare process management. The semantic wiki should act as a collective online memory containing training material that is accessible to authorized users, thus enhancing the training process with collaboration and cooperation capabilities. It is proposed that the wiki is stored in a secure virtual private cloud that is accessible from anywhere, be it an excessively open environment, while meeting the requirements of redundancy, high performance and autoscaling. Keywords. Semantic wiki; healthcare processes; user training; cloud computing.

1. Introduction The drive in healthcare to reduce cost and improve quality requires enhanced cooperation and collaboration among disparate healthcare units. Hence, considerable attention has been paid to designing process models of healthcare delivery and on developing healthcare information systems that support intra- and inter-organizational healthcare processes, focusing on reducing (or eliminating) medical errors and improving quality of care [1,2]. In many circumstances, a lack of patient care coordination and teamwork is identified. Well-defined healthcare processes and interoperable health IT will enable virtual care teams to cooperate in the care of patients across organizational boundaries [1,2]. Thus, one important consideration in healthcare process management is to enable and foster active user participation, since users are required to think of their activities as constituents of healthcare processes and, hence, to instil their knowledge and expertise in the definition and automation of healthcare processes while paying due regard to culture. 1

Corresponding Author

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D. Papakonstantinou et al. / A Cloud-Based Semantic Wiki for User Training

The development and management of value-added healthcare processes requires extensive and continuing education of healthcare professionals. Properly designed user training material should enable users to understand process-oriented healthcare delivery, visualize intra- and inter-organizational healthcare processes, assimilate the logic underlying existing processes and identify areas where redesigning existing processes is required in order to adapt to today’s dynamic healthcare environment [1,2]. The knowledge that inter-organizational healthcare processes contain (e.g., flow of activities, resources involved, physical location, coordination requirements) and the data content must be made explicit through training so that users understand the requirements of the environment and participate collaboratively in its development. This paper focuses on the objective of empowering user-to-analyst interaction, being particularly concerned with designing and developing relevant training material for healthcare professionals. In particular, to enable users to understand healthcare process modeling concepts we use a semantic wiki as a collaborative tool that highlights the relevant knowledge expressed by a domain specific ontology and is used to develop and provide a training material. The training system architecture is based on a virtual private cloud environment to allow authorized healthcare professionals to modify the training material in order to adapt healthcare processes to changing requirements, share healthcare process definitions and access them anytime and from anywhere. Further, the cloud-based semantic wiki proposed possesses several advantages including access control (who has access to the information stored on the cloud and under what conditions), redundancy (effective recovery in case of machine/data failure), high performance and auto-scaling (capacity additions/removals into a cloud infrastructure based on actual usage and without human intervention) [3,4].

2. Motivating Scenario Healthcare delivery is, nowadays, characterized by the need for increased cooperation and collaboration among functional units. Hence, considerable attention has been paid on designing new healthcare processes or redesigning existing ones, according to current requirements [1,2,5]. This requires active user participation so that users’ knowledge and expertise is incorporated into healthcare process definitions. In turn, this requires an effective user-to-analyst interaction which can be facilitated by a user awareness activity on healthcare process management concepts which calls for a suitable and adaptable training content to be made available to users anytime and from anywhere. To illustrate the main principles of the training approach proposed, consider a healthcare process scenario concerned with drug prescriptions (ePrescribing service). The benefits accrued from the implementation of an ePrescribing service are many: for example, the service puts eligibility, insurances and formulary information at the physician’s fingertips at the time of prescribing. This enables physicians to select medications that are on formulary and are covered by the patient’s drug insurance. It also informs physicians of lower cost alternatives such as generic drugs. In addition, physicians can access a timely and clinically sound view of a patient’s medication history at the point of care, decreasing the risk of preventable medication errors [6]. This scenario shows an example implementation of a cloud-based ePrescribing service: A physician uses an ePrescribing application which is interfaced to a PHR


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system, reads the summary record of his/her current patient and selects one or more drugs from the Insurance Organization’s formulary based on information regarding eligibility status and ID numbers of the medication list covered. Upon selection of one or more drugs by the physician, the ePrescribing application performs validation checks (e.g. regarding drug interactions, patient allergies and medication history) to either clear the prescription or return alert information to physician. In case of a clear prescription, the prescription is stored, as pending, in the medication profile area of the insurance organization’s designated data center. A pharmacist connects to the insurance organization’s cloud infrastructure, selects the patient’s prescription and executes it. Thus, the patient or a delegated person thereof collects the prescribed drugs from a pharmacy of his/her choice [6]. Figure 1 shows an ontology for the ePrescribing process which has been constructed in order to be used by the semantic wiki proposed. Design or redesign of a healthcare process model can be performed by manipulating already defined objects, providing flexibility, agility and reusability of the training material designed.

Figure 1. A training ontology for the e-prescription process

3. A Semantic Wiki Architecture on a Cloud Infrastructure The need for providing training material for healthcare process management concepts requires a collaborative and cooperative training environment so than users not only acquire knowledge about the training objects but they also learn the relations between them. Wikis allow for collaborative knowledge and can be helpful in learning models [7,8]. In particular, semantic wikis allow users to add semantic annotations to the wiki content, offering better navigation and information retrieval [3,7]. Hence, nowadays, semantic wikis constitute a popular semantically enhanced collaborative knowledge management system, mostly because it tends to make semantic technologies accessible to non-expert users and that they make the inherent structure of a wiki accessible to machines beyond mere navigation [7]. Users can query the annotations directly or create views from such queries, navigate the wiki using the annotated relations and introduce background knowledge to the system.

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In this paper, a prototype system of knowledge acquisition is presented to support training in healthcare process management that consists of the following modules: • An LMS system called JoomlaLMS which supports interfaces with Web2.0 technologies and external applications. • The Semantic MediaWiki (SMW) as a tool to acquire and share knowledge. • The OntologyEditor extension of SMW to develop ontologies and ensure consistency of the knowledge base by a set of knowledge repair algorithms. • The Halo extension of SMW to facilitate the authoring, retrieval, navigation and organization of semantic data in SMW. • The Halo Access Control extension of SMW to protect content, allowing easy administration of access rights and user groups [8]. The training system proposed has been designed to be available on demand (i.e. when and where needed). Hence, it has been implemented onto the Amazon cloud infrastructure that provides a flexible, scalable and low-cost cloud computing platform. The web service of Amazon, Amazon’s Elastic Compute Cloud (EC2) was used to host the required software. Users of the semantic wiki collaborate using the same shared datastore for storing and retrieving the semantic annotations. Amazon Simple Storage Service (S3) is used to provide a highly durable storage infrastructure, while S3 security enables a determination of how, when, and to whom have to be exposed the information stored on the cloud, using also proven cryptographic methods to authenticate users [9,10]. For networking and security issues, Amazon Virtual Private Cloud (Amazon VPC) was used, which integrates with EC2 and functions as a secure bridge, enabling the healthcare organization that provides the training material to connect their existing infrastructure to a set of isolated Amazon web services and compute resources via a Virtual Private Network (VPN) connection using industrystandard encrypted IPsec VPN connections [10,11]. In this context, it is ensured that only users with specific IPsec VPN connections can access the training material included in the semantic wiki developed.

4. Results The approach proposed in this paper is concerned with capturing the knowledge found in healthcare processes and in structuring this knowledge in terms of an ontology that contains all relative concepts, instances of concepts and relations between them. The semantic wiki relates the basic entities defined in the ontology with the corresponding text. Thus, the training material user is enabled to search through the semantic wiki for an ontology construct, understand its meaning and usage with the help of the supportive text and navigate to associated ontology constructs. In this way, an in-depth understanding of each healthcare process is ensured. Semantic wikis address core problems of traditional wikis: consistency of content (same information on many pages), accessing knowledge (finding and comparing knowledge from different pages) and reusing knowledge. With regard to the creator of the training material, the main advantage of the proposed model is content reusability. From the trainee’s point of view, the main advantages are semantic search, knowledge or conceptual navigation and knowledge dissemination and ease of use without further education and training. The cloud solution has significant advantages to healthcare organizations such as cost saving, accelerated time to delivery, offloaded maintenance and management to


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the cloud, elastic resources, redundancy and scalability. More importantly, due to the information sharing capability, healthcare professionals can share standardized and best practice medical protocols thus improving the quality of care provided. In addition, the virtual private cloud approach ensures that training content will always be available to authorized users.

5. Concluding Remarks Healthcare is an increasingly collaborative enterprise involving a variety of activities (administrative, paramedical, nursing and medical) that are interconnected into healthcare processes in a manifold manner and are performed within and outside healthcare organizations. This paper takes the stance that a process-oriented view of healthcare delivery contributes to cost containment and quality improvement that healthcare processes should be designed (or redesigned) through active user participation and that healthcare professionals need an effective training aid that facilitates their participation. Thus, a prototype approach to structuring training content in healthcare process management is proposed. The approach is based on a semantic wiki implemented on a cloud environment. Thus, it defines a general ontology, refines the general ontology by adding all ontology constructs required, implements the semantic wiki infrastructure and implements the semantic wiki in a virtual private cloud. Due to the encouraging results of the approach described, it is intended to evaluate it extensively using more complex healthcare processes.

References [1]

Makris A., Papakonstantinou, D. Malamateniou F, Vassilacopoulos, G. Using Ontology-based knowledge networks for user training in managing healthcare processes, International Journal of Technology Management, 47 (2009), Nos 1/2/3, 5-21. [2] Wieringa, R.J. Blanken, H.M. Fokkinga M.M. and Grefen, P.W.P.J. Aligning Application Architecture to the Business Context, Lecture Notes in Computer Science, Springer-Verlag, 2681 (2003), 209-225. [3] Oren, E. A semantic wiki approach for integrated data access for different workflow meta-models, Digital Enterprise Research Institute, 2006. [4] Fitzgerald J. and Chalk, D. CLOUD TECHNOLOGY: Clear Benefits: The Emerging Role of Cloud Computing in Healthcare, DELL Services, 2010. [5] Lenz R. and Kuhn K.A. (2004), Towards a continuous evolution and adaptation of information systems in healthcare, International Journal of Medical Informatics, 73(1) (2004), 75-89. [6] Puustjärvi J. and Puustjärvi, L. Improving the Quality of Medication by Semantic Web Technologies, Proceedings of the 12th Finnish Artificial Intelligence Conference (STeP), 2006, Helsinki, Finland. [7] Landefeld R. and Sack, H. Collaborating web-publishing with a semantic wiki, Studies in Computational Intelligence, 221 (2009), 129-140. [8] Bratsas, C. Kapsas, G. Konstantinidis, S. Koutsouridis G. and Bamidis, P. A Semantic Wiki within Moodle for Greek Medical Education, Proceedings of CBMS 2009: The 22nd IEEE International Symposium on Computer-Based Medical Systems, 2009, New Mexico, USA. [9] Buyya, B. Yeo, C. Venugopal, S. Broberg, J. Brandic, I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, 25 (2009), 599-616. [10] Baron J, Schneider R. Storage option in the AWS Cloud. Amazon Web Services, 2010, Available from URL http://media.amazonwebservices.com/AWS_Storage_Options.pdf [11] Amazon Web Services, Overview of Amazon Web Services, 2010, Available from URL http://media.amazonwebservices.com/ AWS_Overview.pdf.

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Reference Architecture of Application Services for Personal Wellbeing Information Management a

Mika TUOMAINENa,1, Juha MYKKÄNENa University of Eastern Finland, School of Computing, HIS R&D Unit, Kuopio, Finland

Abstract. Personal information management has been proposed as an important enabler for individual empowerment concerning citizens' wellbeing and health information. In the MyWellbeing project in Finland, a strictly citizen-driven concept of "Coper" and related architectural and functional guidelines have been specified. We present a reference architecture and a set of identified application services to support personal wellbeing information management. In addition, the related standards and developments are discussed. Keywords. Citizen empowerment, service-oriented architecture, standards, personal health records, interoperability

1. Introduction According to many political agendas, individual's, citizen's or consumer's personal needs must be at the centre of the development of high quality health and wellnessrelated information services [1,2]. In healthcare, the transition of health care system from provider-centric to patient-centric or consumer view has been seen both necessary and inevitable [3]. This requires empowering individuals to better manage their own wellbeing and health care. Personal Information Management (PIM) solutions have been suggested to promote citizen or patient empowerment [4,5]. The MyWellbeing (OmaHyvinvointi) project was a national-level R&D initiative in Finland which focused on citizen as the center of services ecosystem and developed conceptual and concrete tools and solutions for personal empowerment. The project focused on a holistic concept of a "Coper" to explore and define features of an aid for personal wellbeing. The Coper is designed to help citizens cope with the services they use and to manage them. It also promotes the coordination with and between service providers. In addition, virtual communities and social networks provide information and support, and aid in the decision-making for citizens [e.g. 6]. Platform and service provider interchangeability and use through multiple channels such as internet portals or mobile phones are required characteristics of the Coper. The Coper is not an implemented product as such, but many of its features are supported by existing applications such as personal health records (PHR applications), electronic government services and personal time and content management tools. 1

University of Eastern Finland, E-mail: [email protected]

HIS

R&D

Unit,

POB

1627,

Fi-70211

Kuopio,

Finland,

M. Tuomainen and J. Mykkänen / Reference Architecture of Application Services

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The basic idea of the Coper in healthcare closely resembles that of Personal Health Record (PHR) Systems [e.g. 7,8]. There are, however, many different content, use and implementation models related to PHRs, mainly due to different business models [9]. PHRs or other self-managed digital information collections are also mostly absent in collaborative health information system typologies [e.g. 10]. Furthermore, to be able to support information management in different wellbeing-related services, a reference architecture which could be populated by specific services and components was needed.

2. Materials and Methods The objective of this work was to specify a reference architecture for the Coper and to identify components for personal wellbeing management solutions. A service-oriented architecture (SOA) approach which facilitates reuse and integration of application services [11,12] was used. In addition, a classification of services was pursued. The work is based on literature and standards survey, experience from citizen eService development projects, existing products, and results from nine workshops of the project participants. The workshop participants included two EPR vendor companies, vendor companies for community, citizen and knowledge services, message delivery operator, five research institutes, and four health service provider organizations. The literature survey covered articles on personal information management, studies and comparisons of PHRs, and standards such as [8]. The survey confirmed that many PHR solutions share main features of the Coper. Experience from efforts such as guidelines for eBooking [11] stressed the need to identify benefits for both service providers and consumers. Four out of nine workshops in the project focused on service implementation and specification. The services were further prioritized, identifying combinations which could be realized using the existing offerings. The work was also harmonized with the information architecture for the Coper, and the prototypes for the case groups: persons retiring from work life and families having a baby.

3. Results Instead of an enterprise standpoint, the project used the analysis of citizen needs and activities as a starting point for the solutions and the architecture. The architecture is based on the dual model of services. The citizen has the right to receive a copy of documents from wellbeing services. Information is traditionally stored in the providers’ professional systems, but the customer's copy is under the control of the individual and can be used to combine information from various services. Such combination, if performed by service providers, is often difficult due to legal and privacy constraints. In our design, the wellbeing services offered to the citizen are reflected the identified software services. The SOA services are classified according to functional, platform, information or interactivity requirements (See Figure 1). The classification is generic, and all identified Coper services and functionalities can be located in one of the classes. In addition to the core functionalities of the Coper, personal information repository and user interfaces are basic building blocks of Coper realizations. Based on the results of the workshops and surveys, a total of 62 identified services were classified (See Figure 2). Core functionalities of the Coper include basic entry, management and organization of data and documents. Many basic functionalities

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follow those of PHR systems [8]. Personal information repository holds the documents received from different sources and personal data, including both structured and unstructured formats. Viewing, sorting and searching functions are supported by webbased, mobile or desktop user interfaces. Added value presentation services for different user devices and presentation personalization can also be provided.

Figure 1. Classification of services supporting personal information management.

Various platform services support communication, information management or user management. Communication platform includes secure communication and messaging services, in addition to support services such as technical service directories. Information management platform consists of services for data management such as synchronizing information repositories or translations between different presentation formats. User management platform services include identification, authorization and access control mechanisms supporting also access logs and digital signatures.

Figure 2. Specific personal wellbeing information management services in different service categories.


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Information source services are primary communication channel to import external data into the Coper, in addition to user-entered data. Connections to the systems of the service providers (also including the national eArchive in Finland), document scanning services, as well as connections to different types of personal measurement instruments are supported by these services through dedicated data import interfaces. In addition, there are various added value services which provide functionality related to personal preferences, communication with the selected service providers or communities, or for combining personal information with knowledge repositories. Personal added value services include and link personal tools such as calendars, reminders, diaries or personal trend indicators. Community added value services offer peer-to-peer and other communication and information sharing channels for selected communities. Knowledge services link personal information to external knowledge, interpretation or risk analysis, individual decision support or patient instructions. Finally, provider collaboration services enable transactions and information sharing with the service providers, including eBooking, service directories, prescription renewal and communication with professionals during the patient journey [13].

4. Discussion and Conclusions In contrast to many provider-driven initiatives, only small part of Coper services focus on traditional eServices of health service providers. Many services were identified in readily-made products or completed projects. Prototype implementations of information management and sharing for maternity, as well as document scanning services for persons retiring were implemented in the project. In addition, detailed interfaces were specified for data import from health service provider systems to the Coper and for citizen-oriented decision support. It is not reasonable to expect any given system to contain and integrate all the services, although individual implementations of all services can already be identified. In addition, personal preferences hardly require all the services to be present. The identification of functionalities as SOA services enables stepwise development and individually-driven combination of various services. In addition, service categories promote uniform architecture which is needed to ensure the interoperability of various components. There are readily available standards for many parts of the architecture for integration of services. Many standards are based on the solutions developed for health professionals, but also generic standards can be utilized. Especially relevant are standards for structure and semantics of health information which are in key position for linking personal information to knowledge or provider collaboration services. In our project, the most relevant of these were the national HL7 CDA implementation guides, HL7 Continuity of Care Document specifications and IHE Exchange of Personal Health Record Content profile. Open standards are also available for device connectivity (including Continua and ISO/IEEE 11073 specifications), provider collaboration such as eBooking, and community and web user interface standards, respectively. The service classification is generic and can be used to group electronic services in general. For example, services and interface standards for personal health systems presented in [14] and [15] can be positioned using the framework. The architecture is used and refined in relation to a eService ecosystems architecture produced in an services ecosystems research in Finland (as part of the Mind and Body programme) and

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promoted for a national programme for citizen eServices. Several services have been further specified and refined by the participating organizations. In addition to this architecture for the services, infrastructure decisions such as the use of integration platforms, as well as rules for the information architecture including metadata are among the key decisions to be agreed upon within a given ecosystem of services. From citizen perspective, personal wellbeing information management de-couples the individual from health or wellness service provision and avoids several obstacles related to service providers' view. The concept can be extended to cover personal health records, many different domains (healthcare, insurance, social services etc.), interactive eServices and community and knowledge links. The presented classification of individual-oriented application services serves as a step towards open and extensible ecosystem of electronic wellbeing services. Prioritization of services to be implemented and shared, however, must be based on the needs of consumers and aspects which can be implemented by service producers with an acceptable threshold in a sustainable way. Acknowledgements. The authors would like to thank all the members of the MyWellbeing project, the participants of the workshops and the members of the Mind and Body programme.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Ministerial declaration of eHealth 2003 conference, Brussels, 22 May 2003. Detmer D, Steen E. Learning from abroad: Lessons and questions on personal health records for national policy. AARP Public Policy Institute, Research Report 2006-10, March 2006. Castro D. Explaining international IT application leadership: Health IT. The Information Technology & Innovation Foundation, September 2009. Angst CM, Agarwal R. Patients Take Control: Individual Empowerment with Personal Health Records. Working Paper No. RHS-06-013, 2004. Pratt W, Unruh K, Civan A, Skeels M. Personal Health Information Management. Commun ACM 2006:49(1):51-55. Eysenbach G. Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness. J Med Internet Res 2008;10(3):e22. Iakovidis I. Towards personal health record: current situation, obstacles and trends in implementation of electronic healthcare record in Europe. Int J Med Inf 1998:52 (1-3):105-115. HL7 Personal Health Record Systems Functional Model, Release 1, Draft Standard for Trial Use, HL7 EHR Technical Committee, November 2007. Rocca M, Ritter J, de Faria Leao B, Reynolds M. ISO/HL7 Personal Health Record (PHR) Survey Results. Health Level Seven and International Standards Organization, 17 September, 2008. Balka E, Björn P, Wagner I. Steps Toward a Typology for Health Informatics. Proceedings of CSCW’08, Nov 2008, San Diego, California, pp. 515-524. Mykkänen J, Tuomainen M, Kortekangas P, Niska A. Task Analysis and Application Services for Cross-Organizational Scheduling and Citizen eBooking. Proc. of MIE2009, IOS, 2009, pp. 332-336. HSSP Service Specification Development Framework, version 1.3, Healthcare Services Specification Project, Health Level Seven and Object Management Group, 2007. Richards T. Who is at the helm on patient journeys?. BMJ 2007:335:76. Mikalsen M, Hanke S, Fuxreiter T, Walderhaug S, Wienhofen L. Interoperability Services in the MPOWER Ambient Assisted Living Platform. Proceedings of MIE 2009, IOS, 2009, pp. 366-370. Kaufman JH, Adams J, Bakalar R, and Mounib E. Healthcare 2015 and Personal Health Records: A Standards Framework. Proceedings of IHIC 2008, Oct 2008, Crete, Greece, pp. 19-28.


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Development of a Web-Based Decision Support System for Insulin Self-Titration A.C.R. SIMONa,b,1, F. HOLLEMANb , J.B. HOEKSTRAb , P.A. De CLERCQc, B.A. LEMKESb , J. HERMANIDESb , N. PEEKa a Department of Medical Informatics, b Department of Internal Medicine, Academic Medical Center, Amsterdam, The Netherlands c MEDECS BV, Eindhoven, The Netherlands

Abstract. Insulin is the most potent agent for the treatment of diabetes mellitus. However insulin treatment requires frequent evaluation of blood glucose levels and adjustment of the insulin dose. This process is called titration. To guide patients with type 2 diabetes using once-daily long-acting insulin, we have developed a web-based decision support system for insulin self-titration. The purpose of this paper is to provide an overview of the phases of development and the final design of the system. We reviewed the literature, consulted an expert panel, and conducted interviews with patients to elicit system requirements. This revealed four important aspects: the insulin titration algorithm, the handling of hypoglycemic events, telemedicine functionalities, and visiting frequency monitoring. We used these requirements to develop a fully functional system. Keywords. Clinical decision support systems, telemedicine, self care, diabetes mellitus, insulin

1. Introduction The prevalence of diabetes is increasing rapidly worldwide. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030 [1]. Diabetes, a chronic metabolic disorder, is hallmarked by increased blood glucose levels. Serious long-term effects of high blood glucose levels are blindness, kidney failure and cardiovascular disease. The main therapies for treating diabetes are dietary adjustments, oral glucose-lowering drugs, and insulin. Insulin is the most potent agent in the therapeutic arsenal but it requires frequent evaluation of blood glucose levels and adjustment of the insulin dose, a process which is called titration. Current clinical pathways for supporting type 2 diabetes patients in their titration of insulin involve either frequent clinical visits or routine visits supplemented by frequent telephone calls or e-mail contact. Both options involve exchanging information about blood glucose results and providing advice on adjusting treatment, but they are also very timeconsuming. Delivery of care between visits improves how fast the patient reaches good glycemic control and reduces the risk of exposure to a high glycemic burden for prolonged periods of time. As internet access in patients’ homes will continue to increase over the coming years, a web-based system to support self-management in insulin titration has the 1

Corresponding Author.

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A.C.R. Simon et al. / Web-Based Decision Support System for Insulin Self-Titration

potential to reach a large number of patients at low cost. There already exist systems to guide patients with type 1 diabetes in calculating the optimal pre-meal short-acting insulin dose [2;3]. Existing systems for patients with type 2 diabetes mostly focus on providing weight management, physical activity and diet [4]. However, patients with type 2 diabetes initiating once daily insulin form a specific and relatively large patient group that also requires intensive titration of the insulin dose. They do not benefit from the systems for patients with type 1 diabetes because adjustment of long-acting insulin requires a different strategy than adjustment of short-acting insulin. For this reason, we decided to develop a web-based system that facilitates the clinical process of providing insulin dosing advice to patients with type 2 diabetes using any once-daily long-acting insulin, the Patient Assisting Net-Based Diabetes Insulin Titration (PANDIT) system. Patients should be able to access the PANDIT system on a frequent basis to receive insulin dosing advice. In addition, the system should support communication between patients and caregivers, and provide caregivers the possibility to overrule the system’s advice when they deem that this is necessary for safety reasons. PANDIT should also recognize potentially unsafe situations such as hypoglycemic events. The purpose of this paper is to provide an overview of the phases of development and the final design of the PANDIT system.

2. Methods The first step of developing the PANDIT system was to elicit the system’s requirements by reviewing the literature, by consulting an expert panel, and by interviewing patients. We searched the literature using the following MeSH terms: “clinical decision support systems” OR “telemedicine” AND “diabetes mellitus” AND “insulin” to provide us with an up-to-date overview of studies focusing on insulin titration and decision support for patients with diabetes. As currently no single and uniform care standard for the titration of insulin exists, we organized several meetings with an expert panel consisting of physicians, diabetes nurses and medical informatics specialists to generate decision rules for web-based titration of insulin. The rules were assessed by considering specific premeditated consultation scenarios. We performed semi-structured interviews with five experienced patients and five patients who recently started with once-daily insulin, to investigate patients’ habits and behaviors when performing self-measurements of fasting plasma glucose values, injecting insulin and adjusting the insulin dose. We also asked them if and how they could benefit from a web-based system that generates insulin dosing advices. A researcher (AS) translated the gathered information to a system requirements document. The document was repeatedly reviewed by a clinical diabetologist (FH) and a medical informatics specialist (NP) who are both part of the research team. Based on the specified requirements, a fully functional system was developed. Because generation of insulin dosing advice is the main feature of the system, it was decided to use the GASTON framework [5]. GASTON is a state-of-the-art framework for building decision support-systems, and consists of (i) an ontology-based knowledge representation language, (ii) a graphical modeling tool for encoding clinical decision rules and decision support algorithms, and (iii) an execution engine for reasoning and generation of advice. The graphical user interface (GUI) of the system was developed using Microsoft Silverlight. Patient data are stored in a Microsoft SQL database. SSL (Secure Sockets Layer) is used to provide encrypted data exchange over the Internet.


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3. Results 3.1. Requirements Specifications The elicitation of the requirements for the web-based titration system revealed four important aspects: the insulin titration algorithm, the handling of hypoglycemic events, telemedicine functionalities and visiting frequency monitoring. Insulin titration algorithm – The effect of diabetes treatment is evaluated by the widely accepted marker HbA1c (glycosylated hemoglobin), which reflects the blood glucose levels over a period of six to eight weeks. We used an existing treat-to-target titration algorithm for once-daily basal insulin in type 2 diabetes patients that has already been proven to be effective in lowering HbA1c when used systematically [6]. Discussions with the expert panel revealed that it would be advisable to adapt the existing treatment algorithm in such a way that it incorporates the variables weight and age, e.g. a patient with a higher weight requires more insulin and if the patient is older than 70 years it was deemed safer to titrate less aggressively (Table 1). The expert panel also concretized the necessity to choose a glycemic target that would preserve the benefits of intensive therapy but minimize the risk of severe hypoglycemia. If a patient experiences frequent hypoglycemic events, the caregiver should be able to tailor the target value of the PANDIT system to the individual patient. Table 1. Insulin titration algorithm Lowest Fasting Plasma Glucose of the Preceding Three to Six Days < 4 mmol/l 4,0 – 5,5 mmol/l 5,6 – 9,9 mmol/l > 10 mmol/l * min = minimum

Insulin Dose Adjustment for People < 70 Yrs -0,02 IU/kg (min* -2 IU) Stable dose +0,02 IU/kg (min +2 IU) +0,04 IU/kg (min +4 IU)

Insulin Dose Adjustment for People > 70 Yrs -0,02 IU/kg (min -2 IU) Stable dose +0,02 IU/kg (min +2 IU) +0,02 IU/kg (min +2 IU)

Safety: handling of hypoglycemic events - Achieving lower blood glucose levels carries an increased risk for hypoglycemia. The literature review showed that hypoglycemia and fear of hypoglycemia are considered the main barrier to attain good glycemic control by patients and clinicians [7]. Therefore, the expert panel set up decision rules with the purpose to prevent patients from experiencing hypoglycemic events. Firstly, the expert panel stated that increases of the basal insulin should be based on the lowest of three recent fasting plasma glucose (FPG) measurements, collected in the preceding three to six days. Patients sometimes measure a single fasting blood glucose value that is disproportionally high or low due to measurement errors and general variations in lifestyle (e.g. exercise, food intake). Using these values could cause overdosing of insulin and lead to hypoglycemia. Titration on the lowest FPG value from multiple measurements will prevent the system from using an erroneous measurement. Secondly, the expert panel concluded that if the patient reached the target value, the titration should be based on the lowest of six recent FPG measurements, collected in the preceding six to twelve days. Most importantly, the system should also include a procedure for handling hypoglycemic events. As currently no uniform care standards for handling hypoglycemic events exist, we used both the literature and expert opinion to set up this procedure. The expert panel stated that every hypoglycemic episode should be graded in terms of severity and safety risks, and if the episode is considered a reason for more intensively guided treatment, the patient should be redirected to the caregiver.

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Telemedicine functionalities - Several patients emphasized the importance of the involvement of the caregiver. Also the expert panel stated that caregivers should be able to maintain their responsibility while delegating the task of providing insulin advices to the system. Telemedicine functionalities should therefore enable caregivers to access their patients’ records of blood glucose values. Furthermore, caregivers should be warned by the PANDIT system when patients experience hypoglycemic events. In such cases caregivers should have the opportunity to overrule the system’s algorithm and directly provide insulin dosing advice through the PANDIT interface for a certain period of time. Visiting frequency monitoring - The expert panel stated that patients should be encouraged to use the PANDIT system frequently in order to achieve good glycemic control. It was decided that patients would be reminded automatically by e-mail or SMS to use the system if they had not entered FPG values for more than three days. According to the patient interviews not all patients would appreciate receiving such reminders from the system; consequently this feature should be optional. 3.2. System Architecture and Implemented Functionalities The PANDIT system consists of three different components: a decision support system, a GUI and a database. The system architecture is shown in Figure 1.

Fig 1. System architecture of the PANDIT system

The PANDIT system uses an interface resembling a plasma glucose diary to facilitate the collection of fasting blood glucose values2. Upon each consultation of the system, the patient updates the diary with recently measured FPG values and the amounts of insulin used, and reports whether he or she has recently experienced hypoglycemic events. Subsequently, the GUI performs a store-operation on the database and sends a request to GASTON. GASTON runs the applicable query on the database and executes the PANDIT algorithm. If necessary, GASTON executes the additional hypoglycemia algorithm. Finally, an advice will be transmitted to the GUI. The GUI stores the advice in the database and displays the advice to the user. The diary also facilitates adding annotations to the blood glucose values. These annotations could have an added value if the titration is redirected to the caregiver. In addition to the automated reminders sent by the PANDIT system, the system includes an e-mail functionality enabling the patient to directly contact the caregiver if considered necessary by the patient.

2

A video presenting the main features of the system is available at www.pandit-online.nl/demo


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4. Discussion In the current study we developed a web-based insulin titration system with telemedicine functionalities, the PANDIT system. PANDIT distinguishes itself from existing web-based self-titration systems as it focuses on patients with type 2 diabetes using once daily long acting insulin. In addition, while most existing systems do not involve the caregiver, the PANDIT system is specifically developed to be embedded in routine care. Earlier studies have already shown that frequent insulin dose adjustments which are set by clinicians according to a predefined algorithm can lead to substantial decreases in HbA1c. Even greater reductions are achieved when such an algorithm is applied by patients themselves [6]. A web based algorithm, like the PANDIT system, will spare the patients calculating a new insulin dose themselves. The challenge of providing automated insulin dosing advices at a patient’s home is enabling caregivers to maintain their responsibility in this process. A strength of the study is that we involved both patients and care professionals in clarifying the system requirements. We therefore believe that the results of our study are useful for the development of future telemedicine tools for diabetes patients. A web-based titration system such as the PANDIT system could be extended to patient groups using multiple injection therapy. Additionally, future studies should aim to develop web-based systems for diabetes patients addressing multiple treatment targets and facilitating integrated care involving a diabetes nurse, a dietician and other health care providers. A limitation of our study is that dietary and lifestyle aspects of diabetes were not considered to be implemented in the system. In the near future we will perform a pilot study and a randomized controlled study to investigate the efficacy of the PANDIT system. During the pilot we will also perform a usability test with patients.

References [1] [2]

[3]

[4]

[5]

[6]

[7]

Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030, Diabetes Care 27 (2004), 1047-53 Hejlesen OK, Andreassen S, Frandsen NE, et al. Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?, Computer methods and programs in biomedicine 56 (1998), 165-173 Rossi MC, Nicolucci A, Di Bartolo P, et al. Diabetes Interactive Diary: a new telemedicine System enabling flexible diet and insulin therapy while improving quality of life: an open-label, international, multicenter, randomized study, Diabetes Care 33 (2010), 109-115 Brown LL, Lustria ML, Rankins J. A review of web-assisted interventions for diabetes management: maximizing the potential for improving health outcomes, Journal of diabetes science and technology 1 (2007), 892-902 De Clercq PA, Hasman A, Blom JA, Korsten HH. Design and implementation of a framework to support the development of clinical guidelines, International journal of medical informatics 64 (2001), 285-318 Davies M, Storms F, Shutler S, Bianchi-Biscay M, Gomis R. ATLANTUS Study Group, Improvement of glycemic control in subjects with poorly controlled type 2 diabetes: comparison of two treatment algorithms using insulin glargine, Diabetes Care 28 (2005), 1282-1286 Cryer PE. Hypoglycemia: the limiting factor in the glycaemic management of Type I and Type II diabetes, Diabetologia 45 (2002), 937-948

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TreC - a REST-based Regional PHR Claudio ECCHERa,1, Enrico Maria PIRAS a, Marco STENICO a a Fondazione Bruno Kessler, Trento, Italy

Abstract. The Personal Health Record (PHR) is progressively becoming a fundamental tool to allow people to control their health. User needs, however, impose to design a PHR solution that must offer a great flexibility in terms of managing heterogeneous health data, composing data in higher level concepts and interfacing the PHR with different devices to collect and visualize data. We propose to adopt REST as core of a regional PHR architecture and present a PHR based on this architecture implemented and tested in our Province. Keywords. PHR, REST architecture

1. Introduction Since few years there has been a growing interest in Personal Health Records (PHR), electronic tools aimed at laypeople to support them in accessing, managing, and sharing their personal health information [1]. This interest is demonstrated by the increasing number of articles about this technology. The vast majority of the literature deals with issues like acceptability (by both institutions and laypeople), business models, fields of application, or present long term scenarios of a revolutionized healthcare sector. On the other hand, the ongoing debate shows little concern about which technical solutions are to be adopted in order to achieve the expected results. Reflections on these issues, though, are not mere details to be left to technologists but they require a specific attention. In this work we present the pathway that led us to choose a REST based architecture for TreC, a regional-wide PHR for the citizens of the Province of Trento (Northern Italy, 400 000 inhabitants) [2], in order to satisfy the requirements emerged from sociological research on future users. In the next paragraph we will present the research project and the requirements analysis carried out in the preliminary phase. In the subsequent paragraph we will discuss the implications for design emerged that suggested the use of REST-style architecture and present it. Finally we will briefly describe the implementation process and the work planned for the future.

2. The Personal Health Record PHR can be roughly described as the laypeople’s counterpart of clinical Electronic Health (or Patient) Records (EHR and EPR). The latter are typically designed around the needs of the healthcare personnel (physicians, nurses, and clerical staff) or the 1

Corresponding author.

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organizations they work for. These professionals have patterns of actions that can be studied and, to some extent, formalized which makes possible to build technologies that fit into their workflow. This is not the case with citizens, whose activities do not fit into clearly defined schemas and consequently a formal data model cannot be defined. The impossibility to represent a “typical citizen workflow on health related activities” is not the only problem. To take care of personal health and wellbeing, in fact, a citizen might need to go beyond strict medical data and to record data related to lifestyle (e.g. cigarettes smoked, miles run). These differences between EPRs and PHRs require carrying out the requirement analysis in different ways: while in the first case a detailed study of the workflow and formal rules of conduct is necessary, in the second case this it is just impossible. What is relevant, though, is to provide a broad conceptualization of how health information is managed by laypeople in search of macro-concepts to be later transformed into system requirements. 2.1. Requirement Analysis We conducted 42 semi structured interviews focused on three major topics: 1) how paper based health records are sorted out and shared among relevant actors in the care process; 2) when and where this happens; and 3) if people produce medical documentation (e.g. personal diaries to keep track of health parameters). The analysis (see [3] for details) led to the following considerations. Everyday observations are defined by individuals and may be meaningful only for the person in the particular context in which they are collected or used. Moreover, to collect and visualize information people can use a number of different devices and applications that constitute a digital ecosystem able to satisfy the users’ needs in different contexts. The PHR, in substance, must allow the collection and aggregation of data without constraining them to be structured in a predefined data model. The main requirements of a PHR are summarized in the following: • • •

•

The PHR must allow accessing relevant information produced elsewhere: e.g., laboratory test and diagnostic imaging reports from the hospital, GP’s drug prescriptions, etc. The PHR must allow collecting and managing heterogeneous data: health data (pressure, blood tests) as well as lifestyle data (diet, physical activity), etc. The PHR must allow a flexible organization of low-level data in higher level concepts with different meaning and importance in different contexts: e.g., the weight associated to diet (lifestyle information) or to heart failure (health data for disease control). The PHR must allow to interface heterogeneous devices and applications to collect and visualize data when, where and how the user prefers/needs but also for receiving messages, alerts, etc.

2.2. An architectural solution for the PHR The choice of the PHR architecture was mainly determined by the last two requirements. The PHR is based on the Representational State Transfer (REST) paradigm, initially described in the context of HTTP, but not limited to that protocol [4]. which offers greater flexibility and control respect to other Web Service architectures [5]. REST-style architectures consist of clients, which initiate requests,

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and servers, which process requests and return responses. Requests and responses are built around the transfer of representations of resources, i.e. any coherent and meaningful concept. The representation of a resource is typically a document capturing its current or intended state. For our purposes, the REST architecture presents several advantages: •

• • •

The resource representation format is independent from the database. REST Web services do not use a single format for representing resources, but they can provide a variety of MIME document types (JSON, XML). This allows different devices to access ecosystem resources. Clients and servers are separated by a uniform interface with a small set of general methods to manipulate resources2, which represent primitive concepts that can be composed by clients in higher level domain concepts. REST applications maximize the use of the pre-existing, well-defined interface and other capabilities provided by the chosen network protocol, and minimize the addition of new application-specific features on top of it. In the last few years, there has been a growing interest in REST-based architectures to develop Web 2.0 applications.

3. The TreC System Architecture The TreC System architecture, depicted in Figure 1, can be divided in three blocks: the TreC REST core, the Data Model (DM) and the database, and the TreC clients.

Figure 1. System architecture of the TreC PHR implemented in the Province of Trento.

2

For example, a HTTP-based RESTful web service implements the methods GET, PUT, POST and DELETE.


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3.1. The TreC REST Core The core of TreC is a RESTful service (e.g. a service respecting the REST principles) which manages resources and transfers their representations to/from the clients. The idea is that a medical concept is a resource, and its transferred representation is structured in a XML or JSON document. One document can contain more resources, depending on the request of the specific client. The REST layer is responsible to store the incoming resource representation into a relational database of clinical concepts and to send the representation of clinical concepts in response to a client request according to the TreC data model (see next section). Moreover, the REST layer interfaces the PHR with other systems providing REST-based access to the external resources. 3.2. The TreC Data Model To allow the maximum flexibility in structuring heterogeneous data at application level, the DM is characterized by the definition of low-level concepts only, each corresponding to a resource in the REST paradigm, that do not depend on the particular context or the application used to collect them. Essentially, DM is base on the Entity Attribute Value (EAV) model, where each entity corresponds to a parameter with a value and several attributes (see Table 1). Primitive data are stored in a relational database; the REST-based access to the DB is mediated by the REST layer. Structured information generated by third parties (e.g., hospital reports) are managed by the REST layer as a resource whose representation is a complex file that can contain different kinds of information: plain text, coded parameters, images, etc. 3.3. TreC REST Clients Essential component of TreC is the set of client applications satisfying specific needs. A client application must know how to re-aggregate in complex concepts the primitive data exposed by the data model. To this end, the clients are thick clients able to build custom data structures to represent context and situation-specific concepts. Clients can also implement complex logic for analyzing and monitoring data according to the specific application domain independently from the PHR core and other applications, allowing managing health parameters even in the case of poor or absent client-server connectivity. Table 1. Simplified example of the data model structure. The most important attributes are: ID of the subject whom the measure refers to, ID of the resource, name, value, unit of measure, parameter code in some medical terminology, and registration time. Parameters for which a code in some terminology does not exist are marked with an internal code. The resource URI is composed by server address, subject ID and resource ID. Values belonging to the same measurement procedure (e.g., the blood pressure) can be identified by the same registration time. Subj ID S-001

ID R-001

S-001

R-002

Name Diastolic Pressure Systolic Pressure

Code SNOMED-CT: 271650006 SNOMED-CT: 271649006

Value 90

Unit mm[Hg]

Time 20/01/2011 12:25:00

140

mm[Hg]

20/01/2011 12:25:00

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At the same time, they can easily inter-operate with other clients exchanging the low level data. As shown in Figure 1, the client applications can be of different nature: applications running on mobile devices for implementing small local service, as well as web-based applications accessible through a browser. In this view, the PHR is an ecosystem of different applications, running on a range of devices. The responsibility of implementing client applications is left to client developers in response to specific user needs.

4. Trec Implementation and Future Work In the last half of 2010 we released a first version of the system for extensive testing in a real life setting, according to the philosophy of the living lab approach [6]. 450 people are currently using the system. On a 2-month base they are asked to respond to an on line survey about general satisfaction, patterns of use and other specific issues. The released system is constituted by the REST core, the database and the Web portal, based on Liferay, that allows to access to a set of client applications implemented as widgets. At the moment the system offers three main functionalities. The first is the possibility to manage on line clinical information produced by the institutions (e.g. lab test results, admission and discharge letters, specialist consultancy reports). These documents can be visualized but also annotated and classified. The second is a structured personal health diary where individuals can keep track of clinical parameters, medications, and reconstruct their past clinical history. The third function allows parents to manage their kids’ personal pages. In 2011 we aim at testing TreC as a communication tool between individuals and physicians to support the personal management of three chronic conditions: pediatric asthma, diabetes and chronic hearth failure and to add a set of mobile applications for collecting and monitoring health data at home.


[4]

[5]

[6]

Tang, P.C., Ash, J.S. Bates, D.W.. Overhage, J.M and Sands, D.Z. Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption, JAMIA, 13 (2006), 121–126. Piras, E.M Purin, B. Stenico, M. and Forti, S. Prototyping a Personal Health Record Taking Social and Usability Perspectives into Account, Electronic Healthcare, Springer, 2010, 35–42. Piras, E.M. and Zanutto, A. Prescriptions, X-rays and Grocery Lists. Designing a Personal Health Record to Support (The Invisible Work Of) Health Information Management in the Household, Comput. Supported Coop. Work, 19(6) (2010), 585–613. Fielding, R. Architectural Styles and the Design of Network-based Software Architectures, PhD dissertation, 2000, University of California Irvine, available at http://www.ics.uci.edu/̃fielding/pubs/dissertation/top.htm, last accessed January 2011. Pautasso, C. Zimmermann, O. and Leymann, F. RESTful Web Services vs. Big Web Services: Making the Right Architetural Decison, Proceedings of the 17th International World Wide Web Conference (WWW2008), 2008. Følstad, A. Brandtzæg, P.B. Gulliksen, J. Börjeso,n M. and Näkki, P. Towards a Manifesto for Living Lab Co-creation, Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II - INTERACT ’09, Uppsala, Sweden, 2009, 979–980.

Decision Support, Knowledge Management, Guidelines



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Next Generation Neonatal Health Informatics with Artemis Carolyn MCGREGORa1, Christina CATLEYa, Andrew JAMESb, James PADBURYc a University of Ontario Institute of Technology, Oshawa, ON, Canada b The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada c Women & Infant's Hospital of Rhode Island, The Warren Alpert Medical School of Brown University, Providence, RI, USA

Abstract. This paper describes the deployment of a platform to enable processing of currently uncharted high frequency, high fidelity, synchronous data from medical devices. Such a platform would support the next generation of informatics solutions for neonatal intensive care. We present Artemis, a platform for real-time enactment of clinical knowledge as it relates to multidimensional data analysis and clinical research. Through specific deployment examples at two different neonatal intensive care units, we demonstrate that Artemis supports: 1) instantiation of clinical rules; 2) multidimensional analysis; 3) distribution of services for critical care via cloud computing; and 4) accomplishing 1 through 3 using current technology without a negative impact on patient care. Keywords. neonatal intensive care, multidimensional data, cloud computing

real-time

analysis,

clinical

rules,

1. Introduction Neonatal Intensive Care Units (NICUs) deploy state of the art medical devices to monitor and support premature babies; however, neonatologists are unable to process the vast quantities of both manually charted data and data collected from medical monitoring equipment. While there has been a sustained effort to move from paper to electronic charting in critical care, including NICUs, these initiatives have not improved the representation of information that can be derived from that charted data or the translation of that information to knowledge for earlier condition onset warnings. Recent research is building a strong case for the benefits of real-time data analysis, with clinical events such as late onset neonatal sepsis (LONS) [1, 2] exhibiting early warning signs in physiological data before the clinical impact is sufficient to exhibit current clinical detection indicators. However, that research takes a condition specific, patient specific or physiological data stream type specific approach. The translation of that knowledge is another ‘black box’ clinical decision support system (CDSS) medical device at the bedside. Patients can develop multiple conditions concurrently or over time and each condition can have a set of behaviours with a pattern of occurrence. An infrastructure that can process currently uncharted higher frequency physiological data 1

Corresponding Author. 2000 E-mail: [email protected]

Simcoe

Street

North,

Oshawa,

Ontario,

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and support the earlier onset detection of multiple conditions has the potential to provide greater knowledge at the bedside than is available today and represents the next generation of informatics solutions for critical care. The provision of this knowledge requires a multidimensional approach as there are multiple conditions and multiple streams of data for which multiple behaviours can exist. In addition, new approaches are needed to enable processing and integration of both real-time synchronous medical device data and asynchronous clinical data to aid in clinical decision-making and improve outcomes for newborn infants. We present the Artemis framework, a platform for real-time enactment of clinical knowledge as it relates to multidimensional data analysis and clinical research. First implemented at The Hospital for Sick Children, Toronto, in August 2009, Artemis has been running continuously since that time. We discuss, with examples, Artemis’ multidimensional approach. Our goal is to provide a comprehensive description of the Artemis platform to date including the introduction of a cloud computing approach to enable distribution and support outsourced service of critical care.

2. Materials and Method Artemis, shown in Figure 1, provides a flexible platform for the real-time analysis of time series physiological data streams extracted from a range of monitors to detect clinically significant conditions that may adversely affect health outcomes. The Data Acquisition component enables the provision of real-time synchronous medical device data and asynchronous Clinical Information Management System (CIMS) data. This data is then forwarded for analysis within the Online Analysis component which operates in real-time. For this real-time component, Artemis employs IBM's InfoSphere Streams, a novel streaming middleware system that processes data in real-time and then enables data storage within the Data Persistency component. It is capable of processing and then storing the raw data and derived data from multiple infants at the rate they are generated [3]. Stream processing is supported by IBM's Stream Processing Application Declarative Engine (SPADE) language, which is the programming language for IBM's InfoSphere Streams middleware. For the Knowledge Extraction component, Artemis utilizes a newly proposed temporal data mining approach [4]. This component supports the discovery of condition onset behaviours in physiological data streams and associated clinical data. New knowledge, once tested through rigorous clinical research techniques, is transferred for use within the Online Analysis through the Redeployment component which translates the knowledge to a SPADE representation. First, this paper tests whether the Artemis platform can enable the instantiation of clinical rules. Second it demonstrates how this platform can support multidimensional analysis. Third, we propose that this platform can be provided not only through an inhouse installation but also through cloud computing providing a service of critical care [5]. This is particularly of interest for remote hospitals whose infrastructure for information technology technical support is much more limited than larger urban centre healthcare organizations. In this way, raw physiological streams and related clinical data can be transmitted securely over the Internet, with de-identified patient identifiers, for processing at the cloud-computing site. Finally, we show that that the current technology is capable of supporting the platform without a negative impact on patient care. The Hospitals’ Research Ethics Boards approved this research.


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Figure 1. Artemis Framework (modified from [3])

3. Results Our first implementation of Artemis is located at The Hospital for Sick Children (SickKids), Toronto. Real-time synchronous data is being acquired from the Philips Intellivue MP70 Neonatal monitors. Asynchronous data is being acquired from CIMS. Clinical protocols require that electrocardiogram derived HR (ECG-HR); transcutaneous oxygen saturation (SpO2); respiration rate (RR); and impedance respiratory rate (IRW) data streams are constantly collected. When available, we also receive the systolic, diastolic and mean blood pressure. We have deployed SPADE code within the Online Analysis supporting our research into early detection of LONS. Data Persistence occurs at SickKids and data is replicated daily to the University of Ontario Institute of Technology (UOIT) where Knowledge Extraction research supports our clinical research into new earlier onset detection of LONS. Artemis has collected data on 174 patients, representing 4.1 patient years of data; all raw and derived data has been stored for retrospective research. Currently supporting eight concurrent patients and collecting approximately 1250 readings a second, Artemis at SickKids is deployed on three laptops: 1) for data acquisition; 2) for online analysis; and 3) for stream persistence. An incremental backup of the data is made to a persistence storage mirror at UOIT and used by the knowledge extraction component. In April 2010 a second Artemis instance began collecting data from the Women and Infants Hospital in Rhode Island (WIHRI), USA. The WIHRI has successfully used a cloud-based deployment, where spot readings taken each minute are collected from the bedside SpaceLabs devices and fed in raw form to the Data Acquisition component, implemented in Mirth, of the Artemis platform running at the UOIT through a secure internet tunnel. In this setting all components of the platform are housed in the Health Informatics Research Laboratory at UOIT. Knowledge Extraction research supports our clinical research into new earlier onset detection of LONS on slower frequency physiological data. To date WIHRI has enrolled 203 patients, representing 10.6 patient years of data. We have implemented a third installation of Artemis that contains only the Data Persistency, Knowledge Extraction and Redeployment components. Using the Knowledge Extraction component, we are performing retrospective data mining on a

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dataset of nearly two years of 30 second spot reading data, obtained from 1151 patients, to further inform our refinement of a clinical rule for earlier detection of LONS which can ultimately be deployed by the Re-deployment component. We have successfully instantiated clinical rules though their implementation in SPADE for deployment by the Online Analysis component for LONS [3], apnoea [6], and hypoglycemia [7]. The three different implementations demonstrate that the platform can support multiple dimensions, shown in Figure 2, including: multiple locations, care providers, patients, conditions, data streams, and data stream behaviors. By care providers we mean that the platform can provide different temporal data summaries to different providers. For example, with apnoea, the neonatal nurse responds to alarms for extended respiratory pauses and falling SpO2 and HR levels indicative of potential apnoea events. Our goal is not to generate further alarms for discrete events, but rather to create integrated temporal summaries of events from multiple data streams. For instance, a single mild apnoea event may not be clinically relevant; however, clusters of such events could be indicative of LONS and this information should be available to the neonatologist.

Figure 2. Multidimensional approach

Our current implementations at SickKids and WIHRI have no impact on care at the bedside. We are collecting and comparing when behaviours in the physiological streams are noted for comparison with current clinical observation and treatment practices. Due to the volume of data collected there were initial concerns expressed by the hospital’s Information Services group about network traffic; however we have found that Artemis consumes less than 0.5% of network bandwidth. We have demonstrated that such a platform is capable of keeping up with the data collected at the speed at which it is received. The two other Artemis environments are currently running at UOIT spread across four servers.

4. Discussion The Artemis platform uses currently available technology to support next generation health informatics, through online analysis and knowledge extraction of currently uncharted higher frequency data. In addition to the three implementations presented here, new implementations of the Artemis platform are in the planning stages for another NICU in Canada, as well as two NICUs in China and one in Australia. Artemis provides clinical decision support in a flexible and transparent manner. Flexibility results from the ability to receive any asynchronous physiological data,


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support the generation of multiple clinical rule representations as autonomous or interrelated SPADE graphs for Online Analysis, and perform multiple clinical research studies within Knowledge Extraction for clinical event analysis. The use of SPADE to represent the clinical rule enables transparency of the representation of the knowledge processing. This is in direct contrast to many CDSSs based on complex mathematical processing, such as artificial neural networks, which from the clinicians’ viewpoint operate as black boxes. While a growing number of studies indicate that properly designed and effectively used CDSSs have the ability to improve quality of patient care [8], black box approaches raise concerns about the possible negative effects of CDSSs, including: potential de-skilling effects if system users do not understand how results were generated; a lack of flexibility and overly prescriptive outcomes; promoting overreliance on software; and difficulty in evaluating outcomes [9]. Artemis is not a black box solution; rather it provides a means to instantiate clinical knowledge into the information processing pathway. From a clinical policy perspective, a number of international regulatory bodies are mandating that CDSSs require regulatory approval [10]. Canada has recently introduced new regulations classifying patient management software as a medical device that must be regulated [11]. The impact this will have on the clinician’s ability to perform updates to CDSSs based on new evidence-based medicine is not yet clear. Acknowledgements: This research is funded by the Canada Research Chairs program, Canadian Foundation for Innovation, an NSERC Discovery Grant, and an IBM First of a Kind award.

References [1]

Flower AA, Moorman JR, Lake DE, Delos JB. Periodic heart rate decelerations in premature infants, Experimental Biology and Medicine 235 (2010), 531-8. [2] Griffin MP, Lake DE, O’Shea TM, Moorman JR. Heart rate characteristics and clinical signs in neonatal sepsis, Pediatric Research 61 (2007), 222-227. [3] Blount M, Ebling M, Eklund J, James AG, McGregor C, Percival N, et al. Real-time analysis for intensive care: development and deployment of the Artemis analytic system, IEEE Eng Med Biol Mag 29 (2010), 110-8. [4] McGregor C. System, method and computer program for multidimensional temporal data mining. Patent # 089705-0009; Canada, Gatineau Quebec (2010). [5] McGregor C, Eklund JM. Next generation remote critical care through service-oriented architectures: challenges and opportunities, Service Oriented Computing & Applications 4 (2010) 33-43. [6] Catley C, Smith K, McGregor C, James A, Eklund JM. A Framework to model and translate clinical rules to support complex real-time analysis of physiological and clinical data, Proc. 1st ACM International Health Informatics Symposium (2010), 307-315. [7] Kamaleswaran R. A SOA method for the integration of heterogeneous data models for decision support, Master’s thesis (in progress), University of Ontario Institute of Technology (2011). [8] Wright A, Sittig DF, Ash JS, Sharma S, Pang JE, Middleton B. Clinical decision support capabilities of commercially-available clinical information systems, JAMIA 16 (2009), 637-44. [9] Open Clinical, DSS Success Factors (2005). Accessed January 2011 from: http://www.openclinical.org/dssSuccessFactors.html [10] Berner ES. Legal and regulatory issues related to the use of clinical software. In Greenes RA, ed. Clinical Decision Support, The Road Ahead. Elsevier Inc., 2007. [11] Health Canada, Software Regulated as a Class I or Class II Medical Device (2010). Accessed January 2011 from: http://www.hc-sc.gc.ca/dhp-mps/md-im/activit/announce-annonce/ md_notice_software_im_avis_logicels-eng.php

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Limitations in Physicians’ Knowledge when Assessing Dementia Diseases – an Evaluation Study of a Decision-Support System a

Helena LINDGRENa1 Department of Computing Science, Umeå University, Sweden

Abstract. There is a need to provide tools for the medical professional at the point of care in the assessment of a suspected dementia disease. Early diagnosis is important in order to provide appropriate care so that the disease does not cause unnecessary suffering for the patient and relatives. DMSS (Dementia Management and Support System) is a clinical decision-support system that provides support in the diagnosis of a dementia disease, which is in use in controlled clinical evaluation settings in four countries. This paper reports the results of evaluations done in use environments in these places during a period of two years. Data in 218 patient cases were collected by 21 physicians during their use of the system in clinical practice. In 50 of the cases the use of the system were also observed and the physicians were interviewed in 88 cases. The collected data and inferences made by the system were analyzed. To summarize the results, DMSS gave appropriate support considering the patient case, available information and the user’s skills and knowledge in the domain. However, the results also illuminated the need for extended and personalized support for the less skilled physician in the assessment of basic information about patients. Keywords. Clinical decision support system, dementia, evaluation, diagnosis

1. Introduction In a larger perspective of developing sustainable knowledge-based system in the health domain, results from iterative user evaluations are ideally fed into new versions of the system [1]. Due to the safety-critical nature of health and medical decision-support systems, the integration of prototypes of such systems in their earlier stages are commonly troublesome (e.g., [2]). As a consequence, the ecological validity of the support provided can not be properly assessed, which is particularly important when developing systems for supporting a continuing medical education in individuals. In order to overcome this constraint on the development, efforts have been done to develop methods to integrate early prototypes in clinical practice using an action research and participatory design approach, Herzum and colleagues provide with one example in [3]. Another example is DMSS (Dementia Management and Support System) in focus for our work [4]. DMSS is a stand-alone prototype of a clinical 1

Corresponding Author: Helena Lindgren, Department of Computing Science, Umeå University, SE-90187 Umeå, Sweden; E-mail: [email protected]

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decision-support system currently used in controlled clinical evaluation settings. The core information needed for assessing types of dementia is typically not collected in electronic patient health records if they even exist, which is one reason to introduce DMSS. What has been seen as beneficial in earlier qualitative studies is the learning potential visible in changes of the user’s assessment procedure and the support in the form of a checklist in the assessment towards deciding upon a diagnosis [5]. The main purpose with the evaluation study presented in this paper was to investigate how the system is used in real clinical settings involving users previously not familiar with the system and being novice or moderately familiar with diagnosing dementia diseases. The work supplements earlier case studies [5, 6] and aims at providing a quantitative evaluation of the outcome of the use (i.e., to what extent does the diagnosis suggestions provided by the system deviate from what the physician assert?), and interpretations of reasons for such noncompliance in the cases when they occur, which can be used for improving the system.

2. Methods The patient data from 218 patient cases was collected by 21 physicians, employed at 12 different health care organizations in four different countries during a period of two years. Three of the 21 physicians were considered experts, since they were enrolled in specialist care for dementia patients. The other participants were considered novices or moderately knowledgeable in the dementia domain, corresponding to typical levels of knowledge among primary care physicians. A range of different specialties was represented in the group, however, sharing a common clinical practice situation in which they need to diagnose dementia more or less frequently. The types of clinics ranged from small family practices with no computers, part from a laptop with DMSS installed, to hospitals with full equipment, where the patients could be either inpatients or outpatients depending on the local organization and the patient’s need. The data was entered into DMSS either as a part of the patient encounter or after the patient encounter. The collected patient data was anonymous, and also the individual physicians were coded and made anonymous in the data sample. Evaluations were done using the set of clinical practice guidelines and consensus guidelines underlying DMSS as baseline for what diagnosis could be regarded as correct in the case of conflicting views on a patient case [7, 8, 9, 10]. The physician’s assessment of specific diagnoses was recorded in the database, and in 50 patient cases the physician was also observed using the system. In addition, the physician was interviewed about his or her reasons for assessment in these 50 cases and in additional 38 cases. DMSS interprets the case as being atypical in the case when the patient data was ambiguous when analyzed using clinical guidelines (Figure 1). In these cases DMSS shows degrees of support for different diagnoses instead of suggesting one particular diagnosis. In such cases, the diagnoses with the highest confidence in the diagnosis were used in the comparison with the physician’s assessment. For instance, if DMSS assesses the reliability in Diagnosis 1 higher (e.g. “probable”) than in Diagnosis 2 (“possible”) and the physician has asserted Diagnosis 1 then their assessments comply, while if the physician has asserted Diagnosis 2 then this is interpreted as a conflict. The patient cases where the assessments did not comply, and the cases where an insufficient amount of information was entered, were subjected to further analysis in order to find reasons for noncompliance and lack of information. These cases and the

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conflicting cases were also re-analyzed using DMSS in order to investigate where the user had stopped filling in information and which information underlies the conflicting views on a case.

3. Results A brief overview of the range of patients that occurred in the sample is the following. Out of 218 patients 125 received a specific dementia diagnosis that the system and physician agreed upon and 26 did not have a cognitive disease according to the physician and the system. 15 cases were in agreement diagnosed with mild cognitive impairment (MCI). This means that in 166 out of 218 cases (76,1%) it was possible to reach as far as a diagnosis agreed upon (or non-diagnosis) based on the collected information. The distribution of different types of dementia among the 125 cases with a specific dementia diagnosis was the following: Alzheimer’s disease (AD) 72%, vascular dementia (VaD) 6,4%, combined AD and VaD 2%, Lewy body dementia (DLB) 8,0%, frontotemporal dementia (FTD) 5,6%, dementia due to alcohol abuse 2,4% and dementia due to Parkinson’s disease (PDD) 1,6%. In addition to these 166 cases, there were cases in which the system and user also agreed upon that it was not possible to come to a diagnostic conclusion based on the insufficient information available. The views on the results of using the system and reasons for incomplete information in these cases were assessed by interviews. The physician agreed with the system that more information is needed, leaded to additional examinations, these however, being out of scope for our evaluation. In total, the physician and the system agreed on a view on diagnosis in 185 of 218 patient cases (84,9%). In additional 17 cases there were incomplete information, however, in these cases the physicians were not available for interviews for evaluating the level of agreement.

Figure 1. Part of an overview of DMSS analyses in an atypical patient case.

In 16 cases (7,3%) there were a conflict between the physician’s assessment and the system’s analyses of the collected data. Four of these cases were identified as caused by system’s failure to assess a correct diagnosis, mainly due to insufficient handling of the type of dementia that is caused by excessive alcohol consumption. When the system had been adjusted, a re-analysis of the four cases generated satisfactory results, thus increasing the proportion of agreement to 189 cases (86,7%). In 10 of the 12 remaining conflicting cases the pattern was seen that the physician assessed


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Alzheimer’s disease based on a set of data in which the physician has asserted necessary symptoms such as episodic memory dysfunction as absent. The remaining cases showed neither a clear agreement nor clear disagreement, and the responsible physicians were not available for interviews. They were characterized by scattered and incomplete data collection and nine of these were collected by two of the physicians with minor experience in dementia diagnosis. One of the physicians had not asserted a diagnosis in four of the cases, and another physician had in five cases asserted a diagnosis, but had not entered enough information so that the system was able to come to any conclusion. In these cases the feed-back provided by the system was either highlighting data necessary to be entered for establishing diagnosis and/or information that the collected data was ambiguous and did not comply with implemented clinical guidelines.

4. Discussion In 10 of the contradicting cases, the system can be viewed as being correct based on the collected data and following the clinical guidelines. This would imply an increase in the “correctness” of DMSS. However, in an interaction design perspective the noncompliance is indeed not satisfactory. If it would be the case that the physician is correct about the memory deficit not being present, then the patient receives an incorrect diagnosis from the physician. If the physician is wrong about the memory function, this indicates that suitable interventions aimed at reducing consequences of cognitive dysfunctions may not be provided. In both cases, the physician needs to become educated in assessing cognitive disorders and their interventions. Reasons why the contradictory information has been entered may be stressful work situations, or lack of knowledge about dementia diagnosis, or simply that the interaction design of the system does not provide enough support to complete the task in a satisfactory way. Regarding the feed-back provided by the system in these cases, the user is given an overview of the support and lack of support/contradicting data for each potential dementia diagnosis. This feed-back is given in order to provide the user with explanations and a chance to reflect upon their own assessment. The reasons for the missing information may have been the same as in the cases where the physicians described that they did not have all the necessary information, or that the entering of information was not possible due to a stressful situation. Another reason may be lack of knowledge about the phenomenon to assess, as observed in earlier studies [6]. There was a set of symptoms that seemed to cause more confusion than other in the assessments. We have already mentioned episodic memory, which seems to be difficult for inexperienced physicians to distinguish. In addition, whether the patient has been exposed to toxic substances (e.g., drugs) and to assess characteristics of the cognitive decline caused difficulties. The physician must value whether the onset of the cognitive decline is rapid or insidious, and whether the decline is progressing. This is difficult, especially when there may be a case of multi-diagnosis with sudden rapid decrease in functioning due to vascular incidents along with a more slow progression due to Alzheimer’s disease. Also evaluating severity levels in different cognitive functions in order to distinguish between normal ageing, MCI and dementia is difficult, but necessary. In addition, at least two of the physician did not seem to know about the importance to enter information about an ongoing Parkinson’s disease so that this information can be valued together with other information. In an earlier study it was

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observed that in spite of knowledge that a related disease was present in health record and in the user, this was not included in the information, and in spite of that the patient showed typical symptoms, the physician assessed these to be absent [5]. This leaded to an agreement between the physician (who did not take the information into account which should be done) and the system, since the system draws conclusions based on the entered knowledge, although possibly not correct. Therefore, in the 185 cases in which the physician agreed with the system’s analysis, there may be agreement but not on the correct diagnostic conclusion, due to inaccurate data entry. This emphasizes the importance to provide the user with support also in the basic tasks of data collection and interpretation, and integrate DMSS locally with general health information systems.

5. Conclusions The work presented in this paper shows how a CDS for supporting dementia diagnosis comply with assessments done by physicians, and reasons for noncompliance are detected and discussed. The results show that the system performs well, with agreement in 84,9% and disagreement in 7,3% of the cases. In the remaining cases (7,8%) the information was incomplete and physician’s view was unknown. The reason for disagreement was in a majority of the cases due to a possible misconception in physicians of necessary symptoms for diagnosing Alzheimer’s disease. Therefore, future work will focus on developing the support in the system for assessing core symptoms since a correct diagnosis depends on correct assessment of basic cognitive functions. The cases will be further analyzed with automated methods in order to find patterns of behavior in the participating physicians that can be responded to when incorporated in a web-based adaptive support system.

References [1] Kaplan B. Evaluating informatics applications – clinical decision support systems literature review. Int J Med Inf. 2001:64:15-37. [2] Kaplan B. Evaluating informatics applications – some alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Inf. 2001;64:39-56. [3] Hertzum M, Simonsen J. Positive effects of electronic patient records on three clinical activities. Int J Med Inf. 2008;77(12):809-817. [4] Lindgren H, Eriksson S. Sociotechnical Integration of Decision Support in the Dementia Domain. Stud Health Technol Inform. 2010;157:79-84. [5] Lindgren H. Towards personalized decision support in the dementia domain based on clinical practice guidelines. UMUAI 2011; DOI: 10.1007/s11257-010-9090-4 [6] Lindgren H. Decision Support System Supporting Clinical Reasoning Process – an Evaluation Study in Dementia Care. Stud Health Technol Inform. 2008;136:315-320. [7] American Psychiatric Association. Diagnostic and statistical manual of mental disorders, fourth edition, text revision (DSM-IV-TR). American Psychiatric Association; 1994. [8] McKeith IG, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology 2005;65(12):1863-187. [9] Neary D, Snowden JS, Gustafson L, et al. Frontotemporal lobar degeneration. A consensus on clinical diagnostic criteria. Neurology 1998;51:1546-1554. [10] Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST. Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review). Neurology 2001;56:1133-1142.


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A Generic System for Critiquing Physicians' Prescriptions: Usability, Satisfaction and Lessons Learnt Jean-Baptiste LAMYa,1, Vahid EBRAHIMINIAa, Brigitte SEROUSSIa, Jacques BOUAUDb, Christian SIMONc, Madeleine FAVREd,e, Hector FALCOFFd,e, Alain VENOT a a Laboratoire d'Informatique Médicale et Bioinformatique (LIM&BIO), UFR SMBH, Université Paris 13, Bobigny, France b AP-HP, DSI, STIM, Paris, France; INSERM, UMRS 872, eq. 20, Paris, France c Silk Informatique, 40 bis avenue du général Patton, Angers, France d Université Paris Descartes, Faculté de Médecine, Département de Médecine Générale, Paris, France e Société de Formation Thérapeutique du Généraliste (SFTG), Paris, France

Abstract. Clinical decision support systems have been developed to help physicians to take clinical guidelines into account during consultations. The ASTI critiquing module is one such systems; it provides the physician with automatic criticisms when a drug prescription does not follow the guidelines. It was initially developed for hypertension and type 2 diabetes, but is designed to be generic enough for application to all chronic diseases. We present here the results of usability and satisfaction evaluations for the ASTI critiquing module, obtained with GPs for a newly implemented guideline concerning dyslipaemia, and we discuss the lessons learnt and the difficulties encountered when building a generic DSS for critiquing physicians' prescriptions. Keywords. Evidence-based guidelines, Dyslipaemia, Drug prescription

Decision

support,

Evaluation,

1. Introduction Clinical guidelines (CG) provide physicians with recommendations, but paper guidelines are difficult to use effectively during medical consultations [1]. This difficulty has led to the development of decision support systems (DSS) based on CG [2]. The ASTI project aims to develop a DSS to help physicians to take into account the treatment recommendations expressed in CG for chronic diseases [3]. ASTI includes a critiquing module that is automatically activated when the physician writes a drug prescription, and which issues an alert if the prescription does not follow the CG. The critiquing module was initially developed for hypertension and type 2 diabetes. However, unlike many DSS which focus on a single CG, ASTI is designed to be generic enough to cover all chronic diseases. To ensure the generic aspect of the 1

Corresponding Author: Jean-Baptiste Lamy, E-mail: [email protected]. LIMBIO, UFR SMBH, Université Paris 13, 74 rue Marcel Cachin, 93017 Bobigny cedex, France.

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system, a new CG concerning dyslipaemia [4] was implemented, and evaluated in the current study. The critiquing module [5] and the validation of its knowledge bases [6] have been presented elsewhere. The CG recommendations are modeled in the critiquing module’s knowledge base, and are then automatically translated into critiquing rules of the form “if physician prescribed treatment X to a patient with clinical condition P, then show criticism C”. An inference engine applies these rules, and has been integrated into éO généraliste, an electronic patient record (EPR) for general practitioners (GPs). Drug prescriptions, laboratory test results and some clinical conditions are automatically extracted from the EPR and used by the critiquing module. Other clinical conditions required by the critiquing module are entered manually by physicians on a special form integrated in the EPR and displayed when a patient is included in the ASTI study at the beginning of the consultation. We present here the results of the usability and satisfaction evaluations of the ASTI critiquing module for dyslipaemia, and we discuss the lessons learnt and the difficulties we encountered in the construction of this generic DSS.

2. Methods A knowledge base has been designed and tested for the CG relating to dyslipaemia, as previously described [5, 6]. It includes 28 decision criteria (patient's clinical conditions, laboratory test results, etc.), 15 drug treatments and 17 recommendations, resulting in 73 critiquing rules. We evaluated the critiquing module for dyslipaemia in the laboratory with 33 GPs. The GPs were éO users who volunteered to participate. Two evaluations were performed with the ASTI critiquing module for dyslipaemia. Usability was evaluated using five simulated cases. These cases were derived, by an expert, from real cases, and were selected to cover the various aspects of the CG. The GPs were first briefly introduced to the use of the ASTI critiquing module. They were then asked, for each case, to code the data for the patient into the EPR and to enter two prescriptions: the usual prescription the doctor would write and a prescription that he or she did not consider to satisfy the CG. For each prescription, the physicians were asked to indicate whether they expected an alert, whether an alert was raised, whether the alert (or the absence of it) was justified, and whether the explanations and proposals accompanying the alert were appropriate. Additional textual comments were possible. Satisfaction was evaluated just after the GPs had used the system. This evaluation was based on seven sentences. For each sentence, the GP had to tick one of four boxes, indicating strong agreement with the sentence, weak agreement, weak disagreement or strong disagreement. The evaluation was followed by a focus group, during which GPs were asked about the system, the way they used it and their feelings about it.

3. Results The usability evaluation involved 299 prescriptions (less than 2x5x33, because some GPs did not reply to all questions, e.g. they rarely tried a second prescription if the first one was already criticized), divided as shown in Figure 1. The system's specificity was 94±1.4% (95% confidence interval) and the sensitivity 84±2.1%. The 136 true positives includes both prescriptions criticized as expected, or unexpectedly if the GP then

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agreed with the criticism; for 114 (84±2.1%) of the true positives, the GP considered the system’s explanations and treatment proposals as appropriate. In 80±2.3% of cases, the system raised an appropriate criticism or was silent with good reason.

Figure 1. Results of the usability evaluation.

The results of the satisfaction evaluation are shown in Table 1. The physicians were interested in receiving automatic criticisms about their prescriptions and they found the ASTI critiquing module easy to use. However, they also felt that the use of the module interfered with doctor-patient relations. Further discussions with physicians showed that this problem was related to the time required to code the clinical context for the patient in the form displayed during the first consultation with the patient. Table 1. Evaluation of satisfaction, expressed in percent (%) Question I would like to receive automatic criticisms or suggestions relating to my prescriptions The ASTI critiquing module is easy to use The response time of the ASTI critiquing module is satisfactory The ASTI critiquing module is ergonomic The ASTI critiquing module can be effectively integrated into my daily practice The ASTI critiquing module interferes little with my relationship with the patient Extending the ASTI critiquing module to other guidelines would be a major step forward

Agreement Strong Weak 39 58

Disagreement Weak Strong 3 0

3 73 18 28

88 24 73 66

9 3 9 6

0 0 0 0

0

27

67

6

33

64

0

0

4. Discussion and conclusion In this study, we evaluated the usability of and satisfaction with the ASTI critiquing module for dyslipaemia, a condition different from the hypertension and type 2 diabetes initially used for designing the system. Many other chronic diseases (e.g. asthma, cystic fibrosis) also have complex drug treatments evolving over long periods of time, the optimal treatment depending on several factors (lab test results, clinical conditions,...). The system's specificity was high, and the GPs expressed an interest in critiquing systems. Similar evaluation designs, based on simulated cases, have already been used for evaluating DSS [7, 8]. It would be interesting to carry out further evaluations of the critiquing system on real practice. Performing evaluation on voluntary GPs is a possible bias since they are usually enthusiastic, but this can hardly be avoided. The first lesson learnt from this study is that it is possible to design a generic DSS supporting several CG for chronic diseases, despite the considerable heterogeneity of

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the various CG, which follow different treatment strategies (e.g. the CG for type 2 diabetes follows a “waterfall”-like linear strategy, depending on the stage of the disease, whereas the CG for dyslipaemia follow a “star”-like non-linear strategy depending on the type of dyslipaemia) and are often based on implicit knowledge (e.g. the CG does not generally mention that drug doses can be lowered to reduce adverse effects). Currently, five CG have been implemented: hypertension, type 2 diabetes, dyslipaemia, tobacco addiction, atrial fibrillation [5]. A few other DSS frameworks, such as Asbru [9] and others [10], have achieved a similar level of genericity. Another lesson is that an automatic DSS, like this critiquing module, requires tight integration with the EPR used by the physician. However, as the various EPR include essentially the same patient data, it is possible to integrate a DSS into many different EPR. Semantic interoperability is easy to achieve, because a DSS usually has a limited number of decision criteria (e.g. 28 for dyslipaemia). During the ASTI project, the critiquing module was integrated into another EPR, ALMA Pro, produced by the ALMA association. The difficulties encountered during the integration process were organizational and financial rather than scientific or technical. A third lesson is that physicians are interested in receiving automatic criticism on their prescriptions. This finding is consistent with other studies showing that physicians prefer automatic “background” DSS over “on-demand” DSS [11, 12]. However, we also learnt that displaying the CG textual excerpts applying to the patient (as in older versions of the critiquing module, but not the one used during the evaluation) is not sufficient for the critiquing of physicians’ prescriptions. Indeed, CG give recommendations such as “when the patient has clinical context C, drug W should be prescribed”. However, they do not explain to the physician why the drug X, Y or Z he prescribed is not appropriate. For a given patient, many prescribing errors are possible and should receive different criticisms: e.g. drug X may be contraindicated due to another disease that the patient has, drug Y may be indicated only as a second-line treatment, and drug Z may already have been prescribed a year ago without success. The major difficulty encountered is the coding of the patient's clinical conditions. The existing terminologies were developed for the coding of patient data in the EPR, but are not always relevant for coding decision criteria from CG. For instance, we were unable to code “family antecedent of myocardial infarction in the father before the age of 55” and “type 2 diabetes discovered at an advanced stage”. Moreover, physicians do not usually code clinical elements in patient records. Instead, they tend to write them in free text, which is not usable by DSS as-is. While it might be possible to convince some physicians to code the principal diseases and antecedents of the patient, they are unlikely to code systematically complex decision criteria, such as those cited above. This problem has also been encountered in the ASTI guiding module [13], and is considered as one of the tenth “DSS grand challenges” [14]. By contrast, the coding of laboratory results and drug prescriptions is less problematic: test result criteria are generally simple in CG, and drug databases can be used for coding drug prescriptions. Other difficulties relate to the CG themselves: they do not always provide clear recommendations, instead sometimes providing only “food for thought”, which is not sufficient for critiquing. The various strength levels of recommendations are useful but not always mentioned in CG. In some situations, two CG may be contradictory. For example, the French CG for hypertension and for dyslipaemia give different formulas for determining cardiovascular risk level. Formalizing CG during their development may help to resolve these problems [15].


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In conclusion, we have shown that the ASTI critiquing module, initially developed for hypertension and type 2 diabetes, is generic enough for application to dyslipaemia with good results. We have also shown that this module is of interest to physicians. The main difficulty is the coding of the patient's clinical conditions, but several approaches could be applied to this problem. First, graphical user interfaces or automated text processing tools could be designed to help physicians with data entry. Second, the coding of some clinical conditions could be done after the possible criticism rather than before, the physician explaining the reasons of his decision to the system. Finally, rather than executing the CG entirely, as the critiquing module does, other DSS might consist in presenting the CG to the physicians in a more usable form than plain text, possibly through graphical approaches. Acknowledgments: We thank the HAS (Haute Autorité de Santé, the French health authority) and the CNAM (Caisse Nationale d'Assurance Maladie, the French health insurance fund for employees) for funding the ASTI project.

References [1]

[2] [3] [4] [5]

[6]

[7]

[8] [9] [10] [11]

[12] [13]

[14] [15]

Dufour J, Bouvenot J, Ambrosi P, Fieschi D, Fieschi M. Textual Guidelines versus Computable Guidelines: A Comparative Study in the Framework of the PRESGUID Project in Order to Appreciate the Impact of Guideline Format on Physician Compliance. In: Proc AMIA Symp. Washington, DC; 2006. p. 219–223. Isern D, Moreno A. Computer-based execution of clinical guidelines: A review. Int J Med Inf. 2008;77(12):787–808. Séroussi B, Bouaud J, Dreau H, et al. ASTI: a guideline-based drug-ordering system for primary care. In: Medinfo. vol. 10. the Netherlands; 2001. p. 528–32. AFSSAPS. Prise en charge thérapeutique du patient dyslipidémique; 2005. Available at http://www.afssaps.fr/content/download/3967/39194/version/6/file/dysreco.pdf Lamy JB, Ebrahiminia V, Riou C, et al. How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions? Methods and application to five guidelines. BMC Medical Informatics and Decision Making. 2010;10:31. Lamy JB, Ellini A, Ebrahiminia V, Zucker JD, Falcoff H, Venot A. Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system. In: Proceedings of Medical Informatics Europe (MIE2008). Göteborg, Sweden; 2008. p. 223-228. Ramnarayan P, Roberts G, Coren M, et al. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Medical Informatics and Decision Making. 2006;6:22. Bury J, Hurt C, Roy A, et al. LISA: a web-based decision-support system for trial management of childhood acute lymphoblastic leukaemia. Br J Haematol. 2005;129(6):746–54. Young O, Shahar Y, Liel Y, et al. Runtime application of Hybrid-Asbru clinical guidelines. J Biomed Inform. 2007;40(5):507–26. Peleg M, Tu S, Bury J, et al. Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc. 2003;10(1):52–68. Van Wyk J, van Wijk M, Sturkenboom M, Mosseveld M, Moorman P, van der Lei J. Electronic alerts versus on-demand decision support to improve dyslipidemia treatment: a cluster randomized controlled trial. Circulation. 2008;117(3):371–378. Ash J, Sittig D, Dykstra R, et al. Identifying best practices for clinical decision support and knowledge management in the field. In: Proceeding of MEDINFO. Cape Town, South Africa; 2010. p. 806–810. Séroussi B, Bouaud J, Sauquet D, et al. Why GPs do not follow computerized guidelines: an attempt of explanation involving usability with ASTI guiding mode. Stud Health Technol Inform. 2010;160(Pt 2):1236–40. Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision support. J Biomed Inform. 2008;41(2):387–92. Goud R, Hasman A, Strijbis AM, Peek N. A parallel guideline development and formalization strategy to improve the quality of clinical practice guidelines. Int J Med Inf. 2009;78(8):513–520.

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An OCL-compliant GELLO Engine Jing MEIa1, Haifeng LIUa, Guotong XIEa, Shengping LIUa, Baoyao ZHOUa a Information and Knowledge Department, IBM Research, Beijing, China

Abstract. GELLO, an expression language for clinical decision support, has been approved as an HL7/ANSI normative standard for years. Unfortunately, there are few GELLO engines available in use, and the limited tooling seems to hamper a widespread adoption of GELLO. The objective of this paper is to validate the availability of implementing an OCL-compliant GELLO engine. Experimental results show that our GELLO engine runs successfully in a clinical guidelinebased decision support system for chronic disease management. Keywords. GELLO, virtual medical record (vMR), clinical decision support (CDS), clinical guideline

1. Introduction GELLO, an object-oriented query and expression language for clinical decision support (CDS) [1], was published in 2005, and approved as an HL7/ANSI normative standard. Syntactically, the GELLO language is based on the Object Constraint Language (OCL) that applies to an object-oriented data model. The underlying data model for GELLO was called a “virtual medical record” (vMR), and recently, the HL7 CDS Work Group embarked on the development of an HL7 vMR standard based on a multi-national, multi-institutional analysis of CDS data needs [2]. Two options appeared in the literature for GELLO implementation. One is to translate GELLO expressions into another language by means of a compiler producing executable codes. The other is to build a GELLO engine to evaluate native GELLO expressions. Obviously, the former option requires ad-hoc translations due to a variety of target languages while the later is more promising towards a generic solution. In this respect, we aimed at implementing a native GELLO engine, and we observed two alternative approaches. One is to regard GELLO as an independent language, and implement a standalone GELLO parser. The GELLO authoring tool [8] developed by Medical Objects is such a representative. Alternatively, we may make GELLO fully compliant with OCL, and leverage a range of well-developed OCL tools such as the Eclipse MDT (Model Development Tools) OCL [9] to implement a GELLO engine. Previous studies on GELLO implementation mainly focused on the relationship between GELLO and OCL, which promoted the HL7 CDS Technical Committee to approve the full compliance of GELLO with OCL [5]. Since then, few related work has been devoted to the OCL-compliant GELLO implementation. In this paper, we introduce our implementation for an OCL-compliant GELLO engine, which has been used in a clinical guideline-based decision support system for 1

Corresponding author: Jing Mei, Diamond building A, Zhongguancun Software Part 19, Dongbeiwang West Road 8, Haidian District, Beijing 100193, China; E-mail: [email protected].

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chronic disease management. As a case study, we also present the experimental results when deploying our system to a clinical setting in the daily care routine of diabetes patients. Finally, strengths, limitations and future directions of our work are discussed.

2. Method The objective of our GELLO engine is to evaluate GELLO expressions against clinical data in order to reaching a clinical conclusion (diagnosis result, therapy plan, etc.), where GELLO expressions are OCL expressions with reference to the data model of vMR. To this end, there are four steps. 1. implement the vMR model 2. define the GELLO expressions 3. feed the clinical data 4. execute the evaluation As shown in Figure 1, we develop a three-layer model of GELLO engine to accomplish the tasks above. We set up a model layer which provides the vMR model implementation in the first step. Specifically, it includes a core module of function implementation for manipulating HL7 data types where an ontology reasoner is employed to compute the implication relationship of concept descriptors. In the second step, as GELLO expressions normally contain terminologies, such as SNOMED CT for observation codes, we develop a configurable ontology repository to load relevant terminologies in the configuration layer. In the third step, in order to accommodate clinical data in non-vMR form, we develop a schema registry in the configuration layer to help the engine understand them. Finally, we build a service layer, which takes clinical data and GELLO expressions as input and puts evaluation results as output. Here, an OCL engine is borrowed to parse the input GELLO expressions, and a vMR transformer is developed to transform the input clinical data into the form of vMR. GELLO expression

Clinical data

Service layer vMR transformer

OCL engine Configuration layer

schema registry

ontology repository Model layer

vMR model implementation function implementation ontology reasoner vMR model Figure 1. Three layers for GELLO engine implementation.

Samples in Table 1 illustrate the input for executing our GELLO engine. The left column is a fragment of clinical data in CDA (Clinical Document Architecture [7]), which describes a blood glucose observation with value of 120 mg/dL in the first row

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and a diagnosis of diabetes mellitus type 2 in the second row. In the right column, we first define a GELLO expression to evaluate whether the patient’s blood glucose level is higher than the threshold of 7 mmol/L, followed by another GELLO expression in the second row to evaluate whether a patient has diabetes mellitus as implied in his/her problem observations. We remark that such data type functions as the comparison of physical quantity (PQ) and the implication of concept descriptor (CD) have been provided by our GELLO engine. So, the engine concludes the first evaluation result is false while the second is true, based on the facts that 1 mmol/L =18 mg/dL (where both mmol/L and mg/dL are standard units for measuring blood glucose), and diabetes mellitus type 2 is subsumed by diabetes mellitus in the SNOMED CT ontology. Table 1. Samples of input to GELLO engine

Clinical data (in CDA) <statusCode code="completed"/> <effectiveTime value="20104071530"/> <statusCode code="completed"/> <effectiveTime value="201004071630"/>

GELLO expressions package vMR context Patient def: BG : CD = self.factory.CD(‘1558-6’, ‘LOINC’, ‘Glucose p fast SerPl-mCnc’) def: threshold : PQ = self.factory.PQ(‘7’, ‘mmol/L’) def: obs : Sequence(LaboratoryObservation) = self.isAssociatedWith.laboratoryObservation obs -> exists(testCode.equal(BG) and value.oclAsType(PQ).greaterThan(threshold))

package vMR context Patient def: DM: CD = self.factory.CD(‘73211009’, ‘SNOMED CT’, ‘diabetes mellitus’) def: obs : Sequence(ProblemObservation) = self.isAssociatedWith.problemObservation obs -> exists(problemCode.imply(DM))

2.1. Model Layer This layer is responsible for implementing the underlying vMR model for GELLO. We first take the latest HL7 vMR domain analysis model (version 2010-03-22 [6]) as an input, and leverage EMF (Eclipse Modeling Framework, a modeling framework and code generation facility for building tools and other applications based on a structured data model) to generate the vMR model code packages automatically. Next, to provide support for HL7 data type functions, we code the implementation by ourselves. Taking the first GELLO expression in Table 1 as an example, the greaterThan is a function of PQ which needs the comparison between measurements with different units. Because of 1 mmol/L =18 mg/dL, the evaluation of “120 mg/dL is greater than 7 mmol/L” answers false. In addition, to provide the mechanisms for creating instances of classes through the Factory method in GELLO [5], we define a Factory class and implement its instantiation functions. As shown in Table 1, “def: threshold: PQ = self.factory.PQ(‘7’, ‘mmol/L’)” is such an example which creates an instance of the PQ class, namely “threshold”.


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It is remarkable that, using an ontology reasoner [4], we implement a unique function in HL7 data types that is the implication of concept descriptor (CD), aka CD.imply (we do not use CD.implies because “implies” is an OCL reserved keyword). Recalling the example in Table 1, the evaluation of diabetes mellitus type 2 implying diabetes mellitus answers true (in terms of the SNOMED CT ontology). 2.2. Configuration Layer In order to adapt to various deployment environments across clinical institutions, we provide two configurable components in the configuration layer. One is the schema registry. If the clinical data as input to the service layer is not in vMR form, the registry is configured to assist the engine in understanding the schema of the input clinical data and transforming them into vMR. The other is the ontology repository. For those terminologies appearing in GELLO expressions, the engine is able to reason on them with the access to the repository of the referenced ontology. 2.3. Service Layer Clinical data and GELLO expressions are input to this layer, with output of evaluation results. As mentioned above, if the input clinical data is in other format rather than vMR, a vMR transformer will perform the transformation task, where the schema of the input clinical data has been registered via the configuration layer. Meanwhile, considering that GELLO is fully compliant with OCL, we utilize an OCL engine (the Eclipse MDT OCL [9]) to parse the input GELLO expressions, where all functional computations are passed to the underlying vMR model implementation. Note that our methodology is vendor-independent and other OCL tools could be applicable as well.

3. Result We have implemented our GELLO engine in a clinical guideline-based decision support system for chronic disease management. The prototype system has been successfully deployed to Peking University People’s Hospital (one of the largest health providers in China), for managing diabetes patients. Specifically, we computerize a diabetic guideline (as defined by consulting an expert and referring the literature) as a clinical decision process which represents decision conditions with GELLO expressions and assists clinicians in the following three aspects. First is to raise health alerts to patients, such as a continuously high blood glucose alert. Second is to provide prescription advices, in process of oral glucose control therapy and insulin therapy. Third is to make referral suggestions, for transferring patients from primary to secondary care, and vice versa. In total, 36 GELLO expressions are defined, two of which are presented in Table 1. Besides PQ and CD, another 13 HL7 data types are used in these expressions such as TS (Point In Time) and IVL (Interval of Point In Time). Almost 80% of the 15 HL7 data type functions are implemented, including minus, plus, imply, equal and et al. When deploying our system to the local clinical setting where the clinical data is represented in the form of CDA, we register the corresponding CDA schema and develop the vMR transformer to transform CDA into vMR using the technique of XSLT transformation. Furthermore, as SNOMED CT is used in our GELLO expres-

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sions to represent clinical concepts, we build up a SNOMED CT ontology repository and leverage the ontology reasoner [4] for subsumption reasoning. At runtime, our GELLO engine is plugged in a process engine namely FileNet P8 to go through the diabetic guideline-based process where decisions are made through evaluating GELLO expressions. Those 36 GELLO expressions are all correctly evaluated against the real clinical data.

4. Discussion Compared with the standalone GELLO implementation like a GELLO authoring tool [8] developed by Medical Objects, an OCL-compliant GELLO engine undoubtedly could profit from the utility of well-developed OCL tools. This paper validates the feasibility of implementing an OCL-compliant GELLO engine with minimal effort. Particularly, if the HL7 vMR model updates (which is currently a draft), our modeldriven development facilitates the code re-generation, and moreover, the manual part (for function implementation) will not be overrode. In this respect, we pave a way for GELLO tooling, so as to improve the GELLO widespread adoption. Moreover, the novel features of our GELLO engine include providing support for the HL7 data type functions and the GELLO factory initialization functions. To the best of our knowledge, this paper is the first to bring an ontology reasoner into a GELLO engine, making the implication of concept descriptor sound and complete. However, as pointed in [3], overlapping and semantically non-compatible terminologies are in concurrent use, which is a significant challenge for scalable clinical decision support. By far, we provide support for ontology reasoning within one single terminology, and our ongoing work addresses the problem across terminologies. Another imperfection is that we currently do not provide full support for all HL7 data type functions. The justification is that some HL7 data type functions such as type conversions of promotion and demotion are rarely used in practical GELLO expressions. More importantly, we notice that such functions can be replaced by OCL operations, e.g., the oclAsType() operation is a good candidate for HL7 data type conversions. Finally, as GELLO expressions could also serve as queries to fetch vMR data, we also consider enriching our GELLO engine as a vMR query engine in future.

References [1] [2]

[3]

[4] [5] [6] [7] [8] [9]

Sordo M, Ogunyemi O, Boxwala AA, Greenes RA. GELLO: An Object-Oriented Query and Expression Language for Clinical Decision Support, AMIA Annu Symp Proc. 2003: 1012. Kawamoto K, Del Fiol G, Strasberg HR, Hulse N, Curtis C, Cimino JJ, Rocha BH, et al. Multi-National, Multi-Institutional Analysis of Clinical Decision Support Data Needs to Inform Development of the HL7 Virtual Medical Record Standard, AMIA Annu Symp Proc. 2010: 377-381. Kawamoto K, Del Fiol G, Lobach DF, Jenders RA. Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress, the Open Medical Informatics Journal, 2010 (4): 235-244. Mei J, Liu S, Xie G, Kalyanpur A, Fokoue A, Ni Y, Li H, Pan Y. A Practical Approach for Scalable Conjunctive Query Answering on Acyclic EL+ Knowledge Base, ISWC Proc. 2009: 408-423. HL7 GELLO standard, available from http://www.hl7.org/v3ballot/html/infrastructure/gello/gello.htm HL7 vMR wiki, available from http://wiki.hl7.org/index.php?title=Virtual_Medical_Record_(vMR) HL7 CDA standard, available from http://www.hl7book.net/index.php?title=CDA Medical-Objects GELLO wiki, available from http://wiki.medical-objects.com.au/index.php/GELLO Eclipse Modeling OCL, available from http://www.eclipse.org/modeling/mdt/?project=ocl


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Improvement of Inter-Services Communication through a CDSS Dedicated to Myocardial Perfusion Scintigraphy Julie NIESa1, Gersende GEORGb, Marc FARAGGIc, Isabelle COLOMBETd, Pierre DURIEUX d a MEDASYS, Espace technologique de St Aubin, Gif-Sur-Yvette Cedex, France b French National Authority for Health (HAS) Saint-Denis La Plaine, France; c Department of nuclear medicine and dMedical Informatics Department at Georges Pompidou European Hospital, Paris, France

Abstract. This study addresses the question of communication between medical wards and the nuclear medicine department for the realization of myocardial perfusion scintigraphy. It analyses the effects of a reminder for completing the content of an order form. It shows that the CDSS impacted ordering practices. It could be seen as a system enabling to structure the information and improve the quality of orders. Keywords: medical ward – technical service communication, organization, CDSS, CPOE

1. Introduction Clinical Decision Support Systems (CDSS) have demonstrated their efficacy in improving clinical practices and patient outcomes [1-3], particularly in the form of onscreen computer reminders [4]. However, previous experimental works, set up in different domains, show the absence of learning effect associated with the reminder effect. For example, Weingarten et al. evaluated telephone reminders to encourage rapid discharge of patients with chest pain without increasing the risk of post discharge complications. Using an alternating-time series design, they showed that the degree of medical compliance to guidelines decreased back to its pre intervention level [5]. Similar effects were reported by Durieux et al. with a CDSS dedicated to venous thrombosis prevention: each time the system was inactive, medical practices came back to the initial level before intervention [6]. The present work consists in implementing an on-screen computer reminder to help ordering Myocardial Perfusion Scintigraphy (MPS). It was performed in the Georges Pompidou European Hospital (HEGP), a university teaching hospital in Paris, France. Since its opening in 2000, the hospital has an entirely computerized Hospital Information System (HIS) with patient centered Electronic Health Record (EHR), DxCare® [7]. The EHR allows the computerized prescription of drugs, imaging and laboratory tests by means of a Computerized Physician Order Entry (CPOE) system. 1


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All MPS orders are made by physicians through the CPOE. Physicians of the nuclear medicine department answer demands and schedule examinations on the basis of information transmitted by prescriber through the CPOE, in a free-text field associated to orders (thereafter called “comment”). This information should describe patient characteristics and the aim of the examination. However, numerous orders are transmitted to the nuclear medicine department with no comment. This lack of information on clinical context leads to the cancellation of many scheduled examinations. Some studies demonstrated that lack of information sharing could lead to misunderstanding [8]. Some common representation is required to communicate about a shared task [9]. Indeed, information contained in the comment (i.e. objective of the examination) is a precondition for the nuclear medicine department to perform the examination. The implementation of a CDSS attached to MPS ordering was required by the nuclear medicine department to improve the transmission of specific patient data needed to schedule the examination. This study analyses the effects of content of MPS orders, by checking the existence of a comment associated to orders and seeking for information useful for MPS realization in the comment.

2. Methods 2.1. Intervention The myocardial scintigraphy consists in creating functional images of the myocardium showing where the blood is flowing, by following over time the distribution of tracers injected into the blood stream. The MPS may or may not be performed during a stress test (i.e. exercise), measuring to which extend myocardial perfusion and oxygen consumption adapt to exercise. This examination is therefore performed to search for myocardial ischemia and its functional consequences in patients for whom this primary diagnosis is suspected or in case of documented and already treated coronary artery disease for patient and therapy monitoring: evaluation of residual myocardial ischemia under medical therapy, search for post-infarction myocardial viability. Therefore, some knowledge of clinical context and diagnosis objective is needed to anticipate the conditions of tracer administration (i.e. at rest, during a muscular effort or during a pharmacological stress) and therefore to appropriately schedule the examination and prepare patients. All physicians of the hospital could order MPS. The aim of our work consists of characterizing the missing information in the orders which could help them to identify the objective of the examination. The content of the reminder and a dedicated questionnaire have been designed with the physicians performing the MPS. The reminder proposed one or several MPS types to the prescriber according to the patient characteristics and to prevent undesirable or fatal events which could occur in case of medical contraindication for the stress test. It also reminded the prescriber with the dedicated data to be transmitted to the nuclear medicine department. During the MPS ordering, a dedicated questionnaire appeared once by patient stay. This questionnaire helped the prescriber to complete clinical data required by the patient-specific reminder: 1) coronary disease history, myocardial infarctions and/or revascularization interventions; 2) coronary risks factors when needed, in case of primary diagnosis objective; 3) contra-indications for stress test.


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The reminder was displayed to the prescriber, proposing a pre-formatted text to be pasted in the comment attached to the order (Figure 1). The memo proposed by the CDSS is a well structured resume of all the data contained in the questionnaire. An explanation justifying the proposed CDSS memo is also provided to improve the adherence of the prescriber. The memo is not automatically integrated in the order window. The prescriber can have different choices to complete his order: 1) copy/past the CDSS memo in the comment area of the order window, 2) modifies the CDSS memo, or 3) writes his own comment.

Figure 1: Example of a Myocardial-Scintigraphy-CDSS display: a framed memo and a text justifying the proposed decision support. The reminder content has been translated from French.

2.2. Quantitative Evaluation During the study period (31 months, from January 2005 to July 2007), the CDSS was activated during two periods (A1 and A2) and not activated during two control periods (C1 & C2). The length of each period was: • C1 – 23 weeks from 1st January 2005 to 13th June 2005 • A1 – 43 weeks from 14th June 2005 to 13th April 2006 • C2 – 23 weeks from 14th April 2006 to 27th September 2006 • A2 – 43 weeks from 28th September 2006 to 31th July 2007. In the HIS, it was not possible to directly link the imaging orders with their realization. Thus, we could not verify if the reminder had an impact on the number of MPS cancelled. We analyzed the content of the comments which should be directly affected by the CDSS display. We analyzed the alternated series with ‘the number of comments influenced by the CDSS memos’ as primary outcome and ‘the number of empty comments’ as secondary outcome. We evaluated the number of MPS orders and the presence (or not) of associated comments according to the distinct experiment periods. Comments were blindly classified by two authors (JN and GG) in 4 categories: ‘Identical’, ‘Modified’, ‘Different’, and ‘Empty’ (see Table 1 for categories description). Divergences were resolved by consensus. Comments classified as ‘Identical’ or ‘Modified’ correspond to comments influenced by the CDSS.

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2.3. Qualitative Evaluation We performed also a comparative study of the comments content for every period. We used software dedicated to statistical analysis of texts: TropesTM. We focalized on concepts used by the CDSS and appearing in C2 period.

3. Results 3.1. Quantitative Results Comments typed as ‘Identical’ and ‘Modified’ show that the CDSS recommendation has been followed in the A1 and A2 periods, 288 (36.9%) and 314 (39.2%) times, respectively. The percentage of empty comments decreased during and after the first activated period (Table 1). Table 1: Description of the comments epidemiology according to experiment periods: n (%) [95%CI]. 95%CI: 95% Confidence Intervals for proportions were computed using exact binomial distribution. Type of comment Identical (copied and pasted from the CDSS memos) Modified (partly copied and pasted from the CDSS memos with additional information; totally written by the prescriber containing information from the CDSS memos, with or without complementary information) Different (with no link with the CDSS memos) Empty

C1 (N=859) N/A

N/A

A1 (N=779) 75 (9.6%) [7.6-11.9%]

213 (27.3%) [24.2-30.6%]

C2 (N=323) N/A

N/A

A2 (N=801) 57 (7.1%) [5.4%-9.1%]

257 (32.1%) [28.8%-35.4%]

739 (86.0%)

414 (53.2%)

314 (97.2%)

455 (56.8%)

[83.5%-88.2%]

[49.5-56.6%]

[94.7%-98.7%]

[53.3%-60.2%]

120 (14%)

77 (9.9%)

9 (2.8%)

32 (4.0%)

[11.7%-16.4%]

[7.8%-12.1%]

[1.3%-5.2%]

[2,7%-5,5%]

3.2. Qualitative Results TropesTM analysis demonstrated that some concepts are present in every study periods, such as the goal of the examination, e.g. ‘search for ischemia’ or ‘search for viability’. However, some concepts which didn’t exist in C1 appeared in C2, e.g.: ‘contraindications’ (179 occurrences), ‘asthma’ (11 occurrences), and ‘aneurysm’ (10 occurrences). Others concepts are more represented in C2, e.g. 6 occurrences representing beta-blocking drugs were retrieved in C1, 37 in C2. All these concepts were used in the memos proposed in A1. We can thus deduce a type of learning or sensibility to the information to be communicated to the nuclear medicine department.


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4. Discussion and Conclusion Our study suggested that the CDSS could have impacted the MPS orders. The quantitative analysis showed that the percentage of empty comments decreased after the first activated period and that the contents of comments were directly influence by the CDSS display. The qualitative analysis showed that the prescribers still used the CDSS concepts during CDSS inactivation periods. The CDSS could therefore be seen as a system enabling to structure the information and improve the comments quality. In previous studies [5, 6], the support compensated an error or omission: as long as the system was active, the reminder was efficient but all effects stopped when the system was disabled. In our experiment, the support was used to structure reasoning which is always done by the prescriber but which is not reported along with the order. Our study has some limits. 1) The CDSS which was stopped in April 2006 was reintroduced in September 2006 upon request from the nuclear medicine department physicians who had noticed a difference in the comments content. Consequently, study periods have different length. The reintroduction of the CDSS confirmed their observation. 2) Patient profiles were not analyzed but we have no reasons to believe that patient profiles changed over time. In a further work, we will specifically analyze the content of the ‘Modified’ comments from A1 and A2 periods in order to determine the information which is not displayed by the CDSS but considered important to be communicated by the prescriber.

References [1] [2] [3] [4] [5] [6] [7] [8]

[9]

Garg AX, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Jama 293 (2005), 1223-38. Mollon B, et al. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials. BMC Med Inform Decis Mak 9 (2009), 11. Pearson SA, et al. Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature. BMC Health Serv Res 9 (2009), 154. Shojania KG, et al. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev 3 (2009), CD001096. Weingarten SR, Riedinger S, et al. Practice guidelines and reminders to reduce duration of hospital stay for patients with chest pain. Intern Med 120 (1994), 257-63. Durieux P, et al. A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior. Jama 283 (2000), 2816-21. Degoulet P, et al. The HEGP component-based clinical information system. Int J Med Inform 69 (2003), 115-26. Beuscart-Zephir MC, Pelayo S, Anceaux F, Maxwell D, Guerlinger S. Cognitive analysis of physicians and nurses cooperation in the medication ordering and administration process. Int J Med Inform.76 (2007), S65-77. Epub 2006 Jul 7. Cooke NJ, Salas E, Cannon-Bowers JA, Stout RJ. Measuring Team Knowledge. Human Factors 42 (2000), 151-73.

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Prognostic Data-Driven Clinical Decision Support - Formulation and Implications Ruty RINOTTa,1 Boaz CARMELI a Carmel KENTa, Daphna LANDAU a Yonatan MAMANa Yoav RUBIN a, Noam SLONIMa a IBM Haifa Research Labs, 165 Aba Hushi st., Haifa 31905, Israel

Abstract. Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach. Keywords. Clinical Decision Support, Data Driven, Machine Learning, Prognostic

1. Introduction The need for Clinical Decision Support Systems (CDSSs) increases rapidly [1]. Most existing systems are rule-based systems focused at guidelines adherence, drug prescribing and monitoring, etc. [2]. The increasing pace by which Health Care Organizations (HCOs) adopt Electronic Health Record (EHR) technologies and the increasing recognition of personalized medicine importance suggest great potential for another type of CDSS, aiming to predict the outcomes of treatments considered for an individual patient via statistical and machine learning algorithms. We suggest a formal general description for such a prognostic data-driven CDSS (pdd-CDSS) and highlight an inherent difficulty associated with the development of such a system, related to the inherent bias in HCO's clinical data. We then propose a general strategy to address this difficulty and demonstrate our approach over clinical data of hypertension patients.

2. Methods 2.1. Defining Relevant Concepts We consider a patient who is at stage k of disease d. The pdd-CDSS should assist the physician by predicting the expected outcome of relevant candidate treatments for this 1


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individual patient, through mining the HCO's clinical data. Let T be a random variable with values {t1 ,…, tNt}, representing distinct candidate treatments. Let O be a random variable with values {o1 ,…, oNo}, representing distinct outcomes. We assume that the HCO maintains data about Nf clinical features, denoted by the random variables, {f1 ,…, fNf}. The sample population for the pdd-CDSS consists of Np patients that have already been at stage k of disease d and their received treatment and resulting outcome are recorded in the HCO's database. These Np patients can thus be divided into mutually exclusive and exhaustive treatment groups, according to the their treatment value, T, denoted {gt1 ,…, gtNt}.2 The data mined by the pdd-CDSS can thus be represented by a matrix M, where M(i,j) indicates the value of the i-th patient according to the j-th feature. The treatment and the outcome variables can be represented via two additional column vectors. Finally, we denote a new patient by the index i*, and the data associated with her is represented via an additional row in M, while T(i*) and O(i*), are obviously unknown. All these notations are depicted in Fig. 1a. 2.2. Treatment Groups are Inherently Biased Our first observation is that from a statistical perspective, different treatment groups often represent different populations, reminiscent to an observational study [3]. As an extreme example, let us assume that gender, denoted for example by fj, affects treatment success. We further assume that in the HCO's data for all patients in gt1, fj=M, while for all patients in gt2, fj=F, e.g., due to the HCO's guidelines. Next, we consider a new female patient. Since there are no examples in the data for female patients who received treatment t1, and assuming gender affects the treatment success, machine learning and statistical analysis algorithms will not be able to properly predict the outcome of applying t1 to this new patient based on the HCO's records. In practice, we do not expect the distinction between the treatment groups to be that obvious. However any bias in baseline covariates between treatment groups will affect prediction ability and must be considered in the design of a pdd-CDSS. Next, we propose one strategy to address this issue.

Figure 1. (a) Notations. (b) A flow chart for the proposed pdd-CDSS.

2

For simplicity, if a patient received more than one treatment during the same stage of the disease, her assignment to a treatment group is done based on the most recent treatment she received.

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2.3. A Valid Flow for pdd-CDSS In the example above, while we could not predict the outcome of applying t1 to the new patient, we could have predicted the outcome of applying t2 to that patient. Thus, if the “customary”3 treatment can be determined for a new patient, the outcome of that treatment may be reliably predicted. This suggests a strategy of limiting outcome prediction to “customary” treatments. However, identifying the "customary" treatment for a new patient might be far from trivial, involving complex considerations. Here, we propose to first exploit the bias in treatment allocation to predict the HCO's “customary” treatment. If a treatment group is clearly identified, it implies that the patients in that treatment-group are relatively similar to the new patient, in particular in the context of the covariates that distinguish the different treatment groups. Hence, outcome prediction can be reliably performed in that treatment group. Thus we propose to decompose outcome prediction for a new patient into two separate tasks (cf. Fig 1b): • Treatment prediction: predict T(i*), i.e., the HCO's “customary” treatment for the new patient, using all Np patients as training data. • Outcome prediction for the predicted treatment: predict the outcome only for the predicted treatment; namely, predict O(i*) given that the treatment is T(i*), using only patients who underwent T(i*) as training data.

3. Results We demonstrate our methodology over clinical data collected for hypertension patients as part of the Hypergenes project4. We identified three major possible treatments in the data: non-drug therapy (t1); angiotensin II receptor blockers (t2); and beta blockers (t3). We focused on patients that suffer from Stage-1 hypertension and for which: (a) the treatment group is known and the date in which this treatment was assigned is known5; (b) Systolic and diastolic blood pressure (BP) were measured when treatment was selected and at an additional later time point. This led to a dataset of Np=1771 patients with respect to 181 clinical features. Decrease in BP to below hypertension levels (diastolic < 90, systolic 0.7) [19] except the value of the first question of OB which however exceeded 0.6 and decided to remain at the model [20]. For internal consistency, the values of composite reliability exceeded 0.7 [21], thus considered reliable [21]. Furthermore, convergent validity was assessed based on the Fornell and Larcker [22] cut-off value of 0.5 for the Average Variance Extracted (AVE), being greater than 0.5 thus considered reliable [22]. At last discriminant validity was assessed based on the squared root value of the AVE for each construct [21,22] and produced reliable results. Continuously, the structural model was investigated by applying a bootstrapping technique (with 1000 resamples) and three statistically significant levels: p Copy data from previous record to anamnesis (patient case history) > Copy data from anamnesis (patient case history) to care plan > Nursing plan

Daily or weekly 76% 85% 79%

n 92 93 91

>> Use Nursing plan example >> Make Multi-disciplinary problem in care plan >> Report on Category

10% 29% 63%

69 87 90

>> Report on Health pattern >> Report on Multi-disciplinary problem or nursing plan

44% 51%

89 90

The highest level of the NIS consists of tabs. The use of tabs is mandatory and not shown. Each submenu is marked by >. A sub-submenu is marked by >>. All percentages differ significant from filling out at random (χ2-test at all columns equal, p < .05).

Analysis of the results in Table 1 shows, that the NIS is used intensively for making care or nursing plans, but supporting functions, such as the nursing plan example and the option to start a multi-disciplinary problem, are hardly used. The NIS supports the provision of care, especially by presenting accurate patient information, which enables the nurse to have a quick overview of the patient’s need of care (Table 2). The responses on the statements of the NIS on the collaboration with other disciplines are inconclusive. A possible explanation is that not all disciplines use the NIS. Table 2. Support of provision of care. Support of provision of care Quick first impression Quick complete view Quick insight in necessary care Quick overview of provided care Quick insight in goals Care is provided according to NIS Information for after-care is in the NIS Everyone enters data in the same way Everyone knows meaning of abbreviations Reports are kept regularly and up-to-date. Oral and written reports are not contradictory Patient data are not entered in wrong record I enter orders immediately in the NIS I enter change of care immediately in the NIS Support collaboration of disciplines is better

fully agree 32% 10% 24% 20% 7% 14% 10% 8% 2% 34% 25% 7% 30% 29% 13%

partially agree 37%Mdn 40%Mdn 41%Mdn 39%Mdn 31% 47%Mdn 23% 18% 14% 43%Mdn 48%Mdn 24% 32%Mdn 34%Mdn 30%

neutral 12% 18% 15% 16% 30%Mdn 26% 39%Mdn 27%Mdn 38%Mdn 13% 19% 19%Mdn 27% 27% 27%Mdn

partially fully disgree disagree 14% 4% 25% 7% 15% 4% 21% 4% 26% 7% 11% 2% 21% 7% 30% 18% 32% 13% 7% 2% 7% 1% 30% 20% 4% 7% 7% 3% 17% 13%

All percentages differ significantly from filling out at random (χ2-test at all columns equal, p < .05). Category contains median value.

n 91 89 91 90 90 90 89 90 90 90 89 90 90 90 90 Mdn

=

A remarkable result is the high frequency of users, who disagree that data of patients are not entered in the wrong record. This could be related to the small percentage of nurses who agree that every colleague enters data in the same way, and every colleague knows the meaning of abbreviations and symbols. This suggests that the NIS is not used consistently. Although the NIS does not seem to make nursing easier or faster, the nurses do have a positive opinion on the NIS and prefer the NIS to the paper record (Table 3). The use of the NIS raises the quality of recording according to the respondents. Törnvall et al. also found that using an electronic standardized

342

M.B. Michel-Verkerke / Nursing Information System

wound record improved the quality of documentation [8]. A hindrance in usability is the frequent occurrence of computer errors. Due to technical problems with the Computer-on-wheels (COW’s), entering data at the bedside together with the patient is often hindered, which reduces the involvement of patients in their care process. The NIS cannot be accessed by the patient, but the patient can request for a printed summary. The nurses consider this to be a draw-back of the NIS and do not consider the NIS to be patient friendly. Table 3. Usefulness of the NIS. To what extent do you agree? Perform tasks better. Care process passes more smoothly. Spend more time on direct care. Perform tasks easier. Useful and usable in my job. Do not want without anymore. Precisely provides the information I need. Precisely offers the functionality I need. No superfluous functionality. Contains all information I need. Contains all functionalities I need. No superfluous information. Can enter all information Access to all information anytime Can use all functionality anytime Access to all information anywhere Can use all functionality anywhere Quality of recording raises Advantages compensate the disadvantages Many advantages above paper record.

fully agree 7% 8% 2% 6% 22% 23% 11% 8% 29% 13% 12% 22% 18% 13% 12% 15% 14% 22% 25% 26%

partially agree 27% 21% 16% 17% 47%Mdn 33%Mdn 33% 33% 29%Mdn 36% 39%Mdn 38%Mdn 44%Mdn 41%Mdn 38% 35% 30% 39%Mdn 29%Mdn 33%mdn

neutral 34%Mdn 39%Mdn 28% 36%Mdn 16% 18% 34%Mdn 36%Mdn 26% 26%Mdn 25% 32% 17% 23% 25%Mdn 23%Mdn 26%Mdn 23% 25% 25%

partially disagree 19% 14% 29%Mdn 25% 9% 8% 14% 17% 9% 16% 15% 6% 15% 20% 21% 23% 23% 8% 13% 8%

fully disagree 14% 19% 24% 16% 6% 17% 9% 6% 7% 9% 9% 2% 6% 3% 5% 5% 7% 8% 8% 7%

n 86 87 86 87 87 87 86 86 87 87 87 87 87 87 87 87 87 87 87 87

All percentages differ significant from filling out at random (χ2-test at all columns equal, p < .05). Mdn

= Category which contains the median.

The questionnaire ended with three open questions. Multiple answers were given by the respondents. Top of the list of advantages were readability (45%), orderly (27%), easy and quickly to use (24%) and copying previous recorded information (22%). Disadvantages are dominated by technical problems (50%) and usability issues, such as complexity of some specific functions (23%), time consuming (21%), many mouseclicks (20%), no overview (18%), mistakes cannot be corrected by nurse (14%), and not everybody uses the NIS accurately (11%). Also 15% report the lack of direct access for the patient as a disadvantage. The desires for changes were all suggestions to resolve previously mentioned issues.

4. Discussion and conclusion Nurses intensively used those functions of the NIS, which were essential for reporting or retrieving patient information. Elements of the NIS meant to structure patient data, to support making nursing plans, and to improve the quality of recording were used less frequently. According to [9] predefined nursing plans stimulate nurses to make nursing plans. This study shows that only part of the nurses use these. It seems that these functions are not micro-relevant enough to overcome the extra effort of additional

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mouse-clicks. The NIS is mainly appreciated for supplying unhampered access to complete, legible, structured patient data, anywhere, anytime. This is in line with the outcome of a previous study on physicians [7]. Although training is necessary, overall the NIS was considered easy to use. The nurses thought the NIS did not save time, except for copying data. Some functions were regarded complex and error-prone. It would be interesting to investigate whether the doubt on accurate use by colleagues is based on facts. Technical problems in using bedside computers, made the NIS less patient-friendly than the paper record, but do not hinder the nurses to express their preference for the NIS. This confirms that usability problems do not obstruct perceived usefulness and use [1]. Combining qualitative methods with quantitative methods added value to the research, since results of the questionnaire could be explained and interpreted better by comparing the answers to the open questions and with the results of the interviews. The socio-technical approach reveals that not only system quality or system features determine or explain success or failure. The way colleagues use the system is equally important. The NIS is micro-relevant because it solved the information problem, and can be regarded as an improved version of the paper record, but does not solve the time problem. Information quality is probably more micro-relevant than time. It would be interesting to investigate whether micro-relevance is a relative or absolute phenomenon. If the use of functions that seem to be irrelevant, raises when the technical problems are solved, micro-relevance is likely to be relative.

References [1] Venkatesh, V. and H. Bala, Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 2008. 39(2): p. 273-315. [2] DeLone, W.H. and E.R. McLean, Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 1992. 3(1): p. 60-95. [3] Spil, T.A.M., R.W. Schuring, and M.B. Michel-Verkerke, Chapter IX: USE IT: The Theoretical Framework Tested on an Electronic Prescription System for General Practitioners, in E-health Systems Diffusion and Use: The Innovation, the User and the USE IT Model, T.A.M. Spil and R.W. Schuring, Editors. 2006, Idea Group Publishing: Hershey, USA. p. 147-177. [4] Babbie, E., The Practice of Social Research. Seventh Edition ed. 1995, Belmont: Wadsworth Publishing Company. [5] Garrity, E.J. and G.L. Sanders, Dimensions of information success, in Information Systems Success Measurement, E.J. Garrity and G.L. Sanders, Editors. 1998, Idea Group Publishing: Hershey, USA. p. p.13-45 [6] Michel-Verkerke, M.B., What makes doctors use the Electronic Patient Record? Master Thesis. 2003, Enschede: University of Twente. [7] Michel-Verkerke, M.B., An Electronic Patient Record in a Nursing Home: One Size Fits All?, in Nursing 2010: Rotterdam. [8] Törnvall, E., L.K. Wahren, and S. Wilhemsson, Advancing nursing documentation - An intervention study using patients with leg ulcer as an example. International Journal of Medical Informatics, 2009. 78: p. 605-617. [9] Ammenwerth, E., et al., A Randomized Evaluation of a Computer-Based Nursing Documentation System. Methods of Information in Medicine, 2001. 40(2): p. 61-8.

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GP Connector – a Tool to Enable Access for General Practitioners to a StandardsBased Personal and Electronic Health Record in the Rhine-Neckar Region a

Oliver HEINZEa,1 , Holger SCHMUHLa, Björn BERGH a Center for Information Technology and Medical Engineering of the University Hospital Heidelberg, Germany

Abstract. Electronic health records (EHR) as well as personal health records (PHR) are in widespread use today. Since several years the University Hospital Heidelberg is implementing a so-called personal and electronic health record (PEHR). The joint approach is standards-based and includes several needed services. However a remained unresolved issue is how to connect general practitioners (GP) and their systems to the record. This work describes a tool called GP Connector that provides access for GPs to the PEHR within the law. GPs can profit from all advantages of the PEHR usage. Only adding documents to the record comfortably through standards-based interfaces remains still open. Thus, deep integration of the PEHR into primary systems is preferable anytime. Yet the continuous trend towards multi-institutional health network may also pave the way for standards-based interfaces also in the field of practice management systems. Keywords. PHR, EHR, PEHR, eConsent, Standards-based, GP

1. Introduction In the last fifteen years electronic health records (EHR) have dominated eHealth projects around the world [1]. In the last years the usage of personal health records (PHR) rose in order to empower patients enlarging their role to actively manage their health [2, 3, 4, 5, 6, 7, 8]. Since several years the University Hospital Heidelberg (UHH) is implementing a personal and electronic health record (PEHR) to improve communication with partner hospitals in the region and to give patients a tool to manage their health. The concept foresees an integration of an EHR with a PHR according to the definitions of the Healthcare Information and Management Systems Society (HIMSS, see [9, 10]) using the advantages of both record types. The ownership of the whole longitudinal, lifelong record is given to the patients. The information exchange among their healthcare professionals is achieved by using the PEHR as central source of all relevant data. Due to data privacy legislation in Germany, the patient has to give his consent in order to 1

Corresponding Author: Dipl. Inform. Med. Oliver Heinze, University Hospital Heidelberg, Center for Information Technology and Medical Engineering, Speyerer Str. 4, 69115 Heidelberg, Germany; E-mail: [email protected]

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allow connected primary systems sending data to or receiving data from the record. From a technical point of view the PEHR is based on profiles of Integrating the Healthcare Enterprise (IHE) like PIX/PDQ for patient identification and XDS.b for document sharing via a central repository as well as international standards like HL7 and DICOM. The PEHR provides an integrated web-based view to enable access to patients and professionals [11]. Due to the lack of consent management functionalities of the deployed record system a centralized consent management was developed for the PEHR [12]. Healthcare providers and their organizations can be identified by a provider and organization registry service (PORS) [13]. Hospital information systems (HIS) are directly connected to the PEHR. Single sign on mechanisms provide the capability to seamlessly integrate the web-based access to the PEHR within the context of the HIS [11, 14]. In contrast to the successful interconnectivity between HIS and PEHR, the situation is quite different when it comes to the practice management systems (PMS) for general practitioners (GP) in Germany. A deep integration into PEHR like mentioned above is not possible because PMS neither do support international standards in order to share structured or unstructured information nor could their proprietary interfaces be used for that purpose at full extend. The German market for PMS is huge and diverse. PMS companies are often small dreading the effort to develop appropriate interfaces. Direct usage of the PEHR web interface is not possible due to the fact that on the one hand the PEHR vendor does not integrate the developed consent management deeply and on the other hand the PMS currently do not provide any means for consent management. Therefore the objectives of this paper are to describe a solution how to give GPs access to the PEHR in the Rhine-Neckar-Region without having a deep integration of their systems and without harming German privacy laws and regulations using the regional consent management service.

2. Method In expert workshops with computer scientists and physicians the requirements for the accessibility of GPs to the PEHR and the underlying workflows have been analyzed taking the systems landscape of the PEHR into account in order to identify technical possibilities as well as limitations of an integration approach. Based on the outcome the system architecture of GP connector (GPC) has been designed under the premise to reuse as many software components as possible and to use Open Source Software wherever possible. As database engine PostgreSQL was chosen. Apache Tomcat was used as servlet container and the Open eHealth Integration Platform (IPF) from the Open eHealth Foundation as middleware handling the messaging. The application logic was written using Java Spring and Spring Security. The interface of GPC is written in HTML5 and CSS3.

3. Result The GPC is a software component fitting into a service-oriented architecture enabling GPs in the Rhine-Neckar Region to access the PEHR without having a deep integration of their PMS and without harming data privacy laws.

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3.1. Workflow and Functionalities GPC provides a user management for GPs who want to participate in the regional network. If they do not have a user account they can request for one. The administrators of the network then do the initial authentication and provide them with the referring login credentials as well as a client certificate. In addition, the data of the GP is squared with the PORS to uniquely identify the GP within the network. After being logged in, a GP can search for a patient. Access is granted to the PEHR of the searched patient if its consent contains a policy that allows the GP to see existing data or to add some new. Otherwise the search will return no results. Due to privacy reasons it remains unclear if there are no rights or the patient has no record (See Fig. 1).

Figure 1. Workflow using GP Connector

3.2. Architecture GPC consists out of a three-tier architecture (See Fig 2). The presentation tier provides the user interface for GPs to login and to search for their patients. The logic tier is the most important tier. It is responsible for the whole program interaction. It coordinates the different service queries and acts according to their results. It is the connecting link between interface, backend and the external services. The third tier is the data tier being responsible for data storage of the user accounts and the GPC configuration. If a GP is logged in and searches for a patient by given name, last name and date of birth, the GCP passes the request to the master patient index of the PEHR using IHE patient data query transaction (ITI-21). The results also contain the master patient index identifications (MPI_ID). Together with the unique identifier of the GP from the provider and registry service (PORS_ID), the GCP queries the consent manager by using an HL7 conformance based query (QBP). The consent manager uses PORS_ID and MPI_ID to verify if there exists a policy granting access for this GP. Every authorized match is displayed in a list on the interface. Now, the GP can access the PEHR of these patients by simply clicking a button within GPC. This starts a new browser window containing the interface of the PEHR. No further login is required. Technically this is done by a SSL secured http POST request using the single sign on interface of the PEHR. The functionality is encapsulated in the Java-based PEHR launcher that is also used by the context-based single sign on from the


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connected hospital information systems. Inside the PEHR the GPC is registered as authorized system the same way like it is done for a HIS. It transfers its parameters including a password and receives a one-way token. This token together with the PEHR login credentials of the GP is used to authenticate him at the record. In addition, the MPI_ID of the patient is transferred allowing opening its record directly. Due to security and privacy reasons GPs are not allowed to search inside the PEHR directly. They always have to use the GPC search in order not to bypass the consent management.

Figure 2. Architecture of GP Connector

4. Discussion The GPC is a tool for all physicians in the Rhine-Neckar Region not working in a hospital. It provides easy accesses to the PEHR of their patients without having a deep integration into their primary systems like the hospitals in the region have. The GPC as solution meets the current privacy laws and regulations in Germany. The involvement of the consent manager was feasible. It is for the moment the only viable solution to adequately enable GPs access to the PEHR. Thus, they and their patients can benefit from all PEHR advantages. Accessing contents from the PEHR is not different from having a deep integration when being logged in into the web-based GPC-Interface. The main disadvantage exposes when it comes to add new information to the PEHR. For GPC users this is still a manual process due to the missing standards-based interfaces and integration into their primary systems. Therefore a deep integration is preferable anytime and still stays worthwhile. Yet the continuous trend towards multi-institutional health network may also pave the way for standards-based interfaces also in the field of PMS. Future developments will be carefully observed. As soon as a standard-compliant

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interface would be available for a specific PMS, it could be integrated the same way like is already done for HIS. The choices of the applied technologies exposed to have been quite sufficient. Open Source technologies provided cost efficient, reliable programming libraries and tools making it easy getting quick results. Due to the service oriented and web-based environment of the PEHR it opened up to use well proven Java-based tools like Tomcat and IPF which made it quite easy to handle the messaging aspects. Spring was chosen due to the build-in login and security functionality which points out to have been a good decision. At the moment GPC is in alpha release phase and will be open sourced with its first release candidate.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14]

Iakovidis, I., Towards personal health record: current situation, obstacles and trends in implementation of electronic healthcare record in Europe. Int J Med Inform, 1998. 52(1-3): p. 105-15. Hassol, A., et al., Patient experiences and attitudes about access to a patient electronic health care record and linked web messaging. J Am Med Inform Assoc, 2004. 11(6): p. 505-13. The future of healthcare - it's health, then care, W. Koff and P. Gustafson, Editors. 2010, Leading Edge Forum CSC: Falls Church, Virgina, USA. Norgall, T., B. Blobel, and P. Pharow, Personal health--the future care paradigm. Stud Health Technol Inform, 2006. 121: p. 299-306. Blobel, B., Introduction into advanced eHealth -- the Personal Health challenge. Stud Health Technol Inform, 2008. 134: p. 3-14. Kaelber, D.C., et al., A research agenda for personal health records (PHRs). J Am Med Inform Assoc, 2008. 15(6): p. 729-36. Ahmadi, M., et al., A Review of the Personal Health Records in Selected Countries and Iran. J Med Syst, 2010. Li, Y., et al., Electronic Health Record Goes Personal World-wide. Yearb Med Inform, 2009: p. 40-3. HIMSS. PHR Definition. 2008 April 2011]; Available from: http://www.himss.org/ASP/topics_FocusDynamic.asp?faid=228 HIMSS. EHR Definition. April 2011]; Available from: http://www.himss.org/ASP/topics_ehr.asp. Heinze, O., A. Brandner, and B. Bergh, Establishing a personal electronic health record in the RhineNeckar region. Stud Health Technol Inform, 2009. 150: p. 119. Birkle, M., O. Heinze, and B. Bergh, Entwurf eines elektronischen Einwilligungsmanagements für ein intersektorales Informationssystem, in eHealth 2010. 2010: Wien. p. 113-119. Heinze, O., A. Ihls, and B. Bergh. Development of an Open Soruce Provider and Organization Registry Service for Regional Health Networks. in Third International Conference on Health Informatics (HealthInf 2010). 2010. Valencia, Spain. Heinze, O. and B. Bergh. Experiences integrating RIS/PACS into personal electronic health records. in 13th World Congress on Medical and Health Informatics. 2010. Capetown, South Africa.


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Proposal of an End-to-End Emergency Medical System Samir EL-MASRIa,1, Basema SADDIKb College of Computer and Information Systems, King Saud University, Saudi Arabia b College of Public Health and Health Informatics, King Saud Bin AbdulAziz University for Health Sciences a

Abstract. A new comprehensive emergency system has been proposed to facilitate and computerize all the processes involved in an emergency from the initial contact to the ambulance emergency system, to finding the right and nearest available ambulance, and through to accessing a Smart Online Electronic Health Record (SOEHR). The proposed system will critically assist in pre-hospital treatments, indentify availability of the nearest available specialized hospital and communicate with the Hospital Emergency Department System (HEDS) to provide early information about the incoming patient for preparation to receive and assist. Keywords. Emergency, EHR, Ambulance, Mobile Web Services, SOA, GPS

1. Introduction In this paper, we are a proposing a new comprehensive emergency system. The objective of this system is to respond to the needs of an efficient and error free emergency system which, in cases of car accidents or other emergencies can quickly and accurately locate the right ambulance and send it to the place of accident. Our proposed system will operate without or at least with minimal or limited human intervention in order to reduce human errors and to accelerate the lifesaving process. All of the current ambulance systems rely on calls from people who give information about the accident and the accident’s approximate location. Most human operators use a type of traditional or computer aided dispatching system to find an ambulance according to the information given by the caller. The challenge with these types of systems is the potential for errors from the caller, or from the transfer and entering of wrong data into the dispatch system. These may put the patient at risk and cause substantial harm or loss of life as a result of human errors or late arrival of an ambulance, wrong information or treatment. Many organizations and governments have realized the importance of building better systems to spare the lives of patients or the injured involved in an accident. In the last decade, there has been a lot of effort to improve and automate emergency dispatching systems. One of these serious efforts is that of the Victoria 1

Correspondent author: Samir El-Masri, Associate Professor, Department of Information Systems, College of Computer and Information Sciences, KSU, P.O. Box 51178 Riyadh 11543, Kingdom of Saudi Arabia. E-mail: [email protected]

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Ambulance department in Australia [1]. The Victorian system was introduced in 1998 and provides clinical information about the patient to the hospital as well as recommending care from the hospital to the ambulance. Another advanced system is the Hospital & Emergency Ambulance Link (HEAL) which was implemented in Singapore. The HEAL system is based on wireless data communication between ambulances and hospitals an assists hospitals and doctors at the emergency departments with providing information about the incoming patients arriving by ambulance [2]. More research works have been conducted and a variety of new systems have previously been proposed [3, 4, 5, 6, 7]. El-Masri [8] proposed a preliminary version of the current proposed system in 2005.

2. System Components and Architecture The proposed emergency system consists of 5 components as shown in Figure 1 and is listed as follows: 1. Emergency requester device (Emergency application for mobile devices): This is s a mobile phone equipped with a Geographical Positioning System (GPS) 2. Main Central System (MCS): This is the main server for the whole system 3. Ambulance system: Each ambulance system will be equipped with a GPS and navigation system. The system will utilize touch screen (to indicate availability and reaction) 4. SOEHR: Smart Online Electronic Health Record 5. HEDS: Hospital Emergency Department System

Figure 1. System components and communications

3. System Processes The system processes and the communications between components are:


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3.1. Reporting an Accident The start of the system will be triggered by the emergency requester device reporting an accident. With a simple mobile application installed on the device, the caller can quickly and easily enter information about the accident (for example: the number of injured people and the number of cars involved in the accident). The application will automatically send the coordinates and the number of the mobile phone to MCS. The mobile application will then send the approximate accident location using general packet radio service (GPRS) in case GPS coordinates are not available.MCS automatically and without human intervention receives the request and searches for a suitable ambulance. In case the details for the accident are insufficient, a human operator will then be warned and will intervene immediately by calling the accident requestor and asking for more details. MCS can alternatively receive normal requests through the phone by talking to a human operator. 3.2. Finding an Ambulance MCS receives the emergency request from the requester and again without human intervention; sends a request to all available ambulances to report their GPS coordinates (other algorithms are also available where ambulances continually report coordinates to MCS. The selection of a specific algorithm will be based on how busy the environment is). MCS will then compare accident and ambulances coordinates and send a job request to the nearest ambulance based on the navigation system map rather than the direct distance. The ambulance officer has 10 seconds to accept or reject the request. If the request is accepted, MCS will send the accident’s coordinates to the ambulance and automatically, the ambulance system shows the road map to the accident location. If the ambulance officer rejects the job request or does not reply within 10 seconds, the MCS will pick up the second nearest ambulance to the accident, assuming that in 10 seconds the positions of ambulances don’t change much. In cases of longer delays MCS will restart the process from the beginning.

4. Ambulance System Processes 4.1. Setting up Availability and Communicating with MCS The ambulance officer can setup up the status of the ambulance to available or not available. They can also accept or reject job requests. In cases of rejection, the reasons for rejection should be entered into the system. When an ambulance accepts the job, the ambulance’s status will show “in mission”. 4.2. Accessing SOEHR After picking up the patient or the injured, the ambulance system can access the SOEHR using the patient’s fingerprint and the officer can then quickly and easily enter the current patient conditions such as injuries, fractures, or level of consciousness. SOEHR, based on the patient’s medical history and the current conditions will recommend urgent and pre-hospital treatment.

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SOEHR is a separate and independent system which is under construction by few countries such as Australia, New Zealand, and recently USA, Saudi Arabia and others. SOEHR is a unique electronic health record system which can quickly retrieve all details about the patient from different hospitals and clinics, process the medical histories and the current conditions and come up with a pre-hospital treatment recommendation. Accessing the online health record, can be done from the ambulance systems, HEDS or from the hospitals. Each access will be different in terms of security and the quantity of information required. For example, access from the ambulance will require quick and light information whereas access at HEDS and from the hospitals will require more detailed information in which, healthcare professionals can explore more details, track any visits, medications, or check results from any hospital. 4.3. Finding Hospital The ambulance needs to find the right hospital for the patient onboard. The ambulance system, based on the patient’s conditions, distance, availability and specialty of hospitals, will select the appropriate hospital and the roadmap on the navigation system will automatically be shown. Identity of the patient will be communicated to HEDS. All hospital GPS coordinates will already be available in the database of the ambulance system. 4.4. Preparing for Incoming Patients and Monitoring Incoming Ambulances Once the ambulance selects a hospital, the Hospital Emergency Department System (HEDS) books a bed or place for the incoming patient and accesses the patient’s health record. The ambulance system starts to continuously send the ambulance coordinates to HEDS so the department’s medical staff can monitor the incoming ambulance on the HEDS. HEDS will show in real-time all the incoming ambulances on a map with a list of information about distances and time. Department staff will prepare what they plan to do for the incoming patient which will include the operation theatre, medications, and consultants if needed. Through HEDS, staff can update the availability of beds or operation theatres based on the discharge or transfer of patients.

5. Advantages of the Proposed System over other Existing Systems Advantages of the proposed system over other systems can be listed as follows: 1. The system is fully computerized from start to end 2. It is very comprehensive and includes all components and involved parties. 3. It is based on new advanced technologies 4. It eliminates all human errors and reduces time to find and send ambulance 5. By accessing SOEHR, the ambulance officer can efficiently treat the injured, in comparison to traditional systems where the officer has no information about the patient’s medical history and has no system to assist 6. It identifies and selects the right hospital and communicates patient details to the selected hospital 7. It allows the emergency department to efficiently prepare and monitor incoming patients and ambulances


8.

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The ambulance officer can access SOEHR only with the patient’s fingerprint HEDS staff can only access SOEHR after the ambulance system sends the patient ID thus ensuring privacy and security of patient details

6. Conclusion and Future Work Using advanced technology such as Mobile Web Services and SOA, a new comprehensive emergency system has been proposed. We are currently in the process of developing all the components of the system. The new system will respond to all the needs of medical emergency from the initial emergency request until the transfer of the patient to the hospital. This system includes components that no other systems have proposed before. Upon the completion of system development, a pilot system will be deployed and tested. The system will be evaluated on a small scale and results and performance will be studied and compared with existing systems. Acknowledgement: This work is part of a two year research project fully funded by a grant through KACST/National Plan for Science and Technology in the Kingdom of Saudi Arabia. Grant number: 09-INF880-02.


[4] [5]

[6] [7]

[8]

Metropolitan Ambulance Service, The Metropolitan Ambulance Service, http://www.ambulance.vic.gov.au/Ambulance-Victoria.html [accessed: 11 February 2011.] Anantharaman V, Lim Swee Han. Hospital and emergency ambulance link: using IT to enhance emergency pre-hospital care. International Journal of Medical Informatics 61 (2) (2001)147-161. FitzGerald G, Tippett V, Elcock M, et al. Queensland Emergency Medical System: A structural and organizational model for the emergency medical system in Australia. Emergency Medicine Australasia [serial online]. December 2009;21(6):510-514 Atkin C, Freedman I, Rosenfeld J, Fitzgerald M, Kossmann T. The evolution of an integrated State Trauma System in Victoria, Australia. Injury [serial online]. November 2005;36(11):1277-1287 Romsaiyud, W, Premchaiswadi W. SOA context-aware mobile data model for emergency situation. Proceedings of Knowledge Engineering, 8th International Conference on ICT 2010, pp.93-97, 24-25 Nov. 2010 Sandeep Chatterjee, James Webber. Developing Enterprise Web Services, Prentice Hall PTR. 2004 Hameed SA, Miho V, AlKhateeb W, Hassan A. Medical emergency and healthcare model: Enhancement with SMS and MMS facilities. Proceedings of Computer and Communication Engineering (ICCCE), International Conference 2010, pp.1-6, 11-12 May 2010 El-Masri, S. Mobile Comprehensive Emergency System using Mobile Web Services. A book chapter, In Unhelkar B, editer. Handbook of Research on Mobile Business: Technical, Methodological and Social perspective. Idea Group, 1 (2005) 106-112

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The General Practitioner in the Giant’s Web Vigdis HEIMLYa,b,1 Norwegian University of Science and Technology b Norwegian Centre for Informatics in Health and Social Care Norway a

Abstract. Most General Practitioners (GPs) in Norway use Electronic Health Record (EHR) systems to support their daily work processes. These systems were developed with basis in local needs. Electronic collaboration between the different actors has developed over time. Larger national projects like the ePrescription and the Core EHR are examples of projects that interact with the GPs EHR systems. The requirements from these projects need to be addressed by the vendors of the EHR systems. At the same time the GPs see a need for further development of their EHR systems to make them more suited as tools to support the daily work processes. This paper addresses the how GPs can influence on the design and development of their EHR systems in a situation with a preexisting installed base of systems and increasing requirements from many actors. Keywords. National deployment, Electronic collaboration, Electronic Health Record Systems, General Practitioners, Practice Consultants, Requirements

1. Introduction More than 95% of the GPs currently use EHR systems[1]. These systems were developed in a local setting and deployed on a national basis. The process resembles a bootstrapping process as described by Skorve and Aanestad [2]. The GPs use the EHR systems actively in their clinical work and they do not keep paper records. EHR systems are also in widely use in hospitals and in nursing homes. The development of all these systems has been done with the local actors needs in mind and not the needs of the collaborating actors. Electronic collaboration is wanted by all actors, but how to coordinate at a national level and still provide room for further development of EHR systems based on the different user groups needs? One of the main challenges is how to balance between influences from national actors like regional health authorities and smaller actors that do not have a strong organization to represent them nationally.

2. Method The paper is based on experiences from participation in the EHR-monitor study [1], the initial ELIN-project [3], the GPs’ national reference group and a study of available project documentation. 1


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3. Analysis In a study from 2009 [4] T. Christensen concludes that “EPR systems in Norwegian primary care that have been developed in accordance with the principles of usercentered design have achieved widespread adoption and highly integrated use. The quality and efficiency of the clinical work has increased in contrast to the situation of their hospital colleagues, who report more modest use and benefits of EPR systems.” The study was based on a national, cross-sectional questionnaire survey in Norwegian primary care. They found that the GPs got assistance from their EPR system while conducting most of their clinical tasks, but the GPs also saw the need for improvements of their EHR systems. This was further documented in second study [5]. Examples of missing functionality were decision support that could be adjusted to the individual patient, extended possibilities for electronic collaboration and integration of the GPs EHR with personal health records. The EHR monitor survey [1] has also shown that one of the most evident challenges for the GPs currently is missing functionality of the existing systems. The ELIN projects are examples of a project family where GPs are involved at a national level [3]. A panel of experts created functional requirements for electronic communication in health care with basis in the existing systems, standards and the expert’s local needs. These requirements were implemented in the EHR-systems. The EHR-vendors costs were partly funded by Innovation Norway2. The rest were covered by licenses that were paid by the users of the EHR systems. This project model has worked out well, but the challenge for the EHR-vendors is that there are many ELINprojects (health station, community care, general practice, dentistry,..) and the same vendors have obligations in several of the projects. The growing need for collaboration has become more and more evident over the years. With a large installed base of EHR systems installed by the collaborating actors, it is not an easy task to develop collaboration systems and deploy them in full scale at a national level [6]. This is a complex interplay between the development of standards, technical solutions and the people who use these systems as a part of their daily work processes. Like many other users, the GPs might to be skeptic to requirements that are established by external actors. If they do not feel an ownership to new systems and modules that they are supposed to use, they can refuse to use them. There must also be some obvious benefits. Even the rumor about missing functionality of a system that is defined by another party might make the deployment process difficult. As an example, one of the interviewed GPs in an electronic referral project said: “I have heard that the hospital’s requirements are too detailed and that the system is time consuming for us GPs to use, so I have never tried it.” One way to narrow the gap between clinicians in primary and secondary care can be by establishing a practice consultancy system [7-10]. Practice Consultants are GPs that work in part time positions at hospitals with issues that are related to collaboration between primary care and specialized care. The Practice Consultants can be regarded as boundary spanners [11] who try to engage clinicians both in primary and secondary care to take part in a common Community of Practice [12]. E. Wenger has defined 2 Innovation Norway promotes nationwide industrial development profitable to both the business economy and Norway’s national economy, and helps release the potential of different districts and regions by contributing towards innovation, internationalization and promotion.

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Communities of practice as groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly. Work processes where the Practice Consultant participate would typically include strategic plan processes at the hospital, distribution of documentation from general practice to health workers and administrative staff at the hospital, participation in design and deployment of electronic collaboration projects, arrangement of meetings and seminars with GP in then hospitals local area and development of guidelines for GPs in collaboration with the specialists. A survey related to the introduction of the referral system with decision support showed that GPs tended to trust the practice consultant because they were experienced and regarded as one of their own[6]. Experiences with the Practice Consultants have been good both in Denmark and Norway, although it has been challenges to fund the system [8], [10]. 3.1. The Vendor’s Challenge As a wide range of ICT systems have been installed by actors that collaborate with the GPs, an increasing need for extensions and changes to their local systems have emerged. A lot of requirements are put on the vendors of the GPs EHR systems for actors like the Directorate of Health, the regional health authorities, insurance companies, the national insurance scheme etc. The development of national systems like ePrescription, Core EHR, and new ELIN-projects are funded in national strategies, standards and architectures. Originally most of the projects started at a local level. The challenge now is how you can balance the external factors and limitations that are set on the development with the local needs. Fewer and fewer of the projects that the GP vendors move into are projects that are only intended for a local market. The vendors need to know that their products can be sold and deployed at a wider scale. The vendors are also short of money for further development of their systems because scarce resources are being kept by other actors. There is a contradiction between the local needs and the potential for moving into a broader market. The users groups will also vary from project to project and there is no link between them. During the recent years the EHR vendors have been obliged to satisfy national requirements. The vendors have user forums and user groups, pilot users claim that the vendors cannot afford to prioritize the local needs to the same extent as before, because they have to pay more attention to needs from the collaborating actors and the authorities. The GPs do not have a national body where that can represent them in a national setting. They are linked to the Municipal Authority (KS) and the Norwegian Medical Association, but these organizations have a wide focus that the GPs consider to be too wide. 3.2. The Gps’ National Reference Group A group of ICT experienced GPs took the initiative to establish a national reference group in 2010. The Norwegian Medical Association has a subgroup named the Norwegian Association for General Practice (NFA), where the reference group is connected. Their focus is on further development of electronic health record systems in General Practice. Most of the GPs in the group have broad experience from being pilot users in various ICT projects, practice consultants or users representatives in the vendor’s users groups. The GPs also have a very active online forum where they


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discuss ICT related issues vividly. The reference group also uses this forum to get active feedback on the work that they do.

Figure 1. Development model for EHR in General Practice

The reference group has so far come up with a list of more than 30 action points where they want improvements of their EHR systems. Some of these action points are general and wide (decision support) while other are more concrete and limited (suggestions for improvements of the interface of a communication module). Some of the action points only have implications for the systems in general practice, while others are related to the collaboration with external actors. The tasks that the GPs wanted to give priority to first were the development of medication synchronization modules, NEKLAB (Norwegian coding system for laboratory services), synchronization tables and new functionality for transfer of EHRs between GPs. The GPs have also experienced that promising pilots have been stopped, because there are not available resources for the deployment process and want more focus on deployment processes. The reference group first of all wants more money and programming resources to the vendors, in order to ensure that they can continue to improve the EHR systems based on the GPs needs. The GPs are willing to pay parts of this bill by increased licenses, but they also try to get national funding from the Directorate of Health and Innovation Norway. These actors have been positive in terms of supporting the initiative that seems promising. So far this process is at an early stage and it remains to see how this Reference group will find its role among all the other national actors. A possible model for further development of the EHR systems in general practice is illustrated in the Figure 1. Recommendations for development: • • • •

Requirements from local projects and users are discussed and prioritized in user forums The GPs national reference group coordinates and prioritizes tasks with national project, and works for funding. Vendors develop new functionality in collaboration with GPs, Practice Consultants and other collaborating actors. Practice Consultants partake actively in the deployment of new functionality.

One of the challenges that need to be sorted out, is how the GPs in the reference group should be compensated for the time they spend on coordination task. As a starting point they have partly been compensated by a project linked to NFA, but most

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of the work has been done on a volunteer basis. One model could be to provide the GPs with a 20% position that is linked to their national role. This position could be funded by actors like the Department of Health, KS or the Norwegian Medical Association.

4. Conclusion External actors put an increasing pressure on the EHR system vendors in terms of requirements for new functionality. The GPs own possibilities for influence on the EHR system development has decreased simultaneously. The development of new functionality should still have a basis in the local needs, but coordination at a national level is also needed. A model with a national reference group that is initiated by the GPs has been tried out and seems promising. Based on the experiences from this work, a more permanent model for the involvement of the GPs should be established at a national level. Experiences from Danish and Norwegian collaborations projects also show that active involvement of Practice Consultants in design and deployment of collaboration functionality can be recommended.

References Heimly V, et al. Diffusion and use of Electronic Health Record Systems in Norway. Studies in Health Technology and Informatics, 2010. 160: p. 381. [2] Skorve E, Aanestad M. Bootstrapping Revisited: Opening the Black Box of Organizational Implementation. Scandinavian Information Systems Research, 2010: p. 111-126. [3] Christensen T, Grimsmo A. Development of functional requirements for electronic health communication: preliminary results from the ELIN project. Informatics in Primary Care, 2005. 13(3): p. 203-208. [4] Christensen T, et al. Norwegians GPs’ use of electronic patient record systems. International Journal of Medical Informatics, 2009. 78(12): p. 808-814. [5] Christensen T, Grimsmo A. Expectations for the next generation of electronic patient records in primary care: a triangulated study. Informatics in Primary Care, 2008. 16(1): p. 21-28. [6] Heimly V. Collaboration across Organizational Borders, the Referral Case. Studies in Health Technology and Informatics, 2010. 157: p. 106. [7] Kvamme O, Olesen F, Samuelsson M. Improving the interface between primary and secondary care: a statement from the European Working Party on Quality in Family Practice (EQuiP). Quality in Health Care, 2001. 10(1): p. 33. [8] Kvamme OJ, Eliasson G, Jensen PB. Co-operation of care and learning across the interface between primary and secondary care - Experiences from two workshops at the 15th WONCA World Conference 1998. Scandinavian Journal of Primary Health Care, 1998. 16(3): p. 131-134. [9] Kvamme OJ, Olesen F, Samuelsson M. Improving the interface between primary and secondary care: a statement from the European Working Party on Quality in Family Practice (EQuiP). Quality in Health Care, 2001. 10(1): p. 33-39. [10] Risanger V. Mind the Gap. Master thesis, 2008. [11] Levina N, Vaast E. The emergence of boundary spanning competence in practice: implications for implementation and use of information systems. Management Information Systems Quarterly, 2005. 29(2): p. 8. [12] Wenger E. Communities of practice: Learning, meanings, and identity. 2007: Cambridge university press. [1]


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When Information Sharing is not Enough a

Berit BRATTHEIMa,1, Arild FAXVAAGa, Pieter TOUSSAINTa Norwegian EHR Research Centre (NSEP), Institute of Neuroscience, Faculty of Medicine, NTNU, Trondheim, Norway

Abstract. This paper explores information sharing in multidisciplinary clinical collaboration between three hospitals. Our study draws on qualitative interviews with surgeons and radiologists in two county hospitals and one university hospital. The analysis shows that the actors shared a restricted amount of information about the patients they have in common and that different actors used the shared information in different ways. However, much communication was still needed to clarify and negotiate the meaning of shared data and its implications for collaborative care. To conclude, while the arguments for a shared information space may appear convincing, the communication practice observed should illustrate that IS also needs to support the communicative process in clinical collaborative work. Keywords. Shared record, communication support, transinstitutional collaboration, aortic aneurysm, surgery, radiology

1. Introduction The process of planning and subsequent execution of clinical activities, including the coordination of information and transfer of patients, works reasonably well in small clinical units. Actors that are involved in the care of a patient have access to the same clinical information in a shared record system. At the same time, the actors have excellent access to each other, facilitating discussions and negotiations on care issues by allowing less formalized exchange of information. In multidisciplinary contexts, this practice might cause different disciplines to use presumably the same information elements in multiple ways [1]. Most clinical domains are characterized by a steady introduction of new clinical methods and techniques, innovations that must be accompanied by education and more specialized training of the personnel [2-4]. Clinical units that deploy new and improved services by taking sophisticated techniques into use, rapidly find themselves attracting patients from other hospitals. The less innovative clinical units might find a new role as a collaborating and contributing partner. In such situations, collaboration will have to be extended across institutional borders. Clinical domains characterized by trans-hospital collaboration face particular challenges with regards to achieving efficient clinical information exchange [5]. It has been assumed that establishing shared information spaces will lead to more effective collaboration [6], for example when healthcare actors have to exchange information within or across units to provide patient care. Even if the involved actors get access to 1

Corresponding author: The Norwegian EHR Research Centre, Medical technical research centre, NO-7489 Trondheim, Norway, E-mail: [email protected]

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information system (IS) that is shared between multiple institutions [7], this will not suffice. The actors might have other unmet clinical needs that must be satisfied to support effective clinical collaboration. In this paper we have addressed this question in the context of collaboration between members of a multidisciplinary care team that provides advanced endoscopic surgical services (endovascular aneurysm repair (EVAR)) to patients with abdominal aortic aneurysms (AAA) asking the following questions: What information is actually shared between the collaborating clinicians? How is the information used by the different actors, and how is this information shared?

2. Method Healthcare setting: One university hospital and two county hospitals, all being part of a Norwegian regional healthcare service. The information infrastructure consisted of a radiological IS on a shared inter-hospital server, deployed at all public hospitals in the particular health region. Identical Electronic Patient Record (EPR) systems were applied as a stand-alone installation within each hospital. Study design: Semi-structured interviews with 12 key clinicians. The interview guide was inspired by a prior observation study focusing on one episode of monitoring for AAA patients potentially eligible for surgery [4]. From the county hospitals we interviewed two vascular surgeons and four radiologists. At the university hospital, three interventional radiologists and three vascular surgeons were interviewed, all being members of the EVAR care team. Each interview lasted 45-60 minutes and was tape-recorded for subsequent transcription. The analysis was inspired by a ‘grounded theory’ approach [8] and followed an inductive strategy [9]. For the purpose of this paper, we present only excerpts of the empirical material to illustrate the particular issues in question. The study was approved by the Regional Committee for Medical Research Ethics and the Norwegian Social Science data Services.

3. Results In our case of multidisciplinary trans-hospital collaboration we found three different characteristics of information sharing. First, the exchange of information necessitated supplementary discussions to clarify and negotiate essential care concerns. Second, for non-emergency patients, timing was important, but not critical, and the communication could take place in an asynchronous way. Finally, the amount of overlapping information elements indicated a rather modest common dataset. Further details are given below. 3.1. What Information to Share? Making a decision on whether to offer EVAR to a patient required collaboration between experts from both surgery and radiology departments. The transfer of patient information from the county hospital to the university hospital involved two key datasets: One set holding a processed excerpt of focal clinical information extracted from the medical record, and a second set holding more specific information stored in


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the radiological record. Interestingly, the EVAR surgeons focused primarily on the clinical data set, while the EVAR radiologists drew mainly on the second one. In general, a rather restricted amount of information was shared. To exemplify this point, Table 1 depicts an illustrative EVAR case, describing the different information elements as well as the overall communication pattern. 3.2. How is the Information Used by the Different Clinicians? As illustrated in Table 1/Figure 1, different actors had different perspectives on the shared information. The county surgeon considered the submitted dataset as a means to mediate important clinical risk factors, highlighted with key radiological information. The EVAR surgeons, on the other hand, viewed the same dataset within the context of deciding whether EVAR surgery was an option. Hence, one task included a request to the EVAR radiologists about working out an anatomical EVAR suitability assessment. This should be based on the delivered CT information combined with notes indicating clinical risk factors. A second task implied to consider the received clinical risk information and, if needed, collect supplementary considerations on preoperative risk factors. Further, the county radiologists viewed the radiological part of these datasets (e.g the CT images and report) as a means to support the local surgeon’s decisionmaking by providing CT-derived diagnostic information of the AAA and its surrounding arteries. Some of them even included EVAR specific measurements of the arteries, intending to contribute to the EVAR radiologists’ assessments. However, to the EVAR radiologists, this CT information did not suffice. They had to acquire additional data by getting hold of the CT source dataset collected at the county hospital. In general, the EVAR radiologists viewed this source dataset to be fundamental for their image processing, grounding the radiological decision on anatomical EVAR suitability, as well as guiding both their choice of stentgraft components and the actual EVAR intervention. Table 1. Principle communication pattern for eligible EVAR candidates – an illustrative case.

County hospital: 71-year-old-patient attending the regular surveillance of his AAA. Having balanced the risks and benefits of surgical repair versus ongoing surveillance, the surgeon recommends EVAR surgery. A radiological CT scan supports the surgeon’s decision-making. In agreement with the patient, the surgeon sends an EVAR referral letter to the vascular surgery team at the university hospital, including important hand-over information: e.g. considerations on the patient’s comorbidities and preoperative risks. In addition, the surgeon gives access to sharing of CT data between the two hospitals. University hospital: The vascular surgeons (=EVAR surgeons) request the EVAR radiologists to consider the patient’s CT-scan with respect to anatomical EVAR suitability, including some notes about the patient’s risk factors. If needed, the surgeons also collect supplementary clinical considerations, arranging for a separate patient-surgeon consultation and/or tests. As for the radiological work, the EVAR actors draw on the patient CT data or, more precisely, the CT source data collected at the county hospital. The existing IS does not offer a source data transfer utility, but an informal arrangement has been set up between the EVAR radiologists and the county ones to support this function. In a face-to-face meeting between the EVAR experts, the two disciplines share their information.Then follows a discussion including clarification of various risk factors accompanied by negotiations on the difficult trade-offs between anatomical and clinical risk factors. In case of EVAR, the radiologists will be responsible for the ordering of customized components/stentgraft.

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Figure 1. Principle interaction pattern for EVAR collaboration across hospitals.

3.3. How is the Information Shared? Throughout the course of the EVAR suitability assessment, collaboration unfolded as partly asynchronous, discipline-specific work tasks, interspersed with multiple communicative acts. The existing IS solution supported parts of the communication. The actors also communicated by phone, by sending formal paper letters and by exchanging handwritten notes. Information about the outcome of the multidisciplinary face-to-face meeting to decide upon the crucial EVAR inclusion at the university hospital was particularly important. In this meeting the different actors presented, discussed, and negotiated pro and cons of further actions, in particular to balance clinical risk factors against anatomical conditions. Further, some of the county radiologists reflected on the lack of feedback from their colleagues at the university. They pointed out that feedback on their delivered CT work could have helped them improve their EVAR diagnostic-related CT skills. These actors illustrated how the communication could have taken place by presenting examples on how they collaborated with colleagues at other hospitals in other clinical settings.

4. Discussion In this case report, we have shown that having access to a shared information space does not suffice to establish an effective collaboration between clinicians that collaborate across institutional borders. As our data indicate, communicative processes are also necessary, because substantial parts of the collaboration consisted of giving multiple meanings to information from different perspectives and to negotiate the implications for further actions. The information shared was rather modest, leading to discussions to clarify and negotiate the meaning of the shared data as well as their


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consequences when approaching collaborative care concerns. From this, it might seem that seeking to enhance clinical collaboration by providing a shared information space does not suffice when dealing with a limited amount of overlapping information elements. This view is in line with that of Ash et al [10] who argued that the varying and changing bulk of information put strict demands on the specification of shared minimum data sets to avoid information systems causing new types of errors. The use of both asynchronous and synchronous communication channels indicated that not all EVAR tasks were time-critical. Asynchronous communication was often enough. This emphasizes the need for support of asynchronous information exchange in the IS-solutions (e.g. email functionality, discussion forums and chat). In conclusion, IS support should both support communication and negotiation within cross-organizational clinical activities as well as facilitate the sharing of data. This has implications for many initiatives that aim to improve the coordination of care services, such as the Norwegian National Health Plan [11]. Despite the limited number of cases, our study has shown that today’s IT-systems make it difficult to support care that is provided as collaboration across institutional and professional borders. To accommodate for this, we propose to apply an information needs approach [12, 13] as the first step for process support in evolving clinical treatment processes. Acknowledgments: We thank the participants from the three hospitals for their contribution. We also thank A. Landmark for technical assistance and K.M. Lyng for valuable input on Mol’s work.

References [1] [2] [3] [4]

[5] [6] [7]

[8] [9] [10] [11] [12] [13]

Mol A. The body multiple: ontology in medical practice, Durham: Duke University Press. XII; 2002. Hartswood M, Procter R, Rouncefield M, Slack R. Making a case in medical work: Implications for the electronic medical record. CSCW 2003. 12(3): 241-66. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn't. BMJ 1996. 312(7023): 71-2. Brattheim B, Faxvaag A, Tjora A. Getting the aorta pants in place: A ‘community of guidance’ in the evolving practice of vascular implant surgery. Health (London) 2010 [Epub ahead of print]. DOI: 10.1177/1363459310376300. Bardram J. Pervasive healthcare as a scientific discipline. Methods Inf Med 2009. 47(3): 178-5. Blomberg J. Negotiating meaning of shared information in service system encounters. Europ Man J 2008. 26(4): 213-2. Mäenpää T, Suominen T, Asikainen P, Maass M, Rostila I. The outcomes of regional healthcare information systems in health care: A review of the research literature. Int J Med Inform 2009. 78(11): 757-71. Strauss AL, Corbin JM. Basics of qualitative research: techniques and procedures for developing grounded theory. Thousand Oaks, Calif.: Sage; 1998. Creswell JW. Qualitative Inquiry and Research Design: Choosing Among Five Traditions. Sage Publications; 1998. Ash J, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 2004. 11(2): 104-12. Ministry of Health and Care services: The National Health Plan. Available at www.government.no. Denekamp Y. Clinical decision support systems for addressing information needs of physicians. Isr Med Assoc J 2007. 9(11): 771-6. Häkkinen H, Korpela M. A participatory assessment of IS integration needs in maternity clinics using activity theory. Int J Med Inform 2007. 76(11-12): 843-9.

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Information and Communication Needs of Healthcare Workers in the Perioperative Domain Børge LILLEBOa,1, Andreas SEIM b, Arild FAXVAAG a Norwegian EHR Research Centre, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway b Department of Computer and Information Science, Faculty of Information Technology, Mathematics and Electrical Engineering, NTNU, Trondheim, Norway a

Abstract. Perioperative work requires the collaborative efforts of a multitude of actors. Coordinating such collaboration is challenging, and coordination breakdowns may be very expensive and jeopardize patient safety. We studied the needs for status information and projection of future status and events for key actors in the perioperative environment. We found that information and projection needs differed significantly between actors. While just-in-time notifications sufficed for some, others were dependent on projections to provide high quality and efficient care. Finally, information on current status and support in projecting the future unfolding of events could improve actors situated coordination capabilities. Keywords. Collaboration, awareness, patient management,

1. Introduction The perioperative departments, i.e. the admission wards, operation suites and the postoperative recovery are among the costliest hospital resources. Because of the problem solving nature of surgical work and the unpredictable influx of emergency cases, what actually gets done regularly differs from that inscribed in the schedule. In such an environment, the coordination of actors, patients and operating rooms becomes a challenge. In recent years, one has seen the emergence of information systems that display information on large, wall-mounted boards [1]. By creating a shared information space, these electronic whiteboards improve collaboration and selfcoordination by making actors aware of other actors’ work [2]. According to Endsley [5] the highest form of situation awareness is the ability to project future status (here “situation awareness” means “knowing what is going on”). As part of an effort to develop a next-generation system for supporting situated [3] coordination, we sought to understand the needs for projection of future status and events for key actors in the perioperative environment. In particular, we were interested in involved actors’ need for projection related to transfer of patients from a surgical ward to an operating room (OR), preparation for the operation including induction of 1

Corresponding author. E-mail address: [email protected].

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anesthesia, surgery, emergence from anesthesia, transfer of the patient to the postanesthesia care unit (PACU), cleaning of the OR, monitoring of the patient at the PACU and final transfer of the patient back to the ward. We carried out a modified Goal directed task analysis to study these needs for projection.

2. Methods We studied the largest surgical unit of an 800-bed university hospital in Norway, consisting of two post-anesthesia care units, six surgical wards and 13 operating rooms. The unit performed acute and elective gastroenterologic, urologic, plastic, orthopedic, breast, endocrinologic and vascular surgery. We conducted 31 semistructured interviews with perioperative actors during a field study within the perioperative environment. Interviews lasted 5-120 min, averaging 47 min and totaling 1460 min. Some of the interviews were with more than one worker at the same time. In total we spent 32 hours together with anesthesiologists, cleaners, nurse anesthetists, operating room (OR) nurses, OR suite coordinators, OR technicians, post-anesthesia care unit (PACU) nurses, surgeons and ward nurses. Data were collected as handwritten notes. The interview guide was developed and modified based on pilot observations and interviews. Interviews focused on the goals and tasks of various perioperative actors and what information they ideally would like to have in order to reach those goals on time - without considering whether or not that piece of information was available with current technology. This approach was based on a cognitive task analysis known as 'Goal directed task analysis' [4]. 2.1. Ethics The National Committee for Medical and Health Research Ethics (NEM) and The Norwegian Directorate of Health approved the study. All participants gave their informed consent prior to data collection.

3. Results Many informants confirmed the importance of the ability to project future status, pointing out potential personal benefits pertaining perioperative work. However, different classes of actors were interested in the status of different phases of perioperative work (see Figure 1) and for a variety of reasons. Moreover, the actors inferred their projections from a multitude of sources.

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Figure 1. Illustration of a typical trajectory of a patient undergoing surgery which links status shifts in that trajectory to actors that could benefit from the ability of knowing in advance when those status shifts would occur.

For some, projection of future events was necessary for delivering the required quality of care. A ward nurse emphasized that: “I have to know when the operation will start in order to do required patient preparations such as fasting, showering and premedication.” Others were interested in potential gains in efficiency. A surgical resident explained “It would have been nice to know in advance approximately when the operations would start. In my case a message one hour before the operation begins would be nice. That would give me enough time to take care of an ED [emergency department] patient in the meantime.” The degree of uncertainty in projections made by the actors was at times substantial. One surgeon said “My impression is that e.g. when they bring the patient down to the OR, there is approximately one hour left [until the operation starts], but this depends on the type of operation and anesthesia.” Actors often relied on colleagues’ notifications for updating their projections. Although some of these notifications were done in advance, often communicated through pagers and phones, most notifications were done just-in-time. They acted more as a last minute reminder that would require immediate action and limiting situated coordination. Cleaners explained “We know when we should go and wash an operating room the moment they page us. Usually they do that when they are about to transport the patient out of the room. Sometimes they notify us before the patient is out, then we wait or start some minor washing with the patient in the room.” Similarly, a PACU nurse noted “When the patient has recovered sufficiently we call the ward and ask them to come and get the patient. We call them when we know for certain that the patient is fit enough to be transported to the ward. We don't call and say that the patient will be ready in half an hour...” While such just-in-time notifications were convenient for some, such as a surgeon who noted “They page me when I have to come to the OR”, others were much more dependent on projections. A PACU nurse expressed that “We like to prepare equipment and make sure all other things are done before the patient arrives from the OR. Yesterday that didn't happen, three patients suddenly appeared here without us knowing they were on their way... It works out anyway though, but we prefer knowing it in advance...” Some of the informants described how they used the workspace to project future events. One OR technician explained that “Anyway, it is about walking around and looking through the OR windows. I don't know if there is anything particular that I look


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for, such as the surgeon having finished his work or something like that. But I try to project when each operation will finish and next patient will arrive”. There were also examples of how technology could be used to get almost the same kind of awareness information. PACU nurse: “We pay attention to the electronic OR scheduling system throughout the day. It is possible to see when the patient has arrived, when the operation started and so on. This gives us an indication on when the operation will be done (...) What matters to us is when the patient is expected to arrive here. It is nice to know that approximately 30 minutes before he comes, because then we have the possibility of sending one of the other patients out if we are full” Finally, the coordinator had a particular need to project the future status of multiple perioperative actors. One OR suite coordinator said “ I miss simpler tools to control whether or not our plans are feasible - e.g. if a surgeon has been planned to be in two places at the same time.”

4. Discussion In this study we have illustrated that facilitating access to situation awareness information could improve situated coordination and thereby improve the overall coordination of perioperative activities. Many of those involved in the perioperative processes are located far away from operating rooms - the center for perioperative activity. A ward nurse is usually located nearby the patients he/she is responsible for at the patient ward while a surgeon on call is often wandering between various patient wards, operating rooms, emergency departments, radiology departments and more. These differences in routines and workplace environments require special consideration. Perhaps wall-mounted electronic whiteboards is an insufficient tool for distributing sufficient information? Perhaps a mobile, single user device is more appropriate for this task? The divergent information needs of our informants indicate that a personal device might be better than a common shared information space. Many informants pointed out that being able to project future events also could be beneficial. According to Mintzberg [7] situated coordination2 takes place when “Two or more people simply adapt to each other as their work progresses, usually by informal communication.” In other words situated coordination depends on awareness of the current activities of your coworkers. Whether an information system should purport to support coordination by projecting future events, is an open question. What we do know about projections within this domain is that the duration of surgery is inherently hard to predict accurately, even for operations that have started as planned and are being performed by experienced surgeons [6]. Those in need of coordinating themselves might be capable of inferring future events given they were offered highly updated information about ongoing events and the whereabouts of their colleagues. This could be accomplished by improving the communication means of the actors, making it easy for them to update and share information about their activities. Our study was limited to the activities related to perioperative patient handovers. Involved actors also participated in other activities throughout the hospital. Information and projection needs pertaining to these activities are outside our scope. Moreover, our work was limited to actors’ subjective beliefs about their personal benefits from status

2

Mintzberg uses the term ”mutual adjustment”

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information and projections. Such beliefs have high face validity, but lack objective verification. In conclusion, we found that many actors saw use of or depended on projections of future status and events in their efforts to deliver efficient high quality care. However, the actors’ needs differed substantially, both with respect to which perioperative phases and events they were interested in and how long in advance they felt they needed projections. Information on current status and support in projecting the future unfolding of events could improve actors’ situated coordination capabilities.

References [1]

[2]

[3] [4] [5] [6] [7]

Aronsky D, Jones I, Lanaghan K, Slovis CM (2008) Supporting Patient Care in the Emergency Department with a Computerized Whiteboard System. Journal of the American Medical Informatics Association 15, 184 -194. Bardram JE, Hansen TR, Soegaard M (2006) Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work ACM, New York, NY, USA, pp. 109–118. Lundberg N, Tellioglu H (1999) Understanding complex coordination processes in health care. Scand. J. Inf. Syst. 11, 157-181. Endsley MR, Bolté B, Jones DG (2003) Designing for situation awareness, Taylor & Francis. Endsley MR (1995) Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 37, 32-64. Macario A, Dexter F (1999) Estimating the Duration of a Case When the Surgeon Has Not Recently Scheduled the Procedure at the Surgical Suite. Anesth Analg 89, 1241. Glouberman S Mintzberg H (2001) Managing the care of health and the cure of disease--Part II: Integration. Health Care Management Review 26, 70-84; discussion 87-89.


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Clinical Situations and Information Needs of Physicians During Treatment of Diabetes Mellitus Patients: a Triangulation Study Gudrun HÜBNER-BLODER a,1, Georg DUFTSCHMIDb, Michael KOHLERb, Christoph RINNERb, Samrend SABOORa, Elske AMMENWERTHa a UMIT-University for Health Sciences, Medical Informatics and Technology. Hall in Tirol, Austria b Section for Medical Information Management and Imaging, Medical University of Vienna, Austria

Abstract. Physicians should have access to the information they need to provide the most effective health care. Medical knowledge and patient-oriented information is dynamic and expanding rapidly so there is a rising risk of information overload. We investigated the information needs of physicians during treatment of Diabetes mellitus patients, using a combination of interviews, observations, literature research and analysis of recorded medical information in hospitals as part of a methodical triangulation. 446 information items were identified, structured in a set of 9 main categories each, as well as 6 time windows, 10 clinical situations and 68 brief queries. The physician’s information needs as identified in this study will now be used to develop sophisticated query tools to efficiently support finding of information in an electronic health record. Keywords. Physicians’ information needs, Triangulation study, Electronic health record, information overload, Diabetes mellitus.

1. Introduction The goal of health care of the new millennium is excellent care for all. So physicians should have access to the information they need to provide the most effective and efficient care based on the best available evidence [1]. To fulfil this demand of excellent clinical care excellent information support is needed. Medical knowledge and patient-related information is dynamic and expanding rapidly, and physicians need more and more information to provide optimal care for their patients [2]. The electronic health record (EHR) is a repository of patient data. It provides a portal to a large patient information space, and it offers data from a variety of sources that are aggregated into one place [3]. Due to the huge quantity of medical data, there is a rising risk for information overload. Furthermore physicians must review and process previously documented patient history in an ever-shorter period of time [4, 5]. 1

Corresponding author: Dr. Gudrun Hübner-Bloder MSc., Institute of Health Informatics, Eduard Wallnöfer-Zentrum I, 6060 Hall in Tirol, Austria, Email: [email protected].

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“Medicine is a knowledge based business, and experienced physicians use about two million pieces of information to manage their patients” as Smith reported in [6]. As defined in [7] an information need is a personal item about required information. Physicians seek out information in response to a problem at hand. While physicians need access to the entire patient history, they often seek answers to particular questions and therefore they wish to browse certain subsets of the available data [8]. Within the Austrian Science Fund (FWF) project “Archetype based Electronic Health Record” [9] we develop solutions to assist the EHR user when searching the EHR in the context of the treatment of Diabetes mellitus (DM) patients. The first goal of this project was to investigate the specific information needs of physicians during DM patient’s treatment. The aim of this paper is to report about the study of physicians’ information needs during treatment of DM patients and to investigate, based on the results of the study, how the results will support EHR users during the systematic search of patient-related information.

2. Method The goal of this study was to investigate information needs of physicians during the treatment of Diabetes Mellitus (DM) patients. We decided to use a methodical triangulation, using literature search, interviews, observations and documentation analysis, in order to systematically aggregate different perspectives of the investigated object [10]. Literature research: We analyzed five international evidence-based DM guidelines for clinical diagnostics and medical treatment of DM [11-15] to identify information items needed during DM treatment. To analyze the results, we used a summary qualitative content analysis with inductive creation of categories according to Mayring [16] with the assistance of a qualitative data analysis software (MAXQDA 2007) [17]. Expert Interviews: We performed oral, partial-standardized expert interviews. Objective of expert interviews is the investigation of function-specific know-how. [18]. We conducted expert interviews with 6 internists with specialization DM in the Diabetes outpatient clinics of the University Hospital of Innsbruck, the Regional Hospital of Hall in Tyrol, the Hospital St. Vinzenz in Zams and with one internal physician in private practice. To analyze the results we again used qualitative content analysis [16]. Observation: Additional to the expert interviews, we decided to make participant, unstructured observations of clinical encounters [19], to gain additional insight and to validate information from the interviews. The observation of 22 DM patient encounters took place in the DM outpatient clinic of the internal medicine of the university hospital of Innsbruck. We decided to analyze the data based on grounded theory [20, 21]. This analysis allows inductive concept and theory development during the data collection. Clinical documentation analysis: Another source to investigate the information needs of physicians was to analyze the information documented in the electronic records of hospitals. We analyzed the recorded medical information in three Diabetes outpatient clinics of internal medicine (University Hospital of Innsbruck, Regional Hospital of Hall in Tyrol and the Vienna General Hospital). For analysis these recorded data we again used qualitative content analysis.


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3. Result Categories of information needs: The study resulted in the identification of 446 distinct information items (e.g. items of DM classification (Typ 1 DM, Typ 2 DM, gestational diabetes etc.), onset of DM, weight-height status like body mass index, weight gain, weight loss etc.) which are structured in a set of categories with 9 main categories: I Patient master data, II Self-monitoring of the patient, III Diabetes mellitus classification, IV General medical history, V Diagnosis, VI Recent surgery, VII Recent check-ups, VIII Laboratory findings, IX Medication/Therapy. Each main cateogires comprises up to four sub categories each. Based on the comprehensive information items of the international evidence-based DM guidelines we structured the categories. These categories are adapted with the information items we gained in the expert interviews and the observations. Finally we matched these results with the clinical documentation analysis Time windows: We also investigated the time windows users request when searching for information. We define the time windows as timeframes in which specific information items are important for the attending physician. For example, during a routine check of a DM patient, physicians may want to have access data limited to a 3month-interval or 6-month-interval. Overall, the study pointed out to six typical time windows: I. 0-3 months, II. 0-6 months, III. 0-12 months, IV. 0-36 months, V. 0-60 months, VI. all available data. Clinical situations and brief queries: Both the expert interviews and also the observations showed that information needs are different according to the clinical situations. As a result of this knowledge, we defined ten clinical situations as well as 68 additional brief queries, which consist of queries that supply additional short information (e.g. the progress of HbA1c trend in a specific time-window). Table 1 shows an overview of the clinical situations and brief queries. Table 1. List of the clinical situations and brief queries (exemplary) Clinical Situations 1. 2. 3.

Numbers of Items

Initial clinical interview Routine check - brief data set Routine check - extended data set (exemplary): • Routine check by patients with cardiovascular problems • Routine check by patients with Neuropathy • Routine check by patients with Nephropathy • Routine check by patients with ophthalmological problems (e.g. retinopathy) • Routine check by patients with dermatological problems (e.g. diabetic foot)

202 42 82 58 53 53 63

Laboratory Brief Queries (Exemplary)

Questions

Glucose status • Fasting plasma glucose (FPG) • Postprandial plasma glucose (PG pp) • HbA1c • Oral glucose tolerance test (OGTT)

All pathological values (PG > 100 or PG < 70) All pathological values (PG pp > 130) Progress in time-window (selectable) All data available

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4. Discussion and Conclusion The purpose of the study was to investigate the physicians’ information needs during the treatment of Diabetes mellitus patients. The study revealed 446 distinct information items structured in a set of nine categories, six different time windows, ten clinical situations and the 68 brief queries. Earlier studies that investigated information needs were mostly based only on interviews [22, 23] or observations alone [24-26] or on a combination of interviews and literature research [27]. By using the methodical triangulation as we did, different point of views could be aggregated systematically [10]. In our study we combined interviews, observations, literature research and documentation analysis. We felt that using four different qualitative methods helped to get a more complete and comprehensive picture. “Theoretical sampling necessitates building interpretative theories from the emerging data and selecting a new sample to examine and elaborate on this theory” as reported in [28]. In our study, interviews and observations included six internal physicians. Our goal was to gain a deeper understanding of physicians’ information needs to develop concepts which will be used in our research. At this stage of the project we focused only of the internal physicians information’s needs. Expert interviews with physicians of other medical fields (e.g. cardiologist, neurologists etc.) should be made to extend the focus. While EHRs are designed with all needed patient-related information, the consequence is that this huge amount of available data can overwhelm physicians and making it hard for them to identify the desired information [8]. The identification of information items, time windows, clinical situation and brief queries will now be used to develop pre-defined or modifiable queries that help to search for that information the physician needs in a given situation. For example, for a given clinical situation such as “routine check for patients with neuropathy”, relevant information needs and time windows will be presented to the user, together with additional brief queries. This approach should enable a situation-dependent, optimized view on the patient data. The physician’s information needs as identified in this study will now be used to develop sophisticated query tools to efficiently finding information in an electronic health record [29]. Acknowledgement: The project EHR-ARCHE is being supported by the Austrian Science Fund, project number P21396.

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Godlee F, Pakenham-Walsh N, Ncayiyana D, Cohen B, Packer A. Can we achieve health information for all by 2015? Lancet 2004;364(9430):295-300. Gorman P. Excellent information is needed for excellent care, but so is good communication. West J Med 2000;172(5):319-20. Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008;77(5):291-304. Van Vleck TT, Stein DM, Stetson PD, Johnson SB. Assessing data relevance for automated generation of a clinical summary. AMIA Annu Symp Proc 2007:761-5. Bawden D. The dark side of information: overload, anxiety and other paradoxes and pathologies. Journal of Information Science 2008;35(2):180-191.

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A Constructivist approach? Using formative evaluation to inform the Electronic Prescription Service Implementation in Primary Care, England Jasmine HARVEYa,1, Anthony AVERY a, Justin WARING b, Ralph HIBBERD c, Nicholas BARBER c a University of Nottingham, School of Community Health Sciences, Primary Care (Medical School), Queens Medical Centre, Nottingham, UK b University of Nottingham, Business School, Jubilee Campus, Nottingham, UK c University of London, School of Pharmacy, Department of Practice and Policy, Tavistock Square, London.

Abstract. As part of the National Programme for IT (NPfIT) in England, the Electronic Prescription Service (EPS) is being implemented in two releases. The first release placed barcodes on prescriptions and is widely implemented. Release two (EPS2), the electronic transmission of prescriptions between GP, pharmacy and the reimbursement body, has just started implementation. On the NPfIT agenda, community pharmacies have been predicted to benefit from changes in work practice following the full EPS implementation. The study focused on how the advanced EPS (EPS2) might alter dispensing work practice in community pharmacies on issues such as workflow and workload; and the bearing of these issues on improvement in quality of service and safety. This paper demonstrates how findings of the pre-implementation study were used to provide formative feedback to the implementers. A mixed ethnographical method that combined nonparticipant observations, shadowing and interviews, before and after implementation, was used to qualitatively study eight community pharmacies across three early adopter Primary Care Trusts (PCTs) in England. Key implementation issues were fed-back to the PCTs as part of the EPS2 rolling-out process. Staff access to dispensing terminals needs to be improved if electronic dispensing is to be encouraged. Also, as a safety issue, pharmacists are planning to print off electronic prescriptions (tokens) and dispense from them. Although safer, this could increase workload. The EPS2 could positively alter work practice by improving certain demanding aspects of dispensing whilst reducing human errors. For example, the high demand of customers handing in prescriptions and waiting for them to be dispensed could be reduced through automation. Also, the extreme variation in workload during various times of the day could be evened out to improve workflow and provide a better service; however, in order for this to be fully realized, technical issues such as number of staff per dispensing station and dispensing from tokens would need to be addressed. Keywords. EPS, e-prescription, EHR, EPR, healthcare modernisation, clinical work practice, social constructivism, safety, medication automation, quality of care.

1

Corresponding Author. Dr Jasmine Harvey, University of Nottingham, School of Community Health Sciences, Division of Primary Care, Tower (Floor 14) Nottingham, NG7 2RD, [email protected]

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1. Introduction Community pharmacies in England are part of an ambitious national programme (NPfIT) to computerise health; part of a wider e-Government agenda1. In the UK GP practices are computerised, and virtually all prescribing is done electronically. A paper prescription (called an FP10) is printed and signed by the doctor, and the patient takes that to a pharmacy to be dispensed. The prescription is endorsed by the pharmacist and posted to a national centre which arranges payment for the pharmacist. The concept of the Electronic Prescription Service was one of four strands of a national information strategy first set out in 20022. The EPS implementation commenced in 2008 with the initial phase (EPS1) rolled-out across early adopter Primary Care Trusts (PCTs). The key feature of EPS1 was the inclusion of a barcode on the FP10. The barcode, when scanned in the pharmacy, automatically transferred patient information from the paper to the computer screen, usually eliminating the need to type the medicine labels. The advanced phase (release 2 or EPS2), enables prescribers to authorise and send prescriptions electronically and send them to a centralized system, commonly called the spine (technically called N3). Prescriptions then can be downloaded and dispensed by the pharmacist3. The patient’s role in this is to nominate the pharmacy that will do the downloading and dispensing. Electronic prescriptions open up the possibility of integration with Electronic Health Record (EHR) programme, although the EPS can exist in isolation of EHR. Community pharmacies as key stakeholders of this agenda have been predicted to benefit from the full EPS roll-out in terms of: freeing dispensing staff from work associated with re-keying prescription information; giving dispensing staff scope to streamline workflow by preparing medications in advance; and, managing stock more effectively4. Our study focused on how EPS2 will alter community pharmacies by doing a pre and post implementation study of workflows, workloads and priorities of community pharmacies. The research also explored anticipated issues and perceptions of the full roll-out from pharmacy professionals, how the implementation process was understood, and the pharmacist’s ability to influence patient safety. This paper demonstrates how some of our pre-implementation findings were used to advise key stakeholders.

2. People, technology and the concept of social constructivism in healthcare. A fundamental theory in the study of people and their work practices is that which conceptualises that it is human beings that appropriate technology through formative feedback. Described as social construction of technology, this theory critically opposes technological determinism and theorises that through everyday use, people influence and shape technologies and how they become useful. In the healthcare environment, it is important that the use of technologies does not become a barrier to providing care but are instead tools of know-how that can be appropriated to suit high quality care provision. For example, when May et al5 used an ethnographic study to explore the spatial and temporal relationships between health professionals and patients in the context of how technologies are used in telepsychiatry, they concluded that the technologies needed to be appropriated well in order to avoid interfering with clinical professionalism. May et al5 demonstrated how the boundaries between hard and soft technologies such as the technical and the social are blurred and how the social need to

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be taken into account (for example in a clinician-patient relationship) in order for the technology to work effectively. The theory of constructivism builds on other socio-technological theories demonstrated by Greenhalgh6; Berg and Van der Lei7, Eden et al8 and Harrison et al9. Significantly it recognises that there is an on-going assessment of systems before and after implementation and that it is through the re-engineering by users that the system becomes successfully adopted. Studying how the EPS might alter work practice include attaining a deeper understanding of how it could be shaped by social and organisational processes of its users. The need for this deeper sense of understanding informed the ethnographic framework used in the data collection and analysis.

3. Data and the analytical method Qualitative methods were employed that used an ethnographic framework of nonparticipant observation and shadowing of community pharmacy staff, as well as interviewing. Baseline data were collected in eight sites across three PCTs in the Midland and Northern regions of England. The PCTs were classified as early adopters of the EPS. As the first phase of the service (EPS1) had already been rolled-out, the study focused on the pharmacies that were about to receive the implementation of the second (EPS2) roll-out. These pharmacies were classified as first-of-type sites. The pharmacies were sampled according to which were available as first-of-type or ‘semifirst-of-type’ sites that were due to implement the EPS2, and also according to their geographic location, size and ownership (independent or chain). Overall, 84 hours of observations were conducted in addition to extra hours of shadowing and interviewing staff. The observation and shadowing were written up as case studies. The case studies, together with the interviews were thematically analysed. In the analysis, implementation issues were identified on key themes such as the prioritisation and organisation of work; and the fluidity of work (workflow) and workload.

4. Findings Prioritisation and organisation of work – A majority of the sites tended to prioritise customers who hand in their prescriptions and wait for them to be dispensed. This is termed walk-in (wait-in) dispensing. Most walk-in prescriptions were for acute treatments. Some pharmacists offered a ‘collection and delivery’ service whereby prescriptions were collected from the GP practice, dispensed and delivered to the customer; these tended to be repeat prescriptions. Repeat prescriptions on average were 70% of prescriptions dispensed in each site. In pharmacies that had large numbers of walk-ins, resources were sometimes very stretched as walk-in customers required immediate attention compared to ‘collection and delivery’ customers. As a result, the dispensing of ‘collection and delivery’ prescriptions tended to be fitted around walk-ins. However, as ‘collection and delivery’ prescriptions tended to be greater in quantity than walk-ins, how dispensing was organised and prioritised was sometimes problematic. In order to combat this problem, some of the pharmacies had a prioritisation system of using coloured baskets to organise the dispensing process. Under the EPS2 system, in order minimise this problem and continue to retain current safety practice, pharmacists planned to process electronically transmitted prescriptions as they currently do. This means that even with EPS2, dispensers can print-


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off the electronic prescriptions (called tokens) and process them as they do with a current FP10. Whilst this could indeed retain the current safety practice, it could also increase the time taken (and cost) of dispensing as dispensers will have an added workflow activity of printing prescriptions onto specialized FP10-like paper before processing and dispensing. Workflow and Workload - The amount of work, such as the number of items dispensed in relation to the pharmacy’s dispensing support system, appeared to influence the fluidity of work. Predictably, the greater the pharmacy’s dispensing resource, the higher the workload. The bigger pharmacies, which had more staff, dispensed more items (over 400 items) per day, whilst the smaller pharmacies dispensed around 100-150 items per day. The workload also varied in relation to the type of dispensing service the pharmacy offered. In some of the pharmacies that offered a ‘collection and delivery’ service, the workload tended to range from moderate to very high depending on how many ‘collection and delivery’ items needed to be processed and dispensed. This was done in addition to other duties such as dispensing to walk-in customers, date checking, packing away medicines, answering telephone queries and so on. Under the EPS2 system, these different prescriptions will be streamlined into electronically sent prescriptions (whether acute or repeat), thereby eliminating the extreme workload and workflow variation associated with dispensing. The electronic transmission however introduces a new issue for pharmacies that do not have an adequate number of dispensing stations. Dispensing staff often jostled for terminals which sometimes disrupted the workflow and elongated time taken to dispense prescriptions. If staff have to log in and out whenever they need to use a terminal (in order for the system to record each user’s activity), this issue would be exacerbated, especially if dispensing directly from terminals is encouraged. Since some pharmacy systems are quite sensitive and therefore prone to crashing, the logging in and out of system between too many dispensers could cause potential problems to the dispensing process. In this case, EPS2 would be more beneficial if staff had greater access to dispensing stations.

5. Discussion The introduction of technology into work places radically changes the way work is done and introduces a potential number of ways of doing that work10. Work is therefore engineered through using the most suitable way and an on-going assessment of the technology. MacKenzie and Wajcman11 describe three layers of technology; these are the physical object or the artefact, activities or processes involved with the artefact, and how to operate the artefact. The introduction of EPS 1 and 2 into community pharmacies encompasses changes in all the three layers described by Mackenzie and Wajcman11. Whilst the extended baseline study of this work is currently being examined socio-technically in another article, this paper highlights how the preliminary findings (discussed in the results) were used to inform early adopter PCTs through review reports the study team produced for the PCTs. The constructivist approach enabled the study team to use methods that showed how EPS2 could be socially appropriated to suit current practice of safely dispensing medicines. This was done by observing current practice and providing a platform for potential users to converse about the intended use of the system. As part of the on-going assessment of EPS2, this became a useful information source for key implementer stakeholders, and crucially

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identified some key potential benefits, and implementation issues that could become barriers to effective use of EPS2 in community pharmacy work practice.

6. Conclusion Our preliminary findings indicate that EPS2 has the potential to add value to current dispensing work in terms of smoothing out workflow and improving the management of workloads. There may also be safety benefits for patients and this will be assessed in detail in the final stages of the study. However, issues such as dispensers printing tokens to dispense from, could become barriers to the streamlined workflow and increase the cost of dispensing. In addition, pharmacies need extra technological support such as more dispensing terminals in order to maintain a streamlined workflow. It should however be noted that the benefits and implementation issues identified in this literature are a result of eight site visits to first-of type-sites. Therefore the findings may not be attributable to all implementation sites in terms of the potential effects of the EPS in relation to current work practice. Disclaimer: This report is independent research commissioned by the National Institute of Health Research. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

References Warner, N. A suitable case for treatment: The NHS and reform, Grosvenor House Publishing Ltd, 2011. DoH, Delivering 21st century IT support for the NHSDepartment of Health, UK 2002. Rai, N. EPS put simply. Pharmj.org.UK 56 (2008). CfH, The national programme for IT implementation guide: guidance to support trusts when implementing National Programme products and services. NHS Connecting for Health, 2007. [5] May. C., Gask, L. Atkinson, T. Ellis, N. Mair, F. & Esmail, A. Resisting and promoting new technologies in clinical practice: The case of telepsychiatry. Social Science and Medicine 52 (2001), 1889-1901. [6] Greenhalgh., T. Benefits realization? Lessons from England’s efforts to produce a nationally stored summary record for 50 million people. Conference paper: The International Implementation of Electronic Health Records conference, London. 26.10.2010. [7] Berg, M. Aarts, J. & Van der Lei, J. ICT in health care: sociotechnicial approaches. Methods in Information in Medicine (2010), 297-301. [8] Eden, K.B. Messina, H. L. Osterweil, P. Henderson, C.R. & Marie Guise, J. The Impact of Health Information Technology on Work Process and Patient Care in Labor and Delivery. American Journal of Obstetrics and Gynecology 199 (2008) 307.e1-307.e9. [9] Harrison, M.I. Koppel, R. & Bar-Lev, S. Unintended Consequences of Information Technologies in Health Care-An Interactive Sociotechnical Analysis. Journal of the American Medical Informatics Association 14 (2007) 542-549. [10] Pouloudi, A. Perry, M. & Saini, R. Organisational appropriation of technology: A case study. Cognition Technology & Work (1999), 169-178. [11] MacKenzie, D. & Wajcman, J. The Social shaping of Technology: How the Refrigerator got its Hum, Open University Press, Buckingham, 1985.

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Can Cloud Computing Benefit Health Services? – A SWOT Analysis a

Mu-Hsing KUO a1, Andre KUSHNIRUKa Elizabeth BORYCKI a School of Health Information Science, University of Victoria, BC, Canada

Abstract. In this paper, we discuss cloud computing, the current state of cloud computing in healthcare, and the challenges and opportunities of adopting cloud computing in healthcare. A Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis was used to evaluate the feasibility of adopting this computing model in healthcare. The paper concludes that cloud computing could have huge benefits for healthcare but there are a number of issues that will need to be addressed before its widespread use in healthcare. Keywords. Electronic Health Record, Cloud Computing, Healthcare, SWOT

1. Introduction Despite the many benefits associated with using the EHR, there are numerous obstacles that restrict its adoption such as the [1]: lack of support for startup expenses or reimbursement for implementation costs; lack of standardized technical platforms to support EHR; lack of uniform standards for documentation of clinical services; concerns about the inability to align workflow with a standardized EHR; concerns that automation of clinical charting requires more time than paper charting; need to overcome security and privacy concerns. According to the Accenture’s survey, 58% of survey respondents noted that the expense required to implement EHRs was the area of greatest concern [2]. In 2007, talk of a new on-demand self-service Internet infrastructure (i.e. cloud computing) became more prominent. Many healthcare organizations, managers and experts believe that cloud computing can improve EHR adoption and will change the face of health care information technology [3-8]. The aim of this paper is to discuss the substance of cloud computing, its current applications in healthcare, and the challenges and opportunities of adopting this new approach. 2. What is Cloud Computing? Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service-provider interaction [9]. Examples of similar more limited applications are Google Docs or Gmail. However, cloud computing is different from traditional systems. For example, it provides a wide range 1

Corresponding Author: Dr. M. H. Kuo, School of Health Information Science, PO Box 3050 STN CSC, Victoria, BC, V8W 3P4, Canada. E-mail:[email protected].

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of computing resources on demand any where and anytime; eliminates an up-front commitment by cloud users; it allows users to pay for use of computing resources on a short-term basis as needed; and has higher utilization by multiplexing of workloads from different organizations [5, 9-12]. From a service point of view, cloud computing includes three models: − Software as a Service (SaaS) - The applications (e.g. EHRs) are hosted by a cloud service provider and made available to customers over a network, typically the Internet (e.g. Google Apps and Salesforce.com). − Platform as a Service (PaaS) - The development tools (e.g. OS systems) are hosted in the cloud and accessed through a browser (e.g. Microsoft Azure). With PaaS, developers can build web applications without installing any tools on their computer, and then deploy those applications without any specialized administrative skills. − Infrastructure as a Service (IaaS) - The cloud user outsources the equipment used to support operations, including storage, hardware, servers and networking components. The cloud service provider owns the equipment and is responsible for housing, running and maintaining it (e.g. Amazon EC2). The client typically pays on a per-use basis. To deploy cloud computing, the U.S. National Institute of Standards and Technology (NIST) listed four models as follows: − Private cloud - A proprietary network or a data center supplies hosted services to a certain group of people. − Public cloud - A cloud service provider makes resources (applications and storage) available to the general public over the Internet. − Community cloud - The cloud infrastructure is shared by several organizations and supports a specific community that has common concerns (e.g. mission, security requirements, policy, and compliance considerations). − Hybrid cloud - An organization provides and manages some resources within its own data center and has others provided externally such as Microsoft HealthVault.

3. Current State of Cloud Computing in Healthcare "In the cloud" medical records services, such as Microsoft HealthVault, Google Health, Oracle and Exalogic Elastic Cloud and Amazon’s Web Service (AWS) promise an explosion in the storage of personal health information online [13]. Amazon was one of the first companies to launch a cloud product for the general public, and it continues to have one of the most sophisticated and elaborate set of options. Amazon’s Web Service (AWS) plays host to a collection of healthcare IT offerings, such as Salt Lake Citybased Spearstone’s healthcare data storage application, and DiskAgent which uses Amazon Simple Storage Service (Amazon S3) as its scalable storage infrastructure [14]. In addition, MedCommons, a Watertown, Mass.-based health records services provider, utilizes AWS to build its personal health record (PHR) offering, HealthURL [15]. In most healthcare environment physicians don't always have the information they need when they need to quickly make patient-care decisions, and patients often have to carry a paper record of their health history information with them from visit to visit. To address these problems, IBM and ActiveHealth Management worked together to create a cloud computing technology-based Collaborative Care Solution that gives physicians and patients access to the information they need to improve the overall quality of care, without the need to invest in new infrastructure [16]. American Occupational Network


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(AON) and HyGen Pharmaceuticals are improving patient care by digitizing health records and streamlining their business operations using cloud-based software from IBM MedTrak Systems, Inc. and The Systems House, Inc. Their technology handles various tasks (e.g. online appointment scheduling) as a cloud service through the internet instead of developing, purchasing and maintaining technology onsite [17]. The U.S. Department of Health & Human Services' (HHS) Office of the National Coordinator for Health IT (ONC) recently selected an Acumen Solutions’ cloud computing CRM and project management system to manage the selection and implementation of EHR systems across the country. The software will enable regional extension centers to manage interactions with medical providers related to the selection and implementation of an EHR system [18]. Sharp Community Medical Group in San Diego will be using the Collaborative Care Solution to change the way physicians and nurses access information throughout the hospital group's multiple electronic medical record systems to apply advanced analytics and clinical decision support to help give doctors better insight and work more closely with patient care teams [14]. In Europe, a consortium including IBM, Sirrix AG security technologies, Portuguese energy and solution providers, Energias de Portugal and EFACEC, San Raffaele (Italy) Hospital and several European academics and corporate research organizations announced Trustworthy Clouds (TClouds) - a patient-centric home healthcare service that will remotely monitor, diagnose and assist patients outside of a hospital setting. The complete lifecycle, from prescription to delivery to intake to reimbursement will be stored in the cloud and will be accessible to patients, doctors and pharmacy staff [19].

4. Opportunities and Challenges The SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis is a well-known strategic planning methodology used by organizations to ensure that there is a clear objective defined for a project or venture, and that all factors related to the effort, both positive and negative, are identified and addressed. In this paper, we use the SWOT analysis to evaluate the feasibility of heath sectors adopting cloud computing to improve healthcare services (Figure 1). In SWOT, strengths and weaknesses are internal factors; opportunities and threats are external factors.

Figure 1. The health cloud computing SWOT analysis

Strengths Healthcare, as with any other service operation, requires continuous and systematic innovation in order to remain cost effective, efficient and timely, and to provide high quality services. Many healthcare organizations, managers and experts believe that the cloud computing approach can improve health services [4-9]. In addition, recent

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research indicates that 75% of Chief Information Officers (CIO) indicated that they will need and use cloud computing in the near future [20]. Weaknesses Despite many health cloud computing application examples nowadays, however, there is insufficient evidence to indicate that the new approach is suitable for healthcare. Also, the lack of expertise to evaluate the feasibility of the new approach in healthcare sectors is currently another weakness. Opportunities One of the greatest advantages of adopting cloud computing in healthcare is that the network, server and security headaches that exist for locally-installed, legacy systems are eliminated. Smaller hospitals and medical practices typically don’t have internal IT staff to maintain and service in-house infrastructure for mission-critical applications such as EHRs. Therefore, eliminating the new infrastructure cost and the IT maintenance burdens clearly removes the obstacles to EHR adoption. Also, the cloud computing approach promises to speed deployment while maintaining vital flexibility, i.e. rapid elasticity, and ubiquitous access to health resources. Threats Among the possible threats the cloud computing adoption are the healthcare professionals' lack of trust in the new approach and the lack of national or international mandates or regulations to support full adoption. Armbrust et al indicated 10 top obstacles to users’ trust in the cloud approach [21]: availability of service, data lock-in, data confidentiality and audibility, data transfer bottlenecks, performance unpredictability, scalable storage, bugs in large-scale distributed systems, scaling quickly, reputation fate sharing, and software licensing. Data jurisdiction, data interoperability and some legal issues are also potential major concerns. For example, the US Health Insurance Portability and Accountability Act (HIPAA) restricts companies from disclosing personal health data to non-affiliated third parties unless specific contractual arrangements have been put in place.

5. Conclusion and Discussion Cloud computing is a new style of computing that promises to provide a more flexibility, less expense and more security to end-users. It provides potential opportunities for improving EHR adoption and healthcare services. However, there are still many challenges behind the fostering of the new model in healthcare. In this paper, we use a SWOT analysis to evaluate the feasibility of healthcare cloud computing. We conclude that the pro side includes less up-front capital investment, capability of rapid elasticity and ubiquitous access to health resources. The con side includes the lack of sufficient successful evidence of its application in healthcare, a dearth of domain experts to evaluate the feasibility, less of healthcare professionals' trust, and lack of mandates/regulations to support full adoption. Perhaps the strongest resistance to the adoption of cloud computing in health IT centers on data security and privacy. However, we believe that compared to locallyhoused data, this computing model typically improves security because cloud providers (e.g. Microsoft, Google) are able to devote huge resources to solving security issues that many customers cannot afford, in contrast to the destruction of many medical records and legal documents in the New Orleans Hurricane Katrina disaster.


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Regarding data privacy, some organizations such as the Cloud Security Alliance, a non-profit organization have developed a comprehensive guide to deal with privacy issues [22]. Governments can also play a critical role by fostering widespread agreement regulations for both users and providers. In conclusion, if users, providers and governments act wisely, cloud computing could potentially be very beneficial to healthcare services.

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[3] [4] [5] [6] [7]

[8] [9] [10] [11] [12] [13]

[14]

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Maki, S. E. and Petterson, B., 2008. Using the Electronic health records. Thomson Delmar. Accenture. 2005. Electronic Health Records Survey. [Cited 2010 December 15], Available from: http://www.accenture.com/NR/rdonlyres/407281EB-2187-4A99-8FD72A802D1370EF/0/EHRSurvey.pdf Vouk, M.A., 2008. Cloud Computing – Issues, Research and Implementations. Journal of Computing and Information Technology – CIT, 16(4), 235–246. Han, Y., 2010. On the Clouds: A New Way of Computing. Information Technology and Libraries, 8792. Armbrust, M., et al., 2010. A View of Cloud Computing. Communications of the ACM, 53(4), 50-58. Chatman, C., 2010. How Cloud Computing is Changing the Face of Health Care Information Technology. Journal of Health Care Compliance, May-June, 37-70. Batchelor, J., 2009. Future Forecast: Cloud Computing Brightens Healthcare’s Dark Skies. [Cited 2010 December 15], Available from: http://www.cmio.net/index.php?option=com_articles&view=article& id=16941:future-forecast-cloud-computing-brightens-healthcares-dark-skies Fahrni, J., 2010. Cloud computing and health care - Facing the Future. [Cited 2010 December 18], Available from: http://www.slideshare.net/JFahrni/cloud-computing-and-health-care-facing-the-future Mell, P. and Grance, T., 2010. The NIST Definition of Cloud Computing. Communications of the ACM, 53(6), 50. Iyer, B. and Henderson, J.C. 2010. Preparing for the Future: Understanding the Seven Capabilities of Cloud Computing. MIS Quarterly Executive, 9(2), 117-131. Vouk, M.A.., 2008. Cloud Computing – Issues, Research and Implementations. Journal of Computing and Information Technology – CIT, 16(4), 235–246. Han, Y., 2010. On the Clouds: A New Way of Computing. Information Tech. and Libraries, 87-92. Online storage of your Medical Records with Google Health and Microsoft Healthvault. [Cited 2011 April 4], Available from: http://www.sencilo.com/blog/article/online-storage-of-your-medical-recordswith-google-health-and-microsoft-healthvault/ DiskAgent Launches New Remote Backup and Loss Protection Software as a Service Offering. [Cited 2011 January 18], Available from: http://www.thefreelibrary.com/DiskAgent(TM)+Launches+New+Remote+Backup+and+Loss+Protecti on+Software...-a0182194404 Batchelor J., 2009. Future Forecast: Cloud Computing Brightens Healthcare’s Dark Skies, CMIO. [Cited 2011 January 18], Available from: http://www.cmio.net/index.php?option=com_articles&view=article&id=16941 ActiveHealth and IBM Pioneer Cloud Computing Approach to Help Doctors Deliver High Quality, Cost Effective Patient Care. [Cited 2011 January 18], Available from: http://www03.ibm.com/press/us/en/pressrelease/32267.wss IBM and Partners Help Healthcare Clients Adopt Electronic Health Records and Improve Operations with Cloud Software. [Cited 2011 January 18], Available from: http://www03.ibm.com/press/us/en/pressrelease/26963.wss Acumen nabs ONC cloud computing contract, [Cited 2011 January 20], Available from: http://www.healthimaging.com/index.php?option=com_articles&view=article&id=20648:acumen-nabs-onc-cloudcomputing-contract&division=hiit EU consortium launches advanced cloud computing project with hospital and smart power grid provider, http://www-03.ibm.com/press/us/en/pressrelease/33067.wss Danek, J. 2009. Cloud Computing and the Canadian Environment, [Cited 2011 January 18], Available from: http://www.scribd.com/doc/20818613/Cloud-Computing-and-the-Canadian-Environment Armbrust, M., et al., 2009. Above the clouds: A Berkeley view of cloud computing. Technical Report No. UCB/EECS-2009-28, EECS Department, U.C. Berkeley. Cloud Security Alliance, 2009. Security Guidance for Critical Areas of Focus in Cloud Computing, V2.1. [Cited 2011 January 20], Available from: http://www.cloudsecurityalliance.org/csaguide.pdf


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Medical Providers’ Dental Information Needs: a Baseline Survey Amit ACHARYAa1, Andrea MAHNKEa, Po-Huang CHYOUa, Carla ROTTSCHEITa, a Justin B STARREN a Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI

Abstract. Articulation of medical and dental practices has been strongly called for based on the many oral-systemic connections. With the rapid development and adoption of electronic health records, the feasibility of integrating medical and dental patient data should be strongly considered. The objective of this study was to develop an initial understanding of the medical providers’ core dental information needs and opinion of integrated medical-dental electronic health record (iEHR) environment in their workflow. This was achieved by administering a 13 question survey to a group of 1,197 medical care providers employed by Marshfield Clinic in Wisconsin, United States. The survey received a response rate of 35%. The responses were analyzed based on provider ‘Role’ and ‘Specialty’. The majority of the respondents felt the need for patient’s dental information to coordinate or provide effective medical care. An integrated electronic health record environment could facilitate this holistic patient care approach. Keywords. Integrated Medical-Dental Electronic Health Record, Baseline Survey, Medical Providers’ Dental Data Need, Medical-Dental Holistic Care, Health Information Technology.

1. Introduction The ‘Great Divide’ between dentistry and medicine is a well known fact of the healthcare delivery system. However, it is often said that mouth is the mirror of overall health and there has been many studies linking the oral and systemic connections [1, 2]. The Institute of Medicine (IOM) of the National Academy of Sciences released a report, Dental Education at the Crossroads: Challenges and Change in January 1995 [3]. The IOM report called for a strong cohesion between medicine and dentistry, it states that "Dentistry will and should become more closely integrated with medicine and the health care system on all levels: research, education, and patient care” [3]. An article by Baum, “Will dentistry be left behind at the healthcare station?” [4] indicates that economic prosperity of dental practices and the financial constraints on dental education is keeping dentistry isolated from utilizing biological approaches to managing oral health. Although the healthcare team consists of various specialists trained in different area of expertise, there is often a lack of bi-directional information flow between the 1

Corresponding Author: Amit Acharya, Dental Informatics Scientist, Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, WI 54449, Phone: 1715-221-6423, Fax: 1-715-221-6402, E-mail: [email protected]

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dentists and the medical care providers. Many factors contribute to this lack of effective communication and sharing of patient information between the different groups of care providers in delivering a holistic approach to patient care. Some of the contributing factors could be security issues, lack of infrastructure, and the business model of the practices to list a few. A recent study by Schleyer et al. [5] found that although 55% of the respondents to a survey answered ‘yes’ when asked whether they would allow other providers to access information about their patients, many qualified their response by indicating that they would require a level of security in place. The need for dentistry to be part of National Health Information Infrastructure has also been discussed in literature [6]. Only 32% of the physicians are in solo or 2-physician practices [7], on the contrary almost 73% of all dentists in U.S. are in solo practices [8] representing the different business model of the practices. However, with the advanced technological development and widespread adoption of electronic health records, some of the larger healthcare organizations are in a unique position to explore the feasibility of providing a holistic care approach to their patients through a medical-dental integrated electronic health record (iEHR) environment. The objective of this study was to develop an initial understanding of the medical care providers’ core dental information needs and opinion of medical-dental integrated electronic health record (iEHR) environment in their workflow.

2. Background Founded in 1916, Marshfield Clinic is one of the largest comprehensive medical systems in the United States. This 777-physician, 6519-employee multi-specialty group practice provides patient care, research and medical education across 52 Wisconsin locations. The Marshfield Clinic center works closely with St. Joseph's Hospital a 524-bed acute facility and maintains a joint EHR. Family Health Center of Marshfield, Inc. (FHC) in partnership with Marshfield Clinic has served low-income, underinsured and uninsured individuals since March 1974. FHC has been providing onsite dental services since the fall of 2002. Currently, FHC operates seven dental sites with two additional sites under construction that will be operational in the fall of 2011. Marshfield Clinic made a significant commitment to internal development of its information systems over the past 40 years. Physicians have collaborated with affiliated hospitals, clinics and an in-house development staff of over 300 IT professionals to develop systems. CattailsMD™ is the first internally developed EHR to be certified by the Certification Commission for Health-care Information Technology (CCHIT). Marshfield Clinic is currently developing a robust medical-dental integrated electronic health record (iEHR) environment. The beta version of the dental module, CattailsDental has been implemented and successfully rolled out in all of the seven dental centers. The survey discussed in the manuscript is one of the many studies conducted at the Marshfield Clinic as part of the iEHR environment design and development.

3. Methods The research group developed the survey instrument and pilot-tested to identify any issues. Minor changes were carried out to the survey instrument as a result of the pilot-


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test. The development of the survey was also informed by a literature review that helped identify certain aspects of the survey instrument. The survey consisted of 13 questions with both structured and open-ended questions. Figure 1 illustrates the final survey instrument used in this study to measure the medical care providers’ core dental information needs and opinion of medical-dental integrated electronic health record (iEHR) environment.

Figure 1. Survey instrument used in the study

The final survey and the research protocol were submitted to the Marshfield Clinic Institutional Review Board, which classified the study as exempt under section 45 CFR 46.101(b) and waived requirement for an authorization (FWA00000873). A list of all the medical providers was extracted from the Marshfield Clinic data warehouse that included Physicians, Surgeons, Residents, Registered Nurses, Nurse Practitioners, Certified Nurses and Licensed Practical Nurses. The list identified 1,197 providers from all Marshfield Clinic locations and St. Joseph’s Hospital who were eligible to participate in the survey. The survey was administered through an online survey tool, Survey Monkey (Portland, OR). The survey was administered between January 14th, 2010 and February 15th, 2010. Two reminders were sent, the first on January 25th and the second on February 6th. The providers had an option to be entered into a drawing for an iPod on completing the survey to encourage participation. The survey respondents were grouped based on the ‘Role’ and ‘Specialty’. Groups based on ‘Role’: Group 1 - Physician, Surgeon, Anesthesiologist, Medical Director, Department Chair, Resident and Nurse Practitioner, Group 2 - Certified Nurse, Nurse Midwives, Licensed Practical Nurses and Registered Nurse and Group 3 - Managers and Others. Groups based on ‘Specialty’: 1. Surgery; 2. Cardiology; 3. Emergency Medicine; 4. Primary Care; 5. Oncology; 6. Pediatrics; 7. Neurology; 8. Women’s Health/Obstetrics-Gynecology; 9. Other Specialties. Questions Q5 to Q10 which were ‘structured’ in their format and were analyzed based on the ‘Role’ and ‘Specialty’. P values were derived by performing the Chi-square test. Questions Q11 to Q13 were open-ended and the collected data were coded, analyzed and identified under appropriate major themes. As all the questions in the survey were not mandatory, any missing data from the providers’ response to a particular question were handled by not including that respondent for the analysis of the respective question.

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4. Results The survey was initially emailed to 1221 provider email addresses within the system. There were 24 emails that were undeliverable and those providers email addresses were removed from the original list. Of the original 1,197 eligible providers, 417 completed the survey, which yielded a response rate of 34.84%. The provider’s response to Questions Q5, Q7 and Q8 were statistically significant when analyzed based on the ‘Role’ (P-value < 0.05). On the other hand, when analyzed based on ‘Specialty’, Questions Q5, Q7, Q8 and Q9 were statistically significant (P-value < 0.05). Granular details regarding the responses to individual questions could not be presented in the manuscript due to the space limitation. However, Figure 2 illustrates the overall medical provider’s dental data needs based on the different dental categories. After analyzing the responses to Q13, it was determined that most of the comments fell under Q11 and Q12 and were included under the respective category for analysis. The data collected from the responses to Q11, which represented the respondents’ mentioned advantages of a medical-dental iEHR environment were coded and identified under the following themes: a. access to reliable dental information and history, b. better communication with the dentist, c. holistic care and better continuity of patient care, d. better coordination of patient care, e. easy and faster access to dental information and f. reduce narcotic abuse. Similarly, the data collected to Q12, which represented the respondents’ mentioned disadvantages of a medical-dental iEHR environment were coded and identified under the following themes: a. information overload, b. cost issues, c. privacy concern, d. system slowness and e. coping with dental jargon.

Figure 2. Medical provider’s dental information needs based on the different dental information categories

5. Discussion There is no data from previous literature regarding medical providers’ opinions on an integrated electronic health record environment. The survey results present a baseline


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measure by different medical specialties and major roles. Considering the busy nature of the medical providers and the historic disconnect between how medicine and dentistry has been practiced, the response rate of 35% was encouraging. However, there is further scope for extensive investigation into the articulation of medical and dental iEHR. It would also be interesting to explore how a similar survey would perform on a state, national or an international level. The majority of the respondents felt the need for patient’s dental information to coordinate or provide effective medical care, especially Cardiologists, Emergency medicine physicians, Primary care physicians, Oncologists, Pediatricians and Neurologists. Since there are well-established connections between oral health and these specialties, this reflected the expected outcome. However, only about half of Surgeons and Obstetricians-Gynecologists who responded to the survey expressed the need for patient’s dental information for coordinating or providing effective medical care. Based on the medical providers’ response, there seems to be a strong need for inter-communication between the physicians and dentists regarding their patients’ health information. About 55% of the respondents requested a consult monthly or less and 10% weekly from the general dentist or dental specialist. The need for patients’ oral health status, dental treatment plan, dental problem list and dental diagnosis was of importance for the majority of the survey respondents. Although dentists rarely document diagnostic codes, this calls for an urgent need for documenting diagnosis in their patient records and could be supported by standardized dental diagnostic codes. It is evident from this baseline study that the medical providers have recognized the need for patient’s dental information to provide comprehensive care. Hence, an iEHR environment could facilitate this holistic approach. There is scope for further investigation into specific dental information required for each of the identified specialties. Also a quantitative analysis of the advantages vs. disadvantages of an iEHR environment can further be conducted to explore the feasibility of such an environment.

References [1] [2] [3] [4] [5] [6] [7] [8]

Ostfeld RJ, “Periodontal Disease and Cardiology,” “Report of the Independent Panel of Experts of the Scottsdale Project,” Grand Rounds Supplement September 2007, p. 3. Grossi SG, Genco RJ, Periodontal disease and diabetes mellitus: a two-way relationship. Ann Periodontol. 1998 Jul;3(1):51-61. Field MJ, ed. Dental education at the crossroads: challenges and change. Institute of Medicine Report. Washington, DC: National Academy Press, 1995. Baum BJ., Will dentistry be left behind at the healthcare station? J Am Coll Dent. 2004 Summer;71(2):27-30. Schleyer TK, Thyvalikakath TP, Spallek H, Torres-Urquidy MH, Hernandez P, Yuhaniak J. Clinical computing in general dentistry. J Am Med Inform Assoc. 2006 May; 13(3):344-352. Schleyer TK. Should dentistry be part of the National Health Information Infrastructure? J Am Dent Assoc 2004;135(12):1687-95. PMID:15646601. Boukus E, Cassil A, O'Malley AS. A Snapshot of U.S. Physicians: Key Findings from the 2008 Health Tracking Physician Survey, Center for Health Systems Change, Data Bulletin no 35, September 2009. American Dental Association Survey Center. Survey of dental practice. Chicago, IL: American Dental Association; 2003.

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What Makes an Information System More Preferable for Clinicians? a Qualitative Comparison of Two Systems Habibollah PIRNEJADab, Zahra NIAZKHANIab1, Jos AARTSb, Roland BAL b a Department of Medical Informatics, Urmia University of Medical Science, Urmia, Iran b Healthcare Governance, Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

Abstract. Two different information systems with respect to their ability to afford clinicians’ needs in the chemotherapy medication process were implemented in a large Dutch academic hospital. A commercially available Computerized Physician Order Entry (CPOE) system was not appreciated because clinicians believed that it could not support complex chemotherapy process. Later, a home-grown IT system with the capability of prescribing chemotherapy medications based on standard care protocols was appreciated and fully used by clinicians. We evaluated both systems from their users’ perspective to find the sources of clinicians’ preference and to trace them back to their Systems Development Life Cycle (SDLC). Keywords. Chemotherapy, information system, implementation, design, requirement analysis, qualitative research, user involvement, CPOE, SDLC

1. Introduction There has been an ongoing discussion in the world of health information technology why and how an information system that was adopted in a healthcare setting successfully, fails to be adopted or is adopted sub-optimally in another setting [1]. Is it related to information systems’ design, meaning that a System Development Life Cycle (SDLC) fails to address user requirements, which are very specific for every target organization? Or, is it because of the implementation methodologies, which also should be specific for every implementation site [1, 2]? There is evidence to support the critical role of both design and implementation processes. Studies, for example, revealed that most of the successful implementation and use of decision support systems have been from those institutions that developed their own systems [3]. But, how system development and design can be such [if not the most] important factor in determining successful IT adoption and use? Erasmus University Medical Center (Erasmus MC) implemented a CPOE system in all inpatient wards. Although the system was appreciated and used in most wards, it was not considered appropriate for prescribing chemotherapy medications. Clinicians in hematology and oncology wards continued to use paper-based medication system 1

Corresponding author: Z. Niazkhani, E-mail: [email protected]

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hoping their required changes come in the CPOE system redesign. Few years later, a home-grown IT system with the ability of prescribing chemotherapy medications using standard care protocols was implemented in all hematology/oncology wards of Erasmus MC. The system was appreciated and was fully used by clinicians. In this paper, we looked into the questions that “what made the second system more preferable to clinicians?” and “what lessons can we learn?”

2. Background and Methodology Chemotherapy work can be considered a type of medication process; only it is more complex and takes longer. The complexity in the chemotherapy process comes mainly from the fact that chemotherapy medications have narrow therapeutic windows, necessitating more accurate dose calculation and adjustments, and because multiple parties are involved, making the process more vulnerable to coordination problems and inefficiencies [4]. Guiding patients throughout long-term care protocols as well as keeping high quality prescription records are also very important for chemotherapy process. Erasmus MC is a 1237-bed tertiary academic hospital in Rotterdam, The Netherlands. A commercially available CPOE system (Medicatie/EVS® V 2.30) was implemented in all inpatient wards in 2003-2005. The system had the capability to generate alerts on drug overdoses, interactions, and double medications. We interviewed system users throughout the hospital; among them we conducted semistructured interviews with 2 physicians and 2 nurses from the hematology/oncology department as well as with the project leader, between 2006-2007. The interviews were voice recorded, transcribed, coded, and analyzed for the emerging themes on supportive and non-supportive features of the system in the medication process. More information about our qualitative methods of data acquisition and analysis can be found in [5,6]. In 2007 a home-grown information system, named Kuren, was implemented in all the hematology and oncology inpatient and outpatient clinics, using the same implementation strategy as of the CPOE system. Kuren was designed by a Pediatric oncologist and before being implemented in adult hematology/oncology departments, its early version had been developed gradually and implemented in the pediatric oncology department of Erasmus MC for about 5 years. The system was designed specifically to plan chemotherapy courses based on medical protocols, to adjust chemotherapy medication doses based on patients’ biometric indexes, and to provide decision support in following chemotherapy protocols. One year after the system implementation, we conducted 7 semi-structured interviews with system users (including 4 physicians, 2 nurses, and the project leader). During the interviews, we asked the interviewees to explain what characteristics of Kuren makes it more suitable for chemotherapy prescription process and if possible to compare Kuren with the paper based system and with the CPOE system. The interviews were voice recorded and transcribed. The transcripts were analyzed to find specific reasons for preference of Kuren and to find out more about possible non-supportive features of the CPOE system in chemotherapy process. More analysis on data was conducted by comparing of the preference reasons of Kuren to the non-preference reasons (non-supportive features) of the CPOE system in order to trace the source of differences to system design.

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3. Findings Both systems were implemented through more or less the same implementation strategies. The implementation of the CPOE system was seen overall in the hospital as success. However, the situation with respect to hematology/oncology wards was different. Although, the CPOE system had the capability of being used for prescribing chemotherapy medications, the clinicians thought this functionality couldn’t support the complex chemotherapy process. Therefore, they preferred to continue to use paperbased system and wait for a better functionality of the system. On the other hand, although the interviewed clinicians reported a few problems in working with Kuren, they all liked the system and were very happy with the way it supported the chemotherapy process. Our data analysis revealed 13 reasons for Kuren preference that could be traced back to 3 differences in the SDLC of the systems (Table 1). Table 1. Specific reasons because of them clinicians preferred Kuren, and their source in design process. System Design Differences Proximity of development site to implementation site

Specific Reasons on Kuren Preference Quick and easy communication of feedbacks from system users to system developers

User requirement driven design

Reduced workload of clinicians (they did not have to fill in may forms and there was less double work) Easy to use (e.g., navigation through the system was considered easy) Flexibility (e.g., it was easy to perform changes to patient’s already planed care) Reduced possibility of mistakes in clinicians’ work (the system did exact and accurate dose calculation based on patients biometric indexes) Easy to find different pieces of patient information in the system Offering general overview of patient care (the general scheme of patient care was represented in one screen) Ability to link different pieces of patient information together (in a time related pattern) Providing decision support aid (providing information for clinicians on how to fulfill a step in a care process based on standard care protocols)

Process oriented design

Support applying standard care protocols (physicians could choose a standard care protocol to follow or built their own protocols by combining different standard protocols) Support an overview of patient care (by connecting current patient care to past care as well as planned care) Support synchronization and coordination of the stakeholders where sequence of actions was important (e.g., through the system nurses knew which patients they should expect and what preparations they needed to do for patients before chemotherapy courses arrive at daycare center.) Support communication between the different stakeholders (e.g., the system provided biometric indexes measured by physicians to pharmacists in case they needed to double check the doses.)

3.1. Proximity of Development Site to Implementation Site Following implementation, a system enters into maintenance phase in which not only possible errors with the system during its working life are recognized and eliminated, but also the system is tuned to its working environment. Some of the advantages of Kuren were related to the fact that it was developed onsite. Every comment and/or required change to the system could easily and quickly be communicated to the ICT


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department where the designer and the programmer could sit together to figure out how to adapt the system accordingly. In technical terms such a setting shortened SDLC. The situation was opposite for the CPOE system. The project team had the responsibility to collect the comments and problems of clinicians in working with the system and to communicate them to the system vendor. Twice a year all the countrywide clients of the CPOE vendor gathered in one city where they had the chance to discuss on the required changes to the system. The vendor then had to figure out how the system should or could be adapted in a way to be respondent to its users’ needs in different working environments. Such structured way of SDLC inevitably prolonged the change process. By the time the changes were made to the system, users had already found their way out by working around the system [7]. 3.2. User Requirement Driven Design A thorough user requirement analysis and user involvement is fundamental and prior step to every good system design. We could trace back some important differences between the two systems to the way their user requirements were analyzed and the result were fed into system design and redesign processes. The designer of Kuren was an oncologist, someone with thorough knowledge of chemotherapy work requirements, and someone who has enough experience in working with the paper-based system that was going to be replaced by Kuren. He, moreover, was in close contact with other users and could relatively easily get their feedbacks. This setting created a short cycle of evaluation and consideration of users’ requirements in the design and re-design processes of the system. The result therefore was a specific system for clinical context of Erasmus MC that could respond to many of the clinicians’ needs. Considering users’ concerns in adjusting the system moreover created a sense of system ownership among the users and brought their commitment and close collaboration as a result. Such setting never existed for the CPOE system. Many of our interviewees had complains and/or concerns about the CPOE system. In fact, many of those complaints were never considered in the system re-design and remained as the system’s inflexibility and less user friendliness characteristics. An oncologist noted: “the CPOE system had an alerting system on drug-drug reaction but you had to calculate and combine the chemotherapy medications every time. You had to fill in the information every time. There was no information for physicians and nurses for example about side effects and the way the courses had to be administered.” Contrary to Kuren, entering a new chemotherapy course and/or specifications about a course into the CPOE system could not be done without the help of a technical person. 3.3. Process Oriented Design One of the Kuren’s basic characteristics, which pleased its users, was its ability to tie different stakeholders’ work into a single multidisciplinary care process. This was done at least by four means: First, the system supported using standard care protocols thus reduced variation in care practice concerning who should do what, when, and how. Second, it was connecting different episodes of patient care in the past to planned care episodes in the future along a timeline, giving a general overview on patients past, current, and future care. Third, it improved communication between different parties throughout the process; thus the clinicians did not have to make many phone calls for the purpose of information gathering. Fourth, by providing necessary information from

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one party to the other, the system helped different stakeholders to synchronize and to coordinate better in their interdependent work. On the contrary, we detected many communication problems between the clinicians in working with the CPOE system [7]. The CPOE system, in general, could not support inter-professional work [6] and the patient care process as a whole appropriately. An oncologist explained: “The CPOE system was based on taking only a single chemotherapy course every time you visit a patient. You could not take a chemotherapy strategy for the patient [at once]. You could basically make a mistake by taking a wrong course for a patient. You also did not have an overview on patient care”.

4. Discussion The two evaluated systems were considered successful. Kuren was the system of preference for hematologists/oncologists because it could support the complex chemotherapy process and managed its user requirements better. The advantages of Kuren were built into the system through a user requirement driven and process oriented design as well as due to its onsite development. Our finding in this study demonstrates the fundamental impact of an appropriate SDLC strategy on successful adoption of IT systems. They underscore the importance of user involvement and a comprehensive user requirement analysis in an IT system preference and success. A thorough understanding of a care process is required to design a system to support it. Such thorough understanding (especially if the process is a multidisciplinary one) will only develop gradually and through a close collaboration between system users and its developers. The study did not evaluate the financial aspect of onsite system development.

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[6]

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Wears RL, Berg M. Computer technology and clinical work: still waiting for Godot. JAMA. 2005 Mar 9;293(10):1261-3. Ammenwerth E, Talmon J, Ash JS, Bates DW, Beuscart-Zephir MC, Duhamel A, et al. Impact of CPOE on mortality rates--contradictory findings, important messages. Methods Inf Med. 2006;45(6):586-93. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005 Mar 9;293(10):1223-38. Scavuzzo J, Gamba N. Bridging the gap: the Virtual Chemotherapy Unit. J Pediatr Oncol Nurs. 2004 Jan;21(1):27-32. Niazkhani Z, Pirnejad H, van der Sijs H, de Bont A, Aarts J. Computerized Provider Order Entry System - Does it Support the Inter-professional Medication Process? Methods Inf Med. 2010; 49(1):207 Pirnejad H, Niazkhani Z, van der Sijs H, Berg M, Bal R. Impact of a computerized physician order entry system on nurse-physician collaboration in the medication process. Int J Med Inform. 2008 Nov;77(11):735-44. Pirnejad H, Niazkhani Z, van der Sijs H, Berg M, Bal R. Evaluation of the Impact of a CPOE system on Nures-physician Communication: A Mixed Method Study. Method Inf Med. 2010 (48):350-60 doi:10.3414/ME0572.


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Does PACS facilitate work practice innovation in the intensive care unit? Isla M HAINS,a,1 Nerida CRESWICK a, Johanna I WESTBROOK a Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, Australia a

Abstract. Picture Archiving and Communication Systems (PACS) allow the fast delivery of imaging studies to clinicians at the point-of-care, supporting quicker decision-making. PACS has the potential to have a significant impact in the Intensive Care Unit (ICU) where critical decisions are made on a daily basis, particularly during ward rounds. We aimed to examine how accessing image information is integrated into ward rounds and if the presence of PACS produced innovations in ward round practices. We observed ward rounds and conducted interviews with ICU doctors at three hospitals with differing levels of PACS availability and computerization. Imaging results were infrequently viewed by clinicians during ward rounds in two ICUs: one without PACS and one which had both PACS and bedside computers. In the third ICU, where PACS was only available at a central workstation, images were frequently viewed throughout the daily round and integrated into decisions about patient care. The presence of bedside computers does not automatically result in innovations to work practice. Despite the ability to utilize PACS at the bedside to support decision-making, use was varied. Research to understand how the complexities and context of the ICU contribute to work practice innovation and why practice changes differ is required. Keywords. PACS, Intensive Care Units, Ward Rounds, Decision Making

1. Introduction Diagnostic imaging is a key facet of healthcare and digital imaging is changing the way in which healthcare is provided. Picture Archiving and Communication Systems (PACS) have developed and evolved over the last 30 years and are defined as “comprehensive networks of digital devices designed for acquisition, transmission, storage, display and management of diagnostic imaging studies” [1]. These systems allow x-rays and other diagnostic images to be delivered to clinicians at the point-ofcare, where decisions are made, far more rapidly than hard copies. PACS is now used in healthcare worldwide [2; 3] and has been shown to improve image availability and radiology reporting times, workflows, issues associated with lost images and to reduce the time clinicians spend searching for images [4; 5]. One clinical area where PACS has potential to significantly impact is the Intensive Care Unit (ICU) [6]. Diagnostic imaging can be critical to the care of an ICU patient [7; 8] with imaging examinations conducted on a daily basis. PACS has the potential to allow faster clinical decisionmaking, though studies to determine its impact on the initiation of clinical actions in the ICU have proved inconclusive [9; 10]. Many of these clinical decisions are 1


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conducted during daily ward round in the ICU. The aims of our study were to examine how accessing images and reports is integrated in daily ward rounds in the ICU, and assess if presence of PACS produced innovations in ward round work practices.

2. Methods 2.1. Study Design We observed daily ward rounds in the ICU to understand how use of imaging and imaging reports are incorporated in daily practice. We conducted interviews with ICU doctors to ascertain their perceptions of impact of PACS on their work practices and how it is, or may be, used on a daily basis. Ethics approval was obtained from the relevant hospitals and the University of New South Wales Human Research Ethics Committee. Each participant gave written consent to participate in interviews and to being observed. 2.2. Study Setting and Participants The study was carried out in ICUs at three Australian hospitals, each of which had differing levels of PACS availability and computerization. The characteristics of each site are shown in Table 1. All ICU doctors were invited to participate in the study. Table 1. Study site characteristics ICU Site 1 (Large metropolitan teaching hospital) 2 (Large metropolitan teaching hospital) 3 (Medium metropolitan teaching hospital)

No. of ICU Beds 54 28 13

PACS Availability AGFA IMPAX 6.3.1 – 9 years GE Centricity Web v3.0 – 8 months No

Level of Computerization Bedside computers and central workstations Central workstations Central workstations

2.3. Data Collection Data were collected by IH and NC between April and October 2010. We observed 79 hours of ward rounds and carried out 40 one-on-one semi-structured interviews with ICU doctors (staff specialists, registrars and residents). We asked questions relating to changes or potential changes to practice with PACS; if the sequence of tasks has been or was anticipated to be impacted following PACS introduction; and where PACS is accessed. The interview schedule was adapted throughout the process according to issues arising from earlier interviews. Written observation notes included how often imaging information was accessed during a ward round, where and how the information was accessed and if it was accessed for every patient. 2.4. Data Analysis We (IH & NC) reviewed all observation notes and classified data according to frequency imaging data were accessed, where and how this occurred, either via PACS or hard copy film. We employed NVivo 8 (QSR International) to organize the


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interview data and analyzed it using thematic analysis (IH & NC) [11]. We report results according to themes arising from the combined observational and interview data.

3. Results 3.1. Integration of Imaging into Ward Rounds At Site 1, which has bedside computers with access to an ICU clinical information system, electronic ordering and PACS, imaging data were infrequently viewed and accessed during the majority of daily morning ward rounds. We observed only one ward round where clinicians viewed images during the round and this round was carried out primarily at the central workstation area, with the doctor simply doing a physical examination of the patient at the bedside before going back to the workstation to access data and write notes. Though senior clinicians commented on the ability to “see an x-ray at every bedside without having to walk back and try and find it” and “look up an x-ray at the bedside when you are interested on the ward round”, others stated that images are generally looked at on PACS at the central workstation, outside of rounds, rather than at the bedside. This may be related to their belief that the bedside computer screens are not of a high enough resolution “to have a proper look at PACS” and so they prefer using the high resolution screens at the central workstation, though we infrequently observed this during the round. Additionally it could simply occur because clinicians preferred to keep the ward round “compartmentalized just for the sake of getting the ward round done in a timely fashion”. Similar practices were also observed at Site 3. This ICU does not have PACS or bedside computers and imaging information was rarely used during the ward round. Images are viewed on a multiviewer light box in a corner of the ICU and we observed a total of two instances on two separate rounds (out of six rounds observed) in which a senior doctor accessed an x-ray in this area. However, immediately before the ward round all ICU clinicians attend a “handover round” where imaging data and plans for current ICU patients are discussed. Comments from all doctors also supported the use of imaging information primarily at this handover meeting rather than on ward rounds. Conversely, at Site 2, which has had PACS for a relatively short period of time, we observed the clinicians using PACS to access imaging reports, x-rays and CT scans for the majority of the patients on nearly all rounds when making decisions regarding the patient, with only a few exceptions. 3.2. Ward Round Work Practice Innovation Though there are formal times throughout the week or day in each ICU where imaging results are viewed (Table 2), imaging information was also accessed on the ward rounds, where we observed innovative practice associated with PACS. While the use of PACS during the ward round was seldom observed at Site 1, this was clearly not the case at Site 2 with clinicians demonstrating substantial differences in their integration of imaging information and real-time decision-making about patient care. The organization of ward rounds at Site 2 was observed (and apparent from participants’ reports) to be strongly influenced by both the senior doctor leading the round, and that access to PACS was only at a central workstation area. However many doctors at this site commented that the introduction of PACS has positively changed their practices on

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ward rounds. It “changes the structure of the ward round” from one where all the images are accessed at the beginning or end of the round either in the ICU or through having to go to radiology (mainly for CT images), which one junior doctor stated was “completely impractical”, to one where images are generally viewed at the time the patient is being reviewed. One senior doctor also perceived that PACS allowed the ward round to be conducted in a safer and timelier manner. “I think it increases the ability for us to actually get through the ward round and it makes sure we actually see what we need to see and it also means that the patients are safe because everyone’s on the same page.” Site 2

Though Site 3 did not have PACS, only one doctor perceived that the introduction of PACS would innovate the ward round, while others expected there would be little change, with the only difference being the ‘handover round’ would use a projector screen rather than the traditional multi-viewer light box. Table 2. Image viewing times ICU Site 1 (Large metropolitan teaching hospital) 2 (Large metropolitan teaching hospital) 3 (Medium metropolitan teaching hospital)

Formal Times Imaging Information is Viewed Afternoon x-ray meeting Bi-weekly radiology meeting (in the radiology department) Daily morning handover round (pre-ward round) Bi-weekly radiology meeting (in the ICU)

3.3. Perceptions of Bedside Computers Despite the presence of bedside computers at Site 1, images were rarely viewed at the bedside during the examination of the patient on the round. However, doctors at sites without either bedside computers or PACS believed that the presence of bedside computers would enhance the use of PACS for their decision making on ward rounds. “Whereas if it was at the bedside: examine, look at the numbers, look at the x-ray and then you can formulate a plan.” Site 2 “If it’s available at the bedside… I think the ideal way to do it is...have a look at it when you’re actually seeing the patient or else go there to listen, examine the patient and then, then look at the x-ray. Much more likely to register more from the x-ray when you’ve seen it after looking at the patient.” Site 3

4. Discussion To our knowledge this is the first study which reports on ward round work practice innovation associated with PACS in the ICU. While PACS is an innovation in itself, the presence of PACS did not necessarily lead to significant changes in work practice during ward rounds, despite its great potential to aid in decision-making at the point-ofcare [4; 6]. Though bedside computers are shown to aid clinicians’ work [12], interestingly we found that while doctors (without current access to PACS) perceived that bedside computers would enhance or increase the use of PACS during rounds, our investigation of a site with this capability showed limited bedside use, potentially due in part to screen resolution [13]. This demonstrates an interesting variance between anticipated changes in practice versus what happens in practice. At the site that integrated the use of PACS into their daily ward rounds, clinicians commented on


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improvement and efficiency in ward rounds. Conversely, this was a reason suggested by one of the doctors at Site 1 as to why they did not use PACS during rounds. Studies of impact and changes to structure of ward rounds as a result of PACS in other settings show both positive and negative effects, with conflicting reports of PACS affecting the efficiency of the ward round [13; 14]. The ICU is a complex environment and it is conceivable that context and culture of each ICU will contribute observed differences.

5. Conclusion Although PACS use at the bedside in particular has enormous potential for innovating ward round work practices, we found this was not a consistent outcome. There is a clear need to understand how the complexities and context of each ICU contribute to work practice innovation and why PACS integration can create significant work practice change at some sites and not others. Furthermore, measuring the clinical impact of such work practice changes remains a challenge.

Acknowledgements. The authors thank ICU staff for participating in the study. This research was funded by an Australian Research Council Linkage Grant LP0989144

References [1] Hood, M.N. and Scott, H. Introduction to Picture Archive and Communication Systems, Journal of Radiology Nursing 25 (2006), 69-74. [2] HIMSS Foundation, Picture Archiving and Communication Systems: A 2000-2008 Study, http://www.himss.org/foundation/docs/PACS_ResearchWhitePaperFinal.pdf?src=pr (Accessed 1st November 2010) [3] Sutton, L.N.PACS and diagnostic imaging service delivery--A UK perspective, Eur J Radiol In Press (2010). [4] Bryan, S. Weatherburn, G.C. Watkins, J.R. and Buxton, M.J. The benefits of hospital-wide picture archiving and communication systems: A survey of clinical users of radiology services, Br J Radiol 72 (1999), 469-478. [5] Siegel E.L. and Reiner, B.I.Filmless radiology at the Baltimore VA Medical Center: A 9 year retrospective, Comput Med Imaging Graph 27 (2003), 101-109. [6] Steckel, R.J. The Current Applications of PACS to Radiology Practice, Radiology 190 (1994), 50A-52A. [7] Strange, C. Infection in the Intensive Care Unit: A Clinician's View of the Role of Imaging, Semin Roentgenol 42 (2007), 7-10. [8] Trotman-Dickenson, B. Radiology in the intensive care unit (Part I), J Intensive Care Med 18 (2003), 198-210. [9] Kundel, H.L. Seshadri, S.B. Langlotz, C.P. Lanken, P.N. Horii, S.C. Nodine, C.F. Polansky, M. Feingold, E. Brikman, I. Bozzo, M. and Redfern, R. Prospective study of a PACS: information flow and clinical action in a medical intensive care unit, Radiology 199 (1996), 143-149. [10] Watkins, J. Weatherburn, G. and Bryan, S. The impact of a picture archiving and communication system (PACS) upon an intensive care unit, Eur J Radiol 34 (2000), 3-8. [11] Pope, C. and Mays, N. Qualitative research in health care, BMJ Books, Oxford, 2006. [12] Poissant, L. Pereira, J. Tamblyn, R. and Kawasumi, Y. The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review, J Am Med Inform Assoc 12 (2005), 505-516. [13] Tan S.L. and Lewis, R.A. Picture archiving and communication systems: A multicentre survey of users experience and satisfaction, Eur J Radiol 75 (2010), 406-410. [14] Pilling, J.R. Picture archiving and communication systems: The users' view, Br J Radiol 76 (2003), 519524.

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Innovation in Intensive Care Nursing Work Practices with PACS a

Nerida CRESWICK,a,1 Isla M. HAINS,a, Johanna I. WESTBROOK a Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales

Abstract. Doctors are the main users of x-rays and other medical images in hospitals and as such picture archive and communication systems (PACS) have been designed to improve their work processes and clinical care by providing them with faster access to images. Nurses working in intensive care units (ICUs) also access images as an integral part of their work, yet no studies have examined the impact of PACS on the work of intensive care nurses. Our study aimed to examine whether and how ICU nurses view and use images and whether access to PACS promotes innovation in work practices. We interviewed (n=49) and observed (n=23) nurses in three Australian metropolitan teaching hospital ICUs with varying degrees of PACS implementation. Our study found that nurses with access to PACS were able to independently and easily access images, did so more frequently when required, and perceived that this had the potential to positively impact upon patient safety. Those without PACS usually viewed images more traditionally as part of a ward round. The introduction of PACS to ICU settings promotes changes in nursing work practices by providing nurses with the ability to act more autonomously, with the potential to enhance patient care. Keywords. critical care, nurses, intensive care units, hospital information systems, radiology information systems, evaluation studies.

1. Introduction Picture archive and communication systems (PACS) store and provide faster access to electronic medical images such as x-rays, CTs and MRIs for doctors (including radiologists), and have the potential to assist them in their decision making [1]. Accessing and utilising medical images is an integral part of the work of intensive care nurses [2], for example to determine the position of nasogastric tubes on chest x-rays prior to commencement of feeding. A literature review [3] has highlighted ways in which PACS could innovate nursing practice. These include allowing improved patient care by providing access to more information, its use as a tool to improve handover communication, and in research and education by providing the means for image searching. The only previous studies evaluating PACS use in intensive care units (ICUs) included surveys with nurses as participants, but did not separately report findings regarding nurses [4,5], and an Australian study which found that nurses did not use PACS in the ICU [6]. None have focused on the use of PACS by intensive care nurses, nor its impact on their work practices.

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Intensive care nurses are working in a complex environment in collaboration with doctors to provide care for high acuity patients, integrating clinical information from multiple sources. A socio-technical approach provides a framework which allows information and communication technology (ICT) such as PACS to be examined in the context of its users and their setting [7,8]. The aim of our study was to understand whether and how intensive care nurses access and use medical images in their work, and to examine the impact of PACS on nursing work practice innovation.

2. Method 2.1. Design, Setting and Participants A qualitative study of ICU nurses’ work practices was conducted across three Australian metropolitan teaching hospital ICUs using a multi-method approach with semi-structured interviews to elicit perceptions, and observations to examine practices of access and use of medical images. Interviews and observations provided a means to explore the complex socio-technical network of users, systems and settings [9]. ICU 1 had longstanding (nine years prior) access to PACS (AGFA IMPAX 6.3.1) from bedside and central workstation computers, ICU 2 introduced PACS (GE Centricity Web v3.0) eight months prior, accessed from central workstation computers, and ICU 3 had not yet implemented PACS. Participants were selected to ensure representation across roles, including Registered Nurses (RNs), Clinical Nurse Specialists (CNSs), Clinical Nurse Educators (CNEs) and Nurse Unit Managers (NUMs). We interviewed (n=49) and observed (n=23) nurses for 35.5 hours. Ethics approval was granted by hospital and university ethics committees. Each participant gave written consent. 2.2. Data Collection and Analysis Between April and October 2010 three researchers conducted semi-structured interviews which included asking nurses about how PACS has changed or will change their work practices, patient safety and their role. These interviews were audiorecorded and transcribed. Observations of nurses carrying out their work were conducted and researchers recorded information about the ways in which nurses carried out their day-to-day work, including viewing medical images. Interview transcripts were analysed by one researcher (IH) to categorise direct quotations by the nurses into themes, assisted with QSR NVivo version 8.0. A second researcher (NC) reviewed the categories to achieve triangulation of analyses. Observation notes were reviewed to identify occurrences of nurses viewing medical imaging during their work. Our results present the themes arising from both interview and observational data.

3. Results 3.1. Nurses Viewing Images Independently In ICU 1 where there was bedside access to PACS, nurses stated that they viewed chest x-rays at least once or twice per shift for intubated patients (for nasogastric feeding): at

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the beginning of each shift and when new chest x-ray images became available. Few nurses perceived that they relied on doctors to access and review images. They reported that because PACS is available at the bedside, they are able to access images when required with convenience, without leaving the patient. Nurses in ICU 2 also reported viewing images at the start of their shifts, especially for intubated patients who routinely receive a chest x-ray early each morning, and later in the day if required. “…virtually in the morning in the first hour or so to look at your blood results, look at what the chest xray looks like but then any other further tests that your patient requires through the day then we have to go back…”(CNE/CNS, ICU 2)

The recent replacement of hard copy films with PACS at this site allowed nurses to compare their practices. They reported spending less time searching for x-rays, and that the turnaround time for the availability of images for viewing had decreased. In ICU 3, where PACS was yet to be implemented, nurses who recounted viewing images independently, conveyed that they did so infrequently. Instead, they mainly viewed images at the multi-disciplinary handover round. Many predicted that when PACS is introduced, with access only from the central workstation computers, that it will not be as useful as it would be at bedside computers. 3.2. Collaborative Image Viewing Practices In ICU 2, multidisciplinary ward rounds were carried out each day, with an effort made for each nurse caring for a patient to be present at the bedside while their patient was examined by doctors. If doctors reviewed images during the round they did so at the central workstation where PACS was available, while most nurses stayed with their patient. One nurse commented that if she was able to, he/she viewed the x-ray with the doctors requiring him/her to move away from the patient bedside. In ICU 3, nurses viewed images each morning at the multi-disciplinary “handover round” at the multi-viewer lightbox which they attend while their patient is discussed. Many nurses reported mainly viewing images at these morning handover rounds: “…I would obviously look at them each morning on the morning round with everybody just to get a general understanding but I wouldn't necessarily go back and have a look at it later unless somebody has asked me to go back and have a look.” (NUM, ICU 3)

The introduction of PACS in ICU 3 may change the handover work practices, and nurses may no longer have the opportunity to view the images either in collaboration with doctors or alone: “It may change our handover process in the morning…we, you know, we’ll sit and we would all have a chat about the patient and what’s going on, ask questions about where all the lines are so I don’t know what’s gonna happen…and whether it will change that.” (CNC,ICU 3)

Conversely, in ICU 1, nurses had few opportunities to view images in collaboration with doctors as there were no multi-disciplinary meetings or handovers, and doctors rarely viewed images at the bedside where nurses were usually located. 3.3. Technical Aspects of PACS Some of the nurses at both PACS sites (ICU 1 and 2) perceived that they were not using all the available functions of PACS due to lack of on-going training. There was also some dislike expressed regarding accessing images from small screens compared to viewing large films, the time required to manipulate images, and because the system


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is sometimes down with images unavailable. Some nurses reported that they liked being able to manipulate images in PACS, to “alter the light and all the different brightness and just have our own control over how the film can be reviewed” (RN, ICU 2). Yet others recounted that having to manipulate the images was a problem as they do not possess the expertise do this, and “we’re not really trained to do that...” (RN, ICU 2). Comparing multiple images was perceived to be more difficult with PACS than with hard copies by some: “The advantage with hard films was you could just compare one with the other a lot quicker. Sometimes I find PACS, it’s a bit hard to get a whole series of films and compare yesterday’s and before to today’s x-rays…” (RN, ICU 2)

Others perceived that comparing images was easier with PACS, because at least they could rely on the films being in the system. 3.4. Work Practices and Patient Safety Nurses in ICU 1 believed that by looking at x-rays themselves and not just relying on the doctors they could assess the x-ray, and raise an alarm if they saw an abnormality. “… well I think having the PACS that handy it just allows you for a double check quickly… you know your doctors are always checking but for you to check as well it just feels better you know just being able to look at it yourself.” (RN, ICU 1) “… usually if you got told by the doctor the x-ray said this or that, once they’ve had a look at it in the morning you sort of believed that by word but now you’ve got the access here, so you can sort of go in and it’s a new task, you sort of don’t have to go with what they say now, yeah. And you can actually question a few things and, yeah, so I find that that’s quite good.” (RN, ICU 1)

A number of nurses working in ICU 2 believed that the introduction of PACS had contributed to improved infection control due to films “not actually being handled at the bedside” (NUM, ICU 2). However some nurses found the hard copies delivered to the patient bedside each morning acted as a visual prompt (which they no longer had) to remind them to view their patients’ images. Prior to the introduction of PACS in ICU 2, films were often misplaced around the unit or taken elsewhere in the hospital and locating old films was cumbersome. Many nurses in ICU 2 commented that they perceived access to x-rays had improved with the introduction of PACS simply because they no longer had to search for films. They thought the loss or misplacement of hard films impacted negatively on patient safety, and that the introduction of PACS had eliminated this: “…they don’t get lost and it’s a lot easier, so that saves time instead of searching you know, to and fro. You know if you open up your PACS … you’re going to have your chest x-ray there no matter what, …it’s not going to be erased, it’s not going to be missing.”(RN, ICU 2)

In ICU 3 where PACS had not yet been implemented nurses anticipated there will be less need to “chase up films” with “treatment initiated quicker” (CNC, ICU 3).

4. Discussion By using a sociotechnical approach [7,8] for the design, collection and analysis of data our study was able to examine the technical features of PACS in the context of different settings and its use by ICU nurses in a variety of roles. Our study found that nurses working in ICUs with PACS were able to more frequently view x-rays separately from doctors, to check the position of tubes before proceeding with nasogastric feeding, potentially contributing to the safety of their patients. Other types

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of clinical information technology have been found to allow nurses to act more autonomously and take on extended roles in patient management [10]. The improved access to images for nurses which PACS provides in the settings we examined, allows them to act autonomously and raise the alarm when they detect abnormalities. In the ICU without PACS (ICU 3), and to a lesser extent in the ICU with PACS at the central workstation (ICU 2), nurses had the opportunity to view images alongside doctors, and were able to participate in discussions with them. In the ICU with PACS at the bedside, nurses lacked those opportunities, but they did access images autonomously. Many of the ways for integration of PACS into nursing work practices [3] appear to be coming to fruition in ICU settings with better access to images and improved delivery of education. Further work should examine the impact of these changes on doctor-nurse communication and patient care. The context of the way work is carried out in each of the ICUs appears to influence the ways in which the introduction of PACS innovates work practices.

5. Conclusion The introduction of PACS to ICU settings promotes changes in nursing work practices by providing nurses with the ability to act more autonomously, with the potential to enhance patient care. Acknowledgements. The study was supported by an Australian Research Council Linkage grant in partnership with Sydney South West Area Health Service (LP0989144). The authors thank the participation nurses from the ICUs, and Anne Marks for her work in collecting some of the data.

References [1] Huang H (2003) Enterprise PACS and image distribution. Computerized Medical Imaging and Graphics 27:241-253. doi:10.1016/s0895-6111(02)00078-2 [2] Revell MA, Pugh M, Smith TL, McInnis LA (2010) Radiographic Studies in the Critical Care Environment. Critical Care Nursing Clinics of North America 22 (1):41-50. doi:DOI: 10.1016/j.ccell.2009.10.013 [3] Hood MN, Scott H (2006) Introduction to Picture Archive and Communication Systems. Journal of Radiology Nursing 25 (3):69-74. doi:DOI: 10.1016/j.jradnu.2006.06.003 [4] Cox B, Dawe N (2002) Evaluation of the impact of a PACS system on an intensive care unit. Journal of Management in Medicine 16:199-205. doi:10.1108/02689230210434934 [5] Pilling JR (2003) Picture archiving and communication systems: The users' view. British Journal of Radiology 76 (908):519-524 [6] Yu P, Hilton P (2005) Work practice changes caused by the introduction of a picture archiving and communication system. Journal of Telemedicine & Telecare 11 Suppl 2:S104-107 [7] Berg M (1999) Patient care information systems and health care work: a sociotechnical approach. International Journal of Medical Informatics 55 (2):87-101 [8] Westbrook J, Braithwaite J, Georgiou A, Ampt A, Creswick N, Coiera E, Iedema R (2007) Multimethod evaluation of information and communication technologies in health in the context of wicked problems and socio-technical theory. Journal of the American Medical Informatics Association 14 (6):746 - 755 [9] Berg M (1999) Patient care information systems and health care work: a sociotechnical approach. International Journal of Medical Informatics 55 (2):87-101 [10] Ash JS, Sittig DF, Campbell E, Guappone K, Dykstra RH, Ash JS, Sittig DF, Campbell E, Guappone K, Dykstra RH (2006) An unintended consequence of CPOE implementation: shifts in power, control, and autonomy. Paper presented at the AMIA Annual Symposium, Washington DC.


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Evaluation of Telephone Triage and Advice Services: a Systematic Review on Methods, Metrics and Results Sara CARRASQUEIRO a,1, Mónica OLIVEIRA b, Pedro ENCARNAÇÃO a a Catholic University of Portugal, Faculty of Engineering, Lisbon, Portugal b Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal

Abstract. Telephone triage and advice services (TTAS) have been increasingly used to assess patients’ symptoms, provide information and refer patients to appropriate levels of care (attempting to pursue efficiency and quality of care gains while ensuring safety). However, previous reviews have pointed out for the need for adequately evaluating TTAS. AIMS: To review TTAS evaluation studies, compile methodologies and metrics used and compare results. Systematic search in PubMed database; data collection and categorization by TTAS features and context, type of evaluation, methods, metrics and results; critical assessment of studies; discussion on research needs. 395 articles screened, 55 of them included in the analysis. In conclusion, several aspects of TTAS impact on healthcare systems remain unclear either due to a lack of research (e.g. on long term clinical outcomes, clinical pathways, safety, enhanced access) or because of huge disparities in existing studies on the accuracy of advice, patient compliance, system use, satisfaction and economic evaluation. Further research on TTAS impact is required, comprising multiple perspectives and broad range of metrics. Keywords. Teletriage, e-health, health technology assessment, health services research, economic evaluation, systematic review.

1. Introduction In recent years telephone triage and advice services (TTAS) have been introduced to improve delivery of healthcare services. TTAS are e-health services that combine the use of call centre technology with formal or informal clinical decision systems to evaluate patients’ health condition and advise them or their caregivers to act accordingly. Major objectives pursued by TTAS are to provide education to patients, reducing the fear caused by unknown conditions and empowering to self-care, and to direct patients to appropriate levels of care, increasing the efficiency of healthcare systems and promoting safety and access to care. Although several studies on the impact of TTAS have been conducted, systematic reviews have found flaws in available literature and indicated the need for further studying the impact of teletriage on healthcare systems’ use, safety, cost and on patient satisfaction [1,2]. This study provides a systematic review of evidence about TTAS’ impact on healthcare systems

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Corresponding Author: Sara Carrasqueiro, Catholic University of Portugal, Faculty of Engineering, Estrada Octávio Pato, 2635-631 Rio de Mouro, Portugal, E-mail: [email protected]

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and about the methods and metrics used in these studies2, so as to analyze quality and results from previous studies, as well as to define future research needs.

2. Methods Papers were selected from the PubMed database3 using the keywords listed in Table 1, line 1, which encompass different terminologies associated with TTAS. Other terms like ‘telephone counseling’, ‘counseling call centre’, ‘counseling line’, ‘consultation call centre’, ‘helpline’ and ‘hotline’ were excluded from search because they are mostly associated with specific medical problems’ advice or with follow-up or self support services. Results were successively filtered to: (a) retrieve only evaluation studies (Table 1, line 2); (b) retrieve papers including evaluation from the viewpoint of the healthcare system (line 4); and (c) retrieve papers published from 1994 to present (line 6). The search was run in October 18 2010. All articles retrieved were screened by the first author using title and abstract information, and those outside the scope of this study were excluded. Ambiguous cases were discussed with the remaining authors to achieve a consensus. Table 1. PubMed database search strategy Search Strategy 1. (teletriage OR telephone triage OR telephone consultation OR NHS Direct OR telephone advice OR tele-advice OR health call centre OR (nurs* AND call centre) OR triage call centre OR (consul* AND call centre) OR (after-hours AND call centre) OR triage line OR advice line OR (telephone-based AND triage)[Title/Abstract] 2. (impact OR assess* OR effect* OR evaluat* OR econom*)[All Fields] 3. #1 AND #2 4. (hospital OR visit* OR pathway* OR emergency* OR referral OR utilization)[All Fields] 5. #3 AND #4 6. "1995"[PDAT] : "3000"[PDAT]) AND "0"[PDAT] : "3000"[PDAT] 7. #5 AND #6

Each study was classified according to: Context and Features of TTAS, Objective, Perspective of analysis, Type of economic evaluation, Metrics, Design and Results. Type of economic evaluation category applied the definitions suggested by Drummond et al. [3]. Study design was compiled using the terms and definitions of INHTA Health Technology Assessment (HTA) [4] and The Cochrane Collaboration [5] glossaries. Metrics were grouped by: A. Accuracy of advice; B. Patient compliance to advice; C. Output: C1. Access to care; C2. System use; C3. Clinical outcomes; C4. Safety; C5. Satisfaction; C6. Economics. One should note that a meta-analysis was considered inappropriate because of a large heterogeneity in methods, metrics and context of TTAS studies. Critical assessment of evaluation studies used a modified version of “a check-list for assessing economic evaluation” proposed by Drummond et al. [3] that best fits partial economic evaluations assessing: objective clearness, alternatives adequacy, potential bias, costs and outcomes completeness, data sources, uncertainty allowance and generalizability. 2

Both teletriage and tele-advice by health professionals in their routine work with their patients and teletriage services restricted to one specific disease or telephone advice services for self support (ex. tobacco cessation helpline) are outside the scope of this study. 3 http://www.ncbi.nml.nih.gov/pubmed


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3. Results Figure 1 shows search and screening results. 55 papers were included in our review, of which 50 are original studies. A reference list of these papers is accessible from the website http://echo.fe.ucp.pt/~189903001/ind ex_files/ttas1.html.

Figure 1. Flowchart of selection process and results

Context and Features. 24 of the original studies concern stand alone centralized TTAS, 22 concern TTAS embedded in healthcare delivery units, and the remaining 4 compare different organization models. Most studies report services provided in the UK and USA, some in Australia and few in other countries - Canada, New Zealand, Netherlands, Denmark, Switzerland, France and Japan. 20 studies evaluate 24-hours TTAS services, 18 evaluate TTAS for management of out-of-hours care and the remaining 6 for in-hours care. Most studies relate to TTAS provided to populations of all ages, still 20% relate to pediatric TTAS. Most studies address TTAS provided by nurses supported by computerized systems with embedded protocols and algorithms. Regarding the maturity of the service, 20 studies evaluate services established over three years, 18 evaluate services with three or less years of operation and 18 evaluate pilot experiences. Objective and perspective of analysis. Almost all studies clearly state their objectives, aiming at assessing the impact of TTAS on identified issues or metrics. Many studies do not explicitly state the perspective of analysis (although that can be inferred): the perspective of the system is adopted in most studies, of the provider in some studies, and of patients or professionals in a few studies. Type of economic evaluation. 16 of the 50 original studies do not perform an analysis of alternatives (15 are consequence description studies and one is a costoutcome description). 25 studies are efficacy or effectiveness studies (comparing consequences of alternatives). Only 9 studies assess both costs and consequences of alternatives, although some do not present an overall index or ratio of costs to consequences. Many of the studies comparing alternatives do not use an independent concurrent control but rather a “do nothing alternative” obtained either from “patient intention if the TTAS did not exist” or from “before” data in pre-post designs. Others use a control defined by patients using health care providers who did not contacted previously TTAS, or who live in areas where TTAS is not available. Only 4 studies randomize patients to receive or not care through TTAS.

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Study design. 30 studies are retrospective and 20 prospective. Almost all studies included quantitative assessments, only 2 qualitative studies and 18 observational studies, 31 experimental and 1 decision analysis study. 33 studies do not use an independent control group. Concerning sampling, 14 studies use total/population data, 14 use a randomized sample and 15 use a convenience non-random sample. Metrics and results. Table 2 presents a summary of main metrics, frequent instruments for data collection and key findings for each type of metric. Table 2. Summary of findings for most used metrics, strategies and results for each type of metric (Tm) Tm Metrics A Adequacy of the advised level of care.

Data Collection Strategies Audits to real or simulated calls; Assessment of medical record when patients present to providers (with or without control). Self-reported through survey; Determined through providers databases.

B

Patient compliance to present to level of care advised.

C1

Enhanced access to care.

Self-reported through survey; Analyzed from operations data.

C2

Change in rates or tends of services use. Changes in professionals’ workload.

Determined from difference between self-reported intention and action after TTAS (self-reported or checked from providers’ data); Determined from services’ use trend analysis with or without control (before-and-after); Randomized Controlled Trial (few).

C3

Adverse events (deaths, ED, admissions); Delayed care.

Patients survey; Medical record after service use.

C4

Clinical Outcomes after TTAS.

Self-reported through patients’ survey.

C5

Patient satisfaction in Likert scales.

Self-reported from survey.

C6

Savings from avoided services’ use; TTAS costs.

Analysis derived from system use impact studies.

Analysis of Results Unable to demonstrate high rates of advice appropriateness or service use adequacy gains when compared with control. Varies according recommendation and is affected by other factors (intention, complaint, age, income); Is higher when measured from selfreported data comparing with provider’s database matching and is affected by the time window in metric definition. Not always improved, depending on the considered indicators and system context; Reports of expedited access to hospital for patients with serious symptoms. TTAS usually promptly reduces medical workload but remains unclear whether it only delays it; Evidence on the impact on primary care or emergency department use is diverse; Relevance of influence factors such as: TTAS use rate, geographic location (urban vs. rural) and TTAS organization (central or embedded). Safety is a concern for both patients and professionals; Few adverse events with death reported; Rates of unadvised significant care between 4% and 10%. No studies on long-term clinical outcomes; Some cases resolve with TTAS, others improve, others require additional care. Most studies report high levels of satisfaction (non controlled measure); There are reports of low satisfaction with TTAS when it constitutes a barrier to traditional care (e.g. home visits). Most studies suggest the existence of net benefits from TTAS, but others conclude TTAS does not reduce overall costs; Some studies do not account for follow-up costs and those who do it use different time windows. Some studies use non robust data of service use avoidance. No study evaluated all relevant benefits and costs and all relevant perspectives.


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4. Discussion In line with previous studies [1, 2], results from our review indicate that many aspects of TTAS impact on healthcare systems remain unclear, and further research is needed. Several studies have analyzed the accuracy of advice and the impact on services’ use, but the dispersion of results suggests that it is important to further study TTAS features and context as determinants of success and to overcome inconsistencies in the definition of evaluation metrics and in the choice of evaluation methods, which greatly affect the generalization of results. Most studies report experiences of TTAS in the UK, USA and to a less extent in Australia, remaining unknown whether TTAS has been adopted and/or evaluated in other healthcare systems. Impact on long term clinical outcomes and safety are areas where few results were found - most studies tend to focus on the type of advice given to patients. More research is needed to consider impacts for both patients and professionals. Concerning economic evaluations of TTAS, no study considered a broad range of impacts (e.g. follow-up costs/savings, costs/savings from anticipated/delayed care or adverse events’ rate change, value of recommendations for people, patient clinical pathways), nor all relevant perspectives (e.g. patients perspective was rarely studied). Future evaluation studies of TTAS should attempt to address multiple perspectives and impacts, as well as consider the deployment of multiple organizational strategies within TTAS. Methodologies combining multiple designs and data sources, or using decision analytic modeling could be essayed to achieve these goals. Available evidence suggests that TTAS might be reasonably safe, although advice accuracy rates are not high. It still remains unclear if TTAS promotes access to care, services’ use adequacy, or system efficiency and which are the organization models that enhance TTAS potential gains.

5. Conclusions Further research on TTAS impact is required, comprising multiple perspectives, broad range of metrics and complete care process (from initial call to problem resolution), including clinical pathways and clinical outcomes.

References [1] [2] [3] [4] [5]

Bunn F, Byrne G, Kendall S. The effects of telephone consultation and triage on healthcare use and patient satisfaction: a systematic review, Br J Gen Pract, 521 (2005), 956-61. Bunn F, Byrne G, Kendall S. Telephone consultation and triage: effects on health care use and patient satisfaction, Cochrane Database Syst Rev, 4 (2004), CD004180. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the Economic Evaluation of Health Care Programmes, Oxford University Press, New York, 2005. INHATA – The International Network of Agencies for Health Technology Assessment, Health Technology Assessment (HTA) Glossary, first edition, (2006). Glossary of Terms in The Cochrane Collaboration, version 4.2.5, The Cochrane Collaboration, 2005.

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Human Factors Based Recommendations for the Design of Medication Related Clinical Decision Support Systems (CDSS) a

Sylvia PELAYO a,1, Romaric MARCILLY a, Stéphanie BERNONVILLE a, Nicolas LEROY a, Marie-Catherine BEUSCART-ZEPHIR a INSERM CIC-IT, Lille ; Univ Lille Nord de France ; CHU Lille ; UDSL EA 2694 ; F59000 Lille, France

Abstract. This study is part of a research project aiming at developing advanced functions of medication related CDSS to support the monitoring of patients’ therapeutic treatments based mainly on corresponding lab values. We adopted a user-centred approach to the design of these advanced CDSS functions. We collected existing recommendations in the literature and completed previous Human Factors (HF) field studies and analyses by focused observations and modeling. We present resulting HF based recommendations for the design of such advanced medication CDSS and focus more specifically on two innovative high level recommendations completing those already existing in the literature. For illustration purposes, an example of the operationalization of one of the recommendation is presented. Keywords. Adverse drug event, Clinical Decision Support Systems, Software design, Monitoring and clinical context, Human factors

1. Introduction Medication related CDSS have been found to be beneficial in improving the quality of clinicians’ prescriptions, reducing medication errors and ultimately preventing Adverse Drug Events (ADE) [1]. These systems support clinicians’ therapeutic decision by a real-time checking of the orders through a medication knowledge base providing alerts to the prescribers. In spite of their known positive impact, they remain difficult to implement [2]. These difficulties are partly due to compatibility problems between users’ cognitive characteristics and organisation of work on the one hand, and the model implemented in a given application on the other hand. Thus, alerts do not respect the collective aspects of the healthcare work situations [3]. The medication use process involves different professionals with cumulative roles: physicians monitor the treatment of the patient, pharmacists and nurses are in charge of controlling and executing the therapeutic orders. But alerts are very often, if not always, designed exclusively for the physicians. Additionally, alerts are too often disruptive of the cognitive processes inherent to decision making, due to wrong timing, wrong display mode and wrong/weak content of the information delivered. For example, it is not wise 1

Corresponding author: Sylvia Pelayo, EVALAB – University Hospital of Lille, CHRU de Lille, 2 Avenue Oscar Lambret, 59037 LILLE Cedex; E-mail: [email protected].

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to suggest to the physician an action s/he is just about to carry out, or to alert him/her on a potentially dangerous situation for which s/he has just taken action. Medication related CDSS have been in used for long enough to allow publishing a number of review papers which have tried to (i) identify the key features which should ensure their success and (ii) provide recommendations for the design of applications [4]. The analysis of these key papers allows drawing an overview of the current state of the art for designing acceptable, usable and efficient CDSS. Authors agree on the current limitations of existing CDSS and there is a consensus on a number of recommendations for the design of CDSS: provide decision support automatically as part of clinician workflow and deliver decision support at the time and location of decision making [5], provide justifications for the suggestion and require provider’s documentation of the reason for not following it [6], integrate the CDSS with the Electronic Healthcare Record (EHR) and automatically retrieve data from it [7]. The design of such CDSS requires a perfect understanding and appropriation of the work environment and of its key elements. A lot of studies have already provided valuable insights on the intangible characteristics of the activities within the medication use process. The results emphasize the fact that the therapeutic decision making process is a dynamic process [8]: the patient’s condition evolves depending on the healthcare professionals’ actions but also spontaneously by itself. At each encounter with the patient, clinicians have then to update their knowledge about the patient’s status and his/her evolution, especially as regards new elements in the situation that must be known, e.g. new lab results, unexpected clinical evolution of the patient or actions already undertaken for the patient. Moreover, the medication use process may be characterized as a complex distributed work situation: the information is distributed across the media, such as the EHR or the CDSS but also across the minds of the members of the clinical team. This distribution of the work processes supposes team situation awareness, i.e. a shared understanding of the situation where each professional has a complete vision of the situation allowing the decisions to be adjusted according to the information of the others. The present study is part of the European project entitled “Patient Safety through Intelligent Procedures in medication” (PSIP). One of the major goals of the project is to design CDSS functions and to integrate them into different EHR/CPOE (Computerized Physician Order Entry) systems, so that the resulting CDSS corresponds to the users needs and fits clinical workflows and cognitive processes. We have capitalized on existing knowledge of the medication use process work situations and of the current limitations of the existing CDSS to provide HF based recommendations for the design of medication related CDSS along with clues for the operationalization of the existing recommendations. We completed previous field studies and analyses by focused observations and modelling of the monitoring process of patients’ therapeutic treatments based mainly on corresponding lab values. This paper presents two innovative recommendations complementing those already existing in the literature. An example of operationalization is given to illustrate the adopted approach.

2. Methods 2.1. Study Site The study took place in the Hospital Center of Denain in northern France. The hospital

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has a Patient Care Information system (PCIS) including an Electronic Health Record (EHR) equipped with a CPOE which in this version has very limited CDSS functions (e.g. alerts in case of doubloons). The PCIS is interfaced with a pharmacy system, which allows the pharmacists to check the medication orders and send physicians alerts. The analyses were carried out in two medicine departments: the “cardiology” department and the “internal medicine and infectious diseases” department. 2.2. HFE Methods Over a period of one month, four Human Factors experts observed all tasks related to the medication process carried out by 4 physicians, 6 nurses, 2 pharmacists and 2 assistant pharmacists, with a special focus on all actions related to lab values monitoring. Observation time amounted to 53 hours and concerned 101 patients. It was completed with debriefings and semi-structured interviews. The list of recommendations obtained during the analysis has been submitted for feasibility assessment to all PSIP project’s partners, including two CPOE vendors in charge of integrating the PSIP CDSS in their CPOE, the company in charge of ensuring the connectivity of the system, the designers of the knowledge based system and the designers of the PSIP standalone CDSS.

3. Results The two critical recommendations for the design of medication related CDSS summarize what a CDSS should be, i.e. a teamplayer and a partner to clinicians. 3.1. Make the System a Team Player The system should be a team player to be able to support the elaboration and maintenance of a team situation awareness for the healthcare professionals in charge of the patient. That means the system should (i) provide an indication for all the professionals of the availability of an information; the designers may choose the most appropriate way of indicating the information in the interface, (ii) incorporate functions to support the team awareness about the alert management and its evolution over time (e.g. visible access to how the alert was handled and to the reasons for alert override or rule deactivation if any has been documented), (iii) have the same display of basic CDS information for the case at hand for all professionals and (iv) give access upon request to extended information (justification of the rule, attached scientific documentation, etc.) that should be structured depending on the user profile. 3.2. Make the System a Clinicians’ Partner The clinicians have a critical role since they are the decision makers and those handling the alerts (acknowledgement, deactivation and so on). But the existing CDSS are not able yet to catch elements of the work situation to provide relevant information to clinicians. The CDSS should act as a partner by (i) adapting its behaviour according to a subset of relevant actions taken by clinicians, (ii) adapting its behaviour to the evolution of the outcome at risk over time (i.e. take into account the evolution of the


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targeted lab values to filter the rules and adapt its severity) and (iii) incorporating functions supporting the dialog between the CDSS and the clinician (e.g. acknowledgment / de-activation of the CDSS alert). For illustration purposes of these recommendations, we provide a Unified Modelling Language (UML) model describing the classification process to be performed by a CDSS to catch the monitoring and clinical context of patients identified by the system as being at risk of an ADE (Figure 1). We identified eight typical situations characterizing the current status of drug monitoring. They result from the combination of the status of the lab tests orders on the one hand and the validity and normality of the available lab values on the other hand. For each typical situation we can identify whether the monitoring procedure is appropriate or not, and whether the patient’s clinical status, assessed by the lab value, is alarming or not.

Figure 1. UML model supporting the classification of the situations.

Making the system able to catch the monitoring and clinical contexts opens interesting opportunities for the design of the CDS information content and display mode. For each situation the system displays the rule leading to the identification of the case as being at risk of ADE. But this basic information may be extended or particularized depending n the context. For instance, in contexts 2, 4 and 7 which correspond to situations that are not properly monitored, the CDS could suggest that the clinician order the required lab test and eventually propose a short cut to the lab tests ordering page. On the contrary in context 6, in which a new (recent and valid) lab value came in abnormal, the system could alert the physician on the increasing negative side effect of the drug and invite him/her to reassess the cost benefit ratio of the incriminated drug(s).

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4. Discussion and Conclusion This paper has presented two high-level recommendations for the design of medication CDSS, so that the resulting system correspond to the users needs and fit clinical workflows and cognitive processes. Due to limited space it was not possible to present the entire set of recommendations. These recommendations look promising for improving the capacity of the system to catch the monitoring and clinical context which in turn opens interesting design possibilities. However it is important to assess the technical feasibility of such a set of recommendations. In the PSIP context, all partners have rated the feasibility of the proposed recommendations and unanimously confirmed that most of the recommendations are possible to implement. This attests to their technical feasibility and also to their relevance for the design of advanced medication CDS functions. Those recommendations are quite innovative in the domain of the design of CDSS. For instance, a lot of studies emphasize the inherently collaborative characteristics of the medication use process and the importance to support them efficiently, but to our knowledge, none provides recommendations for the design of a teamplayer CDSS supporting shared awareness of potential ADEs and of the actions taken to prevent them. Similarly, it would be a significant progress for these systems to better catch the monitoring and clinical context. Acknowledgement. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 216130 – the PSIP project.

References [1]

[2] [3] [4] [5]

[6]

[7] [8]

Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review, J Am Med Inform Assoc 15 (2008), 585-600. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 11 (2004), 104-12. Pelayo S, Beuscart-Zéphir MC. Organizational considerations for the implementation of a computerized physician order entry. Stud Health Technol Inform., 157 (2010), 112-117. Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: a review, J Am Med Inform Assoc 14 (2007), 29-40. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success, BMJ 330 (2005), 749:765. Mollon B, Chong JJ, Holbrook AM, Sung M, Thabane L, Foster G. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials, BMC Med Inform Decis Mak , 11 (2009), 9:11. Nies, J Colombet, I Degoulet, P, Durieux. P Determinants of success for computerized clinical decision support systems integrated in CPOE systems: a systematic review, AMIA Annu Symp Proc 2006. Beuscart-Zéphir MC, Pelayo S, Bernonville S. "Example of a Human Factors Engineering approach to a medication administration work system: Potential impact on patient safety", Int J Med Inform. (2009) Sep 7. [Epub ahead of print]PMID: 19740700.


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Making a Web Based Ulcer Record Work by Aligning Architecture, Legislation and Users - a Formative Evaluation Study Anne G. EKELANDa,b1, Eva SKIPENESa, Beate NYHEIMa Ellen K. CHRISTIANSENa a Norwegian centre for integrated care and telemedicine b University of Tromsø, Department of clinical medicine Norway

Abstract. The University Hospital of North Norway selected a web-based ulcer record used in Denmark, available from mobile phones. Data was stored in a common database and easily accessible. According to Norwegian legislation, only employees of the organization that owns an IT system can access the system, and use of mobile units requires strong security solutions. The system had to be changed. The paper addresses interactions in order to make the system legal, and assesses regulations that followed. By addressing conflicting scripts and the contingent nature of knowledge, we conducted a formative evaluation aiming at improving the object being studied. Participatory observation in a one year process, minutes from meetings and information from participants, constitute the data material. In the technological domain, one database was replaced by four. In the health care delivery domain, easy access was replaced by a more complicated log on procedure, and in the domain of law and security, a clarification of risk levels was obtained, thereby allowing for access by mobile phones with today’s authentication mechanisms. Flexibility concerning predefined scripts was important in all domains. Changes were made that improved the platform for further development of legitimate communication of patient data via mobile units. The study also shows the value of formative evaluations in innovations. Keywords. Web based ulcer record, access by mobile phone, collaborative health care delivery, law and security, formative evaluation

1. Introduction In 2007, the Department of Dermatology (DoD) at the University Hospital of Norway (UNN), in collaboration with Norwegian Centre for Integrated Care and Telemedicine, (NST) offered net-based guidance to health staff in the municipal health service as a pilot. Nurses from the home-care service improved their competence, felt more confident when providing treatment, and gained greater skills in making their own assessments. The patients experienced great confidence in the treatment [1]. After completion of the pilot, DoD considered different solutions for electronic collaboration with improved usability. They selected a web-based ulcer record system, pleje.net, available from mobile phones and used in Denmark[2]. Data was stored in 1

Corresponding Author: Anne G. Ekeland.

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one database and easily accessible. In Norway, only employees of the organization that owns data can access the system according to law and security regulations, and mobile units require strict privacy protection and access regulations. As a consequence, the system had to be changed. The solution is now about to be brought into regular use, after a long process of adaptation, where Norwegian legal and safety regulations interacted with the technological options and the health care professionals’ requirements for operability and quality. In this paper we will first describe essential features, scripts, of the Danish ulcer record. We will then proceed to describe requirements from the collaborating health care professionals and from legal and security regulations. We proceed to describe some features of the interaction between the different actors and scripts. Finally we present adjustments of the solution, as well as clarifications of legal and security issues and changes in attitudes and routines from the DoD and Home care nurses. We point to further action. The objective is to demonstrate that in efforts to make new innovations work in a public domain such as the Norwegian health services, different actors are involved and a number of challenges are made visible and addressed. In turn, they created a more informed and realistic platform for additional improvements.

2. Approach, Methods and Data Formative evaluations have been recommended for process studies of complex interventions[3]. They may assess the ways new services and technologies influence, and are influenced by small- and large-scale, interrelated actions. Stakeholders, including patients and researchers, are considered partly objective and partly subjective in these processes. Formative evaluations strive to strengthen or improve the object being evaluated and help to model it by examining the delivery of the program or technology, the quality of its implementation, and organizational conditions, personnel, procedures, inputs, and so on. Formative assessments focus on competing discourses, conflicting scripts, and the socially contingent nature of knowledge. The objectives of the approach are to make different scripts and interests transparent and to articulate (results of) negotiations[4-6]. Within this perspective, researchers are working systematically to link experiencebased knowledge with their theoretical base for reflection related to current problems, in this case to make a web based ulcer record work. It involved a scrutiny of how the system, health professionals’ needs and legal and safety regulations aligned. That is how they shaped ‘making the ulcer record work’, and were mutually reshaped in the same processes. The empirical data are thus generated from a participatory study of/with the actors that were involved in the work to make the record work: competent users with experience from the pilot, the law and security team at NST and the system developers in Denmark collaborated in meetings and discussions, with the goal to arrive at solutions that were legally acceptable, practically useful and technologically feasible. All actors were important and depending on each other. The researchers participated in meetings and discussions, planned and ad hoc, that took place in the period from February 2010 – February 2011. We also included minutes from these meetings and information from participants on additional meetings with authorities and other stakeholders.


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The concept of script that is used, denotes programs of action as inscribed in a technical artifact, or in this case, a ulcer record [7]. I.e. the legal script that the electronic ulcer record carries is a program of action responding to certain legal and security regulations.

3. Results and Discussion 3.1. The Scripts within the Technological Domain Pleje.net is a web-based ulcer record system. The system can be accessed from both a computer connected to the Internet and from mobile phones. One benefit of the system is that all the relevant ulcer data are stored in one database. Health professionals who have the responsibility to provide health care to patients with chronic ulcers collaborate via the system. It is available both for nurses and doctors in the local community, and nurses and doctors in specialist health services, as well as patients and their relatives. The system consists of a database, an application to communicate images and text between participants and a tool to analyze ulcers. The service includes advice between a specialist and the home health care nurses. The system is in used in Denmark and between Roskilde Hospital, Copenhagen and the Faroe Islands. The system simplifies the collection of data both at the DoD, at the GP’s office and the patient's home. Everyone who cooperates in ulcer treatment of the patient has access to the web-based ulcer record system. What they needed in order to utilize the system from a computer was a password and username for access. From mobile phones no authentication was required other than registration of the phone number in the user’s profile in the system. All users had to be registered. 3.2. The Scripts within the Health Care Delivery Domain E-mail based communication with attached images made it possible to intervene immediately if the status of the ulcer changed. Individual consultation provided the opportunity for advising participants on the basis of their level of knowledge. For many ulcer patients, rapid intervention resulted in faster improvement of the condition, and it was assumed that the intervention prevented the need for hospital admissions. The nurses also saw areas for improvement. They wanted to store the ulcer images, and not only send them to the DoD. Thus, they could compare images and see how the ulcers changed over time. They also believed that the images made ulcer documentation less person-dependent, and they could use the images for work based training. They also wanted the patient's physician in the municipality to take part in the treatment in the future. Options to take and send images directly to DoD was ideal. The Danish solution was thus strongly needed. They found the log on procedures and functionality of the system very useful, especially from the mobile phone, as they could use the camera and connect directly to the service from the phone. 3.3. The Script within the Legal And Security Domain In Norway, only those who are employees of the organisation that owns an IT-system or service in the health care sector are allowed to access the system or service. This

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means that if a hospital provides a service or a system, only the employees of this hospital can legally access the system. General practitioners or home care nurses will not be given access. In addition, patient data was considered to be of the highest sensitivity level by the Health Directorate, which requires use of the highest level of security for access to health information via mobile units. This implies among others, two factor authentication for access via external networks. Access to these data by mobile phones thus required the strictest security level. This means that the Danish solution did not comply with Norwegian legislation. The Danish solution has a common database for all actors taking part in the treatment and it is accessible via mobile phones. It operates with dispensation from legislation. 3.4. Interaction Between Domains and Resulting Adjustments The Danish solution had to be adapted in order to comply with the Norwegian legal requirements. Each party had to have their own application and database for which they were responsible, thus a communication service had to be developed in order to share information between the different users’ applications and databases. The Norwegian Pleie.net, which today is the result of the process, has a common portal and login page (www.pleie.net) with general information about the service. Participants from the various service locations might also select their own login page directly. There is one login page for staff in specialist health services, one for general practitioners, one for home care nurses and one for patients. All databases are run at the same server park, and there is a common user database and patient database with demographic information about the users and the patients that all the other databases and applications are linked to. For the home care nurses and the DoD, the functionality of the ulcer service will not be changed. What has changed as a result of the legal requirements is the log on procedures, both for the computer and the mobile phones. For the computer a two factor authentication is required; implying username/password and a onetime password sent via SMS from the server to the phone number registered in the user profile. When the service is accessed from the mobile phone, both user name/password and a onetime link is used. This responds to requirements of one additional security level. For the users this means one extra operation in order to use the system. It was more complicated than they expected from the Danish system, but it is still easier than the system used in the pilot, where a digital camera was connected to a computer with internet access. In the security domain, certain clarifications and adjustments were also obtained. This is a service where patients have to give their consent to be included. More insights into challenges around sharing of patient data were obtained. The Health Directorate’s norms for information security were changed in July 2010 with stricter security requirements. The processes contributed to making the new norms topical and made it clear that different authorities interpreted the risk level for access to health information by mobile units somewhat differently, with consequences for security levels. The processes also implied an adjustment of the security level. The security team at NST interacted with the authorities, and a number of meetings were held. Based on an overall assessment they found that patient information accessible in the ulcer record did not require the strongest level of security. It was due to the fact that the collaborating partners only had access to ulcer data and not to the entire patient record. The latter would imply a stricter legislation.


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In these processes, not only the architecture of the technological innovation was adjusted, but the initial scripts presented by all domains were affected. In this case, technologies, security and legal regulations as well as attitudes and routines of professionals both influenced and were influenced, resulting in the production of a new assemblage. It is a problem that in Norway there are no technological solutions to comply with these requirements for access via mobile phones. The need now is to develop secure solutions for mobile communication, and there are proposals to sophisticate the legislation somewhat. 3.5. Considering the Methodology Following a formative approach, the attention to domains that influenced the development, their scripts and interactions, turned out to be valuable for assessing and conceptualizing characteristics and functionality of the resulting service. This approach can be recommended for assessments of services that are under development in real life settings in order to take part in the work to improve them. It is complimentary to effect studies, assessing the effects of real life use.

4. Conclusions and Further Action Different domains claiming attention and carrying their internal logic or script, which they expected the others to comply with, created complexities that had to be addressed in order to make the ulcer record work. The scripts became subject for changes as they were included in negotiations. Formative assessments, addressing different scripts and negotiations helped display these tensions and contribute to solutions. Formative assessments can thus play a vital role in such processes, in that they address transparency and negotiations, and conceptualize change that has not been anticipated. The process has produced a clearer platform for the ongoing development of web based electronic records and electronic communication by mobile phones between levels of care. At present legal and security challenges are being addressed as a consequence of the processes around the ulcer record, and adjustments are expected. Actions are also taken to develop secure solutions for mobile communication. We are also currently carrying out a formative evaluation study on the ways in which knowledge and actions are being integrated via use of the ulcer record. The experiences from use of four databases will also be assessed.

References [1] [2] [3] [4] [5] [6] [7]

The web page URL: https://www.pleje.net/Info_1.asp. Nyheim B, Lotherington AT, Steen A. Nettbasert sårveiledning. Kunnskapsutvikling og bedre mestring av leggsårbehandling i hjemmetjenesten. Nordisk Tidsskrift for helseforskning. 2010;Volume 6(nr 1). Ekeland AG, Bowes A, Flottorp S. Effectiveness of Telemedicine - a Systematic Review of Reviews. International Journal of Medical Informatics. 2010;79(11):736-71. Shriven M. The methodology of evaluation. In: Gredler ME, editor. Program Evaluation. New Jersey: Prentice Hall; 1967. Rip A, Schot J, Misa T. Constructive Technology Assessment: A new paradigm for managing technology in society. 1995. Oxford Dictionaries Online: Oxford University Press; 2010. Oxford Dictionaries Online. Akrich M. The de-scription of technical objects. Shaping technology/building society. 1992:205-24.

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Assessing the Role of a Site Visit in Adopting Activity Driven Methods a

Irmeli LUUKKONENa1, Kaija SARANTOb, Mikko KORPELAa School of Computing, Healthcare Information Systems Research and Development b Department of Health and Social Management at University of Eastern Finland, Kuopio, Finland

Abstract. Healthcare activities rely heavily on socio-technical information systems. Such systems should be developed according to a socio-technical approach. The Activity Driven (AD) approach has been developed to contribute to the early phases of information system development in healthcare. Multiprofessional and multi-disciplinary education in teams has been used to introduce the approach to prospective analysts, including “lay” healthcare professionals. ‘Almost real life cases’ have been emphasized as promoters of learning. This paper reports on a study on site visits as a crucial element for adopting socio-technical methods of analysis in healthcare. The paper presents feedback collected from an intensive course on health information systems development held in Mozambique. The results indicate the high importance of site visits - not only as a starting point of system analysis but also as a crucial promoter to learning socio-technical methods. Based on the results, needs for improvements are identified to the usability of AD tools and to the practical arrangements of site visits. Keywords. health information system development, socio-technical approach, activity analysis, education, site visit

1. Introduction: How to Teach Socio-Technical Analysis in Healthcare? Healthcare is highly information-intensive; i.e., healthcare activities rely heavily on information being transferred between patients and various care providers, collected, stored, processed and used. The purposeful use of information within activities can be seen as a socio-technical information system (IS) [1, 2], within which information technology (IT; manual or computer-based) is used as a means of work by individual actors or as a means of coordination and communication between actors [3]. To develop such socio-technical systems, the focus should on the work activities as the basic unit of analysis, instead of the IT artefacts embedded in the IS [4]. The Activity Driven (AD) approach on Information Systems Development (ISD) has been studied and developed in the University of Eastern Finland (University of Kuopio until 2009) since the early 1990s [5, 3], with the main focus on healthcare activities and healthcare information systems. It is a socio-technical and participatory approach based on Activity Theory [6] with the primary goal of providing methods that emphasize the intertwined development of work and IS. The approach encourages IS developers and “users” (e.g., healthcare providers) to study collaboratively how 1

Corresponding author: Irmeli Luukkonen, University of Eastern Finland, School of Computing, PL 1627, FI-70211 Kuopio, Finland; E-mail: [email protected].

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different kinds of work activities are actually arranged and conducted, including what kind of information and technology do the actors need within those activities. The approach comprises several interrelated parts, including the Activity Analysis and Design (ActAD) framework [5], the Activity Driven Information Systems Development Model (AD ISD) [3], and a methodology for depicting healthcare “landscapes” [7]. Some initial practical methods and tools for the various parts have been produced and tested by the researchers in cooperation with healthcare providers and IS developers in practical cases. Socio-technical analysis of work and information systems is not possible without analysts who have been trained in the respective methods. However, analysts need not be IT experts; experienced “lay” professionals adopt methods that fit their experience and needs [8]. Multi-professional and multi-disciplinary education in teams has proved out particularly useful in health informatics [9, 10]. Pedagogically, ‘almost real life cases’ have been emphasized as promoters of learning [9]. This paper highlights site visits as a crucial element for adopting socio-technical methods of analysis in healthcare. The paper reports on a study in which four multicultural groups of students used a socio-technical approach that was mostly unfamiliar to them, in a previously unfamiliar context, for the rapid analysis and reporting of a healthcare service activity and its socio-technical information system.

2. Materials and Methods The experiment was implemented in Mozambique as a part of an Intensive Course on Health Information Systems Development and Implementation (6 days) organized by two Finnish and three African universities. The participants (15 students and 11 lecturers) came from Finland and five African countries. The education background of the students was: 7 Information Systems, 4 Health Sciences, 4 Computer Science/IT. The purpose of the course was to introduce the participants with a set of sociotechnical theories and methods for IS needs analysis and implementation in a collaborative, hands-on manner. Pedagogically the main idea was to use a real-life case for group work where the theories and methods were applied by the students. The case site was Macia-Bilene Health Centre, a typical rural-area health facility where the information system in use was mostly paper-based. As preliminary materials, the students were provided with papers about the AD approach and landscape modelling, as well as three lectures on AD methods and Mozambican healthcare service system. For the experiment, the students were divided into four groups (3-4 persons from different universities and countries) and assigned to explore one section each in the health centre, report on their findings and give feedback on the visit and the AD methods. The site visit was arranged in the second day of the course and its duration was three hours. The students reported their findings in four steps: (1) initial observations, (2) tentative and (3) final oral, visual and written reports on the case site and the research process, and (4) written feedback assessing 1) the AD approach and tools and 2) the site visit arrangements. In this paper we focus on step 4, the student feedback. The feedback was gathered with a paper-based questionnaire including 12 questions, 5 unstructured and 7 structured questions with a Likert scale from 1=poor to 5=excellent and option to comment each question freely. The questionnaire was given to the students after the site visit and collected in the last day of the course. All 15

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students returned the questionnaire, while the average answering rate to each question was 80%. The basics of Mozambican health care system were known by 2/3 of the students, while 1/3 had no prior knowledge at all about it. The actual site was unfamiliar to all students. The majority (n=9) of the students had never heard of AD methods. Other systems design and analysis methods were unfamiliar to 6 students. Anonymous research data was analyzed with quantitative and qualitative methods.

3. Results The content analysis of the assessments on the AD approach and tools is summarized below in Table 1, and the average scores and assessments of the site visit are summarized in Table 2. When possible, the factors impacting positively are listed after the subheading Pros and those impacting negatively after Cons. Table 1. Summary of the assessments on the AD approach and tools. Issue (n)

Summary of comments

AD approach compared to traditional systems analysis methods (n=10)

Overall: “field work driven”; observations ‘in vivo’, and ‘in situ’; incorporating stakeholders; “easy to understand” because of “top down approach starting with a broad landscape”, and “zooming into more specific from the big picture”; Models are “near the reality” and “representative”, e.g. showing connections between actors, workflows and information entities

Usability of AD tools: 3 tables and 3 diagram templates (aggregated average rate 4.2 out of 5)

Most used tools: tables and the landscape diagram. Pros: helps in identifying stakeholders (~whom to talk) and important issues and connections between (~what to model); graphical rich representation; models for context mapping; Cons: drawing diagrams is time consuming, some diagrams tend to become too big, some symbols were not easy to understand

New useful ideas

To be used in one’s own work and research: field work driven research, planning systems around workflows instead of existing systems, and incorporating the different stakeholders

(n=6)

To improve the adaptability of the approach: a clear manual for using the approach and more exercises in practice are needed Possible future use of AD methods (n=10)

In research (n= 5); In practical ISD work (n=4); Reasons to use: to promote understanding of the problem domain / research area: identification of essential elements and making the connections between different elements

Although the AD diagrams and their elements were considered easy to understand and representative, some difficulties were identified in two aspects of the use of the models (Table 1). First, “identifying corresponding elements in an unfamiliar field” was difficult. Second, some difficulties were met in “drawing diagrams”, particularly in “deciding what items to include in a diagram and what is the proper level of detail”. On the other hand, the tables were used to ‘identify’ things. In order to improve the adaptability of the tools, both formats should be provided hand in hand, preferably complemented by practical examples describing a very similar target domain. Despite the limitations, the students were able to produce a comprehensible holistic view of their target section of the case site, to use AD tools successfully, and to acquire an idea of a socio-technical perspective to information systems. Although the groups’ reports and presentations are not reported here, it should be mentioned that combined together they provide a valuable starting point to any development activities in the Macia-Bilene Health Centre.


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Table 2. Summary of the assessments of the site visit. Issue

Aver. score (1-5)

Summary of comments

(n) Clarity of assignment

2.9

Pros: prior experience, local knowledge, teamwork; Cons: Unclear goal, changes in plans, no preliminary task division, got the assignment and rubric too late

3.3

Assessing the assignment itself: impacting factors are pre-materials and visit timing in relation to lectures. Assessing the information gathered from visit: impacting factors are visit length, time allocation and knowledge of informants, language barrier, available external information sources

Timing of the visit in terms of background information, (n=13)

2.9

Most preferred time would have been later in the course; visit should be allocated to the working days of informants; preliminary visit to the site before actual information gathering

Length of the visit

2.9

Mainly considered too short, due to changed and unsure plans, language barrier (translations take time), and to interview other sections of the health centre as well

3.3

Mainly considered sufficient; factors impacting the quality: language barrier, informants’ professional age and how long has been in the task; need for documents as additional information sources; need for validation of the results with the informants

4.4

Pros: firsthand experience of the context of research, ‘an eye opener’, interaction with stakeholders and (information system) users in their natural context; Cons: research takes time of health providers thus hindering their daily job with patients

(n=14) Amount of information (n=15)

(n=15) Quality of knowledge of the informants (n=15) Importance of the site visit in terms of adopting AD methods (n=12) Overall comments (n=10)

Positive (n=7): informative, and important to see the work in real context, because it gives better research perspective and promotes learning. Negative (n=3): limited beforehand planning, changed plans, and too short time for visit. Future/ improvement (n=5): visit also other levels of health facilities, re-visit the same site, time allocation of the site’s actors, goal clarification and proper planning

There was a clear positive and even enthusiastic attitude towards the site visit (Table 2). The expression ‘eye-opener’ describes very well the site visit’s importance as a means of learning. The suggestions for improvement mainly addressed the planning and arrangements of the visit. It is important that the task, goals and timetables are properly defined in beforehand. It would be ethically justified if site visits could be beneficial to the site’s development, not only for educational purposes.

4. Discussion In this experiment, the site visit was the heart of the intensive course, providing learning experience of 1) adopting a socio-technical, particularly AD approach, and 2) first-hand knowledge about health care (services and facility) in a rural area in Mozambique. Only the summary of the former is presented in this paper. The site visit concretized very clearly the following points. Since the site was not computerized, the socio-technical view of IS was highlighted. The site visit showed the benefits of multi-professional cooperation, both as a group of researchers, and in interacting with domain experts. The tables were found usable tools, complementing

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the existing diagram templates. Due to the unfamiliarity with the site, fuzzy goals and limited opportunities for planning, the need for improvisation during the visit was highlighted. For such challenging situations, guidelines rather than strict formal instructions are needed to help researchers to improvise and think by themselves. Although the scale of the experiment was small (15 students), it provided clear evidence of the importance of site visits – not only as a starting point of system analysis but also as a crucial promoter to learning socio-technical methods for the early phases of ISD. Based on the feedback from the experiment, improvements can be made to the AD tools and educational artefacts (lectures, guidelines, course programs). Acknowledgements.The intensive course was funded by the North-South-South programme of the Centre for International Mobility (CIMO), Finland, through the INDEHELA-Education project no. 1000202 (2009-2011). The research was supported by the SOLEA project funded by the Finnish Agency of Technology and Innovation (grant 40127/08).

References [1]

Berg M. Patient care information systems and health care work: a sociotechnical approach, International Journal of Medical Informatics 1999;55(2): 87-101. [2] Westbrook JI, Braithwaite J, Georgiou A, Ampt A, Creswick N, Coiera E, Iedema R. Multimethod evaluation of information and communication technologies in health in the context of wicked problems and sociotechnical theory. Journal of the American Medical Informatics Association 2007;14(6):746755. [3] Mursu A, Luukkonen I, Toivanen M, Korpela M. Activity theory in information systems research and practice: theoretical underpinnings for an information systems development model. Information Research 2007;12(3): paper311. Available from: http://InformationR.net/ir/123/paper311.html [4] Alter S. 18 Reasons why IT-reliant work systems should replace the IT artifact as the core subject matter of the IS field. Communications of the Association for Information Systems 2003;12(23):365-394. [5] Korpela M, Mursu A, Soriyan A, Eerola A, Häkkinen H, Toivanen M. I.S. research and development by activity analysis and development - dead horse or the next wave? In: Kaplan B, Truex D III, Wastell D, Wood-Harper AT, DeGross JI, editors. Information systems research – relevant theory and informed practice. Boston: Kluwer Academic; 2004. p. 453-471. [6] Hedegaard M, Chaiklin S, Jensen UJ. Activity theory and social practice: an introduction. In: Chaiklin S, Hedegaard M, Jensen UJ, editors. Activity theory and social practice: cultural-historical approaches. Aarhus, Denmark: Aarhus University Press; 1999. p. 12-30. [7] Korpela M, de la Harpe R, Luukkonen I. Depicting the landscape around information flows: methodological propositions. In: SIG GlobDev Workshop Proceedings, Paris, France, 13 December 2008. Association for Information Systems; 2008. [8] Truex D, Alter S, Long C. Systems analysis for everyone else: empowering business professionals through a systems analysis method that fits their needs. In: Alexander T, Turpin M, van Deventer JP, editors. IT to Empower - 18th European Conference on Information Systems, Pretoria, 6-9 June 2010. [9] Saranto K, Korpela M, Kivinen T. Evaluation of the outcomes of a multi-professional education programme in health informatics. In: Patel VL, Rogers R, Haux R, editors. Medinfo 2001. Proceedings of the 10th World Congress on Medical Informatics, London, 2-5 September 2001. Amsterdam: IOS; 2001. p. 1071-1075. [10] Saranto K. Challenges for multidisciplinary education in health informatics. In: Oud N, Sheerin F, Ehnfors M, Sermeus W, editors. Acendio 2007. 6th European Conference of Acendio. Nursing Communication in Multidisciplinary Practice. Amsterdam: Oud Consultancy; 2007. p. 175-176.


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A Multi-method Study of Factors Associated with Hospital Information System Success in South Africa a

Lyn A HANMERa,1, Sedick ISAACS b, J Dewald ROODE c eHealth Research & Innovation Platform, South African Medical Research Council b HealthTechSA, South Africa. c Department of Information Systems, University of Cape Town, South Africa

Abstract. A combination of interpretivist and positivist techniques was used to develop and refine a conceptual model of factors associated with computerised hospital information system (CHIS) success in South Africa. Data from three case studies of CHIS use in level 2 public sector hospitals were combined to develop a conceptual model containing seven factors associated with CHIS success at hospital level. This conceptual model formed the basis of a fourth case study which aimed to confirm and refine the initial conceptual model. In the third phase of the study, a survey of CHIS use was conducted in 30 hospitals across two South African provinces, each using one of three different CHISs. Relationships between hospital-level factors of the conceptual model and user assessment of CHIS success were examined. A revised conceptual model of CHIS use was developed on the basis of the survey results. The use of a multi-method approach made it possible to generalise results from the case studies to multiple CHIS implementations in two provinces. Keywords. Hospital information system success, Information system (IS) success, multi-method approach, conceptual model.

1. Introduction A conceptual model of computerised hospital information system (CHIS) use has been developed, based on relevant theoretical background and the results of case studies and a survey, to support decision-making about CHIS acquisition and implementation in South African level 1 and level 2 hospitals2. This model takes into account the context in which the CHISs are implemented (environments of limited or vulnerable resources such as skilled personnel and infrastructure; and CHISs of limited scope, i.e., admission/discharge/transfer (ADT) and billing). In this paper, the combined use of interpretivist and positivist approaches to test and refine the conceptual model is described.

1

Corresponding Author. Lyn A Hanmer, eHealth Research & Innovation Platform, South African Medical Research Council, PO Box 19070, Tygerberg, South Africa, 7505. E-mail: [email protected]. A level 1 hospital is a facility at which a range of outpatient and inpatient services is offered, mostly within the scope of general medical practitioners. A level 2 hospital is a facility that provides care requiring the intervention of specialists as well as general medical practitioner services. 2

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2. Methods Two contrasting approaches (positivist and interpretivist) have typically been used in the analysis of the effects of the implementation of information systems in organisations. Much of the literature on information system (IS) success seems to reflect the positivist approach, in which attempts are made to demonstrate the validity of theories of IS success, or the need to modify such theories, based on empirical studies of comparatively large numbers of cases (for example, studies reviewed in [1]). The theoretical work relating to health information system (HIS) success and failure identified to date has generally been based on an interpretivist approach, in which the aim is to deepen understanding of the social and other factors which contribute to the experience of implementing HISs in different environments. In this study, the aim is to develop an understanding of the relationships between an organisation (a level 1 or level 2 hospital), the people in that organisation, and the information system (the CHIS). The aim of some HIS studies has been to develop or extend theories which provide a framework in which to interpret results (for example, [2] and [3]). Other recent studies of factors influencing the success of HISs have taken the form of Delphi studies [4-5]. The interpretivist approach is appropriate to investigating CHIS success or failure because the highly complex nature of the environment being studied makes it difficult to predict outcomes of activities. This study used the opportunity to combine positivist and interpretivist approaches by using in-depth case studies to identify and examine the factors which affect the success or failure of CHISs in the environment of level 1 and level 2 public sector hospitals (a largely interpretivist approach), in combination with a survey of a large number of these organisations in an attempt to explain similarities and differences in the experiences of CHIS implementation across the organisations (a largely positivist approach). The combination of interpretivist and positivist approaches has been advocated by authors such as [6-8]; so that the strengths of each approach can be combined to enrich the analysis of a particular domain. Westbrook et al. [9] are following a multi-method approach in a study of the implementation of a commercial CPOE system in an Australian hospital, describing the analysis of the effects of this implementation as a ‘wicked’ problem, requiring multiple methods of investigation to gain the best possible understanding of the process. The broad framework for the methodological approach used in this study is a reflection of the complexity of the issues being addressed: the socio-technical approach to HIS studies (as in [9-11]) is based on the premise that the implementation of information systems, such as CHISs, results in a complex interaction between the organisation in which the CHIS is implemented and the CHIS itself; i.e., the social and technical aspects of the implementation. This approach is consistent with the intention in this study to examine the implementation of CHISs in the specific context of level 1 and level 2 public sector hospitals in a developing country, based on the premise that access to the resources required for CHIS implementation in these environments is limited and vulnerable. The socio-technical approach provides a mechanism for the incorporation of the context issues in the study design and analysis. The CHISs in use in the study hospitals support mainly patient administrative functions (patient registration, ADT and billing). Most published HIS studies identified in this project refer to clinical information systems, such as computerised physician order entry (CPOE) systems. The lack of published studies of administrative CHISs could imply that the technical and organisational issues related to a CHIS


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implementation like those at the study hospitals are relatively trivial. However, reports of studies in two South African provinces highlight the challenges experienced with the implementation of similar CHISs in those environments [12-13].

3. Results and Discussion The interpretivist component of the current project provided the opportunity to examine the use of a specific CHIS through case studies in three hospitals (the pilot case studies) in order to improve understanding of factors which influence the potential for CHIS success or failure. Once factors had been identified, they were incorporated in the initial conceptual model of CHIS use. This initial conceptual model then provided the framework for the subsequent (fourth) case study. All case study hospitals used the same CHIS. Based on the findings from the fourth case study, and additional insights from the literature and from interviews with HIS experts, the conceptual model was revised to develop an ‘extended conceptual model of CHIS use’, following the structured case study approach described by Plummer [14]. The aim of the survey component of the study was to validate the extended conceptual model by conducting a survey of CHIS use in level 1 and level 2 hospitals in two South African provinces, each using one of three different CHISs. Survey respondents were asked questions designed to confirm (or not) the factors affecting CHIS success, and the relationships between them. This positivist approach was supplemented by a small interpretivist component, since respondents were also asked a few open-ended questions designed to obtain information on additional factors which could affect CHIS success in the study environments. The final version of the conceptual model of CHIS use for this project, the revised conceptual model of CHIS use, was developed based on the results of the survey. The following seven hospital-level factors were identified as being associated with CHIS success: Knowledge and understanding of CHIS; Appropriateness of CHIS design; CHIS performance; Resource availability and allocation; Perception of usefulness; Management commitment to success; and Effective use of CHIS and/or outputs. The survey results and the revised conceptual model are described in [15]. The case studies and discussions with expert informants yielded mainly qualitative data about opinions of the CHISs in use in the study environments. The survey was designed to collect quantitative data, based as far as possible on a 5-point scale to order opinions, and thus facilitate statistical analysis. The design of the questionnaires also made provision for recording qualitative data, both through open-ended questions and by making provision for respondents to record comments. 3.1. Case Studies The use of case studies in examining HIS implementations is well established (for example, as reported in [10]; [13]; [16-17]). In practice, the pilot case studies and the fourth case study resulted in the identification of factors associated with (effective) CHIS use, rather than the more general concept of CHIS success. In keeping with the practice for qualitative research, cases were chosen in order to ensure representativeness of a particular class of cases , rather than on the basis of statistical sampling [18]. The description of the relationship between the identified factors was formalised in the development and refinement of an initial conceptual model of CHIS

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use. The fourth case study differed from the pilot case studies in that it was aimed at investigating the applicability of the initial conceptual model of CHIS use while also clarifying information gained in the pilot case studies, resulting in the extended conceptual model. Yin [19] has made recommendations for enhancing the quality of case studies in health services research. Among the issues identified as being associated with high quality case studies is that they ‘should contain some operational framework’ even if they are exploratory [19, p1215]. For the pilot case studies in this CHIS success study, the framework was provided by the interview framework which was used in all the case studies, and the key IS success models identified by that stage ([1], [20-21]). The initial conceptual model of CHIS use and the same interview framework (as used in the pilot case studies) provided the operational framework for the fourth case study. 3.2. Survey The survey provided data on the CHIS implementations in 30 hospitals. There was no evidence from the available literature that other surveys of similar scope had been conducted either in South Africa or elsewhere, although there have been reports on surveys of the status of clinical information technology in hospitals in Canada and the US [22-23]. While the primary aim of the survey was not to obtain information about the CHIS itself in each hospital, questions were included about the functioning of the CHIS, relating to the factor ‘CHIS performance’ in the conceptual model of CHIS use. The survey was also designed to confirm whether the factors included in the conceptual model of CHIS use do apply in a wider set of hospitals, and to find out whether the relationships described in the conceptual model could be identified from the survey data. Several hypotheses related to the factors in the conceptual model were defined for investigation through the survey. The analysis of the survey data showed that the factors of the conceptual model are associated with CHIS success, and confirmed the relationships between factors of the model in varying degrees.

4. Conclusion The results of the study of factors associated with CHIS success in South African level 1 and level 2 hospitals have been reported in more detail elsewhere [15], [24]. The use of a multi-method approach made it possible to generalise results obtained from the case studies in four level 2 hospitals in the same province using the same CHIS to level 1 and level 2 hospitals in two provinces, using three CHISs. This approach has the potential to support further generalisation of the results of this study.

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Assessing Biocomputational Modelling in Transforming Clinical Guidelines for Osteoporosis Management a

Rainer THIELa, 1 , Marco VICECONTI b, Karl STROETMANN a empirica Communication and Technology Research, Bonn, Germany b Istituto Ortopedico Rizzoli, Bologna, Italy

Abstract. Biocomputational modelling as developed by the European Virtual Physiological Human (VPH) Initiative is the area of ICT most likely to revolutionise in the longer term the practice of medicine. Using the example of osteoporosis management, a socio-economic assessment framework is presented that captures how the transformation of clinical guidelines through VPH models can be evaluated. Applied to the Osteoporotic Virtual Physiological Human Project, a consequent benefit-cost analysis delivers promising results, both methodologically and substantially. Keywords. Biocomputational modelling, VPH, clinical workflow, evaluation, impact assessment, osteoporosis

1. Introduction There is a growing interest for computational technologies in the area of medicine. Whereas Information and Communication Technologies (ICT) already play a fundamental role in medical informatics and practice, bioinformatics, and telehealth, the use of ICT as support to prevention, screening, diagnosis, treatment, and monitoring remains limited. Yet it is by now evident that this is the area of medical technology most likely to revolutionise the practice of medicine in the longer term. Computer models that simulate physiopathological processes can be employed to take clinical decisions on the basis of “what-if” analyses (predictive medicine), to tailor the delivery of care to the specific needs of individual patients (personalised medicine), and to explore pathological scenarios for systemic interactions between multiple physiological processes (integrative medicine). In Europe, the global framework of methods and technologies that will permit the delivery of a predictive, personalised, and integrative medicine has been developed under the name of Virtual Physiological Human (VPH). This initiative has been marked by a demand for measurable evidence that such complex technology is actually worth the cost. The aim of this paper is (1) to introduce a new evaluation framework as developed and applied to predictive computational models for osteoporosis

1

Corresponding author: Rainer Thiel, empirica Communication and Technology Research GmbH, Oxfordstr. 2, 53111 Bonn, Germany; E-mail: [email protected].

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management during the Osteoporotic Virtual Physiological Human Project (VPHOP)2, and (2) to present preliminary results of a cost-benefit assessment (CBA).

2. Method With respect to the conventional definition of Health Technology Assessment (HTA) [1], its application to VPH technology needs to take into account two additional elements: a) the technology involves predictive computer models, which have the potential to revolutionise currently applied clinical guidelines; b) the purpose of the assessment is extended to RTD policymaking, i.e. decisions made during the development of the technology itself. The methodological challenge in comparison to commonly applied health technology assessments is based on two reasons: (1) inherent is the need to assess the technology ex-ante, in very early stages of development [2]; (2) the impact on clinical decision making and practice may be far reaching. For the purposes of assessing biocomputational technologies, standard HTA is not sufficient. To expand on this dimension [3], we suggest considering the complete life cycle of a new or modified technology, ranging across development stages. Therefore, we introduced a particular (VPH) technology readiness level [4]. For the purpose of the VPHOP technology assessment, a new concept assigning fine grained technology readiness levels was introduced across the broader development phases of basic research, experimental validation, pre-clinical validation, clinical validation, and operational usage, providing a overview of the technologies’ maturity at a given time.

3. Result As this paper is of methodological nature, this result section foremost presents the assessment framework. 3.1. Fundamental Attributes of Predictive Computer Technologies We propose that every health technology that includes a predictive model should be assessed with respect to these fundamental attributes: • Capability: substantiation that a computerized model’s reliably represents a conceptual model within specified limits of (inherent) accuracy. Capability assessment requires tightly controlled conditions like laboratory environments. • Clinical accuracy: model accuracy needs to be assessed not only under controlled conditions, but also under operational conditions. Predictive accuracy can thus be truly assessed only in the clinical environment. • Efficacy: efficacy indicates the capacity for beneficial change (or therapeutic effect) of a given intervention in an optimal context. Here the assessment focuses on how medically beneficial is the new clinical pathway for the patient that incorporates the predictive technology (incl. risk). • Impact: for adoption, a health technology should not only be beneficial for the patient, but also present an impact upon the other stakeholders involved 2

EU FP7 #223865, www.vphop.eu.

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(medical professionals, healthcare providers, healthcare payers, policy makers, society at large) that they consider favourable or at least acceptable. 3.2. Central Assessment Framework: VPH Measurement Variables and Indicators All available indicators for each of the four fundamental dimensions above are exhibited in Table 1. The table depicts which indicators can and should be used to assess the four fundamental variables of predictive technology during the four stages of its lifecycle. This matrix serves as the central methodological framework that guided the VPHOP technology assessment. Table 1. Grid of indicators VPH technology assessment Impact Variable

Development Phase

Basic research Experimental verification & validation (inherent accuracy) Pre-clinical verification & validation

Capability

Accuracy

Efficacy

Impact Measure

Verification, validation

Prediction uncertainty

Estimated accuracyefficacy function

Projected cost/time based on simulation

RMS, ROC, AUC

FP/FN accuracyefficacy function

Projected cost/time/risk based on actual use on prototype

Comparative outcome, QALY

Actual cost/time/risk measured Indicators of impact upon patient, provider, payer, etc.

Clinical validation & assessment (clinical accuracy) Operational

Ex-post assessment

3.3. Overall Outcome Measures of Socio-Economic Impact Assessment Before the more concrete developments and application approaches about how to measure the technologies’ capability, accuracy, efficacy and impact were performed, it is worthwhile reconnecting the entire assessment exercise to the ultimate objective of the socio-economic technology assessment task. We can distinguish between two aggregate, overall socio-economic impact outcomes that further guided the further development of measurement variables, indicators and tools: once clinically applied – the ultimate reference point –, the new technologies will affect a) care provider, i.e. the health system, and b) the patient’s health (see Figure 1).

Figure 1. HTA based decision-making and influence of technology

Clinical impact, as the central avenue to approach the impact assessment, is constituted by two elements: (1) clinical management – i.e. the care pathway of the standard of care of the osteoporotic patient and, consequently, its change management; (2) health impact – the disease states and health of the patient (i.e. the expected consequences of fractures avoided) – scaled up to a macro/country level.


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3.4. Efficacy and Definition of Clinical Pathways While accuracy is a concept that can be associated to every technological component, the concept of efficacy can only be defined with respect to a specific clinical pathway, and its associated clinical scope. Once the new multiscale predictive technology has been validated in a clinical context, a new modified pathway will evolve. The comparison between the old and the new pathways represents the initial tool for estimating the expected overall impact of the new technology for the clinical guidelines and thus clinical management. A standard of care pathway (SoC) served as the central comparator of current osteoporosis management with the future VPHOP clinical pathways. For the SoC, all assumptions are based on approximations of current literature and epidemiological data, and constitute a reduced version of the European Guidance algorithm [5]. For the VPHOP clinical pathways, a multi-layered pathways consisting of three levels with the respective technology components assigned was hypothesised for the deployment of the VPHOP technology. 3.5. Cost-Benefit Analysis VPHOP Clinical Pathway Focussing on the outcome variables subsumed under health impact, a first preliminary and integrative cost benefit analyses was performed. The projected costs of the VPHOP clinical pathways (as based on an originally developed costing model robustly estimating costs of each deployable component) were set in relation to the expected benefits the increased inherent accuracy rates in comparison to the costs and predictive accuracy of the standard of care diagnostic pathway. At this early stage of the project, only the technical capability assessment served as the ground work for the consequent impact assessment. In sum, the final output of the dimension impact assessment forms the cost-benefit analysis. The patient flow and output of the VPHOP and the SoC pathway with a hypothetical patient cohort of 5000 patients was comparatively simulated. Health impact was defined to encompass as outcome clinical management and health, formalised as fractures avoided. Each hip fracture amounts to life-time costs of €60.000 when diagnosed and treated in the SoC pathway (including costs of diagnosis, treatment, hospital stays, nursery facility costs, etc.) [6;7]. One of the causes for theses enormous expenses is the low accuracy of the risk assessment of the current standard of care pathway. To reach an estimate of the health impact VPHOP technologies have on avoiding fractures and the derived amount of costs saved, the increased accuracy was multiplied with the costs of fractures. Further, in a conservative estimate, the average ten-year probability to suffer from a hip fracture is around 25%. We assume, furthermore, that the treatment efficacy is 50% in both pathways. For assessing the cost-benefit ratio of the VPHOP technologies, the benefits can be set equal as with the cost savings that derive from the additional prevention of fractures the VPHOP prognosis pathway has achieved in comparison to the standard of care. The costs, in sum, can be defined as the extra costs of the VPHOP prognosis pathway as compared to the costs of the SoC. For the VPHOP clinical pathway, in a simplified manner, with B = Cost savings (fractures avoided) and C = Extra costs, the benefit-cost ratio (BCR) can be calculated as:

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Alternating between conservative and relaxed assumption and data input, the calculated ratio indicated in nearly all instances a positive return. For the simulated patient cohort, and for the VPH technologies, to break even with the costs of the SoC, the number of additional fractures needed to prevent is within realistic reach, once the technology would be deployed in a clinical setting. The CBA exhibited clearly that the extra costs needed to implement the VPHOP pathways are by far offset through the large amount of costs savings that the improved fracture risk prognosis of VPHOP presents.

4. Discussion Through newly developed clinical decision support and pathways, the transformation of biocomputational modelling and VPH technologies into future patient workflows are meant to ameliorate or even replace current clinical management processes, here of osteoporotic patients. The new (VPH) technology assessment framework developed forces many of the implicit assumptions behind such developments to lay bare. Since the assessment perspective is to develop concrete clinical scenarios, the further work, e.g. on VPHOP technologies, will clearly benefit from a much more focused alignment towards producing results that matter within the context of deployable, routine clinical applications. The cost-benefit analysis already at this early stage allowed highlighting some of the fundamental and, most importantly, clinical challenges VPHOP will have to overcome, thereby directing its further research into the clear direction of early clinical triability and later routine clinical deployment.

References [1] [2]

[3] [4] [5]

[6] [7]

Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ, Stoddart GL, Methods for the Economic Evaluation of Health Care Programmes, Oxford University Press (2005). Hartz S, John J, Contribution of Economic Evaluation to Decision Making in Early Phases of Product Development: A Methodological and Empirical Review, International Journal of Technology Assessment in Health Care 24(4) (2008), 465-472. Eisenberg JM, Ten lessons for evidence-based technology assessment, JAMA, 17 (1999), 1865-9. US Department of Defense, Technology Readiness Levels in the European Space Agency (ESA) and the US Department of Defense, Defense Acquisition Guidebook, http://akss.dau.mil/DAG, 2006. Kanis J, Burlet N, Cooper C, Delmas P, Reginster J-Y, Borgstrom F, Rizzoli R, European guidance for the diagnosis and management of osteoporosis in postmenopausal women, Osteoporosis Int 12 (2008), 399-428. Braithwaite R., Nananda F. Col S, Wong JB, Estimating Hip Fracture Morbidity, Mortality, and Costs, J Am Geriatr Soc 51(3) (2003), 364-70. Ström O, Borgstrom F, Zethraeus N, Johnell O, Lidgren L, Ponzer S, Svensson O, Abdon P, Ornstein E, Ceder L, Thorngren KG, Sernbo I, Jonsson B, Long-term cost and effect on quality of life of osteoporosis-related fractures in Sweden, Acta Orthop 79(2) (2008), 269-80.


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Technical Data Evaluation of a Palliative Care Web-Based Documentation System a

Tobias HARTZa,1, René BRÜNTRUPa, Frank ÜCKERTa Institute of Medical Informatics,University Hospital Münster, Germany

Abstract. A technical analysis of the web-based patient documentation system, eKernPäP, was conducted. The system is used by interdisciplinary pediatric palliative care teams in Germany to document outpatient care. The data of the system and the data of an external web analytic system have been evaluated. The results gave an overview how the system is used and what information is generated. A detailed analysis of singular forms showed that not all forms were filled in completely. With the help of the external web analytic system the navigation behavior of the users could be retraced. The users followed the given navigation from top to bottom. An existing exception in this pattern turned out to be misplacement and will be corrected in the next version. The technical analysis proved to be a good tool for improving a web-based documentation system. Keywords. Palliative Care, PCT, EHR.

1. Introduction 1.1. Palliative Care Ambulatory palliative care is about providing comprehensive services to terminally ill patients in their personal surroundings. The goal is to relieve the patients of their symptoms and to improve their quality of life [1]. The physical, mental, social and spiritual needs are the main focus. Palliative care requires multi-professional cooperation. Doctors, nurses, psychologists, social workers and institutions such as hospitals and palliative care teams (PCTs) are involved in the care process. To provide well-balanced care, communication among those health care providers is essential. Therefore access to current patient documentation at all times and from all places is needed [2]. 1.2. The Web-Based Documentation System Based on a prior documentation system which was developed in 2002 using Microsoft Access, a new web-based solution, called eKernPäP, has been implemented [3]. eKernPäP stores the medical data in forms, that each covers one topic. If data is entered or changed, a new version of the entire form is saved. eKernPäP contains a wide range of forms covering diagnosis and therapy related information about the patients. Through the use of the internet and systems equipped with UMTS, the health providers always 1

Corresponding Author: Tobias Hartz, Department of Medical Informatics, University Hospital Münster, Domagkstraße 11, 48149 Münster, Germany; E-mail:[email protected].

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have immediate access to the latest data, regardless of their location. Since the documentation system is running on any modern web browser it is independent of specific hard- or software. To satisfy the high demand for data protection and security the data protection concept of the German TMF e.V. (Technology, Methods, and Infrastructure for Networked Medical Research) [4] has been implemented, in particular by storing identifying data and medical data separately on two different MySQL-database servers. The two data classes are merged locally in the user’s web browser and only when the user has successfully signed on and is involved in the treatment of the patient. The implementation is based on PHP and JavaScript. 1.3. Technical Analysis This paper focuses on an evaluation of the usage of the system. Three PCTs have been using the system for nearly a year by now. Thus, enough data is available to do a comprehensive technical analysis. The purpose of this analysis is to understand how the system is used in order to draw conclusions on how to further improve it.

2. Methods For the technical analysis of the system three different data sources are available. First of all there is the content data, which is entered by the users of the system. In most cases this data is stored in the databases with some additional metadata as the current point of time and the ID of the editing user. The second data source is the integrated audit system that records user actions such as logging in and out and modifying data. As a third source of data the open source web analytics system Piwik [5] was used to collect information about the users’ system configurations, types of internet connection and browsing behaviors. Piwik has the advantage of respecting the users’ privacy to a greater degree by avoiding links between the collected data and the eKernPäP user database. Implementation of Piwik into eKernPäP proved to be a simple method of recording each user’s navigation. The collected patient data of the above mentioned PCTs was retrieved from the servers and anonymized. Using SQL the relevant data was joined and further processed using a spreadsheet application. Analysis focused on single interesting aspects that were found in this process.

3. Results 3.1. Internal Change Log The three PCTs whose data was analyzed have been using the system for almost one year. Up to this point, 61 user accounts were issued and 215 patient records have been created. Not all 33 forms in eKernPäP are used when documenting one patient’s treatment. In general the users choose which forms they need for their documentation. Looking at the data of the internal logging system, a summarization of the usage for those teams can be given. For example, 1,607 address entries were created and in 1,794 cases existing addresses have been edited. The form with pain related questions was

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edited 997 times and 539 distinct data entries can be found in the database. There have been 4,467 memos saved. In contrast, only 25 psychosocial findings have been documented. This first overview reveals the significance of some forms and might suggest the irrelevance of others for the documentation process. Combining this analysis of the internal system data with the extensive statistics from the web analytics system offers further insights. The visited pages and the data of the internal logging system can be matched (tab. 1). The results show that some pages such as medication or comments are not only edited very often, but also viewed even more frequently. Often the ratio between editing and viewing is similar, but there are some forms which show a considerable difference. The high number of views on medication and memo indicates that this information is important not only for the person documenting but also for others using the system. In contrast to that the pain location form has almost as many views as entries. Therefore it seems to be information which is seldom accessed just for the purpose of reading. In most case, when a user views this information, an existing entry is edited or generated. Table 1. Edits and Views of the user.

3.2. Data Completeness of Specific Forms A form consisting of a set of items is always saved as an entire block. Similar to the fact that the users can choose which form they want to use it has been decided in the development not to demand mandatory fields within a form (with the exception of user and patient registration). The reason was that each team keeps its patient records in different detail. Forcing them into a fixed scheme has not been an option within the first step. Even though it would make sense when the data shall be used for comparison and quality management, a more liberal approach gave more flexibility of usage. However, the results of the first internal log analysis, which looks at the forms as an entire block, might be misleading. A high number of edited and viewed forms do not mean that all items within a form have been used. It is important to analyze the data quality of individual forms in more detail. The pain form, for example, had 539 distinct data entries. Some pain forms have been filled in completely but many others only have been partially completed as can be seen in table 2. This observation is important and leads to different consequences. If some aspects of a form do not apply to all patients, the users may omit these parts and this possibility needs to be obvious for the user. But if there are only a few entries due

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because most users consider this information unnecessary, it should either be removed or the importance of these aspects and the need to document them should be clarified. Table 2. Number of data entries of selected items from the pain form. Selection of Items from the Pain Form

Input Type

Strongest pain (24h) What does relieve your pain? Pain relief through treatment Sense of pain: dull, onerous Negative effect of pain concerning vitality

Radio button Text Radio button Radio button Radio button

Data Entries 456 180 96 32 6

3.3. How User Navigate through the System To determine how the users navigate in eKernPäP the probabilities with which the users switch from one form to another were calculated. The results showed (tab. 3) that the users strongly tend to follow the menu structure when documenting a patient contact. The graph shows the likeliness for users navigating from the form on the left to the form on the top. Percentages above 25 % are highlighted. There are only two menu items that are usually not used in the order in which they are positioned in eKernPäP, but exactly the other way around. Thus the position of these two items (“Zeiterfassung” (time registration) and “Gesprächsnotiz” (memo)) will be interchanged in the next version. For the other forms this analysis can be interpreted in two ways: (1) the order of the forms as it is set up in the menu meets the needs of the health providers and respectively the user; (2) the users use the menu structure as a guide for their documentation work, not necessarily indicating that the menu structure is ‘correct’. Misplaced items may negatively influence the users’ documentation. Table 3. Transitions from one form to another 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Origin / Target Select Patient Contact Assessment Anamnese Base data Physical Examination Performance Scale Symptoms overview Symptom controll List of Symptoms Pain Form Pain Localisation Paediatric Pain Profile Time registration Memo

1 5% 8% 8% 4% 1% 0% 3% 1% 1% 2% 1% 17% 20%

2 26% 3% 2% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1%

3 4 5 6 7 8 9 10 11 12 13 14 1% 12% 1% 0% 1% 1% 0% 0% 0% 0% 1% 6% 14% 47% 4% 1% 4% 5% 1% 1% 0% 0% 0% 1% 49% 4% 0% 1% 0% 1% 2% 2% 42% 4% 12% 5% 0% 0% 0% 1% 1% 0% 5% 19% 22% 30% 1% 1% 0% 0% 1% 1% 2% 2% 78% 8% 1% 0% 0% 0% 0% 1% 0% 1% 83% 6% 1% 1% 0% 0% 0% 2% 1% 0% 1% 56% 4% 1% 0% 1% 1% 1% 0% 0% 1% 11% 51% 3% 1% 1% 0% 0% 1% 0% 0% 1% 1% 2% 74% 1% 0% 0% 0% 0% 0% 0% 2% 1% 4% 10% 1% 0% 2% 1% 2% 2% 0% 3% 4% 1% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 25% 0% 0% 0% 0% 0% 0% 0% 33%

Interesting to point out is the fact, that going from top to bottom some forms are omitted. For example after documenting physical examination most users go directly to symptom control leaving out performance scale and symptom overview. Another omission was found for those entries concerning psychosocial documentation. It was already said that only 25 entries of psychosocial findings were saved. Discussions with users have indeed shown that certain forms within this block were not known and are misplaced in the current version. The block psychosocial documentation was often skipped thinking that this only concerned their psychosocial colleges.

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4. Discussion The technical analysis helps to get an overview of how a system is used and what information is generated. Thanks to the web-based architecture this evaluation can be done at any time. The results are not only important for the developers who want to improve the system, but also for users benefiting from system improvements when using the tool and for those users who want to use the data for medical research. Especially in pediatric palliative care, where the number of patients a PCT cares for is small, a central system used by several teams has the chance to generate a useful pool of valid, comparable data. The content of the forms focuses on two aspect: daily work as well as research. Therefore the forms contain items that do not directly affect the care of a patient and are not needed for the daily routine, but are important for research issues. It is important that the users know why they are asked to document this information nevertheless. A technical analysis made it obvious that some forms within the system are not filled in completely. Either the users should be trained to provide this information in the future or the irrelevant items should be removed from the forms. For further improvement the technical analysis can help indicate what features are needed more urgently. The impact of improvement of features which are used very often is much higher than changes to features with minor relevance. Since in most projects the resources are limited, technical analysis can help to select the next mandatory tasks. In addition, the technical analysis can point to shortcomings, for example the analysis of how users follow the navigation helped improve the order of the menu items.

5. Conclusion The technical analysis has been proven to be a useful tool to improve a system and to enhance data quality. Results are easily generated and influence the development and the usage of the system. It would be useful to further analyze the possibilities of this method and to integrate an automatic technical analysis tool into the documentation system, which could provide statistical feedback at any time.

References [1] [2] [3] [4] [5]

Henkel HW, Gerschlauer C, Jan A. Palliativversorgung von Kindern in Deutschland. Monatszeitschrift für Kinderheilkunde; 2005; 153(6). Knapp C. e-Health in Pediatric Palliative Care. American Journal of Hospice and Palliative Medicine; 2010 February 01; 27(1):66-73. Hartz T, Verst H, Ueckert F. Kern-PaeP—a web-based pediatric palliative documentation system for home care. Stud Health Techno Inform.; 2009; 150:337-341. Reng C. Generische Lösungen der TMF zum Datenschutz für die Forschungsnetze in der Medizin. Berlin. Med. Wiss. Verl.-Ges; 2006. Piwik: Open Source Web Analytics [cited 2011 Jan 11]. Available from: URL:http://piwik.org/.


Imaging and Biosignals



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Extracting Gait Parameters from Raw Electronic Walkway Data André DIASa,b,c,1, Lukas GORZELNIAKb, Angela DÖRINGc, Gunnar HARTVIGSENa,d , Alexander HORSCHb,d,e a Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway b Institut für Medizinische Statistik und Epidemiologie, Technische Universität München, Germany c Institute of Epidemiology, Helmholtz Zentrum München, Germany d Computer Science Department, University of Tromsø, Norway e Department of Clinical Medicine, University of Tromsø, Norway

Abstract Spatiotemporal gait parameters are very important for the detection of gait impairments and associated conditions. Current methods to measure such parameters, e.g. electronic walkways or force plates, are costly and can only be used in a laboratory. The new generation of raw data accelerometers might be a cheap and flexible alternative. We conducted a small feasibility study with 50 subjects from the KORA-Age project exploring the output of GAITRite and Actigraph GT3X. We open-sourced a package to extract and process raw data from GAITRite. The most promising location for the accelerometer seems to be at the ankle. The use of accelerometers showed to be simple and reliable, indicating that they can be used in daily life to extract gait parameters. Keywords. Gait parameters, Actigraph GT3X, GAITRite, open source

1. Introduction Objective measurements of spatiotemporal gait parameters are essential in a clinical or research environment to detect possible gait impairments or to monitor the effects of recovery therapy. There are several methods for the assessment of gait parameters, varying in validity, reliability and usability, such as force plates, pressure activated sensors and motion analyses from video. Most of them are either costly, time or labour intensive, or can only be applied to few gait cycles. Because of these limitations they are only feasible in a laboratory, raising questions as to whether such data represents the gait performance in daily life [1]. For these reasons, a portable and easy to use method is of great value, as it allows measurements for many gait cycles in daily living. In the last few years, accelerometer-based gait analysis systems have been proposed for this task [2,3]. Present technology allows us to record data in very high frequencies for long periods, opening a promising window for portable gait assessment. 1

Corresponding author: André Dias Department of Computer Science, University of Tromsø 9037 Tromsø, Norway. E-mail: [email protected]

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The elderly population is an essential target group. However, so far little work has been done on using accelerometers for gait assessment within this group [4].

2. Materials and Methods 2.1. Sensors For assessment of the gait parameters we used the GAITRite portable electronic walkway (CIR Systems Inc., Havertown, USA), 6 meters long, measurement length 4.88 meters, and 0.89 meters wide, with a sampling rate of 80 Hz. For motion sensing we used sets of 4 triaxial accelerometers of type Actigraph GT3X (Actigraph LLC, Ford Walton Beach, Florida, USA) with 16MB and capable of recording data at 30 MHz. This accelerometer has been validated in several published studies for medical and epidemiological research. 2.2. Subjects We asked a subset of 50 subjects recruited for the KORA-Age project [5] to wear accelerometers while walking in the GAITRite, which was part of the KORA-Age study protocol, with no changes due to the accelerometers. The subset was selected by asking every subject who took part in the KORA-Age project on randomly selected days. Therefore the same inclusion and exclusion criteria of KORA-Age study applied [5]. The accelerometers were prepared and attached by the biosensor team, subjects were given a brief explanation of the goal and asked to perform four walks (normal walk, slow walk, fast walk, and walk performing a mental task). The acceptance rate was 100%, i.e. all subjects agreed to wear the sensors. 2.3. Data acquisition and Handling of GT3X Each subject wore one tri-axial sensor at each of the extremities: left and right wrist, left and right ankle. The sensors were configured for raw data mode recording. Care was taken to customise Velcro straps in order to ensure reliable sensor attachment and correct orientation. Time constraints of the KORA-Age protocol did not allow download and reconfiguration of the sensors for each subject. So, each sensor recorded the entire day, without breaks, merging data from several subjects in one session/file. We used one computer for operating Gaitrite and GT3X, to ensure synchronisation. 2.4. Data Processing 2.4.1. Data from GAITRite The main software application provided by the manufacturer is proprietary. Therefore it was not possible to extend the methods and gait parameters extracted from the data. However, the vendor provides a separate program named Gaitraw, which outputs the raw data in a known format, documented by CIR Systems and made available to us. We developed an open-source Java package to process the data provided by Gaitraw. First, as shown in Table 1, seven gait parameters equivalent to those


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computed by the GAITRite were implemented, in order to create a testing ground. Later we extended this set of features by one parameter that we found relevant for medical and epidemiological studies, as well as by a batch processing mode. Table 1 summarises all extracted parameters. The software package, named GaitParser, is available for download and contribution at: http://code.google.com/p/gait-raw-parser/. Table 1. Implemented parameters, at time of writing, in GaitParser. Per walk

Ambulation time (AT)

Duration of the walk

Per footprint

Step length (SL)

Length of a step

Side swing (SS)

Distance of a step from straight walking line (only GaitParser)

Gait cycle time (GT)

Duration of a gait cycle

Single support (SP)

Duration of period only 1 foot on the ground

Per gait cycle

Step time (ST)

Duration of a step

Swing (SW)

Complementary to single support

Double support (DS)

Time when both feet are on the ground

2.4.2. Data from Actigraph GT3X Because the output of each GT3X sensor was one single CSV file including the measurements for all subjects of an entire day, we developed a script that takes the timestamps for each walk as input and splits the file into individual walks. It can process an arbitrary number of files in batch mode. Having the data for each walk, further processing was performed with a set of R statistics scripts. We performed data quality assurance tasks, visualisation and statistical analysis of the data. This paper does not focus on modeling the gait parameters from the accelerometer data, as this is ongoing work.

3. Results 3.1. Comparison to GAITRite In order to test the GaitParser software we randomly selected the data of 7 subjects from the study and did a direct comparison of the results to the GAITRite output. We got an error rate of less than 2% for the common gait parameters as shown in Table 2. 3.2. Output from Actigraph GT3X Figure 1a shows the output from the GT3X sensors mounted on both legs, for one subject walking at a normal speed. We can clearly see the acceleration peaks resulting from the steps in the X-axis (circled). Also identifiable are the time shifts between the two series of the same axis for left and right leg (arrow), indicating the alternate left and right steps. The Z-axis, capturing outward and inward movement, shows signals of low amplitude. We can observe a very stable acceleration pattern for each step, with regular amplitudes and durations.

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The number of steps visually identifiable (4 for left leg and 5 for right leg) corresponds to the output from GAITRite for the same subject. There is a significant difference of amplitude for left and right X-axe, indicating gait impairment. On Figure 1b we can see the matching data for the arms. It seems to contain equivalent information for gait analysis, but amplitudes appear lower than for the legs. The time shift in the series of the same component seems to be less visible in the arms than in the legs. Table 2. Errors in parameters between GaitParser and GAITRite.

Parameter Mean Maximum error Error Percentage

AT (s) 2.85 0.00 0%

SL (cm) 78.32 0.01 0%

GT (s) 1.10 0.02 2%

SP (s) 0.48 0.00 0%

ST (s) 0.56 0.01 2%

SW (s) 0.48 0.00 0.00%

DS (s) 0.20 0.00 0%

Figure 1. Output from the GT3X sensors. a) leg sensors; b) arm sensors. Dotted: Left sensor. Full: Right sensor. Blue: X – axe. Green: Y-axe. Red: Z-axe. Arrow shows time shift. Circles show step pattern.

4. Discussion The preliminary results we achieved from the GT3X sensors indicate that accelerometers capable of recording high frequency raw data may prove to be valuable tools for assessment of gait parameters in daily life. This had been explored in the published literature for young and adult populations. Although quantification is missing, our observations indicate that we may expect similar results for elderly populations. The most promising location for mounting the motion sensors was the leg. The lack of robust functionalities for batch processing and exporting of the calculated gait parameters, in the GAITRite system, makes it hard to use the data for further processing with other tools. Our approach based on open-source code is a first step in the direction to address these restrictions. We were able to visually identify the key characteristics of the signal, but we are still working on methods to numerically process them. The ability of the sensors to record at high sample frequency provides us with enough information to explore pattern-matching techniques to identify the key events of each gait cycle, quantifying all relevant parameters. We do not present a direct comparison of the two systems, as it is a work still in progress. Thus, we can not make the definitive statement that the use of raw data from accelerometers can in fact be successful. Given the ease of use and 100% acceptance by subjects in this study, we believe similar rates will be achieved in clinical application scenarios.


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5. Conclusion Further work is needed to improve the quantification of gait parameters from accelerometer data, in order to make the results reliable enough to be used for medical and epidemiological research. We are working on a complete software package capable of processing the data from GT3X in a simple and user-friendly manner. We have also developed the infrastructure and collected large amounts of data, namely in the scope of cohort studies, to perform robust validation of the method against a de facto standard. We want to encourage other researchers to explore and contribute to the GaitParser package as an open source research tool. Acknowledgments. This research was funded/supported by the Graduate School of Information Science in Health (GSISH) and the Technische Universität München Graduate School. A. Dias is supported by scholarship SFRH/BD/39867/2007 of the Portuguese Foundation for Science and Technology and Research Council of Norway Grant No. 174934. The authors wish to thank Jennifer Reinelt, Matej Svejda, Friederike Thun and Julia Strauß for their essential contributions to the project.

References [1] [2] [3] [4] [5]

Woollacott MH, Tang PF. Balance control during walking in the older adult: research and its implications. Phys Ther 1997;77:646–60. Kavanagh JJ, Menz HB. Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 2008;28:1–15. Henriksen M, Lund H, Moe-Nilssen R, Bliddal H, Danneskiod-Samsoe B. Test-retest reliability of trunk accelerometric gait analysis. Gait Posture 2004;19:288–97. de Bruin ED, Hartmann A, Uebelhart D, Murer K, Zijlstra W. Wearable systems for monitoring mobility related activities in older people; a systematic review. Clin Rehabil 2008;22:878–95. Holle R, Happich M, Löwel H, Wichmann HE. MONICA/KORA Study Group. KORA--a research platform for population based health research. Gesundheitswesen. 2005 Aug;67 Suppl 1:S19-25.

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Safe Storage and Multi-Modal Search for Medical Images Jukka KOMMERIa,1, Marko NIINIMÄKIa, Henning MÜLLERb,c a Helsinki Institute of Physics, CERN, Switzerland b University of Applied Sciences Western Switzerland (HES–SO), Sierre, Switzerland c University Hospitals and University of Geneva, Switzerland

Abstract. Modern hospitals produce enormous amounts of data in all departments, from images, to lab results, medication use, and release letters. Since several years these data are most often produced in digital form, making them accessible for researchers to optimize the outcome of care process and analyze all available data across patients. The Geneva University Hospitals (HUG) are no exception with its daily radiology department’s output of over 140’000 images in 2010, with a majority of them being tomographic slices. In this paper we introduce tools for uploading and accessing DICOM images and associated metadata in a secure Grid storage. These data are made available for authorized persons using a Grid security framework, as security is a main problem in secondary use of image data, where images are to be stored outside of the clinical image archive. Our tool combines the security and metadata access of a Grid middleware with the visual search that uses GIFT. Keywords. grid networks, multi–modal information search, security

1. Introduction Images are getting increasingly important in modern diagnosis and treatment planning. Through the large variety in radiology protocols and modalities, a detailed image interpretation is not always simple. By producing extremely large volumes of imaging data, tomographic modalities such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) but also combined PET/CT (Positron Emission Tomography) and PET/MRI can also lead to an information overload and create a need for new tools to help interpreting images. Content–based image retrieval has been proposed as one of the potential tools to aid diagnosis and use the large amount of visual data available [1]. So far, Grid technologies have been successfully employed inside hospitals to speed up image analysis by distributing the visual feature extraction of images to a cluster of computers [2], and by integrating image analysis, for example with the ProVision PACS of the hospital [3]. This in–house solution has the advantage that images do not need to leave the hospital network for analysis or treatment. The image analysis and retrieval software used in our case is the GNU Image Finding Tool (GIFT). GIFT is used for content–based visual retrieval, whereas so called multi–modal systems combine visual and textual information in retrieval. This has been demonstrated to often give better results than either textual or visual information alone 1 Corresponding Author.

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[4]. Very often, security constraints are not taken into account when discussing an inclusion of image retrieval into the PACS (Picture Archival and Communication System) or RIS (Radiology Information System) [5] and images are stored unencrypted. Steps towards integrating analysis with a secure storage of medical images have been taken in the Medical Data Manager (MDM) software [6]. MDM uses technologies of the EGEE (Enabling Grids for E–science in Europe) project’s gLite middleware [7]. The medical images themselves are stored in an encrypted format in the Disk Pool Man-ager (DPM) Grid storage [8]. Their meta data are stored in AMGA (gLite Grid MetaData Catalog) [9,10]. The symmetric encryption key is split into a number of pieces and stored in the distributed Hydra storage [11,12] according to the well–known Shamir’s Secret Sharing Scheme (SSSS). Even if one node of the Hydra storage is compromised, one piece of the key is not enough to reconstruct the actual symmetric encryption key to de-crypt the data in question. This enhanced security measure is a feature requested by the EGEE BioMedical user group. The gLite security system has been audited by the Centre National d’Etudes Spatiales (CNES) and was validated. In this paper, we describe an integration between GIFT and an MDM–like system to implement on–demand analysis of images. Problems with the initial test for using MDM directly are also described. Moreover, an integration of Grid storage and GIFT is implemented. Two usage scenarios, metadata search and multimodal search are described in Section 2. The components needed to enable these scenarios are described in Section 3. Section 4 contains a discussion and directions for future work. A functional prototype of it has been created as an evaluation of a technical concept in a project for the Swiss Academic Network SWITCH.

2. Functionality The goal of our system is to enable the following two functionalities: • Meta data search: The user has a valid VOMS (Virtual Organization Membership Services) certificate [13]. This issues a command to search the metadata. Access is granted based on the VOMS role and the results of his search are returned. The keys to decrypt the images are obtained and applied and images are returned. • Multi–modal search: In the PACS system, the user selects an image. This image has a DICOM header containing structured inforamtion. Similar images are searched by the visual image content using GIFT and by the metadata using a textual search. Only data matching the user’s privacy level are returned and shown on screen. This requires complete system integration as follows: 1. The image and its meta data are extracted from the PACS system. 2. A request containing the image data is sent to GIFT. 3. A request containing the meta data is sent to AMGA. 4. The results are combined and shown to the user based on the role. The combined system then allows for a safe access to the distributed image data based on the privacy levels of the users. For adding images and metadata to a secure storage system, the following steps are taken: (i) The user is authenticated, (ii) metadata of images is loaded to AMGA, (iii) GIFT carries out a feature extraction of the image and the features are stored by GIFT,

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(iv) the image is encrypted and encryption keys are stored by Hydra, (v) the encrypted image is stored in DPM. As the system tests were outside of the hospital network, a test database of DICOM files and files from the medical literature used in the ImageCLEF2 benchmark were used for testing. In Figure 1 the possibilities for data access are described. The authentication is performed via certificates with the VOMS server. Queries of the metadata can be performed with AMGA and visual retrieval with GIFT.

Figure 1. Work flow of multi–modal search for images and associated metadata..

3. Components The system uses components based on existing Grid tools from the official gLite repositories. The structure is shown in Figure 2. The software components were installed on several virtual machines so that each virtual machine contained a logical collection of the software. On one virtual machine we installed AMGA, GIFT, the glite user interface and one Hydra server. Then, we installed two separate Hydra servers on two separate virtual machines. As VOMS server we used an existing service from the Swiss Multi Science Computing Grid (SMSCG) and a host certificate for every virtual machine. The components are described in the following text. The AMGA Metadata Catalog is an EGEE gLite service allowing metadata handling on the grid. The main usage can be as a front end file metadata service, providing means of describing and discovering data files required by users and their jobs. It can also be used as a Grid–enabled database for applications that require structuring their data, providing a database–like service supporting Grid security features (X509 proxies and the VOMS authentication and authorization system). Finally, an additional feature allows the access of existing relational databases from a Grid environment (worker nodes, user interface, etc.), which enables the addition of Grid security to existing databases. Hydra, part of the European Middleware Initiative (EMI), encrypts data using a distributed key storage system. The passkey is generated and then split into components, which are shared across multiple key stores on different servers and, if possible, in different countries. This is more secure than a central key storage system,

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http://www.imageclef.org/


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which requires only one security breach to be compromised. In contrast, to obtain the passkey generated by Hydra, a coordinated attack on multiple servers is required.

Figure 2. System component network topology for the setup of safe image storage and access..

The GNU Image Finding Tool, GIFT3, is a content–based image indexing and retrieval package developed at the University of Geneva in the late 1990’s. GIFT uses techniques common for textual information retrieval and creates a large set of mainly binary features (global and local color and texture features) [14]. GIFT extracts these features and stores them in an inverted file. In a typical desktop PC, the speed of this feature extraction is about 1 or 2 images per second. An inverted file is created after the feature extraction enabling quick retrieval. Through the Multimedia Retrieval Markup Language (MRML) the system can easily be integrated with other applications.

4. Discussion and Future Work This article describes a safe storage and access system for medical images and associated metadata using methods based on standard Grid tools. The tools allow for an easy integration of safe storage of all data in encrypted form and an access to the data via meta-data search and content-based image retrieval. Initially the use of the MDM (Medical Data Management) system [6] was planned but the software turned out to not be maintained anymore and the security framework was outdated. Thus we decided to change the architecture for the meta data search. The system uses a role-based access via VOMS servers to potentially confidential medical data. Our test system uses several storage servers of the Universities of Geneva and Bern, and a standard VOMS server of the Swiss Grid community. A similar structure can also be implemented inside hospitals, with the encryption keys being distributed on several machines. To limit security risks, all data (images, thumbnails) are always stored in encrypted format and access to meta data is protected by x.509 certificates. Encryption keys are stored in a distributed fashion, so a single security breach does not give access to the encryption keys. Such an architecture also allows data for research projects to be extracted from the PACS and stored in safe format, quicker than accessing via the often overloaded PACS system. Access to all data are via the role definition of a user according to defined access rights. The amount of data that has been stored in the test system is still small, and therefore future studies are 3

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needed to measure the performance, precision/recall of the searches, and usability aspects of the system Acknowledgements. This work was partly supported by the SWITCH AAA project MedLTPC and the European Union in the context of the Khresmoi project (grant agreement no 257528).

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[3]

[4]

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[6] [7] [8]

[9]

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Müller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medicine–clinical benefits and future directions, International Journal of Medical Informatics, 73, pp. 1–23, 2004. Niinimäki M, Zhou X, Depeursinge A, Geissbuhler A, Müller H, Building a community grid for medical image analysis inside a hospital, a case study, Medical imaging on grids: achievements and perspectives (Grid Workshop at MICCAI 2008), New York, USA, pp. 3–12, 2008. Niinimaki M, Zhou X, de la Vega E, Cabrer M, Müller H. A web service for enabling medical image retrieval integrated into a social medical image sharing platform, in MEDINFO 2010, Studies in Health Technology and Informatics, 160, pp. 1273–1276, IOS press, 2010. Eggel I, Müller H. Indexing the medical open access literature for textual and content–based visual retrieval, in MEDINFO 2010, Studies in Health Technology and Informatics, 160, pp. 1277–1281, IOS press, 2010. Welter P, Deserno TM, Fischer B, Wein BB, Ott B, Günther RW. Integration of CBIR in radiological routine in accordance with IHE, in SPIE Medical Imaging 2009: Advanced PACS–based Imaging Informatics and Therapeutic Applications, 7264, 2009. Montagnat J, Frohner A, Jouvenot D, et al. A secure grid medical data manager interfaced to the glite middleware, Journal of Grid Computing, 6, pp. 45–59, 2008. Laure E, Fisher SM, Frohner A, et al. Programming the grid using glite, Computational Methds in Science and Technology, 12(1), pp. 33–45, 2006. Stewart GA, Cameron D, Cowan GA, McCance G. Storage and data management in EGEE, in Proceedings of the fifth Australasian symposium on ACSW frontiers, Darlinghurst, Australia, Australia, pp. 69–77, 2007. Santos N, Koblitz B. Metadata services on the grid, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 559(1), pp. 53– 56, 2006. Koblitz B, Santos N, Pose V. The AMGA Metadata Service, Journal of Grid Computing, 6, pp. 61–76, 2008. Abadie L, Badino P, Baud JP, et al. Grid–enabled standards-based data management, Mass Storage Systems and Technologies, pp. 60–71, 2007. Frohner A, Baud JP, Rioja RMG, et al. Data management in EGEE, Journal of Physics: Conference Series, 219(6), 2010. Alfieri R, Cecchini R, Ciaschini V, et al. VOMS, an Authorization System for Virtual Organizations, Grid Computing, pp. 33–40, 2004. Squire DM, Müller W, Müller H, Pun T. Content–based query of image databases: inspirations from text retrieval, Pattern Recognition Letters (Selected Papers from The 11th Scandinavian Conference on Image Analysis SCIA ’99), 21(13–14), pp. 1193–1198, 2000. Ersboll BK, Johansen P, editors.


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Respiration Tracking Using the Wii Remote Game-Controller J. GUIRAO AGUILAR1, J. G. BELLIKAa, L. FERNANDEZ LUQUE b V. TRAVER SALCEDOc a Department of Computer Science, Faculty of Science and Technology, University of Tromsø, Norway b Norut - Northern Research Institute, Tromsø, Norway c ITACA-TSB, Universidad Politécnica de Valencia, Spain

Abstract. Respiration exercises are an important part in the pulmonary rehabilitation of COPD (chronic obstructive pulmonary disease) patients. Furthermore, previous research has demonstrated that showing respiration pattern helps the patients to improve their breathing skills. We have developed a low cost and non-invasive prototype based on the Wii remote game controller infrared camera to provide BPM (breaths per minute) measurement as feedback. It can also be a comfortable solution without wires, batteries or any kind of electronics but just wearing passive markers. The lab evaluation with 7 healthy individuals showed that this approach is feasible when users are resting of their exercise. The BPM monitored during the tests presented less than 15% of maximum error and the RMSE (root mean square error) was lower than 6% in all the tests. Further research is needed to evaluate and adapt the system for COPD patients. In addition, more work is needed to develop applications that can be built to motivate and guide the users. Keywords. COPD, Wiimote, camera, pulmonary rehabilitation.

1. Introduction Chronic Obstructive Pulmonary Disease (COPD) is a common cause of death. According to the World Health Organization, there are 210 million people with COPD and it accounts for the 5% of all world deaths in 2005 [1, 2]. COPD is defined as a chronic airflow obstruction that can lead to reduced breathing skills and low exercise capacity [3]. Pulmonary rehabilitation is a key aspect of COPD treatment where exercise training and breathing techniques are essential aspects [4, 5]. There are some barriers within the pulmonary rehabilitation, such as lack of motivation and transportation problems [6]. However, a study by Collins et al [7] showed that giving feedback to the patients about how they are breathing has positive effects. If feedback could be combined with game-based rehabilitation, the outcome could be improved based on increased patient motivation [8].

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Corresponding Author: Julián Guirao, Polytechnic University of Valencia. E-mail: [email protected]

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In our study we used a prototype based on the Wii remote controller (aka Wiimote) to acquire and process the user’s breathing signal and visualize the information on a screen. Our approach uses the infrared camera inside the Wiimote, capable of tracking up to four light sources at the same time. Using those infrared cameras in the health domain has already been tested [9]. The Wiimore is a low cost device (its price is below 40€) that can easily be installed in the home of the patient as part of a telemedicine system, or integrated with a computer-game applications for improving motivation [10]. The aim of this project was to implement a prototype to evaluate the feasibility of using the Wiimote's camera for tracking tiny movements such as breathing chest movements and, therefore, to test the possibility of using the Wiimote within respiration rehabilitation. If such approach is feasible, the Wiimote could be used as the user interface for applications aiming at providing respiratory feedback to patients with pulmonary diseases.

2. Methodology The developed prototype comprised of (a) hardware elements to capture the breathing signal and (b) software to process the data and provide feedback to users. 2.1. Architecture The designed prototype includes following parts (Figure 1): • Array of 30 infrared LEDs (light emission diodes) as light source. • Belt with attached markers. These markers were round reflecting metal pieces of 3 cm in diameter and placed approximately 10 cm of distance between them. These markers were made by adapting ice-cream spoons. • Wiimote connected to a PC using Bluetooth connection. • Computer to receive the data and show feedback. The system works as follows: light produced by the LEDs is reflected in the markers and captured in the Wiimote's camera. Therefore the camera is able to track markers on the user's chest and send the data to the computer. As they are moving according to the breathing, the computer processes these variations obtaining a signal corresponding to the breathing pattern Figure 1. Respiration tracking system

2.2. Software A desktop Java application was developed for calculations and signal processing. This application uses two open source Bluetooth libraries: Wiiuse and WiiuseJ. The first of


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them establishes the communication link with the Wiimote to receive the raw data. WiiuseJ is written in Java and prepares the raw data from the Wiimote. The Wiimote, once connected to the computer over the Bluetooth connection, automatically starts to send the raw data. This data is the preprocessed image of the camera, which consists of up to four dots corresponding to detected light sources. The developed application processes the data and visualizes: 1) the breathing signal, extracted from distance between the markers (detected as light sources), 2) a frequency analysis through Fast Fourier Transforms and 3) the “breaths per minute” (BPM) value from the last 20 secs, among some other things. All data and information was saved to files for later analysis. 2.3. Evaluation Protocol During the evaluation, 7 volunteers, 3 women and 4 men, without breathing problems completed some tests. They were asked to wear a belt with markers on the chest in a tight but comfortable way. The exact position was over the abdomen because this is the place with the maximum displacement due to respiration. Every volunteer made 3 tests of 3 minutes each: sitting on a chair, sitting on a stationary bicycle before doing exercise and after doing 5 minutes of exercise. The Wiimote and the light source were placed around 25 cm away from the body. In every respiration, when the lungs were full and the expiration phase was about to start, the user was told to press a button on the Wiimote. Every time the button was pressed, a time stamp was stored in the computer, saving the data to validate systems' outcomes. Finally, data provided by the user and calculated by the system was compared to get the results.

3. Results Octave2 was the chosen tool to address the analysis of the information gathered. Every test was classified in one of the following groups: • Normal: no errors were detected during the test • User induced errors: the user made a mistake pressing the button so the control signal is not valid. • System error: the system failed and the data shown was wrong. From the 21 tests realized (3 each volunteer), 12 of them were classified as normal. Only two of them had a maximum error above 10% (10,17% and 14,04%). The RMSE (root squared mean error), which is a good estimator of precision, was higher than 5% (5,8% and 5,24%) in only two of these tests. It means that difference between both signals was quite low in all these tests. The rest of the tests were classified as user induced errors (3 of them) and system error (6 of them). The errors produced during the evaluation tests were consequence of the impossibility of differentiating chest movements due to respiration from body movements or insufficient light being reflected by the markers.

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4. Discussion The Wii video console has been introduced with success within the rehabilitation field in diseases such as stroke or cerebral paralysis. The COPD patients have also started to play with it to reduce the burden of their symptoms [11, 12]. However, using the Wii remote as respiration sensor, as proposed in this paper, has been never tested. The proposed system acquires the data and provides real-time feedback. The implemented prototype records breathing data and provides feedback to the patient about how slow or fast the respiration is. It is a non-invasive, low cost system with the following components: 1) a standard computer, 2) a US$40 Wiimote, 3) a US$5 belt with markers 4) and a illuminator about $30~40. It is also a comfortable solution without wires, as the patient wears no batteries or any kind of electronics. The patient only wears a Velcro belt with some passive markers. The system developed has proved to be able to acquire breathing signals with surprising precision having in mind the materials involved. All the data collected by the application showed the user's respiration signal without any doubt. Since access to a breathing sensor was not available during the development of the prototype, it was impossible to compare the respiration signal and its accuracy remains unknown. Therefore, a different evaluation protocol was addressed in order to know the potential of the system and its limitations. BPM calculated from this signal was compared to a test signal obtained from the user. Among the tests without any incidents the RMSE was lower than 6% showing a relatively high precision. Maximum error was lower than 15%. As a conclusion it is fair to say that the Wiimote is able to work as a breathing monitor device as a non-invasive, comfortable and low cost alternative. 4.1. Limitations This project is a preliminary study to test the feasibility of acquiring breathing signals with the Wiimote’s infrared camera. There are still many limitations that need to be addressed in order to create applications that are adapted to the needs of patients with COPD. In addition, the accuracy of the new prototype needs to be evaluated using a certified respiratory sensor and not just relay on subjects input. Some of the errors produced during the evaluation tests were consequence of the impossibility of differentiating respiratory chest movements from body movements. In the literature reviewed related to acquire breathing through visual devices, and also in this case, a prerequisite is that the user must be immobile [10]. Detecting respiration with visual devices while exercising, cycling on the stationary bike or walking on a treadmill for example, is something that remains an unsolved challenge. Although some tests using two Wiimotes were addressed, no solutions are proposed to solve this issue. Performance of the markers was also an important limitation. Range of proper operation was very low, allowing the user to be at a maximum distance of the Wiimote of 20-30 centimeters. The cause of this limitation was the material used to develop the reflectors, it did not have the appropriate reflective qualities. These reflectors were modified ice-cream spoons and not expensive reflective devices. The evaluation tests were only carried out by healthy users. COPD patients may have an anomalous breathing pattern or smaller chest movements due to their impaired


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lungs. The outcomes of the prototype with real patients have not been tested and it might present additional issues. 4.2. Future Work At the end of the preliminary study, the conclusion is that the Wiimote is capable of acquire breathing signals. Therefore the next step is to develop the system further on to reach its potential and to find out the real limitations of this approach. The low performance of the markers highlights them as the first thing that should be improved. Better materials must be found in order to have higher reflectiveness from the passive markers. Active markers are another option that should be researched. It would be composed of a LED, a resistor, a switch and a button-size battery, being as lightweight as a passive one. Performance of an active marker seems very likely to outperform passive ones. A larger number of trials with COPD patients is also a milestone to achieve.The real challenge would be to avoid the limitation of body movements. Some studies employed two Wiimotes to achieve a kind of stereo vision or 3D vision that showed to be very accurate [13, 14]. It could be a good approach to avoid this problem.

References [1] [2] [3] [4] [5] [6] [7]

[8] [9] [10]

[11]

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World Health Organization. WHO | Chronic obstructive pulmonary disease (COPD). WHO (cited 2010 June 1). Available from: http://www.who.int/mediacentre/factsheets/fs315/en/index.html Mathers C, Loncar D. Updated projections of global mortality and burden of disease, 2002-2030: data sources, methods and results. World Health Organization. 2005. Anto J, Vermeire P, Vestbo J, Sunyer J. Epidemiology of chronic obstructive pulmonary disease. European Respiratory Journal 17. 2001; 982-994. Gosselink R. Breathing techniques in patients with chronic obstructive pulmonary disease (COPD). Chronic Respiratory Disease 1. 2004; 163-172. Ries AL, Bauldoff GS, Carlin BW, et al. Pulmonary Rehabilitation: Joint ACCP/AACVPR EvidenceBased Clinical Practice Guidelines. Chest 131. 2007; 4S-42S. Smith SM, Partridge MR. Getting the rehabilitation message across: emerging barriers and positive health benefits. European Respiratory Journal 34. 2009; 2-4. Collins E, Laghi F, Langbein W, et al. Can Ventilation-Feedback Training Augment Exercise Tolerance in Patients with Chronic Obstructive Pulmonary Disease? American Journal of Respiratory and Critical Care Medicine 177. 2008; 844-852. Lange B, Flynn Sheryl M, Rizzo A. Game-based telerehabilitation. European journal of physical and rehabilitation medicine 45. 2009; 143-151. Orimoto A, Haneishi H, Kawata N, Tatsumi K. Monitoring and analysis of body surface motion caused by respiration. IEICE 108. 2009; 523-526. Decker J, Li H, Losowyj D, Prakash V. Wiihabilitation: Rehabilitation of Wrist Flexion and Extension Using a Wiimote-Based Game System. Governor's School of Engineering and Technology Research Journal. 2009. Schmidt KI, Porcari JP, Felix M, Gillette C, Foster C. Energy Expenditure of Wii Sports: A Comparison of Five Sport Games. Journal of Cardiopulmonary Rehabilitation & Prevention 28. 2008; 272. Khoo JCT, Brown ITH, Lim YP. Wireless On- Body-Network breathing rate and depth measurement during activity. IEEE Engineering in Medicine and Biology Society. Conference. 2008; 1283-1287. Scherfgen D, Herpers R. 3D tracking using multiple Nintendo Wii Remotes: a simple consumer hardware tracking approach. Proceedings of the 2009 Conference on Future Play. Canada 2009, 31-32. Cuypers T, Van den Eede T, Ligot S, et al. Stereowiision: stereo vision with two wiimotes. 2009.

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A Nomenclature for the Analysis of Continuous Sensor and Other Data in the Context of Health-Enabling Technologies a

Matthias GIETZELTa,1, Klaus-Hendrik WOLFa, Reinhold HAUXa Peter L. Reichertz Institute for Medical Informatics,University of Braunschweig – Institute of Technology and Hannover Medical School Germany

Abstract. Due to the progress in technology, it is possible to capture continuous sensor data pervasively and ubiquitously. In the area of health-enabling and ambient assisted technologies we are faced with the problem of analyzing these data in order to improve or at least maintain the health status of patients. But due to the interdisciplinarity of this field every discipline makes use of their own analyzing methods. In fact, the choice of a certain analyzing method often solely depends on the set of methods known to the data analyst. It would be an advantage if the data analyst would know about all available analyzing methods and their advantages and disadvantages when applied to the manifold of data. In this paper we propose a nomenclature that structures existing analyzing methods and assists in the choice of a certain method that fits to a given measurement context and a given problem. Keywords. Continuous sensor data, health-enabling technologies, ambient assisted living, analysis methods, nomenclature

1. Introduction In research we face a large variety of possible questions. Every problem needs the right tool to produce a useful solution matching the desired question. Especially in dealing with continuously recorded sensor data there is an enormous amount of methods to analyze the data and new analysis methods are often developed with the availability of new data. In the area of health-enabling and ambient assisted technologies there are various use cases for continuously recorded sensor data. In the context of a patient-centered care these sensor data should be seen in combination with data e.g. from a medical health record in order to increase the information gain [1]. Health-enabling technologies are used to detect emergency situations, to give a feedback about the health status, or to assist in daily living [2]-[4]. Due to the interdisciplinarity of this field (e.g. informatics, electrical engineering, medical science and psychology) every 1

Corresponding Author: Matthias Gietzelt, E-mail: [email protected]; Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Mühlenpfordtstr. 23, D-38106 Braunschweig, Germany.

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discipline makes use of their own analyzing methods. In fact, the choice of a certain analyzing method often solely depends on the set of methods known to the data analyst. It would be an advantage if the data analyst would know about all available analyzing methods and their advantages and disadvantages when applied to the manifold of data. It is desirable to know which analyzing method is most appropriate to handle a certain problem.

2. Objectives Our fundamental question was how to choose the most suitable analysis method(s) based on a certain measuring context and a certain problem. To the author’s knowledge, there is no tool that supports the choice regarding the most suitable method(s) and no systematization of methods for analyzing continuous sensor data.

3. Nomenclature for the Analysis of Continuous Data Since a nomenclature is an established approach for systematization (e.g. in economics a nomenclature is used to make an environmental analysis [5]), we developed an open three-axial mono-hierarchical nomenclature in order to structure analysis methods for continuous sensor data. This nomenclature is considered to assist in the selection of one or more appropriate analysis methods in a certain context and with a certain problem. The subsequent semantic dimensions appeared to be reasonable: • Context: description of the situation that was measured; • Problem: underlying problem to be solved; • Analysis method: a scheme of steps for analyzing the measured data 3.1. Context Axis The first axis is the context axis. The context axis gives us information about the situation in which the sensor data was collected. At first, we have to consider the object to be measured. In the area of healthenabling technologies the object is primarily a person. But there are also cases, in which a certain room or an electrical device is primarily measured. It is also necessary to identify the reference system in which the data was collected. The reference system can be a person, a certain room, a flat or a car. E.g., in case of on-body sensors, the reference system is the person measured. For the analysis of the data of some on-body sensors it is crucial to provide information about the wearing position and orientation. In addition, the context axis describes the data source used. It is important to know the specifications of the sensor used to get a deeper insight and a better comprehension for the data. Wolf et al. developed a classification system for sensor-based data sources which describes relevant properties of a sensor [6]. This scheme was refined in [7]. This classification scheme was adopted in our nomenclature. But there may be other (non-sensor-based) data sources based on questionnaires, results of physical examinations, or data from an (electronic) medical record. These data can also be seen as a possible continuous data source and therefore, we added it to the proposed nomenclature.

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The last sub-axis describes the data source’s type, an essential part to choose an appropriate analysis method. Some analysis methods require quantitative, some require qualitative data sources. In addition, one should know how many channels and how many dimensions each channel of the data source has. Since continuous sensor data are recorded in time, a channel can be interpreted as a single sensor measuring a certain physical, chemical or biological characteristic. Please note that each channel can have one or more dimensions measured at the same point in time and a defined sample rate, whereas different channels can have varying sample rates. An example for a continuous one-dimensional signal is a body scale and a triaxial accelerometer measures a threedimensional signal. Multi-dimensional signals can be pictures or data of a computer tomography. Figure 1 shows the context axis and selected sub-axes.

Figure 1. Mind map of the context axis.

3.2. Problem Axis The second axis describes the underlying problem to be solved. Health-enabling technologies can be intended for a broad range of applications [1]. Thereby, a wide variety of aspects must be considered. An example for such a technology is a fall detector [8]. After a detected fall, the system has to initiate an alarm that has to be sent to persons who are able to help. Therefore, it is necessary to identify a strategy for an escalation and a de-escalation chain and to identify one or more suitable communication channels. It would be also helpful to give the helper auxiliary information, e.g. the localization of the affected person. Besides emergency detection, health-enabling technologies can also be used for information and education purposes or even for wellness and sport [1]. Figure 2 shows some use cases for such technologies. This axis may help to go further into the problem, to define the functionality of the complete system, and therefore to define the outcome of the analysis. 3.3. Analysis Method Axis The third axis is the analysis method axis. In this axis we structured methods for analyzing continuous sensor data. The sub-axes are structured in a way that they represent a typical procedure in analyzing continuous sensor data. At first, we have to extract candidate features from the data using filters or a frequency analysis. The second step is to select the most important features with the highest predictive capability. Thereby, we should avoid redundant or highly inter-correlated features. The


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feature selection methods can be differentiated through their search behavior: if a method involves one feature at a time it is a single factor analysis (or uni-variate analysis) and otherwise a multi factor analysis (or multi-variate analysis). The third sub-axis contains the structure identification methods. These methods can be chosen using the information captured by the context and the problem axis. If a qualitative analysis is needed, one should prefer the classification and indexing methods; in case of a quantitative analysis one may choose a regression analysis.

Figure 2. Mind map of the problem axis.

Figure 3. Mind map of the analysis method axis.

3.4. Statement Model The statement model for the nomenclature summarizes all information that was derived from the situation measured: In a certain context x (if measuring the object x1 with reference system x2, using data sources x3 which provide data of type x4) and a given problem y, one should use the analysis method(s) z.

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4. Discussion and Conclusion In this paper we introduced an open three-axial mono-hierarchical nomenclature that may assists in the selection of one or more adequate analyzing methods for continuous sensor data. It covers the description of the context in which the data was collected, and the underlying problem to be solved. It was intentionally designed as an open nomenclature, so that new methods can be added. Within the proposed structure we also considered the typical procedure in data analysis. This first step in the systematization we are conscious about that a nomenclature is not a sophisticated model in choosing analyzing methods. Our future research focuses in enhancing and refining the model in order to choose analyzing methods in a problem adequate manner. 4.1. Limitations In the author’s opinion there is, beside the development of new methods, an interdisciplinary and application oriented demand in research in structuring analyzing methods. But there is also a demand on establishing guidelines for choosing such methods. However, there are also a number of limitations to be stated. First, in spite of an intensive literature review, we did not find any results related to a systematization or a nomenclature for choosing analyzing methods for continuous sensor data. Second, the nomenclature presented is still work in progress and is supposed to be for discussion about its demand and content. Third, the proposed nomenclature has not been evaluated, yet. This will be done in our next step. Therefore, we will systematically analyze existing literature and make focus group discussions with experts in this field.

References [1] [2] [3] [4] [5] [6] [7] [8]

[9]

Haux R, Howe J, Marschollek M, Plischke M, Wolf KH. Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms. Inform Health Soc Care 33 (2008), 77-89. Saranummi N. IT applications for pervasive, personal, and personalized health, IEEE Trans Inf Technol Biomed 12 (2008), 1-4. Arnrich B, Mayora O, Bardram J. Pervasive or Ubiquitous Healthcare?, Methods Inf Med 49 (2010), 65-6. Demiris G. Smart homes and ambient assisted living in an aging society. New opportunities and challenges for biomedical informatics, Methods Inf Med 47 (2008): 56-7. Fleisher CS. Bensoussan BE. Strategic and competitive analysis: Methods and techniques for analyzing business competition, Prentice Hall, New Jersey (USA), 2002. Wolf KH, Marschollek M, Bott OJ, Howe J, Haux R. Sensors for health-related parameters and data fusion approaches. Proceedings of the European Conference on eHealth ECEH 2007:155–61. Koch S, Marschollek M, Wolf KH, Plischke M, Haux R. On health-enabling and ambient-assistive technologies. What has been achieved and where do we have to go? Methods Inf Med 48 (2009), 29-37. Bourke AK, van de Ven PW, Chaya AE, OLaighin GM, Nelson J. Testing of a long-term fall detection system incorporated into a custom vest for the elderly. Conf Proc IEEE Eng Med Biol Soc. 2008:28447. Klein LA. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making. SPIE Publications, 2004.


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Image-based Classification of Parkinsonian Syndromes Using T2'-Atlases Nils Daniel FORKERTa,1, Alexander SCHMIDT-RICHBERGb, Brigitte HOLSTc, Alexander MÜNCHAUd, Heinz HANDELS b, Kai BOELMANSd a Department of Medical Informatics, University Medical Center Hamburg-Eppendorf, b Institute of Medical Informatics, University of Lübeck c Department of Diagnostic and Interventional Neuroradiology; And d Department of Neurology, University Medical Center Hamburg-Eppendorf. Germany

Abstract. Parkinsonian syndromes (PS) are genetically and pathologically heterogeneous neurodegenerative disorders. Clinical distinction between different PS can be difficult, particularly in early disease stages. This paper describes an automatic method for the distinction between classical Parkinson`s disease (PD) and progressive supranuclear palsy (PSP) using T2' atlases. This procedure is based on the assumption that regional brain iron content differs between PD and PSP, which can be selectively measured using T2' MR imaging. The proposed method was developed and validated based on 33 PD patients, 10 PSP patients, and 24 healthy controls. The first step of the proposed procedure comprises T2' atlas generation for each group using affine and following non-linear registration. For classification, a T2' dataset is registered to the atlases and compared to each one of them using the mean sum of squared differences metric. The dataset is assigned to the group for which the corresponding atlas yields the lowest value. The evaluation using leave-one-out validation revealed that the proposed method achieves a classification accuracy of 91%. The presented method might serve as the basis for an improved automatic classification of PS in the future. Keywords. Parkinsonian syndromes, Magnetic resonance imaging, Classification, Computer-assisted image analysis

1. Introduction To date, the diagnosis of Parkinsonian syndromes (PS) is mainly based on clinical criteria. Beside classical Parkinson`s disease (PD), which is characterized by an asymmetric onset of slowness of movements, rigidity, and tremor, other Parkinsonian entities have to be separated, such as progressive supranuclear palsy (PSP). PSP is clinically characterized by vertical gaze palsy or hypometric vertical saccades and postural instability with falls in the first year, in combination with predominantly axial rigidity, and frontal behavioral abnormalities or dementia. Clinico-pathological studies demonstrated that only 41% up to 88% of pathologically proven PSP are correctly diagnosed in life. However, most often PSP was clinically misdiagnosed as PD [1]. In clinically equivocal cases, additional investigation including standardized 1

Corresponding author: Nils Daniel Forkert, University Medical Center Hamburg-Eppendorf, Department of Medical Informatics, Martinistr. 52, 20246 Hamburg, Germany; E-mail: [email protected].

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neuropsychological assessment, electro-oculography or assessment of postural stability can be helpful to distinguish between PD and PSP. So far, there are no disease-specific biomarkers. However, making a correct diagnosis early is becoming more relevant because of potential disease modifying treatment strategies that crucially depend on an accurate diagnosis [2]. To enhance diagnostic accuracy, automated image-based decision support seems to be a promising approach. In contrast to manually defined brain regions, automated techniques map the morphological and/or metabolic patterns across the entire brain and do not require a subjective judgment of a rater. In PSP, for example, the morphological analysis commonly revealed an atrophy of the rostral midbrain and superior cerebellar peduncle. Using a morphological classification scheme, Duchesne et al. [3] analyzed T1-weighted MR image sequences to extract deformation information in the hindbrain region using non-linear registration. The results were used to train a support vector machine, which achieved a 91% classification accuracy to distinct PD from PSP and multiple system atrophy (MSA). Complementary, a metabolic-based approach using Positron Emission Tomography was recently presented by Tang et al. [4], who used voxel-based spatial covariance mapping. Using this automated image-based classification, a high specificity (>90%) in distinguishing between Parkinsonian disorders was achieved. Referring to metabolic pattern, regional brain iron content might be a potential target for an automated classification scheme as brain accumulation and distribution differs between PD and PSP [5]. Paramagnetic substances in the brain such as nonheme iron (ferritin and hemosiderin) create local magnetic field inhomogeneities producing intra-voxel dephasing and shortening transverse relaxation times. Therefore, an estimation of tissue iron can be obtained from T2-weighted image sequences using changes, caused by local susceptibility magnetic resonance imaging (MRI). Here, variations, are particularly sensitive for tissue iron stores and can be calculated by the equation . Graham et al. [6] took advantage of this relation and investigated the iron deposits in the basal ganglia in PD patients and healthy controls in manually defined regions using a T2-weighted PRIME (partially refocused interleaved multiple echo sequence) sequence. The analysis revealed shortened relaxation rates in the substantia nigra and caudal putamen in PD patients compared to controls. To date, the brain iron content has not been analyzed in atypical PS or used for an automatic image-based classification. values could be used The focus of this work was to perform a feasibility study, if for an automatic classification in PS using an atlas-based approach.

2. Material and Methods 2.1. Material 67 MRI datasets were available for the generation and evaluation of T2' atlases, including 24 healthy control subjects (62.8±9.9, 41.9–77.6; mean age ± SD, range), 33 PD patients (61.5±10.4, 41.3–79.9) and 10 PSP patients (66.3±7.8, 55.4-78.8) with a clinically probable diagnosis. All MR scans were performed on a 1.5T Siemens Sonata MR system.


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Figure 1. Representative slice from a T2 sequence (a1-a3) and T2* sequence (b1-b3) and corresponding slice from the calculated T2' map (c).

Among others, the MR protocol contained a T2 and T2* sequence. For T2 determination, a triple-echo sequence using echo times (TE) of 12, 84, and 156 ms was used. The T2* weighted images were performed using an echo-planar imaging sequence at a TE of 20, 52 and 88 ms. Both, the T2 and T2* sequences, offer a matrix of and voxel spacing of mm³ (see Fig. 1). A quantitative qT2 map was calculated by a voxel-wise fitting the exponential function to the signal intensity decay curve given by the multiple TE data of the T2 sequence. In analogy to this, a quantitative qT2* map was calculated using the multiple TE data of the T2* sequence. The dataset can be calculated from the quantitative qT2 and qT2* values voxel-wise by the following relationship: . 2.2. T2' Atlas Generation atlases are generated for the classification of PS, one for In this work, three healthy subjects, one for PD patients, and one for PSP patients. This procedure was employed for two reasons. First, the different atlases allow a visual definition of brain areas with differing values by calculating the difference between each possible atlases. Second, it was supposed that this procedure enables an combination of automatic differentiation of PS. Due to different patient anatomies and positions during the image acquisition, a registration of the datasets is required for calculation of the atlases. For this, one healthy subject was chosen to serve as the main reference for the registration process. This dataset was selected since all important brain areas are covered, no moving artifacts or anatomical abnormalities are present and the head is located in the center of images are very noisy and contain metabolic rather than the image. Since the anatomical information (see Fig. 1c), a direct registration would be error-prone. Therefore, all transformation field calculations were performed on the images of the T2 triple echo sequence with the highest TE, since it exhibits the best tissue contrast. After registration, the calculated transformation fields are used to transform the corresponding datasets. The registration process was divided into two steps. In the first step, the datasets are pre-aligned using an affine registration by optimizing the mean sum of squared differences (mSSD) between the main reference dataset and each other dataset. For this, a manually segmented brain mask of the reference image was

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used to improve registration accuracy. After pre-alignment, an intensity-based diffusion registration as described in [7] was applied to take non-linear differences between the images into account. After both registration steps have been performed and the final dataset, the three atlases were calculated transformation has been applied to each by simple averaging. 2.3. Atlas Classification In order to use the generated atlases for classification of PS, a dataset not part of the atlas generation is separately registered using the same method as described in the previous section. Finally, the similarity of the given and registered dataset to the three atlases can be determined by calculating the mSSD inside the brain mask for each atlas. The given dataset is assigned to the group (PD, PSP, controls) for which the mSSD is the lowest.

3. Experiments and Results For evaluation of the proposed atlas-based registration scheme for PS differentiation, a leave-one-out cross validation was performed. For this, every dataset available was atlases classified using the described method. To prevent biased results, the used were generated again by leaving the dataset to be classified out of the atlas calculation. Table 1 shows the results of the automatic classification using the generated atlases. The results show that 91% of all datasets were correctly classified. The presented method yields a sensitivity of 0.8 and specificity of 1.0 for PSP patients. These quantitative values should be handled with caution since only 10 datasets were available for this syndrome. The presented method yields a specificity of 0.92 and sensitivity of 0.93 for classification of healthy subjects, while a specificity of 0.94 and sensitivity of 0.91 was achieved for PD patients. These results can be assumed to be more significant due to the number of datasets analyzed. Overall, the results are in the range of typical state-of-the art methods, e.g. [3,4]. Table 1. Results of the leave-one-out validation using T2' atlases Group Healthy (n=24) PD (n=33) PSP (n=10)

Classified as Healthy 22 2 1

Classified as PD 2 31 1

Classified as PSP 0 0 8

4. Discussion This paper describes the first stage of the development of an automatic image-based distinction between PD, PSP and healthy controls using atlases. In summary, the current results already reveal a very good discrimination between all three groups. Nevertheless, more datasets of PSP patients are required to obtain more significant results. The approach offers several opportunities for a more sophisticated analysis in the future. For example, so far only the mSSD as a global criterion has been used for the


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classification. Improved results might be possible if only strategic brain areas, which are mainly affected by brain iron metabolism in different PS, are selected and analyzed using more sophisticated classification methods, such as support vector machines. atlases are used to identify these brain areas of clinical Currently, the generated interest for future extensions of the proposed method. For this, the difference between each two generated atlases are calculated and currently visually inspected by a neurologist (see Fig. 2). It should be pointed out that only PD and PSP patients have been applied to the described automatic differentiation. Therefore, patients with other Parkinsonian syndromes like multiple system atrophy or a corticobasal syndrome should be included in future classifiers. Furthermore, classification results might be improved in the future with inclusion of additional MR modalities, like the apparent diffusion coefficient. In summary, the proposed image-based Parkinsonian syndrome differentiation using atlases might enable an automatic classification with reliable results in future.

Figure 2. Selected slices from the main reference dataset (left) and corresponding differences between the T2' atlas calculated from the healthy subjects and T2' atlas calculated from PD patients (right). Bluish colors indicate higher T2' values in control atlas, yellowish colors higher T2' values in the PD atlas .

References [1] [2] [3] [4] [5] [6] [7]

Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service, Brain 125 (2002), 861-70. Tolosa E, Wenning Y, Poewe W. The diagnosis of Parkinson's disease. Lancet Neurol 5 (2006), 75-86. Duchesne S, Rolland Y, Vérin M. Automated computer differential classification in Parkinsonian syndromes via pattern analysis on MRI, Acad Radiol 16 (2009), 61-70. Tang CC, Poston KL, Eckert T, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis, Lancet Neurol 9 (2010), 149-58. Schenck JF, Zimmerman EA. High-field magnetic resonance imaging of brain iron: birth of a biomarker? NMR Biomed 17 (2004), 433-45. Graham JM, Paley MN, Grünewald RA, Hoggard N, Griffiths PD. Brain iron deposition in Parkinson's disease imaged using the PRIME magnetic resonance sequence, Brain 123 (2000), 2423-31. Schmidt-Richberg A, Werner R, Ehrhardt J, Handels H. Landmark-driven parameter optimization for non-linear image registration, Image Processing, SPIE Medical Imaging 2010 (in press)

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Cell Edge Detection in JPEG2000 Wavelet Domain – Analysis on Sigmoid Function Edge Model a

Vytenis PUNYSa,1, Ramunas MAKNICKASa Department of Multimedia Engineering, Kaunas University of Technology, Lithuania

Abstract. Big virtual microscopy images (80K x 60K pixels and larger) are usually stored using the JPEG2000 image compression scheme. Diagnostic quantification, based on image analysis, might be faster if performed on compressed data (approx. 20 times less the original amount), representing the coefficients of the wavelet transform. The analysis of possible edge detection without reverse wavelet transform is presented in the paper. Two edge detection methods, suitable for JPEG2000 bi-orthogonal wavelets, are proposed. The methods are adjusted according calculated parameters of sigmoid edge model. The results of model analysis indicate more suitable method for given bi-orthogonal wavelet. Keywords. Edge detection, edge strength, image segmentation, discrete wavelet transform, wavelet domain.

1. Introduction Scanning microscopes, which had been introduced on the medical equipment market some years ago, keep improving their throughput and are targeting routine clinical use, being able to acquire high quality digital images of the whole slide samples with a scanning times between 1 to 4 minutes per sample (depending on the resolution) in batches containing up to 384 slides (what corresponds to 5.27 Tbyte of uncompressed image data). Then the scanning is followed by a clinical assessment and an imagebased cell quantification, which in ideal situation, should be done automatically. Automatic analysis of virtual microscopy image batches requires considerable computing resources, which recently are available for research purposes (e.g. deploying GRID and cloud computing architectures), but are not yet applied in routine clinical use. Therefore a technique, that could considerably reduce computing resources (or image processing times) needed for microscopy image analysis, will shorten the way of virtual microscopy into wide clinical use. The virtual microscopy images (VMI), suitable for cell quantification, usually are large (80K x 60K pixels and larger), containing 14.4 Gbytes or more data [1]. Naturally, they are stored in a compressed form. And it is the VMI where the JPEG 2000 image compression standard is more widely used than the wide spread conventional JPEG. Thanks to the more sophisticated mathematical techniques, the JPEG 2000 gives 1

Corresponding author: V.Punys, Department of Multimedia Engineering, Kaunas University of Technology. Address: Studentu str. 56-305, LT-51424 Kaunas, Lithuania. E-mail: [email protected]

V. Punys and R. Maknickas / Cell Edge Detection in JPEG2000 Wavelet Domain

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approximately double compression ratio compared to the conventional JPEG. Furthermore, hierarchical compression scheme, implemented by the JPEG2000 (based on the wavelet transform which forms multi-resolution pyramid of image data and wavelet coefficients), coincides with the way the images are reviewed by pathologists. The diagnostic assessment, performed by pathologists, is based on the visibility and staining (intensity, area) either of cell nuclei or cell membranes (together with surrounding areas) in microscopy images (see Figure 1). Technically, visibility and staining are correlated with height and width of object edges in VMI.

Figure 1. Detecting cell membranes and the surrounding stained areas.

There is a general understanding, that the wavelet coefficients “carry” information about both the magnitude and the location of the signal. This is proved by numerous successful research efforts in using the wavelet transform for detection of signal peak or pre-defined shape segment. However, the basic wavelet functions, ensuring good detection results, are far to be the best in image compression applications. The goal of this work is to study what edges (including their parameters: height and width) could be detected in compressed image data structures (wavelet domain) using the biorthogonal wavelets defined in the JPEG2000 standard for lossless (CDF 5/3) and lossy (CDF 9/7) compression [2] without image decompression step. The height and width of detected edges might be used for automatic cell quantification.

2. Edge detection in space and wavelet domain The Canny edge detector is proved to be optimal in sense of good edge detection, localization and minimal response in Euclidean space domain [5]. Usually, the edge strength of objects of interest varies along the edge. It is hard to set the threshold levels, which could discriminate the object of interest among surrounding objects. That is why multi-scale image analysis is helpful. One of multi-scale transformation is a wavelet transformation (WT). Wavelet transform modulus maxima (WTMM) method is analogue to Canny edge detection in multi-scale domain. Since image is 2D discrete digital signal, this method is adjusted to discrete wavelet transform (DWT). In DWT scaling function smoothes the image at different scales removing the noise. Wavelet function detects edges of the same object at different scales. As 2D DWT analyses image in horizontal, vertical and diagonal directions, edge gradient is calculated at every point of the image. The modulus maxima of the WT are defined by the positions where modulus (magnitude) of the WT, i.e. the gradient, is locally maximal in argument direction. Therefore the WTMM method requires wavelet function to be the first derivate of its scaling function [3]. Wavelet functions used in JPEG2000 standard violates this requirement, therefore new methods for edge detection should be searched for.

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3. Edge modelling by a sigmoid function Edges describe properties of objects in given signal. One of mostly used descriptors is the strength of edge [4]. The wavelet coefficients significantly differ from zero at irregular regions of a signal, so they can be used to evaluate the strength of edge. More generally, an edge is described by its three parameters: height , width and position . An edge can be modelled by a sigmoid function in digital image: .

(a) Figure 2. Edge model sigmoid function

(1)

(b) , when parameter c is: (a)

and (b)

DWT w(x,a,b,c) is a discrete convolution between discrete signal and digital high-pass filter (of length ). Edge detection in wavelet domain is performed by analysis of local maximum values of , having DWT coefficients. The range of maximum variation is and . The inverse DWT (IDWT) is multi-valued, as for every wavelet coefficient there is set of pairs of parameters of sigmoid function. Denote local absolute maximum of sigmoid edge at scales . The and of wavelet coefficients are corresponding minimum and maximum values of wavelet coefficients of sigmoid edge having height and width . It is necessary to calculate and analyse all and , of edge, whose local absolute the pairs of parameters at scales having maximums of wavelet coefficients are equal to , .

4. Methods for reverse calculation of edge model parameters Method 1 calculates parameters and of all suitable edges, whose wavelet absolute . This is achieved by calculating maximum coefficients at scales are equal to given intersection of intervals of edge width at every scale. The interval enclosing correct width is in intersection . The more scales are considered, the shorter the interval of edge width is likely to be obtained. The result is the set of ranges of width for every height of an edge. Method 2 calculates height intervals at various widths of edge. The idea of this method is the calculation of edge height , here is homogenous of is composed of wavelet coefficients of edge having unknown degree 1, vector is composed of pre-calculated wavelet height (is taken from given signal), vector coefficients of edge having known height.


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In real applications parameter is usually unknown. Therefore, a set of values is taken (sampled) from a given range. Then for every width the vector of bounds for height range is calculated. In order to make less pairs of parameters more than one function should be involved in the calculation.

5. Experimental results Method 1 gives a set of parameter pairs (a,b), and the interval of detected edge width is analysed. The mean lengths of all detected width intervals were calculated for a given (pre-defined) height and width of edge, for m = 3 DWT scale-levels. The results, presented in Figures 3a and 3b show, that accuracy of detected length is within 0.5 pixel for edges wider than 3 pixels. Analysis of intervals shows better results when detection is performed using CDF 5/3 at almost all widths of given edge. The Method 1 gives slightly worse results for CDF 9/7 – lossy compressed data. As the Method 2 calculates height intervals at various widths of edge, the shorter is the length of these intervals, the higher is accuracy of correct edge height (see Figures 3c and 3d). CDF 5/3 wavelet presented better results (lower mean length of intervals).

(a)

(b)

(c)

(d)

Figure 3. Modelling of detection: Method 1 (a,b) - mean length of detected width intervals;. Method 2 (c,d) - mean length of detected height intervals. Pre-defined edge height: (a,c) a=50; and (b,d) a=150.

Both methods produce many pairs of edge parameters, when local absolute maximum of DWT wavelet coefficients for a modelled edge have same values. The more different values of edge height ai are in pairs (ai , bi), the more difficult it is to distinguish an object. The results, presented in Figure 4, show that it is better to use CDF 9/7 than CDF 5/3 in Method 1, while CDF 5/3 is more appropriate in Method 2. If an object of interest has edge height less than pixels, then it is better to use

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coefficients of DWT with CDF 5/3 in Method 2; otherwise, the Method 1 with CDF 9/7 is more appropriate.

Figure 4. Mean number of different detected edge heights at pre-defined edge height

6. Conclusions Two methods have been proposed for assessment of edge parameters (height, width and position) using wavelet coefficients calculated by the JPEG200 image compression scheme from edges modelled by sigmoid edge function. The modelling results are encouraging to continue the research of edge detection in wavelet domain for virtual microscopy imaging. Analysis of these methods showed different and unambiguous correspondence of edge parameter vectors to wavelet coefficients. Variability of detected edge width for any height does not exceed 0.5 pixel size for edge wider than 3 pixels. Method 1 is more suitable for lossy compressed images, and Method 2 – for lossless compressed ones. Naturally, detection results in lossless compressed images are better than in lossy images, except the case when height of an object exceeds 150 – then the Method 1 is more accurate for height detection in lossy compressed images. Acknowledgements. The work has been carried out within the EU COST Action IC0604 “Telepathology Network in Europe: EURO-TELEPATH”, supported by the EU Science Foundation, R&D programme of the Kaunas University of Technology (Lithuania) and grant COST-42/10 from the Research Council of Lithuania. Authors express their gratitude to all the colleagues involved in the COST Action, especially to Professor Touradj Ebrahimi from École Polytechnique Fédérale de Lausanne for the valuable discussions on the subject of this paper.

References [1] Garcia Rojo, M. et al. Digital pathology in Europe: coordinating patient care and research efforts. Studies in Health Technology and Informatic), vol.150, pp. 997-1001, 2009. [2] Christopoulos, C. Skodras, A. Ebrahimi, T. The JPEG2000 Still Image Coding System: An Overview. IEEE Trans. Consumer Electronics, vol.16, pp.1103-1127, 2000. [3] Mallat, S. Hwang. W.L. Singularity Detection and Processing with Wavelets. IEEE Transactions on Information Theory, vol.38, pp. 617-643, 1992. [4] Kitanovski, V. Taskovski, D. Panovski. L. Multi-scale Edge detection Using Undecimated Wavelet Transform. Proceedings ISSPIT 2008, pp.385-389. [5] Angel, P., Morris C. Analyzing the Mallat Wavelet Transform to Delineate Contour and Textural Features. Computer Vision and Image Understanding, vol.80, pp.267–288, 2000.

Information Modeling, Storage and Retrieval



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Using Multimodal Mining to Drive Clinical Guidelines Development Emilie PASCHEa,b1, Julien GOBEILLa,c, Douglas TEODOROa,b, Dina VISHNYAKOVAa,b, Arnaud GAUDINATa,c, Patrick RUCHa,c, Christian LOVISb a BiTeM Group, Geneva, Switzerland b Division of Medical Information Sciences, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland c Information Science Department, University of Applied Sciences, Geneva, Switzerland

Abstract. We present exploratory investigations of multimodal mining to help designing clinical guidelines for antibiotherapy. Our approach is based on the assumption that combining various sources of data, such as the literature, a clinical datawarehouse, as well as information regarding costs will result in better recommendations. Compared to our baseline recommendation system based on a question-answering engine built on top of PubMed, an improvement of +16% is observed when clinical data (i.e. resistance profiles) are injected into the model. In complement to PubMed, an alternative search strategy is reported, which is significantly improved by the use of the combined multimodal approach. These results suggest that combining literature-based discovery with structured data mining can significantly improve effectiveness of decision-support systems for authors of clinical practice guidelines. Keywords. Multimodal mining, information retrieval, clinical guidelines, resistance profile, antibiotic cost.

1. Introduction Since the early use of antibiotics, it was observed that the selection pressure imposed by their massive employ led to a gradual acquisition of bacterial resistance to antibiotics, rendering them ineffective to treat infectious diseases. Thus it became a priority to regulate antibiotic use and clinical guidelines were developed in that intention. Evidence-based approach is being adopted by most of the organizations developing clinical guidelines, since it provides a very rigorous basis by directly linking the recommendation to evidence [1]. However, the systematic review of the literature required by this approach is a time-consuming and labor-intensive process [2]. As part of the DebugIT (Detecting and Eliminating Bacteria UsinG Information Technology) FP7 European project [3], we aim at facilitating clinical guidelines development and maintenance with the creation of an innovative tool called KART (Knowledge Authoring and Refinement Tool), which gathers literature search and information extraction capabilities based on an advanced question-answering framework. In a previous report [4], we presented an approach to help generating 1

Corresponding author: Emilie Pasche, University Hospitals of Geneva, Division of Medical Information Sciences, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 14, Switzerland; E-mail: [email protected].

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guidelines based exclusively on text-mining. A question-answering engine performed an automatic literature scanning, followed by the identification of hypothetical treatments, thus accelerating systematic reviews. Infectious disease experts can then validate the correct propositions out of the automatically-generated treatments. In this report, we describe how non-textual modalities and in particular clinical data as stored in operational clinical databases can be injected into the baseline system to improve recommendations, using an association model directly inspired by Aronson et al [5]. The structured data used in our experiments gathers clinically-observed resistance profiles, since it is well-known that performing antibiograms before prescription is the optimal way to prescribe an appropriate antibiotic, and prescription cost-related information, assuming healthcare should minimize health costs. The number of data analysis methods that can be used to combine multimodal contents is virtually infinite since learning algorithms and distance calculi are in general highly data independent. In our experiment we borrowed the methodological framework from the Cranfield paradigm [6] and the linear combination approach pioneered by Fox et al [7]. Numerous subsequent works have been reported to improve the basic method; however the original approach applied strictly to textual observations as for instance when several engines are combined to generate a meta-engine. In contrast, our fusion experiments merge text-generated associations with prior probabilities directly extracted from a clinical datawarehouse.

2. Data and Methods In this study, clinical guidelines are represented using a simplified design, assuming the following hypothesis: disease + pathogen + conditions = antibiotics. A questionanswering engine, EAGLi (Engine for Question-Answering in Genomics Literature, http://eagl.unige.ch/EAGLi) [8], is queried with the parameters disease, pathogen and conditions to retrieve a set of the most-cited antibiotics ranked by relevance. The computation of this set is based on the screening of 50 documents from which possible answers are extracted. In our experiments, the target terminology, corresponding to the space of possible answers, consists of 70 antibiotics normalized by their respective WHO-ATC code. A set of synonyms derived from the Medical Subject Headings (MeSH) is used to augment the recall of the answers. Two search engines are used; PubMed, a Boolean and chronological ranking and easyIR, a vector-based similarity ranking. The set of the most-cited antibiotics is then re-ordered based on the injection of costs and resistance profiles. The re-ranking is based on the attribution of penalty or bonus on the original relevance scores, resulting in a new ranking. Thus, expensive antibiotics and antibiotics with high resistance get lower ranks, while cheapest antibiotics and antibiotics with low resistance obtain a better rank. The injection of cost is based on a cost list containing 129 products, corresponding to 17 distinct substances, provided by the HUG (University Hospitals of Geneva) pharmacy supply chain. The very same substance can be mentioned several times (Table 1), representing different routes and/or dosages. We attempt to obtain a daily cost for each antibiotic present in the list. Prescription data of the HUG are used to obtain the number of daily doses usually prescribed given a route/dosage for each product. Moreover, as our system is based on the substance and not the marketed product, different products corresponding to the same substance must be aggregated.

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This is based on the prescription frequency of each product in the clinical data of the HUG. Finally, for antibiotics without cost information, we attribute an arbitrary cost. This value is set during the tuning phase by varying the bonus/penalty value from 0 (which expresses a minimal price) to 100 (which expresses a maximal price). The injection of resistance profiles is based on antibiograms present in the HUG Clinical Data Repository [9] of the DebugIT project. As antibiograms for the pair pathogen-antibiotic were retrieved for only 5% of the data, we decided to search antibiograms for the antibiotic only, disregarding the targeted pathogen. From these antibiograms, we extracted the number of resistant (R) and susceptible (S) outcomes. A susceptibility score is calculated for each antibiotic: S/(S+R). When no antibiogram data is available, an arbitrary susceptibility score is assigned. This score is obtained during the tuning phase by varying the bonus/penalty value from 0 (always resistant) to 1 (always susceptible). Table 1. Extract of the HUG’s cost table. Column Identifier ATC indicates the ATC identifier of the antibiotic. Column Term ATC displays the substance name. Column Int_Art_Ach mentions the name of the drug, as well as its form, dosage and number of doses in the box. Column Public cost indicates the cost of the article in Swiss Franc (for sake of confidentiality, real prices are not displayed). Identifier ATC J01MA02 J01MA02 J01MA02 J01MA02

Term ATC Ciprofloxacin Ciprofloxacin Ciprofloxacin Ciprofloxacin

Int_Art_Ach Ciproxinep.osusp 5g=100ml (pce) Ciproxinep.osusp 10g=100ml (pce) Ciproxinefiol 400mg=200ml (pce) Ciprofloxcpr 250mg (1x20)

Public cost 62.90 104.95 50.95 37.50

A collection of 72 rules extracted from the geriatrics guidelines of the HUG is manually translated and normalized to obtain a machine-readable benchmark [10], following the schema of our simplified clinical guidelines: disease + pathogen + conditions = antibiotics. The collection is divided into two sets: a tuning set of 23 rules used for the design of the optimal recommendation system and an evaluation set of 49 rules used for the validation of the final system on previously unseen contents.

3. Results In Table 2, we provide results of our baseline system, i.e. text-mining results as obtained without any additional knowledge show a top-precision of 40.37% when we use the PubMed engine and 34.28% when the easyIR [8] engine is used. Thus, we can compare two different search models. Although PubMed shows a higher precision, it is worth observing that the relative recall is much lower for PubMed. Thus, the PubMedbased search is able to answer 32 questions out of 49, while easyIR is able to provide answers to all questions. Results obtained when tuning the model with cost-related information are shown in Figure 1A. The best results for easyIR have been found when a null cost is attributed to antibiotics for which no cost information is available. Performances of PubMed-based search decrease with the injection of costs. Final results based on the evaluation set show a top-precision of 43.31% (+9.03%, p3 sec, reduced central pulse volume, decreased level or loss of consciousness) bilateral wheeze (bronchospasm), stridor upper airway swelling (lip, tongue, throat, uvula, or larynx) respiratory distress (tachypnoea, increased use of accessory respiratory muscles, recession, cyanosis, grunting)

Respiratory

Laboratory Gastrointestinal

Minor Criteria generalized pruritus without skin rash generalized prickle sensation localized injection site urticaria red and itchy eyes reduced peripheral circulation (tachycardia, a capillary refill time of >3 sec without hypotension, a decreased level of consciousness) persistent dry cough, hoarse voice difficulty breathing (no wheeze or stridor) sensation of throat closure sneezing, rhinorrhea Mast cell tryptase elevation > upper normal limit Diarrhoea, abdominal pain, nausea, vomiting

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Figure 1. A. ‘Anaphylaxis’ network and centrality metrics for the top 20 nodes; for illustration purposes, node labels are not presented and hub centrality diagram is reversed. B. ‘Anaphylaxis’ island and pattern.

4. Discussion This work demonstrates the potential use of NA for pattern identification in VAERS as this was discussed in our first study (also the first in the area) that dealt with the same issue [9]. Filling the gap of traditional approaches, we analyzed the multiple interactions of the critical terms (vaccines and PTs) in VAERS reports using a dataset related to adverse events reported after H1N1 vaccination. Through the anaphylaxis example, we showed that it is possible to isolate the densest region in a network using certain metrics and algorithms. Using a certain standard (e.g. BC criteria) this region

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could be characterized as a pattern that deserves further investigation. While not the focus of our study, NA might serve as an efficient way to begin development of Standardized MedDRA Queries [10]. This study has some limitations. First, we did not apply a statistical framework for identifying the anaphylaxis pattern but empirically evaluated the results of NA. Second, we did not follow a validated rule for selecting the node interval in the ‘islands’ algorithm; it was considered that this number should be adequate to reveal a strong pattern. It could be also argued that our sample included retrospectively classified reports and this might reduce the value of our analysis; however, our main scope was the investigation of the possible benefits from applying NA to VAERS data. Various algorithms have been applied before for the detection of clustered regions in a network. For example, Newman described the identification of communities based on the concept of modularity [11]. The evaluation of other approaches in addition to the ‘islands’ algorithm should be included in the next steps of our work. The evaluation framework should be extended to include a statistical aspect e.g. a thorough analysis of the centrality metrics. The current study is one step in evaluating the NA potential to recognize safety patterns in VAERS. We plan to further study this approach by addressing the aforementioned limitations and the application of our ideas to prospectively collected data for prediction purposes. Acknowledgements: This project was supported in part by an appointment to the Research Participation Program at the Center for Biologics Evaluation and Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. We thank the Medical Officers at FDA who evaluated the reports and those who reported them.

References [1]

Reblin T. AREPANRIX™ H1N1 Vaccine Authorization for Sale and Post-Market Activities. 11-122009. Canadian Ministry of Health. [2] Varricchio F, Iskander J, Destefano F, et al. Understanding vaccine safety information from the vaccine adverse event reporting system, The Pediatric Infectious Disease Journal, 23 (2004) 287-294. [3] Stephenson WP, Hauben M. Data mining for signals in spontaneous reporting databases: proceed with caution, Pharmacoepidemiology and Drug Safety, 16 (2007) 359-365. [4] Newman MEJ. Networks: an introduction., Oxford University Press, New York 2010. [5] Freeman LC. Set of Measures of Centrality Based on Betweenness, Sociometry, 40 (1977) 35-41. [6] Zaversnik M, Batagelj V. Islands, Sunbelt XXIV, 2004. [7] Batagelj V, Mrvar M. Analysis of Large Networks with Pajek, Sunbelt XXIX, 2009. [8] Ruggeberg JU, Gold MS, Bayas JM, et a. Anaphylaxis: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data, Vaccine, 25 (2007) 5675-5684. [9] Ball R, Botsis T. Can network analysis improve pattern recognition among adverse events following immunization reported to VAERS? Clinical Pharmacology & Therapeutics (in press). [10] Bate A, Evans SJW. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiology and Drug Safety, 18 (2009) 427-436. [11] Newman MEJ. Fast algorithm for detecting community structure in networks, Physical Review, 69 (2004).


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Using Pharmacogenetics Knowledge to Increase Accuracy of Alerts for Adverse Drug Events Yossi MESIKAa,1, Byung Chul LEE b, Yevgenia TSIMERMAN a, Haggai ROITMAN a Heon Kyu PARK b a IBM Research, Haifa, Israel b IBM Ubiquitous Computing Laboratory, Seoul, Korea

Abstract. Adverse drug event (ADE) has significant implications on patient safety and is recognized as a major cause of fatalities and hospital expenses. Although some medical systems today can help reduce the number of ADE occurrences, these primarily take into account clinical factors-even though recent studies show the significance of genetic profiles in ADE detection. Incorporating pharmacogenetics knowledge and data from genetic test results into these systems can improve the accuracy of preliminary alerts about potential ADEs. However, pharmacogenetics knowledge is unstructured, making it inappropriate for use in a system that involves automatic processing. We propose a methodology that can help incorporate the pharmacogenetics knowledge. Specifically, we show how pharmacogenetics knowledge can be expressed in a medical system and used together with the patient genetic data to provide alerts about ADEs at the point of care. Keywords. Pharmacogenetics, Adverse Drug Events, Warfarin

1. Introduction Adverse Drug Events (ADE) are usually defined as an undesirable effect in a patient, caused by a drug or the inappropriate use of a drug [1]. A common example of ADE is the administration of an overdose or underdose of a drug to a patient. In the US alone, ADEs are responsible for 6.7% of the hospitalizations, ranking just below emergency department visits [2]. A similar rate in the UK suggests that ADE is a serious worldwide phenomenon. Studies have indicated that about 28% of ADEs could be prevented [3]. Clearly, a reliable prediction system would increase patient safety. Apart from patient safety concerns, the prevention of ADEs also has significant economic implications for hospitals due to the expenses incurred by unplanned hospitalization. The return on investment (ROI) of a hospital that invests in methods to prevent ADE will be substantial [4]. ADE prevention can be achieved by incorporating an ADE alert system at the point of care. Such a system can either function as a component complementing the existing electronic medical records (EMR) system or integrated within the EMR system. Although some hospitals have developed ADE prevention systems, these solutions and 1

Corresponding author: Yossi Mesika, IBM Research, Haifa 31905, Israel; E-mail: [email protected].

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the integrated ADE knowledge remain proprietary. Furthermore, in many health organizations, the knowledge about ADE lies buried in the physicians' expertise, and gaining access to this knowledge could require a long formal process of education based on their experiences. In both cases, the ADE knowledge remains inaccessible to patients who store their medical records in public services such as Google Health2 or Microsoft HealthVault3. Traditionally, ADE knowledge is based on studies that determined a range of clinical factors explaining the variability of different patient reactions to certain drugs. However, in recent years, advances in the genetics domain and new pharmacogenetics research have shown a high correlation between specific genetic variations and ADEs [5, 6, 7]. For example, Han Chinese patients carrying the HLA-B*1502 haplotype have significantly higher risk for severe skin reactions associated with Carbamazepine, a drug commonly prescribed for the treatment of seizures [8]. Ideally, this new brand of pharmacogenetics knowledge could be used before initiating treatment to help reduce or completely eliminate ADEs. The idea is even more relevant today, now that genetic testing is more accessible and less expensive, and the number of direct-to-consumer (DTC) services that offer a comprehensive range of genetic testing to the public has increased [9]. The integration of genetic test reports and pharmacogenetics knowledge into existing health provider's medical system is not currently supported by existing ADE alert systems. This prohibits the prediction of ADEs that consider both clinical and genetic data at the point of care. Designing a generic system capable of providing ADE notifications based on a genetic profile of a patient is a difficult challenge. There are two main hurdles that need to be tackled to design such a system. First, the clinical and genetic factors are vast and usually cannot be found within only one type of document. Second, the research outputs are unstructured and therefore cannot be processed by a machine. A semantic data warehouse can assist in overcoming the first challenge [10]. In this paper, we target the second challenge with a systematic methodology that uses a rule-based approach for expressing pharmacogenetics knowledge and executing it over harmonized patient data. Our work proposes a generic system that can integrate patient data (clinical and genetic) and transform pharmacogenetics knowledge into a machine processable form. This ADE alerting service can be integrated into legacy medical systems (such as EMR) and provide caregivers with more accurate alerts.

2. Methods There is a knowledge gap between two domains that are required for developing of ADE alerting services. The first domain is the information technology (IT), which contributes the technical solutions for developing a complex medical system. The second domain is pharmacogenetics, where the knowledge exists in unstructured forms such as published papers, articles, research reports, and so on. The challenge of building an ADE alerting service is twofold. On the one hand, it is extremely difficult to integrate the pharmacogenetics knowledge because its unstructured form is difficult to process automatically. In addition, it is usually difficult for IT specialists, who are not familiar enough with pharmacogenetics, to express such knowledge. On the other 2 3

http://www.google.com/health http://www.healthvault.com


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hand, pharmacogenetics information is clear to medical experts; yet, they generally lack the technical skills and familiarity with machine languages needed for adding the knowledge to the system. We propose bridging this gap by breaking the development process into two separate phases, where each phase targets a different type of domain expertise. In the first phase, we extract the knowledge from the sources of information and generate an abstraction model. In this phase, we make use of concept mapping [11], a method for visualizing different concepts and the relationships between them. A concept mapping tool allows medical experts to describe the pharmacogenetics knowledge in a structured form without requiring any programming skills. After modeling several cases, we were able to determine common concepts and relationship types that can be predefined and reused across different models. For example, concepts like gene and allele are common to all the models that we constructed and their specific values can imply a variation in normal drug dosage. Aside from its ease-of-use, this library of common reusable concepts allows IT specialists to understand and work with the generated models. In the second phase, IT specialists transform the models into a set of executable rules using a technology specific language such as Java. In short, the first phase can be done by medical experts and the second one can be done by IT specialists. Once the models and rules are created, the results of patients' genetic tests results can be retrieved from hospital genetic labs or from external systems where independent genetic tests are performed. Once the data is available, the system can analyze it by executing the pharmacogenetics rules and provide alerts on drug overdose, drug allergy, interaction with other drugs, and so on. These alerts can be given to physicians at the point of care, allowing them to immediately take action and modify the prescription to minimize the chances of ADE.

3. Results We implemented a generic ADEs alerting system by incorporating clinical information, genetic profiles, and pharmacogenetics knowledge. We then validated this generic approach on common pharmacogenetics cases including drugs such as Abacavir, Azathioprine, Clopidogrel, Irinotecan, Panitumumab and Warfarin. We integrated the pharmacogenetics knowledge for each drug into the system in two phases: model expression and rules generation. The rest of this section provides the details of the process using the common case of Warfarin. Warfarin4 is an anticoagulant used for prophylaxis and treatment of thromboembolic disorders. The Warfarin drugs are responsible for 6.2% of all reported ADEs [2]. This high percentage provides tremendous incentive for creating a systematic solution that can assist in reducing the number of ADEs caused by this drug. Today, several clinical factors are already being taken into consideration to improve the estimation of a patient's initial therapeutic dose; these include: age, body surface area (BSA), smoker status, race, and so forth. Although clinical predictors provide an explanation for 17 to 22% of dose variability, recent pharmacogenetics studies show that 53 to 54% of variability in dose can be explained by including the CYP2C9 and VKORC1 genotypes [5]. As a result, FDA has approved updating the Warfarin label to include recommended therapeutic dose range based on the specific genotypes Table 1. 4

Also known by its brand names of Coumadin, Jantoven, Marevan, Lawarin and Waran

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Table 1. Warfarin expected initial therapeutic dose ranges VKORC1 (rs9923231) GG AG AA

CYP2C9 (rs1799853/rs1057910) *1/*1 *1/*2 *1/*3 (CC/AA) (CT/AA) (CC/AC) 5-7 mg 5-7 mg 3-4 mg 5-7 mg 3-4 mg 3-4 mg 3-4 mg 3-4 mg 0.5-2 mg

*2/*2 (TT/AA) 3-4 mg 3-4 mg 0.5-2 mg

*2/*3 (CT/AC) 3-4 mg 0.5-2 mg 0.5-2 mg

*3/*3 (CC/CC) 0.5-2 mg 0.5-2 mg 0.5-2 mg

The Warfarin pharmacogenetics information already exists on the drug label and in relevant papers and public sources5. However, the information is not structured, making it very difficult to have it automatically processed by a system. In our proposed system, a domain expert who is familiar with the relevant concepts can express the pharmacogenetics information in a visual model. The expert uses an intuitive concept mapping tool6 to describe the information visually by drawing the concepts and the relationships between them. We assist the expert by predefining a library of reusable components that represent common concepts in pharmacogenetics. A model that expresses the pharmacogenetics of Warfarin can be seen in Figure 1. A more comprehensive model that also considers non genetic factors (such as environmental, demographic and clinical) should be incorporated. The scenario above describes how we use our generic systematic approach for one common case of pharmacogenetics. The same approach can be applied for current and

Figure 1. Concept modeling of Warfarin pharmacogenetics.

5 6

Public knowledge sources such as SNPedia, dbSNP, PharmKGB and PubMed Such as ihmc CmapTools that is available freely from http://cmap.ihmc.us/


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future advances in pharmacogenetics, where correlations between drug ADEs and genetic variations are discovered. Some correlations also involve clinical conditions and environmental factors, which should also be part of the process to determine whether an ADE alert is relevant.

4. Conclusions Including pharmacogenetics in the process of determining potential ADE will increase the accuracy of the generated ADE alerts. In this paper, we suggested a methodology for incorporating pharmacogenetics knowledge into an ADE alerting system. This methodology can be extended to also represent knowledge for non genetic factors that have influence on the behavior of a medication. Integrating the various knowledge models related to a specific medication will result in a comprehensive accuracy. We distinguished between two types of expertise, medical and IT, which should be included separately in the process of implementing such a system due to the different disciplines. By bridging these two areas of expertise, we were able to generate an ADE notification service that considers both clinical and genetic data for a patient, ultimately reducing the number of ADEs. In addition to improving the quality of treatment, reducing the number of ADEs also has positive economic implications for care providers.

References [1]

Nebeker J. Clarifying adverse drug events: a clinician's guide to terminology, documentation, and reporting, Annals of internal medicine, 2004. [2] Budnitz DS, Pollock DA, Weidenbach KN, Mendelsohn AB, Schroeder TJ, Annest JL. National surveillance of emergency department visits for outpatient adverse drug events, Journal of the American Medical Association, 2006; 296(15):1858-66. [3] Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D. Incidence of adverse drug events and potential adverse drug events, Journal of the American Medical Association, 1995; 274(1):29–34. [4] Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, Petersen LA. The costs of adverse drug events in hospitalized patients, Journal of the American Medical Association, 1997; 277(4):307. [5] Gage B, Eby C, Johnson J, Deych E, Rieder M, Ridker P. Use of pharmacogenetic and clinical factors to predict the therapeutic dose of warfarin, Clinical Pharmacology & Therapeutics, 2008; 84(3):326– 331. [6] Hetherington S, Hughes AR, Mosteller M, Shortino D, Baker KL, Spreen W. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir, The Lancet, 2002; 359(9312):1121–1122. [7] Hoskins JM, Goldberg RM, Qu P, Ibrahim JG, McLeod HL. UGT1A1*28 genotype and irinotecaninduced neutropenia: dose matters, Journal of the National Cancer Institute, 2007; 99(17):1290-5. [8] Chung W, Hung S, Hong H, Hsih M. Medical genetics: a marker for Stevens–Johnson syndrome, Nature, 2004 [9] Hogarth S, Javitt G, Melzer D. The Current Landscape for Direct-to-Consumer Genetic Testing: Legal, Ethical, and Policy Issues. Annual Review of Genomics and Human Genetics. 2008; 9(1):161-182. [10] Bianchi S, Burla A, Conti C, Farkash A, Kent C, Maman Y. Next Generation Information Technologies and Systems. Springer Berlin Heidelberg. 2009. [11] Novak JD. The Theory Underlying Concept Maps and How to Construct Them. Florida: Institute for Human and Machine Cognition. 2006.

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Schizophrenia Prediction with the Adaboost Algorithm a

Jan HRDLICKAa, Jiri KLEMA a Department of Cybernetics, Czech Technical University in Prague, The Czech Republic

Abstract. This paper presents an adaBoost approach for schizophrenia relapse prediction. The data for the adaBoost are extracted from patients answers to Early Warning Signs questionnaires sent regularly via mobile phone messages. The performance of the adaBoost algorithm is confronted with current ITAREPS system with sensitivity 0.65 and specificity 0.73. AdaBoost has the same sensitivity 0.65 but higher specificity 0.84 and is then ready to became the part of the ITAREPS care program. Keywords. Schizophrenia, Prediction, Sensitivity, Specificity, AdaBoost

1. Introduction Evidence suggests that a relapse of schizophrenia is preceded by psychotic and nonpsychotic behavioural and phenomenological changes. The most common nonpsychotic prodromal symptoms are irritation, sleep problems, tense, fear, anxiety, quiteness or withdrawal [1]. Psychotic early prodromal symptoms resemble those of schizophrenia symptoms but are milder. For example, a milder experience of hallucinations might be termed "perceptual abnormalities", hearing voices etc. The evidences indicate that the prediction of schizophrenic relapse is a realistic goal and therefore an intervention based upon programs of early detection can reduce schizophrenic relapse [4]. ITAREPS (Information Technology Aided Relapse Prevention in Schizophrenia) was developed for rapid and targeted recognition of early warning signs of psychotic disorder relapse by Psychiatric Centre in Prague. This paper uses data from one-year double-blinded follow-up, but normally the ITAREPS is routinely used in clinical practice. The patient concerned in ITAREPS system responds ten questions included in patient’s questionnaire. For example, one of the questions is "Does your feeling of being laughed at or talked about changed for the worse during this week?". The observer – a family member or person close to patient - answers similar questions about patient’s mental health. Both the patient and the observer evaluate change for the worse of particular items by numbers between 0 and 4 and send responses via mobile phone message. Patiens and their family care takers are asked to send mobile phone message once a week. The alert sending algorithm in the ITAREPS is a simple sumand-threshold classifier. Information about the ITAREPS system can be found in [6, 7].

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2. Objectives and Measures Our goal is to predict schizophrenia relapse on the basis of SMS messages sent by patiens and their family informants. So we are looking for appropriate classifier to distinguish between messages preceding the hospitalization i.e. messages with prodromal symptoms and other messages. One of the most popular measures used among medical experts are sensitivity and specificity. The literature concerning these test measures, deals primarily with settings where a diagnostic test result, Y, is measured concurrently with the gold standard disease variable D. The sensitivity also called true positive rate (TPR) and 1-specificity, called false positive rate (FPR) are defined as Sens = TPR = P{Y = 1|D = 1} and 1Spec = FPR = P{Y = 1|D = 0}. Where D=1 indicates the presence of disease and D=0 denotes its absence. Y=1 is positive test result and Y=0 negative test result. In ITAREPS system, the diagnostic test (classifier outcome based on new message sent) is performed every week and it is trying to predict a future event - hospitalization, not the current state of the patient. Because of that, TPR function has to be extended for event times where the success of the prediction depends on a time lag between measure time s (message date) and a patient’s readmission time T. The time-dependent TPR function is then defined as (1) where the time lag is t and τ is some prespecified suitably large time distinguishing between positive and negative messages. The FPR, or 1-specificity, relates to subjects without hospitalization, or at least with hospitalizations having the measurementhospitalization time lag bigger than τ. Therefore, we define the time-dependent FPR as (2) note that the FPR is not a function of time since it accumulates over all subjects for whom t > τ.

3. Methods First, the question whether some types of the answer are bounded together, arises. For the answer, a hierarchical clustering of the questionnaire’s items (particular questions) was performed with complete linkage criterion and 1-correlation as a distance function. As it can be seen in Figure 1 there are some questions in the questionnaire with high correlation (for example 9 and 10) which should be bounded together. After consulting with a medical expert, four clusters were chosen. For patient’s questions these clusters were {1},{9;10},{2;6},{3;4;5;7;8}. The same clustering with four resulting clusters was made for observers questions.

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Figure 1. Dendrogram of hierarchical clustering of the particular questions

For the purposes of messages classification two types of features were extracted. The first is sum of all the past answers in particular cluster with exponential forgetting time weighting. Since there are four patients clusters and four observers clusters these are eight features. The second feature is a difference between the last two messages - again for every cluster separately. So as an input for the classification there are sixteen features. All these features are discretized by minimal entropy algorithm with minimum description length principle stopping criterion (MDLPC) introduced by Fayyad in [3]. In this paper, sample is set of above-mentioned features measured in time s, which is the time the message was sent. For these samples classification(prediction) is made. There are two classes within the data: The class D of the sample i is negative if the time lag t between the sample time s and the event time T is bigger than t. And the sample is positive if the time lag t is not greater than τ. (3)

For classification itself the two-class adaBoost algorithm described by Freund and Schapire in [5] is used. These weak rules for adaBoost algorithm are constructed from the features and the possible feature thresholds. Every rule is in form Feature >= threshold or Feature < threshold. Standard adaBoost algorithm is initialized by setting the same weights to all the training samples. But since there is much more negative samples than positive, it is natural requirement to concentrate on positive samples. So the the weights are initially distributed separately for negative samples: (4)

and for positive samples: (5)


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The base learning algorithm has 30 iterations in total, therefore the final classifier is a linear combination of 30 rules (weak classifiers).

4. Results Table 1 gives overview of the data. There are 71 patients and 15 hospitalizations within 12 hospitalized patients. The patients have sent 5389 messages and 139 messages are positive for τ = 42days. The τ was chosen on the medical expert advise as maxima length of prodromal symptoms. Table 1. Data Overview. Patients Hospitalization Positive Samples Negative Samples

71 15 139 5250

The resulting time-sensitivities, modeled as splines according to Cai in [2], are depicted in Figure 2. On the left of the figure is time-sensitivity of the original ITAREPS while on the right is adaBoost time-sensitivity. Both time-sensitivities are increasing with smaller measure-hospitalization time lag. This means the prediction of hospitalization is easier for messages closer to the hospitalization. Adaboost has lower sensitivity just before the hospitalization, but time-sensitivity is not decreasing with time lag so fast. Cumulative sensitivity and specificity was defined by Heagerty in [8]. Cumulative sensitivity is a ratio of true alerts to all positive samples. Cumulative specificity is same as (2) and is a ratio of true non-alerts to all negative samples. Where positive and negative samples are defined by (3). Youden index is Sensitivity+Specificity-1.

Figure 2. Time-sensitivity for original ITAREPS system and adaBoost classifier.

All the results can be seen in Table 2. Overall (cumulative) sensitivities are almost the same for both the ITAREPS and adaBoost while specificity is higher for adaBoost. This specificity increase means the false alerts for the program would decrease from 1403 in current ITAREPS to 824 false alerts with adaBoost. Table 2. Cumulative results Measure Specificity Cumulative Sensitivity Youden

ITAREPS 0.73 0.65 0.38

AdaBoost 0.84 0.65 0.49

All the adaBoost results were gathered through the patient-wise leave-one-out crossvalidation.

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5. Disscusion The adaBoost prediction algorithm has significantly higher specificity and thus generates less false alerts then current ITAREPS system. While the overall cumulative sensitivity is same for both the algorithms, adaBoost generates more timelyinterventions, it’s sensitivity decreases slower with measure-hospitalization time lag than in ITAREPS system. The adaBoost classification algorithm is ready to became the part of the ITAREPS care program. Ackowledgements: Jan Hrdlicka‘s work was supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS10/279/OHK3/3T/13. Jiri Klema’s work was supported by the research program No. MSM 6840770012 "Transdisciplinary Research in Biomedical Engineering II" of the CTU in Prague.

References [1]

[2] [3] [4] [5] [6]

[7]

[8]

M Birchwood, J Smith, F Macmillan, B Hogg, R Prasad, C Harvey, and S Beringg, Predicting relapse in schizophrenia - the development and implementation of an early signs monitoring-system using patiens and families as observes, a preliminary investigation. Psychological Medicine 19(3) (1989), 649–656. TX Cai, MS Pepe, YY Zheng, T Lumley, and NS Jenny, The sensitivity and specificity of markers for event times. Biostatistics 7(2) (2006),182–197. UM Fayyad and KB Irani, On the handling of continuous-valued attributes in decision tree generation, Machine Learning, 8(1) (1992), 87–102. PB Fitzgerald, The role of early warning symptoms in the detection and prevention of relapse in Schizophrenia, Australian and New Zealand Journal of Psychiatry, 35(6) (2001), 758–764. Y Freund and RE Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1) (1997), 119–139. F. Spaniel, P. Vohlidka, J. Kozeny, T. Novak, J. Hrdlicka, L. Motlova, J. Cermak, and C. Hoeschl, The Information Technology Aided Relapse Prevention Programme in Schizophrenia: an extension of a mirrordesign follow-up, International Journal of Clinical Practice, 62(12) (2008), 1943–1946. Filip Spaniel, Pavel Vohlidka, Jan Hrdlicka, Jiri Kozeny, Tomas Novak, Lucie Motlova, Jan Cermak, Josef Bednarik, Daniel Novak, and Cyril Hoschl, ITAREPS: Information technology aided relapse prevention programme in schizophrenia, Schizophrenia Research, 98(1-3) (2008), 312–317. Heagerty PJ, Lumley T, Pepe MS, Time-dependent ROC curves for censored survival data and a diagnostic marker, Biometrics, 56(2) (2000), 337-344.


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Applying One-vs-One and One-vs-All Classifiers in k-Nearest Neighbour Method and Support Vector Machines to an Otoneurological Multi-Class Problem a

Kirsi VARPA a1, Henry JOUTSIJOKI a, Kati ILTANEN a Martti JUHOLA a Computer Science, School of Information Sciences, University of Tampere, Finland

Abstract. We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurological data using 10-fold cross-validation 10 times with kNearest Neighbour (k-NN) method and Support Vector Machines (SVM). The results showed that the use of multiple binary classifiers improves the classification accuracies of disease classes compared to one multi-class classifier. In general, OVO classifiers worked out better with this data than OVA classifiers. Especially, the OVO with k-NN yielded the highest total classification accuracies. Keywords. multi-class classification, binary classifiers, otoneurology, k-nearest neighbour method, support vector machines

1. Introduction Multi-class classification problems can be difficult to understand. Especially, if the application domain is not so familiar before, it can be hard to conceptualize the domain. Whenever creating new computer systems into new domains, it is important to have understanding about domain concepts, their relationships and differences. In order to distinguish classes better, one way is to convert the multi-class problem into multiple two-class problems [1, 2]. This may also help separation of classes. Earlier we have studied otoneurological data, for example, by using machine learning (ML) methods like decision trees [3] and neural networks [4]. Previous studies have shown that certain disease classes are difficult to recognize: they easily mix up with other classes [5]. From the literature, studies can be found where this kind of problem has been eased with using One-vs-One (OVO, also called round robin or pairwise class binarization) [6] and One-vs-All (OVA, also known as one-against-all, one-vs-rest) [7] solutions (i.e. using several binary classifiers instead of trying to classify all the classes at the same time with one classifier). Beforehand, it is not possible to say which of these solutions is better than others. Therefore, we examine the use of multiple binary classifiers to help the classification of vertigo data, and to find out which classifier solution seems to work the best with this data. 1 Corresponding author: Kirsi Varpa, Computer Science, School of Information Sciences, FI-33014 University of Tampere, Finland; E-mail: [email protected].

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K. Varpa et al. / Applying One-vs-One and One-vs-All Classifiers

In this paper, we examine the effect of using multiple binary classifiers instead of using only one multi-class classifier. Binary classifiers used are OVO and OVA classifiers with a k-Nearest Neighbour (k-NN) method [8] and Support Vector Machines (SVM) [9].

2. Data and Methods The k-NN classifier is a widely used, basic instance-based learning method that searches for the k most similar cases of a test case from the training data [8]. It can be used with both binary and multi-class problems. The k-NN classifier used in this research was implemented in Java. The nearest cases were searched with k= 1, 3, 5, 7, 9, 11 and 13. The best k-NN varied between classes, so, we selected NN classifier with k=5 (5-NN) into the comparison to SVM. (In addition, 5-NN was used in our earlier study [5]). The k-NN method used Heterogeneous Value Difference Metric (HVDM) [10] since our data included nominal, ordinal and quantitative attributes. SVM is a newer, more sophisticated ML method to be used in the separation between two classes [9]. It is a kernel-based classification method [11, 12]. Originally, it was made for the binary classification tasks, but later it has been extended for the multi-class cases [13]. The basic idea in SVM is to generate an input space dividing hyperplane such that the margin, the distance between the closest members of both classes, is maximized. The use of SVM was expanded by the invention of kernel trick, where the input space is mapped with a non-linear transformation into higher dimensional space [14, 15]. In the research, we used the binary SVM implementation of Bioinformatics Toolbox of Matlab with the Least-Square method [16] as a basis for the multi-class extensions. SVM runs were made with linear, polynomial (d=2,3,4,5), Multilayer Perceptron (MLP) (scale κ in [0.2,10]; bias δ in [-10,-0.2]) and Gaussian Radial Basis Function (RBF) (scaling factor σ in [0.2,10]) kernels with box constraints [0.2,10] (κ, δ and σ with intervals 0.2). The best kernel functions, linear and RBF, were selected into comparison. ML methods were tested with an otoneurological data containing 1,030 vertigo cases from nine different vertigo diseases (Table 1). Data was collected at Helsinki University Central Hospital during several years [3]. The dataset used in this research consists of 94 attributes concerning a patient’s health status: occurring symptoms, medical history and findings in otoneurologic, audiologic and imaging tests. More detailed information about the collected patient’s information is provided in [17] and in [4] 38 main attributes are described. From the 94 attributes, 17 were quantitative (integer or real) and 77 were qualitative: 54 binary (yes/no) and 23 categorical attributes. Clinical tests are not done to every patient and, therefore, values are missing in several test results. In total, the data had about 11% missing values, which allowed using imputation. Imputation was needed due to calculation of the SVM method. Missing values of qualitative attributes were imputed (substituted) with class modes and missing values of other attributes with class medians. The imputed data was used with k-NN in order to keep it comparable to SVM. A 10-fold cross-validation (CV) was repeated 10 times using each time different random data divisions. Training and test set divisions into 10-fold CV were created with Matlab. In divisions, the ratios of disease classes were maintained in different CV folds. CV was used with both ML methods.

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In OVA runs, we had nine (n_classes) binary classifiers: each one of them was trained to separate one class from the rest. A test sample was input to each classifier and a final class for the test sample was assigned according to the winner-takes-all rule from a classifier suggesting a class. For OVO runs, we trained 36 (n_classes·(n_classes − 1)/2) binary classifiers between all pairs of the classes. A test sample was solved with each binary classifier. Table 1. Nine disease classes and their absolute and relative frequencies in the otoneurological data. Average true positive rates (TPR) of disease classes, median of TPR and total classification accuracies with machine learning methods 5-NN and SVM linear and RBF using OVO and OVA classifiers from ten 10-fold crossvalidation runs in percents. Used kernel parameters with SVM linear and RBF presented below the table. OVO Classifiers Disease Name (Abbreviation)

Cases 1,030(100%)

OVA Classifiers

5-NN

5-NN

SVM linear

SVM RBF

5-NN

SVM linear

SVM RBF

Acoustic Neurinoma (ANE)

131 (12.7)

89.5

95.0

91.6

87.2

90.2

90.6

90.7

Benign Positional Vertigo (BPV)

173 (16.8)

77.9

79.0

70.0

67.0

77.6

73.5

78.6

Menière's Disease (MEN)

350 (34.0)

92.4

93.1

83.8

90.1

89.8

87.8

91.5

Sudden Deafness (SUD)

47 (4.6)

77.4

94.3

88.3

79.4

87.4

61.3

58.1

Traumatic Vertigo (TRA)

73 (7.1)

89.6

96.2

99.9

99.3

77.7

79.9

96.7

Vestibular Neuritis (VNE)

157 (15.2)

87.7

88.2

82.4

81.4

85.0

85.4

84.3

Benign Recurrent Vertigo (BRV)

20 (1.9)

3.0

4.0

20.0

16.5

8.0

21.0

8.0

Vestibulopatia (VES)

55 (5.3)

9.6

14.0

16.5

22.8

15.8

15.3

13.5

Central Lesion (CL)

24 (2.3)

5.0

2.1

26.0

28.5

15.0

19.0

15.8

Median of TPR

77.9

88.2

82.4

79.4

77.7

73.5

78.6

Total Classification Accuracy

79.8

82.4

77.4

78.2

78.8

76.8

79.4

Linear kernel with box constraint bc = 0.20 (OVO and OVA) RBF kernel with bc = 0.4 and σ = 8.20 (OVO), bc = 1.4 and σ =10.0 (OVA)

In OVO, the results of pairwise decisions were combined, thus having 36 class suggestions (votes) for the class of the test sample altogether. The final class for the test sample was chosen by the majority voting method, the max-wins rule [1]. A class, which gained the most votes, was chosen as the final class. If a tie situation occurred in the max-wins (OVO) or winner-takes-all (OVA) rules, the final class, within the tied classes, was solved in SVM by 1-NN, whereas k-NN searched for the nearest case from the classifiers belonging to the tied classes and selected the class with minimum distance to the test case. If the test case did not get any class by using k-NN with OVA (every classifier voted 0), the class was searched from the whole learning set with normal 1-NN.

3. Results In the Table 1, mean true positive rates (TPRs) and total classification accuracies of the ten 10-fold cross-validations are presented for 5-NN and SVM with linear and RDF

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kernels. Both methods were run by using OVO and OVA classifiers. The 5-NN method was also run in a basic way by using all nine disease classes in a classifier, i.e. all of the training cases class labels were used when searching for the nearest case to the test sample. The basic 5-NN was used as a baseline in the comparison of the predictive accuracies of the methods. The mean number of tie situations occurring during 10 times repeated 10-fold CV with OVO classifiers was 20.3 with 5-NN (standard deviation SD=4.8), 7.2 using SVM linear (SD=2.6) and 2.6 with SVM RBF (SD=1.5). With OVA classifiers, the number of ties was higher, as expected: 5-NN 167.1 (SD=3.8), SVM linear 49.8 (SD=4.7) and SVM RBF 25.8 (SD=4.2). With 5-NN OVA classifier all of the ties (16.2%) happened when a case could not be classified at all, ties with all nine classifiers, whereas 5-NN OVO classifier had ties (2.0%) with two or three classes (mainly BPV, MEN and VES). The results show that the use of multiple binary classifiers improves the TPRs of disease classes. The best results were yielded with OVO in 5-NN: it had the highest median of TPR and total accuracy. With this data, the OVO classifiers mainly increase TPRs and the total accuracies, whereas OVA classifiers have slightly decreasing effect on classification. However, there were exceptions also with this: SVM with MLP and polynomials 4 and 5 worked better with OVA classifiers. Usually, MLP is one of the best kernel functions used in SVM, but with this data it did not work at all (total accuracy 25.5% with OVO and 68.5% with OVA). It could also be seen with k-NN that the bigger k, the closer the results with OVO, OVA and the basic k-NN came (except with disease classes SUD and TRA).

4. Discussion In this research, we concentrated on studying the effect of splitting the multi-class problem into several binary classifiers and the voting procedure within two different ML methods, the k-NN and SVM classifiers. Splitting a problem into several binary problems helps to understand data better, especially with OVO classifiers in k-NN. The OVO classifiers aid to see which classes are difficult to separate and which ones distinguish well from the others. Diagnosis of the otoneurological disorders is demanding. For example, in [18], 1,167 patients participated in research but only for 872 patients could be made confirmed diagnosis and in [19], ten of the 33 test cases had to be excluded from the test because even the expert physician could not give them a definite diagnosis. Diseases can simulate each other in the beginning having symptoms of similar kind and symptoms can vary in time making recognition difficult [18, 20]. Classification accuracy of the medical professionals with the data of this study having 1,030 cases has not been tested because this would be an enormous task for them to do. However, a smaller number of cases (23) have been classified with a group of physicians [19]. We need to remember that classification tasks in this research were performed with the imputed data. In real life, there usually occur missing data because clinical tests are not done to every patient automatically. Thus, TPRs and total classification accuracies in this research might be a little bit higher than with the original data having missing values. There occur some differences in the way how ML methods used in the research handle data. SVM treats each attribute as quantitative, whereas k-NN using HVDM


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distance metric makes a different calculation depending on the type of the attribute (quantitative or qualitative). In the future, we shall expand the use of the voting procedure to involve handling the results of several different classification methods (e.g. k-NN, nearest pattern method of an otoneurological expert system [21] and Naive Bayes [8]), thus, forming a hybrid decision support aid. Being able to use results of several ML methods simultaneously strengthens the support of decision making. Acknowledgements: The authors wish to thank Erna Kentala, M.D., and prof. Ilmari Pyykkö, M.D., for their help in data collection during the years and their valuable aid in domain expertise. The first and second authors acknowledge the support of the Tampere Doctoral Programme in Information Science and Engineering (TISE).

References [1] [2] [3] [4] [5]

[6]

[7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

Friedman JH. Another approach to polychotomous classification. Stanford University; 1996 Oct.14 p. Allwein EL, Schapire RE, Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res. 2000;1:113–141. Viikki K. Machine learning on otoneurological data: decision trees for vertigo diseases [PhD Thesis]. Tampere, Finland: University of Tampere; 2002. Siermala M, Juhola M, Kentala E. Neural network classification of otoneurological data and its visualization. Comput Biol Med. 2008;38(8):856–866. doi:10.1016/j.compbiomed.2008.05.002. Varpa K, Iltanen K, Juhola M. Machine learning method for knowledge discovery experimented with otoneurological data. Comput Methods Programs Biomed. 2008;91(2):154–164. doi:10.1016/j.cmpb.2008.03.003. Fürnkranz J. Round robin rule learning, In: Brodley CE, Danyluk AP, editors. ICML-01. Proceedings of the 18th International Conference on Machine Learning; 2001. Williamstown, MA: Morgan Kauffman; 2001. P.146–153. Rifkin R, Klautau A. In defense of one-vs-all classification. J Mach Learn Res. 2004;5:101–141. Mitchell T. Machine Learning. New York: McGraw-Hill;1997. Debnath R, Takahide N, Takahashi H. A decision based one-against-one method for multi-class support vector machine. Pattern Anal Appl. 2004;7(2):164–175. doi:10.1007/s10044-004-0213-6. Wilson DR, Martinez TR. Improved heterogeneous distance functions. J Artif Intell Res. 1997;6:1–34. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–297. Vapnik VN. The Nature of Statistical Learning Theory. 2nd ed. Springer; 2000. Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw. 2002;13(2):415–425. Christiani N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press; 2003. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2:121–167. Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters. 1999;9:293–300. Kentala E, Pyykkö I, Auramo Y, Juhola M. Database for vertigo. Otolaryngol Head Neck Surg. 1995;112:383–390. Kentala E. Characteristics of six otologic diseases involving vertigo. Am J Otol. 1996;17(6):883–892. Kentala E, Auramo Y, Juhola M, Pyykkö I. Comparison between diagnoses of human experts and a neurotologic expert system. Ann Otol Rhinol Laryngol 1998;107(2):135–140. Havia M. Menière’s disease prevalence and clinical picture [PhD Thesis]. Helsinki: Department of Otorhinolaryngology, University of Helsinki; 2004. Auramo Y, Juhola M. Comparison of inference results of two otoneurological expert systems. Int J Biomed Comput. 1995;39:327–335.

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Roogle: An Information Retrieval Engine for Clinical Data Warehouse Marc CUGGIAa, Nicolas GARCELONa, Boris CAMPILLO-GIMENEZa, Thomas BERNICOTa, Jean-François LAURENTb, Etienne GARINb, André HAPPEc, and Régis DUVAUFERRIERa a UMR 936 Inserm, Faculté de médicine de Rennes. France b CRLCC Centre Eugène Marquis, Rennes, France c Intermède – Guignen. France

Abstract. High amount of relevant information is contained in reports stored in the electronic patient records and associated metadata. R-oogle is a project aiming at developing information retrieval engines adapted to these reports and designed for clinicians. The system consists in a data warehouse (full-text reports and structured data) imported from two different hospital information systems. Information retrieval is performed using metadata-based semantic and full-text search methods (as Google). Applications may be biomarkers identification in a translational approach, search of specific cases, and constitution of cohorts, professional practice evaluation, and quality control assessment. Keywords. Information retrieval, electronic patient record, ontology, indexing

1. Introduction As of today, medical informatics is going through a strong shift of paradigm. Data from medical reports are most of the time not structured. They are however of high medical value as they correspond to the expert interpretation of the clinician. These information sources consist therefore in a data repository potentially highly relevant for scientific research. From this perspective, the combined exploitation of metadata and information contained in exam reports on a large data corpus by tailored search engines becomes very relevant. The main objective of the R-oogle project is to implement a system aimed at offering to researchers the possibility to exploit, for a scientific goal, the huge amount of medical data that are metadata and exam reports with the ease of Googletm search engine, combining search methods for structured data elements and full text. The objective of R-oogle is to implement a platform consisting of: (i) A Clinical data warehouse (CDW) containing a large collection of patient data coming from different hospitals (ii) A search engine combining semantic search and full text search (with semantic enrichment) exploiting information contained in the exam reports.

2. Background Building and exploiting a multi-domain medical CDW built from Electronic Health Records (EHR) is currently an active research topic, e.g. Rubin et al [1] or the open

M. Cuggia et al. / Roogle: An Information Retrieval Engine for Clinical Data Warehouse

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source platform (I2B2) [5] developed by Harvard. Information retrieval (IR) is also a very active research domain and the medical field appears to be very suitable for such techniques, despite medical documents were, as for semantic ambiguity, more suitable for indexing than documents from other domains [2]. Ehrler, Ruch et al proposed in 2007 [3] an approach based on the full text indexing of medical reports, exploiting the context in which can be found information on the report’s structure (motive of the exam, description, conclusion). In this vein, Spat and Cadonna [4] described a system for document retrieval based on the metadata enriched by automatically extracted concepts from the reports indexed in German. A literature review done in 2008 [6] on 174 publications showed the increased scientific activity around IR on EHRs, despite significant storage and processing limitations to implement systems outside of experimental context. In a previous publication, we evaluated contribution of full-text search versus encoding data with the Diagnosis Related Groups (DRG)) in an epidemiological study context [7]. This study highlighted the contribution of full-text search to the DRG database search. In this work, we added semantic enrichment to the search engine, and we compared document retrieval with or without semantic enrichment. Shultz and al. developed MorphoSaurus, a German concept-based document search engine, connected to hospital information system in order to support search across the whole corpus of patient discharge letters and other clinically relevant documents [8].

3. Material and Methods Building the biomedical data warehouse: A “star” data base schema is used. CDW includes patients, full-text documents and structured documents linked to thesauri. Each document is defined as a medical production by a medical department on one patient, at a specific date. Data come from different software, so common kind of information have been defined to all documents (i.e. patient’s identification number, date, author, title, type of document, or text). Into some full-text documents, zones of specific texts can be extracted: motive, results, conclusion, technique, exam, and medical issues. These metadata have been added in the document description to help targeting a search. Structured data have been recorded into a separate table with a scalable architecture to allow integration of heterogeneous data. Each data is attached to a document as a data element, wherever applicable a thesaurus (such as the Logical Observation Identifiers Names and Codes for laboratory data (LOINC)), combined with a data value (such as a date, a number or text), wherever applicable a thesaurus for the data value (such as the French procedure classification (CCAM) used for encoding the DRG data). The CDW was implemented with the Oracle® database management system. Scripts using the Open Source Talend ETL were programmed to feed the database. The first step consisted in loading documents produced between 2005 and July 2010 from different sources. These scripts will be used to feed the CDW “on the fly”. Patient identity mapping: Data sources come from two distinct medical hospitals, so we implemented an algorithm mapping the different identification numbers of the two establishments. The mapping is successively realized following four methods, from the most accurate (identical surname or maiden name, first name, sex and birth date) to the least one (surname or maiden name, sex, date of birth and the first four letters of the first name). The accuracy criterion is applied when mapping so that

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manual mapping of patient identification numbers is allowed for the least accurate method. The identification numbers of the Rennes Cancer Institute are stored to ease patients mapping in future imports and to provide a way to search the data warehouse by its own identification numbers. Semantic enrichment and indexing documents: Medical concepts are extracted from reports using NOMINDEX [9], a concept extraction tool based on the ADM tool (Aide au Diagnostic Médical). Medical concepts associated to each document are stored in the structured part of the data warehouse. Concepts are restricted to the MeSH thesaurus. Lucene, an Open Source full-text engine written in Java by Apache, performed document indexing with tri-grams [10]. Full-text search is optimized and very elaborate queries may be composed (such as boolean, fuzzy, joker) on the entire information contained in a document or on part of the metadata (e.g.: +(mediator benfluorex) +conclusion:valvulop All French synonyms issued from the UMLS (Unified Medical Language System Metathesaurus) and the hierarchical parents of concepts are also integrated in indexing. Subsomption is therefore taken care of during the indexing of the document and not at the query time, the whole Lucene capabilities remaining intact for allowing complex queries. Developing multi-criteria search engine: The first part of the engine consists in a high-level search: patient’s sex, age range and medical department producing the document. The second part consists in a full-text search using Lucene syntax and the third part consists in the structured search. This way the user will have the capability to build structured queries on whole disjoined documents or only on specific parts of documents (e.g. motive, conclusion). Information retrieval assessment: We placed the evaluation in a non-habit user perspective, without complex structured queries. The corpus of assessed documents consisted of the textual part of multidisciplinary prostate cancer reviews. This one was selected for the natural language properties of disease descriptions contained into this part, because our main objective was to assess the potential benefit of documents’ semantic enrichment in a full text search problematic. The assessment was performed with two contextual designs, one where search terms were presumed to be found with a high level of occurrence in the whole documents (i.e. prostatic adenocarcinoma), and one with a low level of search term occurrence (i.e. heart failure). Four types of search process have been conducted on the corpus of documents: one with and one without semantic enrichment by the search engine, one exact match term search by a human medical expert, and one textual search with clinical interpretation of each document by a human medical expert too. We used recall, precision with their 95% confidence interval and f-measure to describe the whole assessment results.

4. Results Status of the data warehouse and the search engine: The CDW was fed by six sources of heterogeneous data, five sources managed by Rennes hospital (pathology reports, radiology reports, hospitalization/consultation reports, gastrointestinal endoscopy reports, DRG data) and one source managed by Rennes Cancer Institute (imaging reports). We are not yet reaching completeness, and the CDW contains as of today 2 115 581 documents. The results of a query are displayed as a table of the retrieved documents. When the user opens a document, the text searched through the full-text


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search engine is highlighted to ease validating the document (like Google cache). The user can then display all the documents on the patient as a sortable list or according to a temporal representation modeled as a Gantt diagram. The user has then the possibility to add the selected patient in a “cohort” he/she is building or, on the contrary, rule out this patient. All the documents of enrolled or ruled-out patients for the current cohort will be displayed as attached to patients already enrolled or ruled out, and won't appear to to be checked again. Other functionalities will provide another general view of the CDW. Table 1. Results of the evaluation in the high term prevalence context TP

FP

TN

FN

Recall [95% CI]

Precision [95% CI]

Fmeasure [95% CI]

nWES search engine 142 1 100 15 0,90 [0,86-0,95] 0,99 [0,98-1,00] 0,95 * HEM search WES search engine 157 26 75 0 1,00 0,86 [0,81-0,91] 0,92 * HEM search nWES search engine 141 2 45 70 0,67 [0,60-0,73] 0,99 [0,97-1,00] 0,80 * HCI search WES search engine 180 3 44 31 0,85 [0,81-0,90] 0,98 [0,97-1,00] 0,91 * HCI search nWES : without semantic enrichment ; WES : with semantic enrichment; HEM : human exact match; HCI: human clinical interpretation; TP: true positive; FP: false positive; TN: true negative; FN: false negative; CI: confidence interval.

Evaluation results: We evaluated the contribution of semantic enrichment of the document database for the search engine. Two hundred and fifty eight notices of prostatic cancer multidisciplinary meetings have been analyzed. Related to the high term prevalence context using the combined query: “adenocarcinoma” AND “prostatic”, the search without semantic enrichment retrieved 143 documents, the search with semantic enrichment retrieved 183 documents, the human exact match search retrieved 157 documents and the human search with clinical interpretation retrieved 211 documents. Related to the low term prevalence context using the combined query: “heart” AND “failure”, the search without semantic enrichment retrieved 0 documents, the search with semantic enrichment retrieved 7 documents, the human exact match search retrieved 0 documents and the human search with clinical interpretation retrieved 8 documents. To compare complete results of the search engine and the human evaluation, see table 1 and table 2. Table 2. Results of the evaluation in the low term prevalence context TP

FP

TN

FN

Recall [95% CI]

Precision [95% CI]

Fmeasure [95% CI] -

nWES search engine 0 0 258 0 * HEM search WES search engine 0 7 251 0 0 * HEM search nWES search engine 0 0 250 8 0 * HCI search WES search engine 4 3 247 4 0,50 [0,15-0,85] 0,57 [0,21-0,94] 0,53 * HCI search nWES : without semantic enrichment ; WES : with semantic enrichment; HEM : human exact match; HCI: human clinical interpretation; TP: true positive; FP: false positive; TN: true negative; FN: false negative; CI: confidence interval.

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5. Discussion - Conclusion In this paper we demonstrated the feasibility and applicability of a CDW that benefits from full-text search capabilities, as opposed to I2B2 that is mainly based on a structured data approach and does not address French NLP specificities. We however applied NLP methods for annotating reports using relevant concepts as well as synonyms and ancestors. This enrichment permitted to deal with subsumption issues using full-text search, and also to cluster cases by projecting reports on a MeSH hierarchy. Results show that semantic enrichment provides a better recall while precision stays quite stable. This is the best situation for prescreening patient for clinical trials. Prescreening aims at spotting patients better too often than too seldom. Knowing that each document returned by the searching engine, can be quickly checked and validated by an end-user (with the keyword highlighting feature), it is then really easy to rule out un-relevant documents. Even though temporality is taken care of through the Gantt diagram representation and the multi-documents search within a single hospitalization is possible, the search engine could be improved to address specific situations, e.g. retrieving hospital-acquired infection cases would require the detection of a positive bacteremia at 48h or later after the admission. We encountered some issues related to reference terminologies we used to encode patient data in the CDW (e.g concerning the integration of lab test, as for Cormont et al [11], we were confronted with the lack of coverage and the missing French translations of LOINC). As perspective, we are currently working a web portal intended to research technicians and investigators. This portal aims at managing the workflow to access to the CDW. Acknowledgement: We would like to thank the CRITT Santé Bretagne for their financial support and Delphine Rossille.

References Rubin DL, et al. A data warehouse for integrating radiologic and pathologic data,. J Am Coll Radiol, 2008. 5(3): p. 210-7. [2] Ruch P, et al. Comparing general and medical texts for information retrieval based on natural language processing: an inquiry into lexical disambiguation. Stud Health Technol Inform, 2001. 84(Pt 1): p. 2615. [3] Ehrler F, et al. Challenges and methodology for indexing the computerized patient record, Stud Health Technol Inform, 2007. 129(Pt 1): p. 417-21. [4] Spat S, et al. Enhanced information retrieval from narrative German-language clinical text documents using automated document classification, Stud Health Technol Inform, 2008. 136: p. 473-8. [5] Murphy SN, et al. Integration of clinical and genetic data in the i2b2 architecture. AMIA Annu Symp Proc. 2006:1040. Available at: Consulté novembre 30, 2010. [6] Meystre SM, et al. Extracting information from textual documents in the electronic health record: a review of recent research, Yearb Med Inform, 2008: p. 128-44. [7] Cuggia M, et al. A full-text information retrieval system for an epidemiological registry, Studies in Health Technology and Informatics, vol. 160, n°. 1, p. 491-495, 2010 [8] S. Schulz, Daumke P, Fischer P, Müller ML. « Evaluation of a document search engine in a clinical department system », AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium, p. 647-651, 2008. [9] Happe, A, et al. Automatic concept extraction from spoken medical reports, Int J Med Inform, 2003. 70(2-3): p. 255-63. [10] Hatcher E, et al. Lucene in action, Action series. Manning Publications Co., Greenwich, CT, 2004. [11] Cormont S, et al. Construction of a dictionary of laboratory tests mapped to LOINC at AP-HP, In Actes AMIA Annual Fall Symposium 2008, page 1200, Washington, DC, novembre 2008. AMIA

[1]


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Truecasing Clinical Narratives a

Markus KREUZTHALERa, Stefan SCHULZa,b,1 Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria b Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany

Abstract. Truecasing, or capitalization, is the rewriting of each word of an input text with its proper case information. Many medical texts, especially those from legacy systems, are still written entirely in capitalized letters, hampering their readability. We present a pilot study that uses the World Wide Web as a corpus in order to support automatic truecasing. The texts under scrutiny were Germanlanguage pathology reports. By submitting token bigrams to the Google Web search engine we collected enough case information so that we achieved 81.3% accuracy for acronyms and 98.5% accuracy for normal words. This is all the more impressive as only half of the words used in this corpus existed in a standard medical dictionary due to the excessive use of ad-hoc single-word nominal compounds in German. Our system performed less satisfactory for spelling correction, and in three cases the proposed word substitutions altered the meaning of the input sentence. For the routine deployment of this method the dependency on a (black box) search engine must be overcome, for example by using cloud-based Web ngram services. Keywords. EHR, NLP, WWW

1. Background Most significant patient-related content in electronic health records is contained in free text narratives [1, 2]. The scenarios in which these texts are produced vary across institutions, and their quality depends on the authors, the target readers, and institutional quality standards. Hastily written notes, typed by a physician or a nurse directly into the computer, tend to exhibit a lower quality in spelling, grammar, style and layout [3], compared to discharge letters, which are first dictated by a resident, then transcribed by a typist, proofread by the author, and finally validated by the staff physician before being sent out to another clinic or to the patient’s GP. As well as the hurried speed in which texts are often produced, there may be technical factors responsible for the bad quality of text. Although most up-to-date text entry interfaces offer the levels of functionality users are accustomed to in modern word processors or e-mail clients, legacy systems still exist which restrict the text entry to 7bit ASCII, thus not permitting lower case characters or diacritics. As a consequence, users familiar with these systems often persist in writing in this style even after migrating to a new system. Although it is simply a matter of time before new texts are no longer produced under these restrictions, and writers of these texts will have familiarized themselves with 1

Corresponding Author: Stefan Schulz.

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the production of correctly capitalized texts, 7bit ASCII text still continues to exist in clinical text corpora. Such legacy data is not only an important resource for retrospective research and clinical care, but also for the training of statistical natural language processing (NLP) systems [4]. The distribution of capital letters inside of a text token depends on its current context, which strongly impacts on the intelligibility of texts [5]. Practical applications of truecasing include the processing of raw input text, such as the output from speech recognition systems, as well as spelling and grammar correction systems. Just as other NLP approaches, truecasers rely on tagged corpora for the training of statistical models such as MaxEnt or SVN. Most truecasing experiments have been performed on newspaper corpora, for which the main use case was the identification of proper names characterized by initial capital letters. Languages differ in their capitalization rules, and German constitutes a special case; in contrast to most languages initial capitals are mandatory for all nouns (and nominalised adjectives and verbs) and therefore are not specific to proper names.

2. Materials and Methods Corpus: 3,542 German-language pathology reports, containing a total of 83,818 words, were extracted from the Graz University Hospital Information System, covering a broad range of clinical disciplines. The texts had been dictated by physicians and entered by typists into a character-based user interface. The reports are entirely in upper case and do not use diacritics such as "Ä", "Ö", or "Ü". Dictionary coverage: the coverage of these words using a German-language medical dictionary [6] was calculated. Sampling: A random sample of 100 sentences was taken, with an average of 9.3 words (SD = 7.9; MIN = 2; MAX=38, Median = 7) per sentence. The following characters were considered sentence delimiters: [.;:!?]. Periods within abbreviations (e.g. "etc.") were not considered as delimiters. Preparation: all remaining punctuation characters and parentheses were removed. Gold standard: for each sentence a corrected version was created. Corrections included not only the restitution of the case, but also spelling and grammar corrections where necessary according to the 1996 German orthography reform, the medical spelling rules in accordance with German medical publishers, and [6]. Reference corpus: The case information was extracted employing the WWW as a corpus. The Google search engine was used for harvesting correct case information. Algorithm: Each sentence with n characters was dissected into overlapping bigrams B1 ... Bn-1. All bigrams are sent to the search engine as a phrase search (quoted). The hits (as displayed in bold face in the summary) of the pages returned by the search engine are saved within a map data structure. The two maps from the same token (Tk+1 which is the second token in Bk and the first token in Bk+1) are merged. A weight W is assigned, directly proportional to the number of occurrences in the map and indirectly proportional to the Levenshtein edit distance [7] of the term to be corrected. The token with the maximum calculated weight is accepted as the corrected token. The edit distance is used because the search engine can also return near matches for quoted phrase searches if, for example, there are very few exact matches for that phrase on the Web.


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In the case that a token is not resolved by either bigram, a single-token (quoted) search is performed. If even this search does not yield any results, the token is decapitalized (with an upper case initial character) and diacritics are restored by applying the rule ["ae" "ä"; "Ae" "Ä"; "oe" "ö"; "Oe" "Ö";"ue" "ü"; "Ue" "Ü"], according to German character transcription rules. The algorithm was implemented in Java, using JDOM, Tagsoup and XPath (XML Path Language). The search requests were spaced by moderate delays so that the strain on the search engine was minimal.

3. Results Table 1 shows a typical correction result and clearly visualizes the increase in readability after truecasing. Table 1. Original text (left), automatically corrected text (right).

CHRONISCHE HEPATITIS MIT GERING BIS MITTELGRADIGER AKTIVITAET (HEPATISCHER AKTIVITAETSINDEX 6 VON 18) UND MITTELGRADIGER BIS HOEHERGRADIGER PORTALER UND MITTELGRADIGER INKOMPLETTER UND KOMPLETTER PORTOPORTALER UND PORTOZENTRALER FIBROSE (FIBROSESCORE 4 VON 6)

Chronische Hepatitis mit gering bis mittelgradiger Aktivität (hepatischer Aktivitätsindex 6 von 18) und mittelgradiger bis höhergradiger portaler und mittelgradiger inkompletter und kompletter portoportaler und portozentraler Fibrose (Fibrosescore 4 von 6).

A comparison of the types in the entire corpus with the Pschyrembel clinical dictionary [6], a standard reference for German clinical terminology, showed an astonishingly low lexical coverage of 51%; of 7500 types in the text corpus only 3808 match any token in the entire dictionary corpus. This result is mainly due to the high productivity in single-word compounding (a similar result can be seen in [8]) and, to a minor extent, the use of spelling variants.

Figure 1. Typical search result. The bigrams in bold face are picked by the algorithm.

Figure 1 shows a fragment of a typical search result, from which the sequences in bold face are extracted. Table 2 exemplifies the decision algorithm.

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Table 2. Decision algorithm for "CHRONISCHE". Input Bigram 1 Frequency Bigram 2 Frequency

GERINGGRADIGE CHRONISCHE GASTRITIS GERINGGRADIGE CHRONISCHE Geringgradige chronische geringgradige geringgradige" CHRONISCHE GASTRITIS Chronische Gastritis chronische

7 15 6 2 9 14 5

Merged

Frequency

Chronische Gastritis Geringgradige chronische geringgradige geringgradige"

Decision

chronische

9 14 7 20 6 2

After the automated correction procedure, 55 of 100 sentences resulted in being equivalent to the spelling and truecasing gold standard. If equivalent expressions and acceptable spelling variants are included this rate increases to 62 and 72, respectively. In several cases it was observed that a word with standard spelling was converted to the non-standard spelling variant, as the latter occurred sufficiently more frequently on the Web. It is well known that few health professionals are perfectly proficient in spelling standard Latin. Some rules are complicated; situations require that a "c" in a Latin word stem should be converted to "k" or "z" as soon as they are no longer in a Latin syntactic context, e.g. "Ulcus ventriculi" but "Magenulkus". A synopsis of the results is given in Table 3. Table 3. Results.

Correction Phenomenon Right case correction of normal words Right case correction of acronyms Meaning of sentence affected by correction Spelling / grammar error corrected New grammar error after processing

896 13 3 1

Total

Units

909 16 100 5 1

tokens tokens sentences sentences sentence

The figures show an impressive accuracy of 98.5% of capitalized non-acronym tokens which were transformed into the correct case. The rate is not as good for acronyms (81.3%). Also, the procedure affected the meaning of three of the one hundred sentences. Only one of five known spelling errors was corrected, and one additional grammar error was introduced after processing. The accuracy of 98.5% slightly outperforms the truecasing result reported by [4] on news articles. However, our method does not clearly separate between truecasing and spelling correction. This was justifiable under the constraints of our research, as the motivating factor for this work was the restrictive nature of the 7-bit ASCII character set which does not only preclude the use of lower-case characters but also of diacritics (in the case of German, mainly the "ä", "ö", "ü", and "ß" characters). The dependence on the Google Web search interface, and its non-predictable output in those cases


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where there was no match, led to strange corrections such as, for instance, proposing "maximaler" as a correction for "minimaler". This distortion of a document's content is, of course, not acceptable, and challenges the unsupervised applicability of the truecasing system. In a future version we will therefore introduce a more conservative edit distance threshold for corrections (after applying the diacritic transcription rules). Additionally, the dependence on Google Search as a black box system, which can not tolerate any major upscaling, is an unknown quantity upon which no routine system could realistically be based. An alternative would be to use Web n-gram services made available by Yahoo!, Google, and Microsoft Research [9].

4. Conclusions We demonstrated that the use of the World Wide Web as a corpus can impressively improve the legibility of legacy texts in medical record systems that use 7-bit ASCII encoding. As the texts under scrutiny were German-language pathology reports, both German diacritics and its associated capitalization rules had to be taken into account. By submitting token bigrams to the Google Web search engine we collected enough case information so that we achieved an accuracy of 81.3% for acronyms and of 98.5% for normal words. This is all the more impressive as only half of the word types used in this corpus could be found in a comprehensive standard medical dictionary. Our system performed less satisfactory for spelling correction, and in three cases proposed word substitutions that altered the meaning of the input sentence. For the routine deployment of this method the dependency on a (black box) search engine must be overcome, for example by using cloud-based Web n-gram services.

References [1] Barry J. Value of unstructured patient narratives. Current EHRs capture most information--patient demographics, medications and problem lists--as structured data, and often codify the details to support billing instead of clinical activities. Health Management Technology. 2010; 31 (7): 6-7. [2] Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors. New England Journal of Medicine. 2010; 25; 362(12): 1066-1069. [3] Peters AC, Nohama P, Pacheco E, Schulz S. Análise de erros de linguagem em sumários de alta.. XII Congresso Brasileiro de Informática na Saúde, Oct 18-22, 2010, Porto de Galinhas, Brazil: http://www.itarget.com.br/newclients/cbis2010.com.br [4] Lita LV, et al. tRuEcasIng. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL 2003), July 7-12, Sapporo, Japan. [5] Batista F, et al. Language Dynamics and Capitalization using Maximum Entropy. Proceedings of ACL08: HLT, Short Papers (Companion Volume), pages 1-4, Columbus, Ohio, USA, June 2008. [6] Pschyrembel W. Pschyrembel Klinisches Wörterbuch Version 2. CD-ROM for Windows 3.x/95/98 de Gruyter, Bln. 1999; ISBN: 3110166208. [7] Levenshtein VI. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet Physics. Doklady, volume 10, pages 707-710, 1966. [8] Schulz S, Hahn U. Morpheme-based, cross-lingual indexing for medical document retrieval. International Journal of Medical Informatics 2000 Sep; 58-59:87-99. [9] Zhai, et al. Web N-gram Workshop. Workshop of the 33rd International ACM SIGIR Conference (2010) http://research.microsoft.com/en-us/events/webngram/sigir2010web_ngram_workshop_proceedings.pdf.

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Checking Coding Completeness by Mining Discharge Summaries Stefan SCHULZa,c,1,Thorsten SEDDIGa, Susanne HANSERa, Albrecht ZAIßa, Philipp DAUMKEb a University Medical Center Freiburg (UMCF), Germany b Averbis GmbH, Freiburg, Germany c Medical University of Graz, Austria

Abstract. Incomplete coding is a known problem in hospital information systems. In order to detect non-coded secondary diseases we developed a text classification system which scans discharge summaries for drug names. Using a drug knowledge base in which drug names are linked to sets of ICD-10 codes, the system selects those documents in which a drug name occurs that is not justified by any ICD-10 code within the corresponding record in the patient database. Treatment episodes with missing codes for diabetes mellitus, Parkinson's disease, and asthma/COPD were subject to investigation in a large German university hospital. The precision of the method was 79%, 14%, and 45% respectively, roughly estimated recall values amounted to 43%, 70%, and 36%.. Based on these data we predict roughly 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes. Keywords. Clinical Coding, Diabetes Mellitus, Parkinson's Disease, Obstructive Lung Disease, Natural Language Processing, Electronic Patient Records

1. Background Health services research, outcome assessment, disease reporting and reimbursement in hospitals require valid and complete data on diagnoses at discharge. It is well known that clinical coding results exhibit extensive weaknesses [1, 2]. Reimbursement systems based on diagnosis related groups (DRGs) tend to increase coding quality [3]. For the clinical controller the main question is whether coding optimization prevents the loss of revenue, whereas for the clinical epidemiologist there is a concern that the coding performed by DRG-savvy coders penalizes the documentation of those conditions that are known to be irrelevant for reimbursement. We present an approach that is suited to bring to light undocumented diagnoses, i.e. conditions that play a certain role in the clinical process but are non remembered (or deemed irrelevant) when it comes to the assignment of ICD codes at discharge. Our hypothesis is that medication at discharge can give an important hint to which ICD codes may be missing. However, manual review of the patient record with the aim to identify missing information and to re-code the discharge profile is time consuming, and requires in-depth medical knowledge. The most trustworthy source here is the discharge summary, as a comprehensive structured documentation of medication is 1

Corresponding author: E-mail: [email protected]

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often missing. In the treatment episodes under scrutiny summaries mostly finish with a "drugs at discharge" list, because this information is important for the follow-up treatment by the patient's general practitioner. The objective of this study is to employ a simple text mining approach to predict missing codes. We focus on three diseases, which were known to be readily omitted in coding, according to the long-lasting experience of two of the authors (SH, AZ) in the UMCF medical control department: (i) diabetes mellitus, (ii) Parkinson's disease, (iii) bronchial asthma and chronic obstructive pulmonary disease (COPD).

2. Materials and Methods Documents. We used a corpus of 34,865 in-patient discharge summaries from UMCF, covering all clinical disciplines (except psychiatry) for one year. Each discharge summary represents one treatment episode (one patient may occur more than once). The corpus was split by random into a training corpus (n=17,000) and a test corpus (n=17,865). The summaries show a broad variation between clinical departments. Information on drugs occurred in several sections (family history, patient history, lab, evolution, medication at discharge) with large disparities in layout and formatting. Annotations. Via a unique ID each summary is linked to one treatment episode and a list of one to many ICD-10 codes with which the episode has been manually annotated for reimbursement, based on the German DRG (diagnoses related groups) system. Rule Bases. For the three diseases under scrutiny a rule base was built, relating drug names with their indications. The official indications were acquired from two databases, MMI and RL [4, 5], completed by additional drug indications found in the training corpus in order to capture off-label usage. Both commercial drug names and ingredient names were included. Unspecific name parts like "sodium" or "hydrochloride" were ignored. For each drug a rule was encoded as a triple (D, P, N) with D (drug name) being a string of characters, P a list of ICD codes (p1…pn) for the diseases under scrutiny ("positive list"), and N a list of ICD codes (n1…nm) detailing other indications for this drug ("negative list"). Only the first there ICD digits were mandatory. E.g., in the Parkinson rule base, for D = "Madopar", the positive list contains the code fragments P = {G20, G21, G22}, covering also more specific codes like G21.3. Extensive negative lists had to be built for anti-Parkinsonian and bronchodilatatory drugs, whereas, no negative list was necessary for antidiabetic drugs. Filter algorithm. For each target disease the rule base was applied to the entire document corpus using the following algorithm, implemented as a Python script (documents had been made available as plain text, extracted from the original RTF format): For each document: For each drug name: If drug name matches text token: If no match between any discharge ICD code and any code in the negative or positive list: Return the document ID

Thus, all those discharge summaries are selected that mentioned a drug for which no ICD annotation justified its administration. The execution of this algorithm on the training data discovered some sources of error, e.g. drug names that are homographs of

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patient names, others which also occur in laboratory results, as well as treatment episodes in which the drug can theoretically be justified by a code from the negative list, although the summary clearly tells that this drug was prescribed to treat a disease from the positive list, mentioned in the record but not coded in the patient management system. Finally, there are cases where antidiabetic and antiparkinsonian substances occur in lab procedures. Such cases were difficult to decide without further information and therefore constitute a source of potential false positive candidate documents. Evaluation methodology. For calculation of the precision a samples (n = 3 * 50) of the candidate texts retrieved by the above algorithm was analyzed by a domain expert. A gold standard for roughly approximating the recall was created as follows: The filter was tested on a document set which has already been annotated with a ICD code of interest. So we modified the above algorithm in order to obtain a rough estimator. For each document: If annotated with ICD code from positive list For each drug name: If drug name matches text token: If drug name is not justified any code from the negative list: Return the document ID

by

The number of documents returned by this procedure divided by the number of documents annotated with a code from the positive list yields the recall estimator for the given rule set (by disease). A low rate of documents retrieved by this code indicates either (i) that the medication is missing in the document or (ii) that there is no drug treatment at all. The first option is not very frequent because all discharge summaries are forwarded to the patients' GP, who generally expects a complete list of drugs at discharge. Note that the set of correctly coded episodes is not representative and the derived values must be interpreted as very rough estimates.

3. Results For the test set (n=17,865) Table 1 shows the output of algorithm 1 and the result of the relevance assessment for the estimation of precision. As an overall result 1.3 percent of all cases lacked an ICD code for either diabetes, asthma / COPD or Parkinson's disease. The differences in precision can be explained by the fact that antidabetic drugs are very specific to diabetes, while antiparkinsonian drugs are used for a broad range of diseases. Table 1 Candidates for missing codes as returned by algorithm 1 and estimated precision after rating of 50 treatment episodes per disease.

As introduced above, we estimated the recall by applying the filter on the set of already coded cases (Table 2). All these treatment episodes are annotated by some code for


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diabetes, Parkinson's or asthma. The high recall for Parkinson's is consistent with the fact that most of these cases take a specific medication. The lower rates for the other two disease groups comply with the cases of diabetes treated by diet only and the lighter obstructive lung diseases which are only treated in case of exacerbation. Table 2 Recall estimation based on correctly coded diagnoses.

Taking in account the recall estimates, and considering the test set and the entire data there are approximately 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes. Table 3 Analysis of false positive cases

The analysis of false positive cases (Table 3) reveals insulin administration in cases of intensive care, provocation tests, as well as a reference to the measurement of probably endogenous insulin in blood. Table 4 explanation for false negative cases

For Parkinson's, false positives derive from the fact that for a broad range of rare neurologic diseases anti-Parkinsonian drugs are used. Antiasthmatic drugs, finally, are used in a series of severe pulmonary diseases like lung cancer for which the bronchial obstruction is rather a symptom than a disease on its own. Most of false negative cases are due to disease cases which are not treated by drugs and, to a minor extent, drugs that are missing in the rule base, or misspelt drug names.

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Recent studies have applied various text mining approaches for the extraction of drug or substance names from medical texts [9, 8, 7, 6, 10]. [11] emphasizes importance of the drug / disease relationship. These studies highlight that extraction of drug names extend the medical record use case we were focusing on. Equally important is the mining of literature abstracts in order to extract generic medical knowledge.

4. Conclusions A computationally simple, high-throughput text mining approach retrieved missing secondary ICD-10 codes of hospitalized patients. For three selected chronic diseases we obtained a rate of together under 2% undercoded treatment episodes, which demonstrates a fairly good coding quality, although the rate is expected to be higher considering a broader array of typical secondary diseases. It supports the observation that although DRG-based reimbursement systems have led to an increased coding quality for major diseases, diseases deemed secondary or unrelated to the actual clinical problem tend to be omitted, given that that they have no impact for DRG grouping. Precision and recall of the proposed information extraction system can be increased in two directions. The rule base must be improved, as our data clearly demonstrate questionable quality of the pharmacopeia used. Off-label indications added, and, ideally, additional knowledge should be acquired by medical experts. This became especially evident when we analysed the potential additional indications for antiparkinsonian drugs. For better investigating the justifications for drugs often used for symptomatic treatments (such as bronchodilatators) additional knowledge associating symptoms with the underlying diseases would also be helpful. The information extraction system can be improved by allowing fuzzy string match and by better identifying the discourse context in which the text string of interest occurs (thus ignoring, e.g. the occurrence of substance names in the lab result section). The latter will also support the harvesting of additional diseases names which occur in the summary but are not coded.

References [1]

Sackett DL. Clinical disagreement. How often it occurs and why. Canadian Medical Association Journal, 123:499–536, 1980. [2] Barnum JF. The misinformation era: the fall of the medical record. Annals of Internal Medicine 10: 482–484, 1989 [3] Stausberg J. Die Kodierqualität der stationären Versorgung. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitschutz, 20:1039–1046, 2007. [4] Medizinische Medien Informations GmbH: www.mmi.de, last accessed 5th February, 2011 [5] ROTE LISTE®: www.roteliste.de, last accessed 5th February, 2011 [6] Schönbach C, Nagashima T, Konagaya A: Textmining in support of knowledge discovery for vaccine development. Elsevier, Amsterdam (ISSN 1046-2023: 2004, vol. 34) [7] Jimeno A et al. Assessment of disease named entity recognition on a corpus of annotated sentences. BMC Bioinformatics. 2008; 9 (Suppl 3): S3 [8] Hauben M, Reich L: Data mining, drug safety, and molecular pharmacology: potential for collaboration. The Annals of pharmacotherapy, Whitney, Cincinnati (2004) [9] Garten Y, Altman R: Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text. BMC Bioinformatics, 2009; 10 (Suppl +2): S6 [10] Dunkel M, Günther S, Ahmed J, Wittig B, Preissner R: SuperPred: drug classification and target prediction. Nucleic Acids Res. 2008 Jul 1;36 (Web Server issue):W55-9. [11] Phoebe M. Roberts, William S. Hayes: Information needs and the role of text mining in drug development. In Pacific Symposium of Biocomputing 2008, 592-603.

Privacy and Security



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Healthcare Professionals’ Experiences With EHR-System Access Control Mechanisms Arild FAXVAAGa,1, Trond S JOHANSENa, Vigdis HEIMLYb, Line MELBYa, Anders GRIMSMOa a Norwegian EHR Research Centre, Faculty of Medicine, University of Science and Technology (NTNU), Trondheim, Norway b Department of Computer and Information Science, Faculty of Information Technology, Mathematics and Electrical Engineering, NTNU, Trondheim, Norway

Abstract. Access control mechanisms might influence on the information seeking and documentation behavior of clinicians. In this study, we have surveyed healthcare professionals in nursing homes and hospitals on their attitudes to, and experiences with using access control mechanisms. In some situations, the access control mechanisms of the EHR system made clinicians postpone documentation work. Their practice of reading what others have documented was also influenced. Not all clinicians logged out of the system when they left a workstation, and some clinicians reported to do some of their documentation work in the name of others. The reported practices might have implications for the safety of the patient. Keywords. Electronic health record systems, Information security, Access control, Patient safety

1. Introduction Modern healthcare is information-intensive in the sense that clinical work depends on access to relevant and updated information about the patient while it at the same time leads to new information about the patient. The data, which often is of very sensitive nature, must be made available to those providing care while at the same time kept protected from unauthorized access. To this means, most jurisdictions have developed personal health data protection acts. Increasingly, patient-specific health data are being presented through electronic health record systems (EHR-systems). The balance between securing legitimate access and protecting data from unauthorized access is achieved through the use of authentication and authorization mechanisms [1], [2]. Because of a more extended use of advanced technologies and more specialized personnel, an increased number of healthcare personnel come into contact with the patient. Increasingly, more than one healthcare institution collaborates on providing the care [3]. This adds further complexity to the issue of keeping the data secure while ensuring sufficient access. 1

Corresponding author: Arild Faxvaag, The Norwegian EHR Research Centre, Medical-Technical Research Centre, N-7489 Trondheim, Norway; E-mail: [email protected]

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The details of access control mechanisms might have profound impacts on the information seeking behavior of healthcare personnel. Many studies have addressed how access control mechanisms should influence on the information working conditions of healthcare professionals, but little is known on how such mechanisms actually influences on the work of healthcare personnel. Access control mechanisms might make access to relevant information more cumbersome and time consuming, not only leaving information less available but also jeopardizing the processes of updating the patients records. Different methods of implementing information security policies might have different impacts on the users of the system. Some aspects of the behavior of users might be inferred from the study of access logs [4], but how healthcare professionals perceive the use of access control mechanisms is largely not known. EHR Monitor is an annual survey that monitors the implementation and use of EHR systems in Norway [5]. The survey is directed towards GPs, municipal care and hospitals and collects data by means of questionnaires. In the 2009 survey, we wanted to explore healthcare professionals attitudes to, and experiences with using access control mechanisms was included in the study. We here present the results.

2. Materials and Methods 2.1. Development of the Questionnaire In the last ten years, we have surveyed healthcare professionals on their use and perceived benefits of using EHR-systems e.g. [6]. As paper-based health records gradually have been withdrawn from clinical workflow, we have observed that healthcare professionals an have become more dependant on access to the EHRsystems for doing their work [7]. Informal observations of clinical work indicated numerous problems with getting access to EHR information and doing documentation work. To explore this, we developed a set of questionnaire items related to the respondents’ perceived use of time while presenting his user name and password to the access control component of the EHR-system and the effects of the automatic logoff mechanism. Other questionnaire items were related to the impact of the access control mechanisms on how the respondents work with the patient’s EHR. Response was given on a five-point Likert scale (For the first five questions: Strongly disagree – Disagree – Neither agree or disagree – Agree – Strongly agree; For the remaining six questions: Always – Most of the time – Half of the time – Rarely – Never). The 11 questions were distributed as a section in the EHR Monitor questionnaire form. An English translation of the wording of the questionnaire items is given in Figure 1 and 2. 2.2. Selection of Participants The survey was directed towards healthcare professionals in nursing homes and hospitals. The municipalities are responsible for the care provided by the Norwegian nursing homes whereas the hospitals are owned by the state. Nursing homes: 45 of a total of 430 Norwegian municipalities were selected on the basis of size and geographic distribution in such a way that they could be regarded as representative for the national average. In municipalities with more than one nursing home, only one of these was selected. The nursing homes were contacted by e-mail and phone and invited to participate in the study. 29 nursing homes agreed to participate. 590 questionnaires


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were distributed. Of these 239 (41%) were returned. Hospitals: For each of the 21 selected hospitals, two clinical departments were invited to participate in the survey. These employed a total of 1352 clinicians. We developed an electronic version of the questionnaire (Questback), distributed a link to the questionnaire to the leaders of the clinical departments and asked them to forward this link to their employed clinicians. A total of 206 questionnaires (15%) were returned. The participants used many different EHR-systems. All systems have implemented access control mechanisms as this is mandatory according to Norwegian law [1]. The user has to present his user name and password to gain access to patient information in the EHR-system. Also the user has to log on to the work station.

3. Results Clinicians must log on to the EHR-system before they start reading in the patient’s EHR or do documentation work. A majority of clinicians reported that they spent too much time dealing with the access control mechanism. Approximately two out of three agreed that every login to the system took too much time (60% in the nursing homes, 62% in the hospitals). Most (56% in the nursing homes, 60% in hospitals) reported that they rarely failed to log on to the system, but as much as 32 % of respondents in the nursing homes and 27% of the hospital respondents reported to have experienced the opposite (figure 1). In most clinical settings, documentation work and patient work is performed in a sequence. The information security regulations make it mandatory for the user to log off the system once he or she is done with the current work process in the EHR-system. This also has the effect that the workstation becomes available for the next user. Sometimes clinicians leave the workstation without logging off the system. 36 % of the respondents in nursing homes and 45 % of hospital employees reported that they often had to log other users off before they could start using the system (Figure 1).

Figure 1. Clinicians’ opinions about and experiences with logging on to EHR systems.

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Six questionnaire items concerned the effects of access control mechanisms on the efficiency, timeliness and some quality aspects of documentation work (figure 2).

Figure 2. Consequences of the use of access control mechanisms / login routines.

More than one out of three reported that the login routines often contributed to delays in their work. In the nursing homes, 52% reported that the access control mechanisms had them relay messages about the patient orally rather than documenting in the patient’s EHR. Access control mechanisms also influenced on use of the EHR system for reading purposes. 46% of the respondents from the nursing homes reported that the logon procedure contributed to that they fail to look up in the patient’s EHR in advance of providing care to the patient. Many reported that they often postponed documentation work until they could find the time to log on to the system. This practice was most prominent in the nursing homes. Among the hospital employees, 16% reported that documentation work always or often was done in the name of others (figure 2).

4. Discussion In this study we have surveyed clinicians in Norwegian hospitals and nursing homes on their experiences with access control. The access control mechanisms of today’s EHRsystems might fulfill the formal demands of the information security regulations but having to use the very same mechanisms are perceived to have a negative impact on clinical work. Access control mechanisms make clinicians postpone documentation work but also alter their practice of reading what others have documented. Not all clinicians log out of the system when they leave the workstation, and some clinicians report to do some of their documentation work in the name of others (i.e. while another user is logged on to the system). Since the survey participants report of behaviors that not are in accordance to the regulations, one might assume that they would tend to underestimate. The practices that have been uncovered might therefore be more common than estimated in the survey. Another limitation of the study is that the users


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only have responded by picking the most appropriate response on a Likert scale. Why the clinicians postponed documenting is not clear. This, and other aspects of the participants’ use of the access control mechanisms could have been uncovered by interviews. The results might be interpreted in the perspective of time constraints and the fact that clinicians do much of their work away from a workstation. Throughout the workday at a ward, many clinical activities take place at the bedside. The patients have conditions that require continuous attention and care. Spending time taking care of the patient must be prioritized. From the perspective of clinicians, maneuvering through an access control mechanism takes time and therefore is perceived as having a cost. Due to time constraints, clinicians might have to choose between working with the patient and spending the same precious minutes logging on to an EHR system. In this situation some of the reported practices might be interpreted as workarounds, developed to accommodate their use of the EHR-system with other clinical tasks [8]. Workarounds like those that have been uncovered in this survey have also been observed in Danish hospitals [9]. The findings are relevant in the perspective of patient safety. Delayed updates of the patient’s EHR, and preferring to share patient information through oral communication makes the information in the EHR less reliable. Not checking the patient’s EHR before providing care to the patient is a potentially dangerous practice. The results should also inform information security policy makers. The results should also have implications for those implementing access control mechanisms. To our knowledge, none of the EHR systems in use have done usability tests of their access control implementations.


[4] [5] [6] [7] [8] [9]

The Norwegian Directorate of Health. Available from http://www.helsedirektoratet.no/vp/multimedia/ archive/00012/Summary_of_The_Code__12645a.pdf Rindfleisch, TC, Privacy, information technology, and health care. Communication of the ACM 80 (1997) , 92-100. Meingast, M, et al. Security and privacy issues with health care information technology. Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE. 5453-5458. Røstad, L, Access Control in Healthcare Information Systems PhD thesis (2009) Available at http://www.idi.ntnu.no/~lilliaro/docs/lr_phd_final.pdf (PDF). Heimly, V, Diffusion and use of Electronic Health Record Systems in Norway. Stud Health Technol Inform 160 (2010), 381-5. Lærum, H, et al. Doctors' use of electronic medical records systems in hospitals: Cross sectional survey BMJ 323 (2001), 1344-1348 Lium, J-T, et al. From the front line, report from a near paperless hospital: Mixed reception amongst health care professionals. JAMIA 13 (2006), 668-75. Ash, JS, Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors, JAMIA 11 (2004), 104-112. Mabech, H, [Electronic prescription in clinical practice] PhD thesis (2008). Available at http://vbn.aau.dk/files/16956379/Elektronisk_medicinering.pdf (PDF).

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Personal Health Information on Display: Balancing Needs, Usability and Legislative Requirements Erlend Andreas GJÆRE a, Inger Anne TØNDEL b, 1, Maria B. LINE b, Herbjørn ANDRESEN c, Pieter TOUSSAINT a a Dep. of Computer and Information Science, NTNU, Trondheim, Norway b SINTEF ICT, Trondheim, Norway c Dep. of Private Law, University of Oslo, Norway

Abstract. Large wall-mounted screens placed at locations where health personnel pass by will assist in self-coordination and improve utilisation of both resources and staff at hospitals. The sensitivity level of the information visible on these screens must be adapted to a close-to-public setting, as passers-by may not have the right or need to know anything about patients being treated. We have conducted six informal interviews with health personnel in order to map what kind of information they use when identifying their patients and their next tasks. We have compared their practice and needs to legislative requirements and conclude that it is difficult, if not impossible, to fulfil all requirements from all parties. Keywords. Personal health information, de-identification, privacy, coordination

1. Introduction The COSTT2 project aims at supporting coordination in the peri-operative hospital environment by visualising status information regarding current operations and patients under treatment on large wall-mounted screens. This will help the personnel predicting when their time and effort are needed, and which colleagues are available for advice or assistance. As a result, both physical resources and staff can be utilised more effectively. Research on similar computerised coordination systems implemented as electronic whiteboards are also presented by Bardram et al. [1] and Aronsky et al. [2]. In order to maximise coordination support, the screens should be placed at locations where the relevant health personnel are likely to see them, e.g. in corridors. This however makes them available to everybody present, including patients, their relatives, and personnel not directly involved in patient treatment (e.g. cleaners and technicians). Such availability has consequences for the privacy of patients and employees. In previous work [3] we have introduced the concept of flexible de-identification, and described how it is possible to present patient information at various levels of 1

Corresponding author: Inger Anne Tøndel, SINTEF E-mail: [email protected] 2 Co-operation support Through Transparency, http://costt.no/

ICT,

N-7465

Trondheim,

Norway;

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details, both with regards to identifying information and the medical condition. Three perspectives have to be taken into account when developing solutions for deidentification. The first perspective is that clinical personnel require a certain amount of identifying information for the medical information presented to be meaningful and useful. The second perspective is that laws and regulations restrict the amount of patient identifying information that can be presented. The last perspective is usability. A system that requires users to log on to multiple systems in order to obtain patient information, might fulfil both the information need and requirements set by laws and regulations, but is not very usable in a dynamic work environment where clinicians work under time pressure. These three perspectives generate different demands, and designing the right level of de-identification means balancing these different demands. The rest of the paper is organised as follows: Section 2 presents the results of unstructured interviews with personnel working in the surgical clinic at a Norwegian hospital, and Section 3 outlines the Norwegian legislative requirements. Then, Section 4 discusses how needs, usability and legislative requirements can be balanced, and Section 5 concludes the paper.

2. Interviews In order to improve our understanding of the information needs of health care personnel, and specifically their need for identifying information, we conducted six unstructured interviews at Trondheim University Hospital, during NovemberDecember 2010. Six different identification approaches were explored (see overview in Table 1), where the one with highest identification level used initials and birth year of the patient. The less identified approaches aimed to identify the patient by his location or his relation to health care personnel, possibly in combination with the test or surgery type performed. In the interviews we wanted to gain feedback on whether the less identifying approaches still resulted in useful status information for health care workers. The participating clinicians included one senior physician and two ward nurses from the Department of Gastrointestinal Surgery, one junior physician and one nurse from the Department of Emergency, one ward nurse from the Department of Breast and Endocrine Surgery, and one charge nurse from a ward at the Department of Orthopaedic Surgery. Their ages ranged from 25 to 55, and all had been in their position for some while. The informants were recruited randomly during work hours, and interviewed straight away in their regular work environment. They were each asked to comment on some early-stage paper-based prototypes of information visualizations, containing message examples related to the treatment progress of patients, e.g. “CT-image description is ready” and “Patient has been scheduled for surgery”. We explored in total four different prototypes, but only one or two were presented to each informant. Some status messages were added during the process, and two of the prototypes were modified slightly in-between interviews, due to feedback given. The prototypes mainly differentiated on how information was organised and how the patients were identified). We used the prototypes to investigate whether the clinicians would be able to tell patients’ identities apart with the different identification approaches, and to evaluate how these related to current practices. The feedback was recorded with handwritten field notes, and written out directly afterwards. The results of the interviews are summarised in Table 1. Generally, clinicians were positive to the idea of integrating status updates from several systems. Most were still

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reluctant to the immediate thought of placing any patient information more publicly available than workstations or personal devices. Though the approach where patients are identified by initials and birth year stood out as the most convenient option, our main impression is that health care personnel have varying needs for patient identification, depending on their role and the context where identification should happen. We also discovered that clinicians commonly used patients’ diagnosis or treatment history as de-identification in conversations between colleagues, (e.g. “he with ileus who needs another operation in three days”). Table 1. De-identification approaches explored in the interviews, based on the paper-based prototypes. Approach Initials and birth year of patient

Example JD59

Summary of Responses Will normally provide fairly good accuracy. Patients having the same birth year and initials (or last name) do however occur. Clinicians still found this convenient as they are used to working with basis in the patients and their name/age (various combinations of name and birth date are used today).

Room number/location

(Plotted on a map of wards)

Patients move around (this may leave room lists temporarily inconsistent) or they can even be placed in the corridors. Room numbers are commonly used for reference today, but in combination with other identifiers, e.g. name, diagnosis or sex. It seems hard to remember the patients’ exact locations.

Initials of responsible physician (first two letters of both first name and last name)

DAJO

Patients are not followed up by only one physician, and physicians attend many patients at each ward. Nurses will not necessarily know the name of the physician providing care for each of their patients at a specific time.

Blood test indicators, time and responsible nurse

Hb, Na, INR 10:41 (HAPE)

Blood tests are ordered as standardised batches, so important indicators, if any (e.g. INR may decide whether to operate or not), do not stand out. Tests for several patients are often ordered at the same time, and by the same nurse, too.

Radiology type, level of urgency, time and referring physician

CT abdomen (red) 11:00 (DAJO)

Some results (MR) take days to arrive, and often 20-30 patients with abdominal pains arrive daily. Hence, a list of pending results may become overloaded and hard to interpret.

Operation room number, surgery type, scheduled time and surgeon initials

OP3: Appendicitis 11:00 (PT)

Nurses rarely know exactly what room an operation will take place in. But as it is uncommon to have several patients from the same ward undergoing surgery at the same time, they may still be able to deduce which operation to follow.

3. Legislative Requirements In Norway, rules and regulations on the obligation of secrecy, and the criteria for sharing or disclosing data, are mainly found in the Personal Health Data Filing System Act [5] which implements the EU personal data protection directive [4] for the health domain, and in the Health Personnel Act [6] which are national rules of conduct for health personnel. The authorisation rule for granting access to health data [5] consists mainly of two criteria. The first is a general need-to-know restriction: “Access may only be granted insofar as this is necessary for the work of the person concerned” [5]. The second criterion is that access must be “in accordance with the rules that apply regarding the duty of secrecy” [5]. The general rule on secrecy goes beyond a mere duty to “keep silent”. It is a proactive duty on institutions as well as individual health personnel to “prevent others from gaining access to or knowledge of information relating to people’s health or medical condition” [6]. There are a few derogations to the


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secrecy rule [6], mainly the need to share information with co-operating health personnel, the duty to supply patient administrative systems with key data, and a few more rules on sharing information with a patient’s next of kin, and with students, health care assistants or data processing expertise. However, there are no general permissions for making health data available to other patients, or to other patients’ next of kin. There are, in principle, two possible strategies on how to make the envisioned wall-mounted displays legitimate under data protection law. The first strategy would be to generalise or trivialise the data in ways that put the information content below the threshold of “relating to people’s health or medical condition”. An example could be to make the displayed data read something like “patient x to be present in room 101 from 9:30 to 14:00” without revealing what activities would take place there. The second strategy would be some sort of de-identification of the patient, in order to avoid that the displayed data pertains to a specific part of the definition of “personal health data” [5], namely a criterion that it “may be linked to a natural person”. Norwegian law contains several useful concepts for de-identification [5]. These legal concepts were initially aimed at central health registers, spanning information originating from different hospitals, but they could also be relevant for de-identification purposes within a single hospital. The definition of “de-identified personal health data” has two components. First, any identifying data is removed. Second, any reidentification shall be dependent on re-supplying the data that was removed. This second component implies a high threshold; an acceptable level of de-identification may not be pro forma, and re-linking data to the right patient cannot be easily accomplished by guessing. An alternative is to aim for “pseudonymous health data”, which implies that identifying information is encrypted.

4. Discussion The interviews indicate that status updates for patients under treatment are useful. Health care personnel would like to know when test results are ready, how operations proceed, etc. Making such information easily available on wall-mounted screens will however expose the information to everybody who has physical access, something that is not permitted by Norwegian legislation. As mentioned in Section 3, two main strategies are available in order to adhere to the legal restrictions: Removing all healthrelated information or de-identifying the information. The first strategy may work for some events, but using it as a general strategy, will probably render the system useless. The second strategy seems more appealing, as it can supply more useful information. Finding an appropriate level of de-identification that makes personnel able to identify patients yet remains a challenge. Results from the interviews reveal that variations over name and birth date are commonly used for identification. At a ward with a limited number of patients, this close to identifies most patients. The other de-identification techniques tested in the interviews, such as using the room number or the identity of health care personnel, turned out not to be usable. Thus we need to work on alternative de-identification methods. Existing literature on de-identification of health information [7] is mainly concerned with de-identification of large datasets that are to be used for secondary purposes (e.g. research). Still we plan to look into how existing techniques such as pseudonymisation can be used for our setting. We will also investigate to what extent information will still be useful if all identifiers are removed.

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If it turns out that the level of de-identification required by legislation will render the system useless, we are left with no option but to limit access to the information to authorised personnel only. This can be ensured by placing the screens at locations where only health personnel have access or by access control mechanisms on the screens, although this will exceedingly reduce the usability for coordination purposes. If such an approach is necessary, it will be important to investigate smart ways of doing access control, e.g. by providing more details on a personal handheld device, or by mechanisms that automatically detect who is present and present information based on the access rights of that group of people. Reducing the level of identification will result in an increased risk of erroneous interpretation of information. Though this will reduce the benefits of the coordination support system, it is important to state that the system will not replace any of the medical information systems. These will still use full identification for all medical data, and thus there should be no increased risk of treatment errors.

5. Conclusion Public display of health information poses an obvious risk to patient privacy, and thus there is a need to determine the appropriate level of identification. As the legislative requirements are in conflict with the needs of health personnel, it may be impossible to fulfil all the legislative requirements, without sacrificing usability. Acknowledgments: We would like to thank Børge Lillebo for his work on the prototypes and cooperation on the interviews. Thanks also to our other colleagues in the COSTT project, Arild Faxvaag especially, for useful comments and discussions. This work was supported by the Norwegian Research Council’s VERDIKT program (grant no. 187854/S10).

References [1]

[2]

[3]

[4]

[5] [6] [7]

Bardram, J.E. Hansen, T. Soegaard, M. AwareMedia – A Shared Interactive Display Supporting Social, Temporal, and Spatial Awareness in Surgery, Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06) (2006), 109-118. Aronsky, D. Jones, I. Lanaghan, K. Slovis, C.M. Supporting Patient Care in the Emergency Department with a Computerized Whiteboard System, Journal of the American Medical Informatics Association 15 (2008) , 184-193. Faxvaag, A. Røstad, L. Tøndel, I.A. Seim, A.R. Toussaint, P.J. Visualizing Patient Trajectories on Wall-Mounted Boards – Information Security Challenges, Studies in Health Technology 150 (2009), 750-759. Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data Act on personal health data filing systems and the processing of personal health data [Personal Health Data Filing System Act] Act of 2nd July 1999, no 64 relating to health personnel etc. [The Health Personnel Act] El Emam, K. Fineberg, A. An overview of Techniques for De-identifying Personal Health Information, Health Canada, January 2009.


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Watermarking – a new way to bring evidence in case of telemedicine litigation Gouenou COATRIEUXa1, Catherine QUANTINb, François-André ALLAERTc, Bertrand AUVERLOTb, Christian ROUXa a . Inserm U650, LaTIM; GET ENST Bretagne, Dpt. ITI b. Dpt. of Biostatistics & Medical Informatics, Inserm U866, CHU de Dijon c Dpt. of Epidemiology and Biostatistics, Mc Gill University, Montreal Canada

Abstract: When dealing with medical data sharing, in particular within telemedicine applications, there is a need to ensure information security. Being able to verify that the information belongs to the right patient and is from the right source or that it has been rerouted or modified is a major concern. Watermarking, which is the embedding of security elements, such as a digital signature, within a document, can help to ensure that a digital document is reliable. However, at the same time, questions arise about the validity of watermarking-based proof. In this paper, beyond the technical aspects, we discuss the legal acceptability of watermarking in the context of telemedicine applications. Keywords: Telemedicine, practitioner liabilities, watermarking, Medical Imaging.

1. Introduction The evolution of medical information systems, supported by advances in information technology, has made it possible for information to be shared between distant health professionals and to be manipulated and managed more easily. Telemedicine applications illustrate this evolution. However, at the same time, more attention should be paid to information security, which is intimately linked to the liability of physicians. Security can be defined in terms of confidentiality, availability, integrity and authenticity (1). In this paper, we focus on the security of multimedia medical data, which means the protection of documents or medical content (images, text, , databases, and so on) shared between different health professionals in the context of telemedicine. Among the different measures to ensure the protection of content, watermarking is awaiting acceptance by health professionals before being deployed in real practice. Basically, watermarking is defined as the invisible embedding or insertion of a message in a host document, an image, for example. As we will show later in this paper, watermarking makes it possible to introduce new security and management layers much closer to the host data: in the signal itself. Even though most of the work on watermarking has concerned medical images in order to verify image integrity or improve confidentiality (2), watermarking can also be applied to any other kind of digital data. Technical aspects of watermarking concerns ways to modify the host

1 Corresponding Author: G. Coatrieux; E-mail : [email protected]. LatIM Inserm U650, Dpt. ITI, Telecom Bretagne, Technopôle Brest-Iroise - CS 83818 - 29238 Brest Cedex 3 – France.

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document for message insertion, and questions arise about whether or not it acceptable evidence and conveys proof of a physician’s liability. In this paper, we aim to answer these questions in the context of telemedicine applications. Thus, in sections 2 and 3, we recall physicians’ liabilities and security concerns that need to be satisfied in telemedicine applications. In section 4, we discuss the security applications watermarking can be used for and conclude with open questions about this technology.

2. Medical Liabilities in Physicians’ Collaboration In France, regarding medical malpractice suits against multiple physicians, assessing the liability of each individual is the classical approach from a legal as well as ethical standpoint. Such assessments aim to identify negligence in practitioner’s behavior, as it is this negligence and not the diagnosis or the drug used, which must be proved to demonstrate the physician’s liability. The legal bases of this obligation go back to the jurisprudence of the Court Of Cassation and the Mercier ruling (1936) in which it is stated that “the obligations that the medical practitioner must meet include providing the patient with conscientious and attentive treatment that is in accordance with the current state of medical science”. In other words, about “obligation of care”, French legislation mentions an “obligation of means”, i.e. an “obligation to make the best effort” and not an obligation to achieve a particular result. If the technical or intellectual means normally used by a competent and diligent professional are not employed, this constitutes criminal negligence. A doctor cannot be sanctioned for not being able to make a difficult diagnosis, e.g. in studying an X-ray film or an anatomopathological examination slide. In contrast, if the practitioner fails to diagnose a common and obvious lesion, the facts show that the professional has not given the care “based on data known to science” (French Medical Code of Ethics, art.32), i.e. those ordinarily known by a competent and diligent practitioner, who “must always make use of, if necessary, the aid of competent third parties”. In telemedicine, physicians’ diligence must be appraised according to their personal involvement in the diagnostic process. Although two practitioners may exchange data in a symmetrical way, they do not necessarily consider these data from the same standpoint. The requester of the opinion has access to all the available information, while the referent, the practitioner whose opinion is sought, generally only receives a part of the information, selected by the first doctor. This selection must be made by a competent person, able to choose the information relevant for the diagnosis and to interact effectively with the referent. This is the most common situation in tele-expertise as defined by French legislation, where two doctors are most often of the same specialty and are accustomed to such dialogues (3). Although nowadays it is possible to send whole images of a radiological or anatomopathological file via the internet, this task does not solve the problem of the acceptability of evidence in cases of litigation. The fact of not having all the information available does not exonerate the referent from his responsibility with respect to the advice he gives. In cases of doubt or of difficulty in diagnosis, it is up to him to ask for additional information, and to decline to give an opinion if this information is insufficient for his needs, or if he feels not competent to do so (4). Furthermore, the referent and patient never meet; it is therefore hard to believe that recourse to tele-expertise truly stems from the wishes of a patient, and that this patient is able to validate the choice of the referent, regarding the “ideal vision of free choice”


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(P. Fernandez). Without patient agreement, there is thus no legal contract between the patient and referent practitioner, as mentioned in the French Medical Code of Ethics (art. 60). From the standpoint of the patient, the liability of a referent doctor, who comes to the assistance of a requester, may not be contractual but of tort nature which may also make him or her more vulnerable in terms of civil liability.

3. Identifying the Facts When a patient suffers a prejudice related to a diagnostic error, it is necessary to determine the respective liabilities of the different medical practitioners involved in the diagnostic/therapeutic process. As mentioned, in France, professional negligence can only be argued if the practitioner provided care in an unsatisfactorily way, i.e. falling short of what might have been expected from him regarding his field of competence. To assess this possible lack of efficiency, and, thus, possible involvement, several questions will rise at the heart of the judge's task: who requested? What? When? For who? Providing which document? Who answered? What? When? Regarding which documents (additional material could be requested to properly answer)? Therefore, all elements involved in the transaction must be carefully stored, with no means of modification (Need 0 (N0)), and the following are thus necessary: Need 1 (N1) - Whole transmitted images are saved with name of the practitioner, name of the patient, date and time of transaction, and these data must be rendered unreadable from an unauthorized access. Need 2 (N2) - Sender must be identified in such a way cannot repudiate the message; Need 3 (N3) - Date, time and substance of answer of referent practitioner must be strongly linked to documents received to make diagnosis before returning them; Need 4 (N4) - The referent must be identified, in a way he cannot repudiate the reply. Need 5 (N5) - Data have to be stored on a non-erasable medium for the 10-year prescription period required by national law.

4. Security Services Based on Watermarking A general schema for watermarking is depicted in figure 1, it relies on two processes: embedding and reading. At the embedding stage, the message is inserted by modifying the host document in an “imperceptible” way. Such a host can be a signal, an image, a text as well as a data base. “Imperceptible” means that the watermarked document can be used instead of the original document without interferences. Applied to an image; embedding consists in slightly modifying its pixel gray level values to insert the message. Image pixel values are modified or modulated so that they can be interpreted or demodulated by the reader to gain access to the message. An example is given in figure 2, where a Magnetic Resonance Image (fig.2(a)) has been watermarked (fig.2(b)) applying the method proposed in (5). The image of difference in fig.2(c) corresponds to the image signal variations, variations which encode the inserted message. Thus, image watermarking can be viewed as the addition of a signal, a watermark w, to the image I. Several techniques have been proposed for medical imaging. The reader must refer to (2) for more details about how these methods preserve the image diagnosis value.

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Watermarked document Iw

Host document (I)

Embedder

Reader

Message (m)

Secret Key

Figure 1. A general schema of watermarking.

(a)

(b)

(c)

Figure 2. Illustration of the reversible watermarking method (5) on a Magnetic Resonance Image, (a) original image (256x256 pixels, encoded on 12 bits), (b) watermarked image (c) Signal of difference, it is the watermark w whose amplitude equals +/-1 or 0. The reversibility property allows the recovery of the original image by inverting image distortion introduced by the embedding process.

According to previous definition, watermarking provides a hidden communication channel where different security elements can be made available. Its main interest resides in the fact that information is dissimulated in the signal itself and can be retrieved even if the image file format is changed. It can also be made very difficult to modify the embedded message without definitively destroying the host content. Embedded data can be accessed by compliant systems, meaning systems that hold the appropriate watermarking plug-in and authorizations, i.e. knowing the watermarking key – see figure 1. Watermarking has been proposed for several applications. Considering data exchange and security needs presented in section 2, it can be used for: − Verifying data integrity by embedding a cryptographic hash of the image itself (N0). This image footprint can be digitally or cryptographically signed by the emitter to also satisfy non repudiation (N1, N2) (6). It can also be used not only to detect image modification but also to indentify the nature of such modifications, e.g. a transmission error or the result of malevolent behaviour (7-9). − Maintaining link between patients and their health records (N1) by watermarking patient identifier/pseudonym (10-11) along with a unique document identifier (e.g. DICOM UID – see standard http://medical.nema.org/). In the same way, content can be traced (N2, N4) by inserting sender and recipient identifiers. − Contribute to N3 by securely linking any documents involved in the collaborative session. Images can be watermarked with the digital signature and identifier of the referent’s report and, simultaneously, images’ digital signatures and identifiers can be included in this report (possibly watermark it) (12). It becomes thus possible to retrieve documents linked to their contents and also more difficult to tamper with documents, as they must be modified as a whole. Watermarking can offer more services than integrity or non-repudiation. For example, embedding the recipients’ identity allows identification of those who disclose or reroute data. Inserting content access or transmission authorization makes it possible to verify if the patient has consented or allowed the sharing of his/her data (13). Embedded patient data is also more difficult to access. Information has to be extracted before being decrypted (need to know watermarking and encryption keys); and, in some cases, embedding may reduce the amount of data to be transmitted (14).


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5. Conclusion Thanks to its transparency, watermarking can contribute to the improvement of medical data security, especially regarding telemedicine, where it can help to overcome most of the major issues mentioned above. As watermarking is a protection mechanism applied after creation of the document, it also allows the user to have access to the multimedia contents while maintaining content protection. Finally, watermarking is specific in that it allows information to be embedded inside the document itself. However, some questions still need to be answered. Without being exhaustive, the invisibility of the watermark is one of the major concerns. Though various solutions have been proposed for images (2), they cannot satisfy all of the needs N1 to N5 at the same time. Furthermore, the way to update the watermark content also has to be studied in depth. As for cryptography, there are symmetric and asymmetric watermarking schemes, but asymmetric methods do not allow the embedding of a large amount of data. At the same time, it may not be possible to embed every type of data. In fact, privacy concerns may limit or restrict information embedding. For instance, if the patient ID is embedded, a specific anonymization procedure will have to be designed. To be accepted, watermarking needs to be combined with cryptographic mechanisms, like digital signatures, which provide legally accepted proof.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

[12] [13] [14]

Coatrieux G, Maître H, Sankur B, Rolland Y, Collorec R. Relevance of watermarking in medical imaging. ITAB00; 2000 Nov; Arlington, USA. Coatrieux G, Lecornu L, Sankur B, Roux C. A review of image watermarking applications in healthcare. Conf Proc IEEE Eng Med Biol Soc. 2006;1:4691-4. Allaert FA, Dusserre L. Telemedicine: responsibilities and contractual framework. Stud Health Technol Inform. 1998;52 Pt 1:261-4. Allaert FA, Dusserre L. Tele-expertise; users' and suppliers' liabilities. Brender J, et al ed. Press I, editor1996. Coatrieux G, Puentes J, Roux C, Lamard M, Daccache W. A low distorsion and reversible watermark: application to angiographic images of the retina. Conf Proc IEEE Eng Med Biol Soc. 2005;3:2224-7. Pan W, Coatrieux G, Cuppens-Boulahia N, Cuppens F, Roux C. Medical image integrity control combining digital signature and lossless watermarking. Lect Notes Comput Sci, 2009,5939,153-162 Liew SC, Zain JM. Reversible Tamper Localization and Recovery Watermarking Scheme with Secure Hash. European Journal of Scientific Research, 49(2), 2011; p.249–264. Huang H, Coatrieux G, Shu H, Luo L, Roux C. Medical image tamper approximation based on an image moment signature. 12th IEEE International Conference on, 2010; 254-259 Cheng S, Wu Q, Castleman KR. Non-ubiquitous digital watermarking for record indexing and integrity protection of medical images, ICIP05, Genoa, Italy, vol. 2, Sept. 2005. Quantin C, Allaert FA, Gouyon B, Cohen O. Proposal for the creation of a European healthcare identifier. Stud Health Technol Inform. 2005;116:949-54. Hsiang-Cheh H, Wai-Chi F, Shin-Chang C. Privacy protection and authentication for medical images with record-based watermarking.Life Science Systems and Applications Workshop (LiSSA), 2009, 190 193. Puentes J, Coatrieux G, Lecornu L. Secured Electronic Patient Records Content Exploitation. Healthcare Knowledge Management: Issues, Advances, and Successes, Springer Verlag, 2006. Pan W, Coatrieux G, Cuppens-Boulahia N, Cuppens F, Roux C. Watermarking to Enforce Medical Image Access and Usage Control Policy. Int. IEEE Conf on SITIS, 2010, 251-260. Acharya R, Niranjan UC, Iyengar SS, Kannathal N, Min LC. Simultaneous storage of patient information with medical images in the frequency domain, Comput. Meth. Prog. Bio. 76:13–19, 2004.

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Sharing Sensitive Personal Health Information through Facebook: the Unintended Consequences Mowafa HOUSEH a,1 College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), National Guard Health Affairs (NGHA), Riyadh, Saudi Arabia a

Abstract. The purpose of this paper was to explore the types of sensitive health information posted by individuals through social network media sites such as Facebook. The researcher found several instances in which individuals, who could be identified by their user profiles, posted personal and sensitive health information related to mental and genetic disorders and sexually transmitted diseases. The data suggest that Facebook users should be made aware of the potential harm that may occur when sharing sensitive health information publicly through Facebook. Ethical considerations in undertaking such research are also examined.

Keywords. Social networking, Privacy, Health information, Facebook

1. Introduction In 2011, the Markle Survey of Health reported that privacy around the exchange and use of health information was a top concern for physicians and patients [1]. Although the concerns voiced by patients and physicians in the survey are legitimate, they have been a focal point of the healthcare informatics agenda for many years. One of the earliest papers on the subject of privacy and confidentiality regarding health information was published by the New England Journal of Medicine in 1968 [2]. In the paper, the authors advocate for state laws, ethical and clearly defined regulations regarding the protection of health information. It was not until 30 years later that the United States passed the Health Insurance Portability and Accountability Act (HIPPA) to protect the privacy and confidentiality of health information [3]. In addition, with recent advancements made in the collection and analysis of genetic data within the field of bioinformatics, the United States Congress has passed the Genetic Information Nondiscrimination Act of 2008 to protect the improper use of genetically identifiable data collected by health insurers and employers. Similar health information privacy legislation has been introduced in Europe through the Personal Data Directive introduced in 1995. As science makes new discoveries and advances to collect an array 1

Corresponding Author: Dr. Mowafa Househ, King Saud Bin Abdul Aziz University for Health Sciences, College of Public Health and Health Informatics, Riyadh, Kingdom of Saudi Arabia; E-mail: [email protected]

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of new health information, new legislation will be needed to ensure that the privacy and confidentiality of health information is maintained. Historically, HIPPA and other acts to protect the privacy and confidentiality of health information were designed to protect the patient from privacy violations that could impact their employment, relationships, or public perceptions of them. Such violations did occur, as reported in 2003 by the Health Privacy Project [3], and they included privacy violations such as • A woman’s medical records being posted on the internet after she was treated for complications that were a result of an abortion • A man being fired after an insurance company informed his employer that he received treatment for alcohol abuse • A clerk working in a hospital stole social security numbers and applied for credit cards and opened bank accounts • Files of persons living with sexually transmitted diseases being sold by a U.S. state These examples are enough for legislators to propose and pass laws that protect the personal privacy and confidentiality of the patient’s health information from improper use by clinicians, staff, hospitals, and government. With the advent of social networking and the promotion of individualized healthcare, however, there is a growing trend of patients sharing their own personal health information to the world through social networking sites such as Facebook. Within this context, the purpose of this paper was to explore the various types of groups and information shared by various Facebook pages and to make recommendations for concerns surrounding privacy and confidentiality for patients with regard to sharing their health information via social networking sites. The focus of this paper was not the health information people may be sharing about others, but rather the health information they may be sharing about themselves. Ethical considerations while conducting this type of research were also examined.

2. Methodology The research focused on reviewing sensitive health information related to mental disorders (anxiety, depression, eating disorders, and drug addiction), sexually transmitted diseases (HIV, chlamydia, and gonorrhea), and sexual and genetic disorders (cystic fibrosis, hemophilia, and sickle cell) shared through Facebook groups. To limit the scope of the study, the researcher investigated only anxiety, HIV, and cystic fibrosis. Briefly, Facebook is a social networking site that allows users to network with other individuals or groups registered on the Facebook site. Registration is free, and each user sets up their own network of friends and groups. Each user can upload videos, pictures, and hyperlinks as well as engage in live chatting and many other features. In addition, users can set up their own privacy settings, which can range from simple issues such as making their profile public or visible only to the friends they invite. For the purpose of this study, Facebook group pages regarding anxiety, HIV, and cystic fibrosis were searched. Only groups that were publicly made available to all Facebook users were reviewed in this study. The researcher logged in with his own personal account, and the search was carried out on February 3, 2011. The search was filtered to include only anxiety, HIV, and cystic fibrosis groups and to excluded pages

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(e.g., pages with information on the subject) and people with names similar to the search terms. The researcher only selected one group with the most registered users for anxiety, HIV, and cystic fibrosis. Once in the group, the researcher observed discussions of group members and various types of information shared. Data from the Facebook Wall and Facebook Discussion forums were the only data included. According to Facebook, the Wall feature allows users to share text, pictures, videos, and hyperlinks. People can comment on the Wall, which is for all Facebook users to view. The discussion forums are more focused on specific issues and are more detailed in their content. See Figure 1 for an illustration.

Figure 1. Facebook Anxiety Group Page

3. Results For the top group site regarding anxiety, there were 266 registered users as of February 3, 2011. The data showed that there were a total of 15 Wall postings by a total of 12 users between July 1, 2010 and January 31, 2011. Two of the Wall postings were commented on by group members. In general, of the 12 users, 7 generally discussed their struggles with anxiety, 1 posting was an herbal advertisement, 1 was advertising a course on anxiety, and 3 were links to non-relevant websites and advertisements. Much of the wall discussions centered on the individual’s struggles with anxiety. For example, one person was seeking help because their anxiety was leading them to contemplate suicide. Another Facebook user was asking for advice about prescription drugs and strategies to cope with anxiety. In the discussion tab, there were no group discussions surrounding the issue from July 1, 2010 to January 31, 2011. Several posts, however, were found before this time period, with the highest number of discussions focusing on panic attacks. For HIV, the top group on the subject had 926 registered users as of February 3, 2011. There were only two Wall comments made in this group, both of which were from two different users regarding disease-related information. As for the discussion forum, there were two postings for the specified time period. One posting was demeaning, whereas another was about a man sharing his struggles with HIV. This particular Facebook user listed his picture, name, location, and work in his public profile. Although it appeared to be genuine, it was not confirmed given the scope and ethics of the study. For the Sickle Cell group, the top group had 3786 members as of February 3, 2011. On the Facebook Wall, there were 32 wall postings made by 24 members of the Sickle Cell group between July 1, 2010 and January 31, 2011. Most of the postings (a total of 8) shared on the wall were related to general information sharing regarding education, new treatments for the disease, and experiences with physicians or hospitals. There


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were seven postings in which the Facebook user was seeking information or help about a problem they were having related to the disease. There were about 6 postings related to advertisements for fundraising events or links to products and articles. Finally, members posted stories about their life struggles in living with the disease. With regard to the discussion threads, there were only two discussion threads posted between July 1, 2010 and January 31, 2011. The first discussion thread was about a Facebook user suffering from the disease where they expressed their frustration with trying to find adequate care and a job. Two respondents provided the Facebook user with support and advice on what to do. The other thread was about a Facebook user contemplating graduate school who was afraid of not obtaining acceptance because of his/her disease. A Facebook user responded by encouraging the individual and letting them know of another individual they knew who was working while living with the disease.

4. Discussion In this study, it was found that there are Facebook users sharing their personal details along with their health information without realizing the potential ramifications of doing so. According to HIPPA regulations, there are no laws that stop the individual from sharing their personal health information [4]. Trying to interpret the results behind this behavior is difficult to ascertain, given the limitations of this study. One conclusion may be that the individuals sharing their health information on Facebook are unaware that such information could potentially be used against them by unscrupulous organizations or individuals. There have been several recorded instances in the news media where employers have fired Facebook users as a result of their public postings. A recent study on Facebook patient privacy violations showed that numerous privacy violations were carried out by medical residents and students [5]. Therefore, there is a growing need for Facebook to make its users aware of potential abuses that may result from sharing health information online. To remedy this issue, Facebook should provide policies and guidelines and create an awareness campaign for its users regarding the sharing of health information via its social networking site. Another possible interpretation to the nonchalant behavior in sharing health information on Facebook could be a result of the cultural change surrounding patient engagement and empowerment in taking control of their own health. For example, Sunnybrook Hospital in Canada has recently provided its patients full access to their personal healthcare records [6]. Google Health and similar technologies are empowering patients to manage their own health. Facebook provides a platform for individuals to connect with other individuals suffering from the same disease or disorder. With over 500 million users on Facebook [7], the potential to connect with people suffering from the same disease or disorder is higher than on any other alternative social media networking sites known to date. The only drawback to Facebook is the ability to identify individuals sharing personal health information and the potential misuse of this information by organizations and individuals, which may cause harm to the individual.

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5. Limitations and Future Research Based on the results of this study, future research should examine the perceptions of individuals regarding the sharing of personal health information through social media networks such as Facebook, Twitter, and YouTube. Future studies should also examine other diseases and disorders shared through Facebook to explore the potential threats that may arise as a result of sharing sensitive health information. There are several limitations worth noting in this study. The manual data analysis was conducted by the researcher and was not verified by another researcher. As a result, this may have caused bias in interpretation of the results. Furthermore, because of time constraints, the data were limited to the period between July 1, 2010 and January 31, 2011. Finally, there was no way to prove the authenticity of the Facebook users who posted their health information on the Facebook group pages.

6. Ethical Considerations When conducting the research study, various ethical considerations were considered. All of the data used in this study were publicly available data that could be accessed by any Facebook user with a Facebook account. The researcher did not solicit information nor did the researcher ask to join a particular group to gain access to data. While conducting the analysis, the confidentiality of the individuals included in the study was strictly enforced; however, anonymity is not guaranteed given that the information is publicly available to any Facebook user. Consent of the groups to use the data was not solicited. With the pervasive use of social networking sites such as Facebook and YouTube, the issues regarding confidentiality, anonymity, and consent are pushed to the limits, and the ethical considerations within the context of such research studies should be reexamined. Acknowledgements: We would like to thank the King Abduallah Institute for Medical Research for their help in editing this document.

References [1]

[2] [3] [4] [5]

[6]

[7]

Lewis N. (2011) Privacy, accountability, lead health IT concerns. Information Week Healthcare. Webaccess February 3, 2011 [http://www.informationweek.com/news/healthcare/EMR/showArticle.jhtml?articleID=229200192&su bSection=News] Curran WJ, Steams B, Kaplan H. Privacy, confidentiality and other legal considerations in the establishment of a centralized healthdata system. N Engl J Med. 1968;281:241-8. Wager K, Wickham F, Glaser J. (2005). Managing health care information systems: A practical approach for health care executives. Jossey-Bass. San Francisco, CA, U.S.A. pg. 83-84 Khan M, Long H. Interview: HIPPA regulations around health information privacy. February 2, 2011. Thompson LA, Black E, Duff WP, Paradise Black N, Saliba H, Dawson K. Protected Health Information on Social Networking Sites: Ethical and Legal Considerations. J Med Internet Res 2011;13(1):e8. URL: http://www.jmir.org/2011/1/e8/ doi: 10.2196/jmir.1590 PMID: 21247862 Canadian Broadcast Corporation (CBC). Online health records popular with patients. Canada. January 30, 2011. Webaccess [http://www.cbc.ca/health/story/2011/01/30/e-health-recordssunnybrook.html#ixzz1D5SAJCKU] Facebook. Statistics. Webaccess February 3, 2011 [http://www.facebook.com/press/info.php?statistics]


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End-to-End Security for Personal Telehealth Paul KOSTER a,1, Muhammad ASIM a, Milan PETKOVIC a, b a Philips Research, b TU/e, Eindhoven, The Netherlands

Abstract. Personal telehealth is in rapid development with innovative emerging applications like disease management. With personal telehealth people participate in their own care supported by an open distributed system with health services. This poses new end-to-end security and privacy challenges. In this paper we introduce new end-to-end security requirements and present a design for consent management in the context of the Continua Health Alliance architecture. Thus, we empower patients to control how their health information is shared and used in a personal telehealth eco-system. Keywords. security, privacy, consent, telehealth

1. Introduction Healthcare around the world is facing important challenges through a substantial increase of the average age of the population and an increase of chronic diseases. Personal telehealth systems are expected to take an important role in addressing these issues. They extend healthcare from acute institutional care to outpatient care and home healthcare. Technological developments in this area are followed by standardization, policy and marketing activities of more than 230 companies that joined their efforts within the Continua Health Alliance [1] to ensure interoperability and further develop the personal telehealth market. Although personal telehealth technologies bring a lot of benefits, they also create new security and privacy challenges. With personal telehealth services, it becomes simpler to collect, store, and search electronic health data, which in turn endangers people's privacy. Furthermore, mistakes that are made because patient measurements are not available, associated to a wrong patient or modified in an unauthorized way can endanger patient safety. Therefore, technological means that empower patients with control over their health information while preventing security breaches and ensuring information correctness are of utmost importance. Traditionally, security in healthcare treats protection of sensitive data by considering individual systems and communication. For personal telehealth applications like remote patient monitoring common security means are (role-based) access control and secure communication protocols [2]. However, emerging trends to open, distributed and user-centric telehealth architectures call for a more end-to-end approach to security. Only an end-to-end approach can provide a consistent level of security and meet patient empowerment expectations. 1

Corresponding Author: Paul Koster, Philips Research, Koninklijke Philips Electronics N.V., High Tech Campus 34, 5656AE, Eindhoven, The Netherlands; E-mail: [email protected]

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P. Koster et al. / End-to-End Security for Personal Telehealth

In this paper, we present new requirements for an end-to-end approach to security for personal telehealth. Moreover, we describe a digital consent management solution. This work represents ideas that have been contributed to and further elaborated in the Continua Health Alliance. This paper is organized as follows. In section 1, we introduce the Continua Health Alliance. Security and privacy requirements are described in section 2. Section 3 focuses on consent management to empower patients and presents a design addressing Continua requirements. Section 4 concludes the paper.

2. Continua Health Alliance The Continua Health Alliance is an industry alliance formed with the intention to foster the growth of personal telehealth. Its 230+ members recognize the need for alignment and interoperability for applications such as disease management, fitness and aging independently. Continua provides a reference architecture for personal telehealth systems and the Continua guidelines [3], which select and profile standards to realize the interoperability objectives. Architecture. The Continua architecture is characterized by its interfaces and device classes as illustrated in Figure 1. Medical observation devices measure people’s vital signs such as weight and blood pressure. These devices can be stationary, portable or body-worn, and use USB, Bluetooth or Zigbee to transmit the measurements to an application hosting device (AHD). Measurement communication follows the IEEE 11073 standard. The AHD acts as an intermediary between observation devices and remote services. The AHD can be a gateway device, PC or smartphone. It uses the WAN interface to forward observations to the remote services such as a disease management organization (DMO). WAN communication makes use of the IHE Patient Care Device transaction standard, which is based on web-services and HL7 2.6 message standards.

Figure 1. Continua end-to-end reference architecture

A WAN service collects the observation data to provide care, e.g. a DMO employing nurses supported by IT systems to coordinate a patient’s care. If measurements fall outside the expected range then a nurse may prepare a Personal Health Monitoring Report (PHMR) and forward this to care providers e.g. a patient’s family physician. The HRN interface facilitates the exchange of the PHMR documents. HRN services interact with WAN services using IHE XDR (Cross-Enterprise Document Reliable Interchange) and IHE XDM (Cross-Enterprise Document Media

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Interchange) standards. HRN services include electronic health record (EHR) systems belonging to care providers or personal health record (PHR) systems. Security. As mentioned in the introduction, for adoption of personal telehealth systems, trust, security and privacy are very important. The same holds for compliance to legislation like EU Directive 95/46 and HIPAA. Continua acknowledges the importance of these issues amongst others through its E2E Security Task Force. The authors actively participate in this task force. Initial security and privacy issues have been addressed in Continua version 1 guidelines for the PAN and HRN interfaces. Continua version 1.5 guidelines added security features for the WAN and LAN interfaces with e.g. TLS for secure communication and SAML 2.0 tokens for authentication of AHD users, see table 1. Table 1. Security standards in Continua version 1.5. Security Standard TLS 1.0 IHE XDM (S/MIME) IHE ATNA WS-I BSP (TLS 1.0) IHE ATNA WS-I BSP (WS-Security + SAML 2.0) Zigbee security Bluetooth security

Security Objective confidentiality + integrity + authentication confidentiality + integrity + authentication auditing confidentiality + integrity + authentication auditing entity authentication confidentiality + integrity + authentication confidentiality + integrity + authentication

Interface HRN “ “ WAN ” “ LAN PAN

3. End-to-End Security and Privacy Requirements Continua version 1.5 addresses basic security requirements for personal telehealth with a strong focus on point-to-point transport security. However, a telehealth system with such open and distributed nature calls for an end-to-end approach, which follows from the risk analysis for remote patient monitoring performed by ENISA [4]. An end-to-end approach helps service providers to ensure compliance with legislation, empowers patients and eases seamless integration by defining a homogeneous security framework. The next paragraphs sketch end-to-end requirements as identified in Continua. Identity management. A correct association of health information to patient identities is essential to provide high quality and safe personal telehealth services. However, a person typically has different identifiers at the various systems in a distributed architecture like Continua. These multiple identifiers imply linking and cross-referencing of identities at the AHD, WAN and HRN systems and services. Up to now, service providers often provide a vertically integrated solution and deal with identity management out-of-band. However, larger number of patients, operational cost pressure, less vertical integration, and vendor interoperability ask for standardized inband identity solutions. This leads to the following requirements: i) measurement uploads should be unambiguously linked to a particular patient, and ii) identity linking should be in-band using interoperable protocols and preferably user-initiated. Integrity and data origin authentication. Health measurements performed by patients require healthcare professionals to place trust in information that patients report. For example, for a blood pressure measurement it is crucial to know that blood pressure of a registered patient is actually measured on his body, that the measurement is taken with a certified device and that it is not modified on the way to healthcare providers. However, end-to-end integrity is not trivial to guarantee as in the Continua

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architecture health measurements pass through multiple parties and undergo transformations before a health provider obtains them. Therefore, it is required to i) authenticate data sources including users and devices, and ii) prevent or detect unauthorized data modifications while allowing legitimate transformations. A more detailed description of related requirements and security mechanisms is presented in [5]. Consent management. Traditionally, consent has been an important concept in healthcare. Signed paper consent forms are used to grant (opt-in) or withhold (opt-out) consent and enable patients to regulate which care providers have access to their health information. In perspective of user centered care and patient empowerment trends, patients should be in more direct control for distributed applications like personal telehealth. Digital consent addresses this requirement and increases consistency, compliance and efficiency for both patients and care providers. Consequently, highlevel end-to-end requirements include that i) patients should be able to define and manage their digital consent and privacy policies in a user-friendly manner, e.g. on a device at home or online, ii) digital consent should propagate together with the patient data, and iii) systems of care providers and services must enforce digital consent.

4. Design for Consent Management in the Continua Architecture Consent management entails the specification, exchange and update of patient’s digital consent preferences. For maximum effect, a patient should indicate his consent and privacy policies as early as possible such that they can travel together with the patient data through the ecosystem. In Continua, the AHD device would be a practical location for a patient to specify his consent. Alternatively, it could be specified at a WAN service by the patient online or taken care of by a nurse on behalf of the patient. Propagation of consent policies over the WAN and HRN interfaces must be enabled to ensure that disease management organizations and care providers use and share patient data in accordance with the patient’s digital consent policy. The Implementation Guide for HL7 CDA R2 Consent Directive [6] forms the basis for our approach to consent management in Continua. This recently approved draft standard for trial use defines a document format for digital consent and enables the expression of structured patient consent policies. The advantage of this standard lies in the fact that it is based on the CDA R2 standard, which is already used at the Continua HRN interface for the health PMHR document. Similarly, well-defined protocols exist for the exchange of this type of documents through the IHE XD* family of profiles. Figure 2 provides an overview of how these document and document exchange standards realize the consent management interactions at the HRN interface. The WAN interface solution is a subset of the solution for the HRN interface. Consent at the HRN interface is supported through a basic and a more advanced interaction. In the basic interaction the patient consent document is included in the same transaction as the health PHMR document as shown in Figure 2a. This makes use of the IHE XDR transaction in Continua, which allows inclusion of both documents in the existing submission set. In the more advanced interaction the patient consent document is retrieved online on demand as depicted in Figure 2b. Such interaction allows for more flexibility as a receiver may obtain consent documents e.g. when it does not have the required consent to perform its intended task. Technically, this variant involves the IHE XDS standard, which provides a superset of the functionality provided by XDR. To enable online


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retrieval of consent documents, a sender at the HRN interface implements the Document Repository and Registry actors to host the consent documents. The receiver implements the IHE XDS Document Consumer actor. Optionally, the receiver may query and lookup the appropriate consent document identifiers and their location URLs. Subsequently, the HRN receiver requests the consent document through an XDS retrieve transaction. The receiver may include a token in the request to authenticate to the sender and enable personalization of the consent document. Finally, the sender responds with the requested patient consent document personalized for the recipient.

Figure 2. Consent management at the HRN interface

5. Conclusions Novel use cases in personal telehealth cannot be addressed with point-to-point or transport security alone anymore and a more end-to-end approach to security and privacy is required. The user-centered and open architecture of personal telehealth systems introduce challenging end-to-end security needs in the areas of identity management, integrity, data origin authentication and consent management. This paper presents a design to extend personal telehealth with digital consent. This design is applied to and presented in context of the Continua Health Alliance interoperability architecture. It demonstrates how the application and combination of novel standards from the healthcare domain realizes consent management and thereby empowers users.

References [1]

[2] [3] [4] [5] [6]

Wartena F, Muskens L, Schmitt L, Petkovic M. Continua. The reference architecture of a personal telehealth ecosystem. Proceedings of the 12th IEEE International Conference on e-Health Networking, Application and Services (Healthcom); 2010 July 1-3; Lyon, France; 2010. Raman A. Enforcing Privacy through Security in Remote Patient Monitoring Ecosystems. Information Technology Applications in Biomedicine; ITAB 2007; Tokyo; 2007. Continua Health Alliance. Continua Design Guidelines version 1.5. 2010. Chronaki C, et al. Being diabetic in 2011: Identifying emerging and future risks in remote health monitoring and treatment. EFR Pilot; ENISA; 2009. p29 Petkovic M. Remote Patient Monitoring: Information Reliability Challenges. TELSIKS 2009. IEEE Press; 2009. p295-301 HL7 Implementation Guide for Clinical Document Architecture, Release 2: Consent Directives, Release 1. Draft Standard for Trial Use. January 2011; HL7; 2011.


Public Health, Catastrophes, Outbreaks



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The Epidemiologic Surveillance of Dengue-Fever in French Guiana: When Achievements Trigger Higher Goals Claude FLAMANDa,1, Philippe QUENELa, Vanessa ARDILLONa, Luisiane CARVALHOa, Sandra BRINGAYb,c, Maguelonne TEISSEIRE d a Cellule de l’Institut de Veille Sanitaire en Région Antilles-Guyane b Départment MIAp, University Paul-Valéry, Montpellier 3 c LIRMM, CNRS, UMR 5506, Montpellier 2 d TETIS Laboratory Départment of Information System

Abstract. The epidemiology of dengue fever in French Guiana is marked by a combination of permanent transmission of the virus in the whole country and the occurrence of regular epidemics. Since 2006, a multi data source surveillance system was implemented to monitor dengue fever patterns, to improve early detection of outbreaks and to allow a better provision of information to health authorities, in order to guide and evaluate prevention activities and control measures. This report illustrates the validity and the performances of the system. We describe the experience gained by such a surveillance system and outline remaining challenges. Future works will consist in the use of other data sources such as environmental factors in order to improve knowledge on virus transmission mechanisms and determine how to use them for outbreaks prediction. Keywords. Dengue Fever, Epidemiologic Surveillance, Vector-borne disease, Infectious disease, Public Health Surveillance, French Guiana

1. Introduction One of the main objectives of infectious disease surveillance systems is to provide early warning of disease outbreaks to those who can take appropriate response. In the last decade, the critical need for better surveillance became more urgent with the threat of bioterrorism, the recognition of the potential for an influenza pandemic [1] and the emergence or reemergence of infectious diseases in some regions of the world such as the introduction of West Nile Virus in the United States, the Chikungunya in Reunion Island or cholera in Haïti. Dengue virus, which is most commonly acquired through the bite of Aedes aegypti mosquito, is the most important mosquito-borne viral disease affecting humans [2]. This infection is caused by an arbovirus of the Flaviviridae family. There are four viral serotypes designated as DENV-1, DENV-2, DENV-3 and DENV-4. The infection produced a spectrum of clinical illnesses that ranges from an influenza-like illness to potentially fatal dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), encephalitis or hepatitis. Despite the current development of 1

Claude Flamand, Epidemiologist. Cellule de l’Institut de Veille Sanitaire en Région Antilles-Guyane. E Mail: [email protected]

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C. Flamand et al. / The Epidemiologic Surveillance of Dengue-Fever in French Guiana

several dengue vaccines [3], no vaccine and no curative treatment are available for the moment. So the strategies of prevention are limited to vector control and treatment strategies are limited to supportive care aiming at avoiding shock [4]. French Guiana is a 200,000 inhabitant’s French overseas department located in South America. Tropical vector-borne diseases such as dengue fever are responsible for disease outbreaks. Since 2006, a multi-source surveillance system, coordinated by the Cellule de l’Institut de Veille Sanitaire (InVS) en Regions Antilles-Guyane (Cire AG) was implemented to monitor dengue fever patterns, to improve early detection of outbreaks and to allow a better provision of information to health authorities to guide and evaluate prevention activities and control measures. The aim of this paper is to describe the experience gained by such a surveillance system and to outline remaining challenges.

Figure 1. Global architecture of the surveillance system

2. Materials and Methods 2.1. General Description of the Surveillance System The surveillance system integrates health information from multiple data sources, located on the coast and in the inland of French Guiana (Figure 1). 1. Biological laboratories (LABM): From 1991 to 2004, the surveillance system was based on the weekly surveillance of cases diagnosed by the French National Reference Center (NRC) for Arbovirus and Influenza virus, based at the Institut Pasteur de Guyane in Cayenne. From 2004 to 2006, the number of laboratories able to perform the biological confirmation of dengue gradually increased up to 7 laboratories distributed on 5 municipalities located on the coast in the northern part of the country. The definition criteria are virus isolation, viral RNA detection by reverse transcription-PCR (RT-PCR), or the detection of secreted NS1 protein or a positive serological test based on immunoglobulin M (igM)-capture enzymelinked immunosorbent assay (MAC-ELISA) [5; 6]. 2. Sentinel network: In 2006, a sentinel network composed of 30 voluntary general practitioners (GPs) located in the coast (representing around 35% of total GP’s


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activity) was implemented to collect clinical cases. A clinical case of dengue fever was defined by the occurrence of fever (equal to, or more than 38°C) with no evidence of other infection and associated with one or more non-specific symptoms including headache, myalgia, arthralgia and/or retro-orbital pains. Every week, public health nurses of the Health Regional Agency (ARS) surveillance unit call the sentinel GPs to collect the number of cases seen during the previous week. The weekly incidence of dengue fever in the coast area is estimated using the ratio of all GPs to participating sentinel GPs. 3. Hospital Centers: Since 2006, surveillance from Emergency Departments (EDs) of the three hospitals in French Guiana was set up to collect ED visits for “isolated fever” or “suspicion of dengue”. Furthermore, InVS set up a volunteer surveillance network of hospital EDs to collect data on a daily basis [7]. For each patient, age, gender, zip-code, reason for admission and main medical diagnosis based on the 10th edition of the International Classification of Diseases (ICD-10) are collected. Since 2008, the Hospital of Cayenne, the main city, is connected to this network and enables the monitoring of ED activities related to dengue fever medical diagnosis. Follow-up of hospitalized cases of dengue was also set up to monitor the severity of the epidemics. 4. Health Centers (CDPS): The health care system in isolated territories of French Guiana is based on 17 Health Centers, which are remotely coordinated by the hospital center of Cayenne. Since 2006, a surveillance system based on a data transmission by satellite connection enables the Cire AG to collect, from each center, the weekly number of suspected cases of dengue, following the same criteria as sentinel GPs. 2.1.1. Data Analysis and Statistical Methods Data have been monitored from January 2006 to December 2010. An analysis system using the Shewhart Control Chart based on moving ranges (MR) [8] was implemented to allow a continuous real-time assessment aiming at early outbreak detection. This analysis compares the weekly number of reported cases with a control limit calculated on the basis of the average of previous observations and standard deviation estimated by the moving range of size 2. Every week, data were analyzed according to the Program for Surveillance, Alert and Response (PSAGE) for dengue fever. The PSAGE was elaborated in 2008 by a local vector-borne disease committee composed of epidemiologists, biologists, clinicians, entomologists, specialists in charge of vector control. The program aims at specifying the role and the missions of all stakeholders in the integrated vector management, epidemiological surveillance, laboratory diagnosis, environmental management, clinical case management and communication. Five distinct epidemiological situations have been established: − Stage 1: Sporadic transmission − Stage 2: Presence of dengue fever clusters in some areas − Stage 3: Pre-alert epidemic − Stage 4: Confirmation of the epidemic − Stage 5: End of epidemic For each stage, a commensurate combination of preventive and control measures has been determined. The observations from epidemic periods were excluded from the calculation of the alert threshold. The pre-alert epidemic stage was activated if alert

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thresholds were exceeded for two following weeks. We confirm the outbreak if the exceeded thresholds lasted two additional weeks. Others points brought to the table, such as the significant increase of the positivity rate of biological analysis or the re-emergence of a serotype were used to confirm the entry into the next stage. The end of epidemic stage was announced when the number clinical cases and biological confirmed cases returned under the thresholds. Table 1. Description of the outbreaks detected from 2006 to 2010, French Guiana Outbreaks period W2006-012 – W2006-34 W2009-01 – W2009-38 W2009-53 – W2010-38

Cases (N) Clinical Confirmed cases cases 15 700 2 300 13 900 4 129 9 400 2 431

Serotypes

Hospitalization

Deaths

DENV-2 DENV-1 DENV-4, DENV-1

204 241 92

4 2 1

3. Results Confirmed and clinical cases were collected and recorded in the database from the 2006 to 2010. Over the study period, 37 812 clinical cases and 10 724 confirmed cases were recorded. The global activity was strongly influenced by the occurrence of outbreaks periods (Figure 2).

Figure 2. Weekly number of biologically confirmed and clinical cases of dengue-fever and outbreaks periods, French Guiana, January 2006 – December 2010

As shown in Figure 2, three major outbreaks were detected during the study period (Table 1). During these outbreaks, 80 signals were triggered for confirmed cases and 64 for clinical cases. The occurrence of all these outbreaks was confirmed by the vector borne disease committee. The average duration of the epidemics varied between 38 and 41 weeks. According to the PSAGE, health authorities decided upon a reinforcement of collective and individual vector control measures proportionate to the severity and magnitude of the epidemiological situation. Aside from the outbreak periods, 19 and 9 signals were respectively triggered by the control chart for confirmed and clinical 2

The surveillance of biologically confirmed cases allowed identifying the beginning of this outbreak in W2005-48.


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cases. While conducting epidemiologic investigations in order to explain these signals, some relevant clusters also happened to be identified in some municipalities.

4. Discussion The achievements presented in this paper highlight the validity of the surveillance system and its performance to monitor dengue patterns in the whole country of French Guiana, to detect outbreaks and to provide real time information to health authorities. The great variety of data sources constitutes a very sound basis for the analysis and interpretation of the epidemiological situation and an essential tool for decision-making within the vector-borne disease committee. In the future, other statistical methods should be implemented using time-series methodology and taking into account data characteristics such as secular trends, seasonality and abrupt changes. Recent outbreaks showed that the implementation of the PSAGE at a region-wide level was not relevant considering the significant distances between municipalities. Future challenges and developments should focus considering smaller territories by spreading the PSAGE in relevant spatial units. Another major challenge will be outbreak prediction. This step will consist in the use of other data sources for surveillance such as environmental factors (i.e. climatic, meteorological, plant cover and land use) so as to help monitor and predict the spatial and temporal distribution of the virus. A research project is now being developed to use an alternative approach as the spatiotemporal data mining. The approach will consist in highlighting the relevant spatial units and the factors which are associated with a subsequent increase of cases. As an example, the project aims at applying data mining algorithms to identify frequent sequential patterns like < (NDVI ++, Rainfall ++) (BCC ++)>, which means that the combination of high Normalized Difference Vegetation Index (NDVI) and important rainfalls leads “frequently” to an increase in the number of biologically confirmed cases (BCC) of dengue. We’ll follow the spatial and temporal distribution of these sequential patterns to better understand the mechanisms of the virus transmission in order to use them for outbreak prediction.

References [1] [2] [3] [4] [5] [6]

[7]

[8]

M’ikanatha NM, Lynfield R, Van Beneden CA, De Valk H. Infectious Disease Surveillance. Blackwell Publishing. 2007 Gubler DJ. The global emergency / resurgence of arboviral diseases as public health problems. Arch Med Res 2002, 334 : 330-342; Guy B, Saville M, Lang J. Development of Sanofi Pasteur tetravalent dengue vaccine. Human Vaccin 2010. 6(9). Beatty ME, Stone A, Fitzsimons D, et al. Best Practices in Dengue Surveillance: A Report from the Asia-Pacific and Americas Dengue Prevention Boards. Plos Negl Trop Dis. 2010. 4(11):e890. Tran A, Deparis X, Dussart P, et al. Dengue spatial and temporal patterns, French Guiana, 2001. Emerg Infect Dis (2004), 10(4):615-621. Dussart P, Petit L, Labeau B, et al. Evaluation of Two Commercial Tests for the Diagnosis of Acute Dengue Virus Infection Using NS1 Antigen Detection in Human Serum. PLoS Negl Trop Dis. 2008.2(8):e280. Josseran L, Fouillet A, Caillere N, et al. Assessment of a Syndromic Surveillance System Based on Morbidity Data: Results from the Osccour Network during a Heat Wave. 2010. PLoS ONE 5(8): e11984. Montgomery DC. Introduction to Statistical Quality Control, 5th ED. John Wiley & Sons, New York, 2005.

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Prescribing History to Identify Candidates for Chronic Condition Medication Adherence Promotion Jim WARRENa,b,1, Debra WARREN a,b, Hong Yul YANG b, Thusitha MABOTUWANAa,b, John KENNELLYc, Tim KENEALYc, Jeff HARRISON d a National Institute for Health Innovation b Department of Computer Science c Department of General Practice and Primary Health Care d School of Pharmacy The University of Auckland, Auckland, New Zealand

Abstract. Poor adherence to long-term prescription medication is a frequent problem that undermines pharmacological control of important risk factors such as hypertension. A medication possession ratio (MPR) can be calculated from Practice Management System (PMS) data to provide a convenient indicator of adherence. We investigate how well prior MPR predicts later MPR, taking MPRFall opian tube Fallopian tube&+Fetal or embryonic structure Fetal or embryonic structure

Vulva|Vagina] Hematoma Device: Prosthesis>Fall opian tube

Means

Action

Open

Incision

Open

Removal

Open

Implantation of device|Change

Open

Excision&+Re moval

Per Orifice/Tr ansorifice

Reposition: Internal version and combined version &Extraction: Delivery Drainage: Evacuation Implantationof device|Change

Open Open

If the granularity of the one semantic category axis is coarser than the granularity needed by the label, : is appended at the end of the semantic category value set and after the symbol the more fine granularity value is registered (target in case 1). When more than a target or an action were needed, & was inserted denoting and (target in case 2). | was used denoting or where more than two options were available in an axe (action in case 3, target in case 6 and action in case 7).

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B. Jung et al. / The Revision of the Korean Classifications

When one item was associated with the other item then &+ was used meaning associated with (target and action in case 4).&- was used to show something excluded (action in case 5). Sometimes pathologic conditions were actual targets of actions. However they were not listed in target axis in the content model. The target in the target axis was put on the left side of ] to keep the original model, and the pathologic condition was put on the right side (target in case 6). Some actions required more than two targets as in sentence structure with indirect and direct objectives. Target as a role of direct object was located on the left side of >, and the other one as a role of indirect object was located on the right side (target in case 7). The model was successfully applied to the rest of the data. When it was applied to the obstetrics and gynecology data, it could gather the procedures labels with similar properties in a group and make rearrangement of the hierarchy easier as shown in Figure 2.We are currently waiting for the validation by doctors which will be presented during the conference.

5. Conclusion On the whole the ICHI semantic model was able to represent most of the ICD 9CM Volume 3 and the specific Korean coding systems labels. Some difficulties still need to be overcome: to find the Action value, some extensions are needed for the number of accepted Targets and Actions and Pathology as a Target. This work is a case study showing how the ICHI international initiative can support the harmonization of national health interventions coding systems starting from the unofficial standard of ICD 9 CM Volume3.

References [1]

[2]

[3] [4] [5]

Rodrigues J-M, Kumar A, Bousquet C, Trombert B. Standards and Biomedical Terminologies: The CEN TC 251 and ISO TC 215 Categorial Structures. A Step towards increased interoperability. In: Andersen SK, et al. (Eds.) MIE 2008 Proc. IOS Press, 2008; pp. 735-740. Madden R, Zaiss A, Thorsen G, Lewalle P, Rodrigues J-M, Weber S, Ustun, B: World Health Organization Family of International Classifications: Developing the International Classification of Health Interventions: Background, Need and Structure, WHO 2008. Weber S, Rodrigues J-M, Madden R, Pickett D, Zaiss A, ten Napel H, Moskal L, Bartz C, Virtanen M. The ICHI content model, WHO 2009. Madden R. World Health Organization Family of International Classifications: ICHI project plan WHO 2010. Trombert Paviot B, Madden R, Zaiss A, Bousquet C, Kumar A, Rodrigues JM. Towards the International Classification of Health Interventions (ICHI). Step 2.Populating the ICHI content model with existing coding systems. In Proceedings of PCSI International Munich 2010.


759

Web-Based Collaboration for Terminology Application: ICNP C-Space a

Claudia C. BARTZa,1, Derek HOY b International Council of Nurses, Geneva Switzerland b SnowCloud, United Kingdom

Abstract. The purpose of this paper is to describe the ongoing evolution of a nursing terminology that involves users in all aspects of the terminology lifecycle. A terminology will not succeed until and unless it benefits users and contributes to improved client outcomes at the point of care. Since the release of ICNP® Version 1 in 2005, users have been necessary partners in research and development, dissemination and education, and, to some extent, in terminology maintenance and operations. ICNP C-Space was launched in 2008 as a platform for collaboration among users and the ICNP team. C-Space applications include, but are not limited to, the ICNP browser, a multi-lingual browser, catalogue development pages, and group discussion pages. Future uses may include work related to ICN research and networks. C-Space adds value to ICNP, ICN, and nursing worldwide by ensuring that terminology users can contribute their expertise to finding workable solutions and developing important products related to ICNP. Keywords. Healthcare terminology, Terminology life cycle model, ICNP C-Space, Terminology user

1. Introduction As a healthcare terminology matures, there comes a point when developers have to rely on users for continued improvement of the terminology and evaluation at the point of use. The goal of standardized documentation in interoperable health information systems, resulting in automatically collected reusable data, is gaining advocates worldwide. Reusable data means that data are entered only once, preferably electronically, and then are available for multiple purposes [1], such as management decision-making, patient outcomes research and healthcare policy development. To ensure continued development, maintenance, and application of a healthcare terminology, it is essential to have full and productive engagement between terminology developers and users, including clinicians, vendors, informatics professionals, and terminologists. The International Council of Nurses (ICN) approved development of the International Classification for Nursing Practice (ICNP®) in 1989 and the alpha and beta versions culminated in the release of ICNP Version 1 in 2005. Prior to 2005, development of the terminology was based on the work of nurse experts who gathered and organized concepts representing the nursing domain in a multi-axial terminology 1

Corresponding Author: Claudia C. Bartz. ICNP & ICN Telenursing Network Coordinator, International Council of Nurses, 3 place Jean-Marteau, Geneva, Switzerland, CH-1201; E-mail: [email protected].

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C.C. Bartz and D. Hoy / Web-Based Collaboration for Terminology Application: ICNP C-Space

that required combinatorial processes to structure primitive concepts into nursing diagnoses, outcomes, and interventions. From 2005 forward, ICNP development used a formalized language methodology to represent concepts and relationships within the nursing domain. Formal definitions for ICNP are represented in web ontology language (OWL). Versions 1.1 and 2 were released in 2007 and 2009, respectively. ICNP is a compositional terminology that represents the nursing domain of healthcare. ICNP Release 2011 includes 3281 concepts, 669 pre-coordinated diagnosis and outcome statements, and 484 pre-coordinated intervention statements.

2. Purpose As ICNP gained stability in development and maintenance processes, the terminology then needed the creativity and expertise of clinicians and researchers who would implement ICNP in care delivery settings, evaluating its usability and the stored, reusable nursing documentation data. As biennial releases of ICNP continue, its value will only be ensured with productive interaction between the ICNP team and users. The purpose of this paper is to describe a process for the involvement of users in all aspects of the terminology life cycle. Specific objectives of the paper are to (1) describe the evolution of a global nursing terminology from ‘paper and pencil’ structuring of relevant concepts; (2) describe how goals and methods for involving nurses worldwide in the application, evaluation, and quality improvement of ICNP have been implemented and evaluated; and (3) propose future directions for continued collaboration between users and developers. 2.1. Evolving Development Methods The alpha, beta 1, and beta 2 versions of ICNP used 16 axes to organize concepts of the nursing domain. To ensure consistency of use, rules for forming nursing diagnoses and outcomes, and nursing interventions (actions) complied with ISO Health Informatics 18104:2003 [2]. With ICNP Version 1 [3], concepts were coded with unique, randomly assigned 8-digit identifier, consistent with ISO Health Informatics 17117:2007 [4]. However, users voiced their comfort with the former codes (eg, 1; 1.1; 1.1.1) because they were able to add local concepts to the terminology in places that seemed logical [5]. Thus the reaction of users to the ICNP terminology concepts being modeled in web ontology language and given unique codes clearly showed the need for continuous user-developer consultation, collaboration and education. With the release of ICNP Version 1, the increasing number of concepts and the unique codes made it difficult for nurses to use ICNP efficiently and effectively in care delivery settings. The solution for this difficulty was to create subsets of the terminology, or catalogues, with pre-coordinated nursing diagnoses and outcomes, and pre-coordinated interventions. Catalogues would be clinically relevant; applicable to individuals, groups, or communities; and focused on health conditions (eg, diabetes), client phenomena sensitive to nursing interventions (eg, adherence to treatment), specialties (eg, maternal health), or settings (eg, disasters). ICN published guidance for catalogue development [6] and two catalogues [7, 8] with the intent of encouraging users to develop catalogues in collaboration with ICN. While pre-coordinated statements were intended to simplify users’ application of the terminology, users had learned to compose nursing diagnosis, outcome and


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intervention statements for the electronic record using combinations of 8-digit codes. Now these multi-coded statements were being superseded by single, 8-digit codes for the pre-coordinated statements. This change also requires continued discussions between users and ICN. 2.2. Life Cycle Model As the ICNP terminology continued to increase in number of primitive and precoordinated concepts, all modeled within the OWL development environment, and as the additional requirements of the programme increased in scope and complexity, a model was developed to organize all the aspects of terminology development. A model was seen as a way to guide internal operations, aid in setting priorities for the work of the programme, structure quality improvement processes, and inform users about how they can contribute to the development and application of ICNP. The model has three main constructs: research and development, maintenance and operations, and dissemination and education [9]. In addition to catalogue development, users conduct research projects in their work settings (eg, academic, clinical). Translations are an important aspect of ICNP development. Dissemination and education involves professional presentations, publications, and academic and clinical applications related to users’ work with ICNP. The model was validated as fit for purpose as overlays of catalogue development (Figure 1) and quality improvement processes were both found satisfactory [10, 11].

Figure 1. Validating Lifecycle Model with Catalogue Development Process.

3. ICNP C-Space An important goal for continuing ICNP terminology development was to establish some means by which users and the ICNP team could more inter-actively continue ICNP development and application. A web-based platform was devised and tested for feasibility. Since its inception in 2008, the capabilities of C-Space have continued to advance in support of the terminology. An ICNP browser was one of the first features of C-Space. Users can download ICNP files from C-Space, using the site as a centralized portal for distribution. The ability of users to access the online browser moved the terminology forward as users asked for various ways of representing the terminology so that it would be as useful,

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accessible, and as comprehensible as possible. With each biennial release of ICNP, ICN aims to provide the ICNP representations that users need for clinical applications and continued research. When ICNP is downloaded, users sign agreements that allow ICN to track research, development and translation projects from inception to completion. In 2011, the browser was made multi-lingual, showing and encouraging worldwide involvement with ICNP. The multi-lingual browser also supports continued translation of the terminology as biennial releases include progressive improvements, mostly in the numbers of pre-coordinated statements for use in the standardized documentation of nursing care delivery. A catalogue development project on C-Space with collaboration between community nurses in Scotland and the ICNP team resulted in an additional catalogue for users worldwide [12]. The collaboration also tested processes for communication, interaction, content development, and screen designs. This multi-year work resulted in many lessons learned, to include confirmation of the belief that nurses use language in many different ways to mean many different things. Variation of words and meanings is a challenge for the ICNP terminology as it aims to represent the nursing domain worldwide. More catalogue development projects are currently under way on C-Space. Communication groups are in early stages of development on C-Space. One group has been formed to discuss implementation of ICNP. Members use the asynchronous discussion format to describe their work locally and collaborate internationally to advance ICNP use in care settings. Another group consists of the Directors of the ICNAccredited Research and Development Centres, who are preparing for the biennial consortium meeting in 2011. Directors are encouraged to collaborate in ICNP development, eg, one Centre’s focus on the phenomenon of family care could inform another Centre’s focus on disaster nursing. C-Space usage is described in Table 1. Table 1. Usage Statistics April 2010 to March 2011.

Unique Visitors

Visits from 129 Countries

Pageviews

4,791

8,860

111,069

Registered Users 03/2011 1,520

User Groups 03/2011 6

Downloads 03/2011 822

4. Future Directions ICN recognizes the expanding impact of eHealth and the great potential that the use of information and communication technology can have with healthcare assessment, management, documentation, and reporting nationally and internationally. Data about nurses and nursing are rare to non-existent in international reports of healthcare resources and outcomes. ICN further recognizes the potential for nursing communication and documentation that ICNP, as a standardized terminology for representing the work of nursing, can support and propagate, whether the application is used with complex health information systems or mobile technology, such as mobile phones. C-Space can continue to expand its capabilities to include research using core data sets. ICN core data sets are seen as the research tools for electronic data collection and


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analysis in response to focused research questions from any of ICN’s programme areas, eg, regulation, socio-economic welfare, and professional practice [13]. Another potential use for C-Space groups would be to support the ICN Telenursing Network as it seeks to interface with nurses and others professionals worldwide. Collaboration between informatics nurses and telehealth nurses could substantially benefit health technology development, application and evaluation, and support standardized documentation of nurse-sensitive client outcomes that would increase nursing knowledge and improve care delivery.

5. Summary ICNP is increasing in scope of coverage of the nursing domain. Nurses in more regions and countries are implementing clinical applications of ICNP. Among the many challenges for nurses are translation and meeting the technical requirements for clinical applications. C-Space supports a strong network of committed nurses and others who continue to collaborate with ICN to ensure that nurses are able to document their work using ICNP, in a consistent and accurate way to result in reusable data. Then nurses worldwide will be able to describe what nurses do, and what differences nurses make in healthcare outcomes for individuals, families and communities.

References [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Hammond, WE Bailey, C Boucher, P Spohr, M Whitaker P. Connecting information to improve health. Health Affairs 29(2), 2010, 284-288. International Standards Organization. International Standard 18104:2003 Health Informatics— Integration of a Reference Terminology Model for Nursing. Geneva Switzerland: International Standards Organization, pp. 3-6. International Council of Nurses. ICNP Version 1.0. Geneva Switzerland: International Council of Nurses, 2005. International Standards Organization. Technical Standard 17117:2007 Health Informatics — Controlled Health terminology — Structure and High-level Indicators, p. 8. Bartz C.C. (personal communication, 16 June 2005) International Council of Nurses. Guidelines for ICNP Catalogue Development. Geneva Switzerland: International Council of Nurses, 2008. International Council of Nurses. Partnering with Patients and Families to Promote Adherence to Treatment. Geneva Switzerland: International Council of Nurses. 2008. International Council of Nurses. Palliative Care for Dignified Dying. Geneva Switzerland: International Council of Nurses, 2009. International Council of Nurses. ICNP Version 2. Geneva Switzerland: International Council of Nurses, 2009. Coenen, A Kim. TY Development of terminology subsets using ICNP. International Journal of Medical Informatics 79, 2010, 530-538. Kim, TY Coenen, A Hardiker N. A quality improvement model for healthcare terminologies. Journal of Biomedical Informatics 43, 2010, 1036-1043. International Council of Nurses, Scottish National Health Service. Scottish Community Nursing Dataset. Geneva Switzerland: International Council of Nurses. in press. Coenen A, Bartz C. (2010). ICNP: Nursing Terminology to Improve Health Care Worldwide. In Nursing and Informatics for the 21st Century: An International Look at Practice, Trends and the Future. 2nd Ed, pp. 207-216.

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Mapping Medical Records of Gastrectomy Patients to SNOMED CT a

Eun-Young SO a, Hyeoun-Ae PARK a1 College of Nursing, Seoul National University, Seoul, Korea

Abstract. The purpose of this study is to explore the ability of SNOMED CT to represent narrative statements of medical records. Narrative medical records of 281 hospitalization days of 36 patients with Gastrectomy were decomposed into single-meaning statements, and these single-meaning statements were combined into unique statements by removing semantically redundant statements. Concepts from the statements describing patients' problems and treatments were mapped to SNOMED CT concepts. A total 4717 single-meaning statements were collected and these single-meaning statements were combined into 858 unique statements. Out of 677 unique statements describing patients' problems and treatments, about 85.5% statements were fully mapped to SNOMED CT. The rest of the statements were partially mapped. This mapping result implies that physicians' narrative medical records can be structured and used for an electronic medical record system. Keywords: information sharing, narrative medical records, terminology system, mapping, SNOMED CT, ICNP

1. Introduction Throughout the healthcare sector, the introduction and utilization of information systems is becoming widespread. Electronic Medical Records, which are the most crucial component of hospital information systems, improve the accessibility of medical information and contribute to the readability and completeness of records, allowing users to search for and use information with more ease through greater integration of information [1, 2]. But in order to use such electronic medical records more efficiently, and to facilitate the smooth sharing and exchange of information between systems and medical institutions, it is imperative to be based on a controlled terminology system[3]. In nursing, an electronic nursing records system based on ICNP, a controlled nursing terminology system, was introduced in early 2003 in Korea[4], and went so far as to use the data gathered by this system in decision-making and research [5]. But in the case of physicians’ records, only fragmentary information such as chief complaints[6], decision-making rules[7], discharge summaries, diagnoses, and operation names [3] has been mapped to SNOMED CT. Records that compose a great part of all medical records, such as admission notes, progress notes, and summary discharge notes are still left in unstructured free text format.

1

Corresponding Author: Hyeoun-Ae Park, College of Nursing Seoul National University, 28 Yongon-dong Chongno-gu, Seoul, 110-799 Korea; E-mail: [email protected]

E.-Y. So and H.-A. Park / Mapping Medical Records of Gastrectomy Patients to SNOMED CT

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Therefore, in the present study, we map doctors’ medical records documented in free-text form to SNOMED CT concepts in order to explore the possibility of structured data input.

2. Method 2.1. Data Collection We analyzed the free-text medical records of patients who were admitted to the Department of General Surgery in a tertiary hospital in Korea, received gastrectomy. Medical records of the patients with gastrectomy were chosen for analysis because gastrctomy is one of the most frequently performed surgeries in Korea with a relatively well defined care procedure. In order to limit the medical records of gastrectomy patients, we eliminated the records of patients who were transferred to other departments before or after the surgery, or who had other operations performed on them simultaneously. Statements were collected in reverse chronological order from the records of the patients admitted on September 30, 2009. Taking into consideration the change of the doctor in charge due to the monthly rotation of the residents at the study hospital, we only included three patients per month in the pool for analysis. We collected the freetext portions of the patients’ medical records, decomposed them into single statements by meaning, and continued the process until there were three patients who no longer yielded statements with new meanings (saturation sampling). As a result, we collected 4717 single statements from the medical records of 36 patients, documented by 19 doctors over a period of 281 days. 2.2. Analysis of Data The collected statements often overlapped in meaning, although they were expressed differently by different doctors. Combining the statements by meaning, a total of 858 unique statements were extracted. We classified the extracted unique statements into those that describe the “medical condition of the patient” (current symptoms, test results, diagnosis and etc.), those that describe “medical procedures performed on the patient” (treatment, administration of medicine, care plans and etc.), and “other statements” (patient’s habits and other administrative information). Of these, the 677 unique statements that describe the “medical condition of the patient” and the “medical procedures performed on the patient” were the target of analysis in this study. First we decomposed each unique statement into concepts and mapped them to SNOMED CT (2009-07-31 international edition) concepts using the CliniClue Xplore browser. The results of the mapping were classified into “fully mapped”, “partially mapped”, and “not mapped”. 2.3. Validation The results of extracting the concepts from statements and mapping them to SNOMED CT concepts were verified by a group of experts. The experts consisted of a surgeon who performs gastrectomies at a hospital, a nurse with a Ph.D degree in nursing

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informatics with experience in SNOMED CT mapping research, a doctoral student with experience in SNOMED CT and nursing informatics research, and a student with a master’s degree who maintains electronic medical records at a hospital using the SNOMED CT. The experts were presented with the results of the mapping along with possible replacement concepts, and asked for their opinions. The mapping results were finally modified based on their verification.

3. Result When 677 unique statements describing the “medical condition of the patient” and the “medical procedures performed on the patient” were decomposed into concepts and mapped to SNOMED CT concepts, 579 unique statements - 85.5% of the total - were fully mapped and the remaining 14.5% were partially mapped. There were no statements that were not mapped to SNOMED CT concepts. In statements without removing redundancy in meaning, 3740 statements - 93.3% of the total - were fully mapped to SNOMED CT concepts. Regarding the types of statements, those that described the medical condition of the patient (91.9%) showed a higher rate of being fully mapped than statements that described the medical procedures (74.4%) (Table 1). A total of 705 concepts were extracted during the course of the mapping.

Table 1. Mapping of Statements by SNOMED CT

Patient Conditions

Treatments Given

Total

No. of total statements (%)

No. of unique statements (%)


No. of unique statements (%)


No.of unique statements (%)

FullyMapped

3071 (96.8)

396 (91.9)

669 (80.0)

183 (74.4)

3740 (93.3)

579 (85.5)

PartiallyMapped

101 (3.2)

35 (8.1)

167 (20.0)

63 (25.6)

268 (6.7)

98 (14.5)

Total

3172 (100.0)

431 (100.0)

836 (100.0)

246 (100.0)

4008 (100.0)

677 (100.0)

Taking the frequency of concepts appearing in the statements into consideration, 705 concepts appeared a total of 9415 times. Out of 705 concepts, 660 concepts – rate of 93.6% were mapped to SNOMED CT. In terms of the types of mapping, 30.2% were lexically mapped, 21.5% were semantically mapped, 13.8% were mapped to a broader concept, 1.1% were mapped to a narrower concept, and 27.0% were mapped more than one concept (Table 2).


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Table 2. Mapping of Concepts by SNOMED CT

No. of unique concept(%) Mapped to SNOMED CT

660(93.6)

No. of total concept(%) 9135(97.0)

Lexically mapped

213(30.2)

1611(17.1)

Semantically mapped

152(21.5)

4390(46.6)

97(13.8)

1204(12.8)

8( 1.1)

53( 0.6)

190(27.0)

1877(19.9)

Mapped to a broader concept Mapped to a narrower concept Mapped to more than one concept Not mapped to SNOMED CT Total

45( 6.4) 705(100.0)

280( 3.0) 9415(100.0)

4. Discussion The results of the study show that most free-text gastrectomy patients’ medical records documented by doctors are able to be mapped to SNOMED CT. This is similar to the content coverage of SNOMED CT to represent the most common nonduplicated patient problems seen at the Mayo Clinic [8]. In the nonduplicated patient problem list comparing research, SNOMED CT, when used as a compositional terminology, can represent 92.3% of the terms used commonly in medical problem lists. This implies that that SNOMED CT can be used to structure free-text doctors’ medical records. In addition, the mapping rate to SNOMED CT was higher with statements that described the “medical condition of the patient”. In the current electronic medical record system, information on “medical procedures” is relatively easy to use, due to procedures being coded because they are used in doctors’ orders and reimbursements. However, the medical condition of the patient - especially the patient’s symptoms or the doctor’s judgments and opinions - usually remains unstructured as free-text records and is therefore difficult to search for. Therefore if such records become structured based on SNOMED CT, the information will prove extremely useful. In mapping to SNOMED CT concepts, statements about test results such as “platelet: */mm” or “total calcium: *mg/dl” were imbued with value judgments regarding the results. Thus the appropriate concepts were first searched for and mapped to concepts in the “clinical finding” hierarchy, then in the “observable entity” hierarchy when no concept matched. However, concepts describing some clinical laboratory tests could not be found in the abovementioned hierarchies, and existed only as a concept in the “procedures” hierarchy. In these cases, the concepts were considered not mapped. An example is the hepatic enzyme “GOT (Glutamic Oxoloacetic Transaminae)” and “GPT (Glutamic Pyruvic Transaminase)”; GOT existed as “aspartate transaminase level (finding)” in the “finding” hierarchy and was thus able to be mapped, but GPT

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only existed as “alanine aminotransferase measurement (procedure)” and was thus unable to be mapped. Such issues of inconsistency were present not only in clinical laboratory tests, but also in some pre-coordinated concepts. For example, “no sputum (finding)” or “not hoarse (finding)” could be expressed in pre-coordinated concepts, but concepts such as “no dyspnea” had no pre-coordinated concepts and needed to be expressed in postcoordinated concepts such as “dyspnea (finding)” and “absent (qualifier)”. Precoordinated expressions did exist for some concepts such as “no vomiting (situation)”, but in these cases, meaning of the concept in the “situation” hierarchy did not match up and thus the concepts could not be mapped. Not all concepts must be expressed through pre-coordinated concepts, but issues of inconsistency may arise when similar types of concepts are expressed partly through pre-coordinated and partly through postcoordinated concepts. In addition, post-coordination may prove to be useful in terms of later data utilization, so certain principles regarding these situations must be established during clinical mapping. However, the present study is limited to gastrectomy patients at the Department of Surgery at a tertiary hospital in Korea, and continued further research into the possibility of structuralization of doctors’ records is necessary through the analysis of medical records from various other areas.

References [1] [2] [3] [4] [5] [6] [7] [8]

Dick RS, Steen EB. The Computer-Based Patient Record: an Essential Technology for Health Care, Rev. ed. Washington DC: National Academy Press. 1997. Ginneken AM. The Computerized Patient Record: Balancing Effort and Benefit. Int J Med Inf 2002; 65: 97-119. Kim SH, Han SB, Choi JW. The Expressive Power of SNOMED CT Compared with the Discharge Summaries. J Kor Soc Med Informatics 2005; 11(3): 265-272. Cho IS, Park HA, Chung EJ, Lee HS. Formative Evaluation of Standard Terminology-based Electronic Nursing Record System in Clinical Setting. J Kor Soc Med Informatics 2003; 9(4): 413-421. Kim EM, Park IS, Shin HJ, Ahn TS, Kim YA, Oh PJ, et al. The Analysis of Standard Nursing Statements at Electronic Nursing Records. J of Kor Clinical Nursing Research 2005; 11(1): 149-164. Chin HJ, Kim SG. Standardization of Main Concept in Chief Complaint Based on SNOMED CT for Utilization in Electronic Medical Record. J Kor Soc Med Informatics 2003; 9(3): 235-247. Kim HY, Cho IS, Lee JH, Kim JH, Kim Y. Concept representation of decision logic for hypertension management using SNOMED CT. J Kor Soc Med Informatics 2008; 14(4): 395-403. Elkin PL, Brown SH, Husser CS, Bauer BA, Wahner-Roedler D, Rosenbloom ST, Speroff T. Evaluation of the content coverage of SNOMED CT: Ability of SNOMED clinical terms to represent clinical problem lists. Mayo Clin Proc. 2006 Jun;81(6):741-8.


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Terminology for the Description of the Diagnostic Studies in the Field of EBM a

Natalia GRABARa, Ludovic TRINQUARTb, Isabelle COLOMBETc CNRS STL UMR 8163, Université Lille 3, rue Barreau, 59653 Villeneuve d'Ascq, France b French Cochrane Center, France, AP-HP, Paris France c Université Paris Descartes, Paris, F-75006 France; HEGP AP-HP, 20 rue Leblanc, Paris, F-75015 France

Abstract. Diagnostic systematic reviews is a relatively new area within the EvidenceBased Medicine (EBM). Their indexing in Pubmed is not precise, which complicates their detection when a systematic review is to be realized. In order to provide an assistance in the selection of relevant studies, we propose to develop a terminology describing this area and the organization of its terms. The terminology is built with a bottom-up approach. It contains 255 terms organized into five hierarchical levels. Only a small proportion of these terms (13%) are already registered in MeSH. This terminology will be exploited in a dedicated web service as a main tool for the detection of relevant diagnostic studies. Keywords. Evidence-Based Medicine; Review, Systematic; Language; Natural Language Processing; Terminology

1. Introduction The aim of systematic reviews (SR) is to provide a synthesis of multiple primary research studies concerned with a given clinical question. Such syntheses are a part of the Cochrane Collaboration effort and published in the Cochrane library. The library is thereby a knowledge base which can be used by health professionals for supporting decisions within the frame of the Evidence-Based Medicine (EBM). The vast majority of SRs addresses the efficacy of interventions to treat or prevent diseases. Other SRs focus on diagnostic or prognostic studies. These reviews can be methodologically challenging. In particular, an essential step is to identify all relevant studies to be included in the review. Identifying diagnostic test accuracy studies is more difficult than searching for randomized trials. First, an exhaustive search strategy should involve several electronic bibliographical databases. Second, the indexing of diagnostic studies is imperfect as there is not a unique keyword for an accuracy study comparable with the term “randomized controlled trial” [1]. Third, methodological electronic search filters for diagnostic studies (which aim to restrict the search to articles that are most likely to be diagnostic studies) are not recommended because they can lead to the omission of a substantial number of relevant studies [2,3]. Fourth, supervised machine learning methods used for the automatic selection of relevant

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N. Grabar et al. / Terminology for the Description of the Diagnostic Studies in the Field of EBM

studies for therapeutic SRs [4-7] are not efficient because of the small amount of existing diagnostic reviews. Consequently, reviewers often have to screen for eligibility very large number of references, most of them being irrelevant to the clinical question of interest. The whole process is performed manually which is a real burden to reviewers. We propose to help the process of selection of relevant articles with a semantic information retrieval system through a terminological resource. To our knowledge, no such resource have been yet designed and published. Two kinds of approaches are distinguished when creating terminologies, namely the top-down (main high-level concepts are defined and then populated) and bottom-up (terms are observed within the exploited material and then organized into classes, sub-classes etc). Corpora of textual documents and Natural Language Processing (NLP) methods are often used in bottom-up approaches [8-9]. Transformation-based approaches have also been proposed, they exploit HTML and XML metadata [10] or databases [11-12]. In our work, we use corpora and NLP methods, because textual material is easily accessible and contains data actually and naturally used in the area of interest. Other related works should be mentioned. For instance, an ontology of EBM has been proposed [13]. It attempts a modelization of this area and it targets particularly relations which may exist between patient records and meta-analysis results. Another work proposes an ontology related to SRs and meta-analyses [14]. It contains 128 elements exploited for manual tagging of five Randomized Controlled Trials studies in neurosurgery. Intra and inter-annotator comparison shows that such ontologies allow to obtain a high annotation agreement (kappa rating from 0.53 to 0.82) and an improvement in the quality of reporting. We aim at creating a terminology dedicated to diagnostic studies.

2. Material and Methods Material. We exploit a set of corpora and the MeSH terminology [15].The main subset of corpora is composed of scientific literature and reports related to diagnostic studies. It contains: 6 reference articles dedicated to description of the STARD initiative and its main concepts, and 20 diagnostic studies, among which 15 are full-text articles and 5 are abstracts. References and full text of these articles are available upon request. These are supposed to be instantiations of the STARD initiative and to describe studies performed within the EBM framework. This diagnostic corpus contains 105,000 occurrences (or words). Additional corpora are used to ensure the specificity of terms, they cover other types of SRs: prognostic (6 citations, 36,000 occ.), therapeutic (7 citations, 36,779 occ.) and observational (6 citations, 39,800 occ.). MeSH terminology [15] is typically used for indexing the scientific literature in Pubmed database, among which for indexing the SRs. We expect that MeSH provides several terms relevant to diagnostic accuracy studies reviews. If new terms are found in the corpora, and according to the expert validation, they may be considered as additional relevant terms for MeSH. Method. Our method carries out extraction of terms and their alignment with MeSH. Another step is dedicated to the evaluation and structuring of the extracted data.


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Automatic acquisition and alignment of terms. Corpora are first pre-processed through the Ogmios platform [16]. This platform performs the segmentation into words and sentences, POS tagging (assignment of part-of-speech categories: cancers/Noun, cancerous/Adjective) and lemmatization (definition of the normalized form of words: cancers => cancer) with TreeTagger [17]. The step of term extraction is carried out with the syntactic rule-based parser YATEA [18]. Once the terms are extracted from corpora, they are aligned with the MeSH terminology. For all the extracted terms, their frequencies are computed in each processed corpus. This information is assumed to help the selection and validation step: frequencies of terms may be indicative of their specificity to the diagnostic area. Indeed, if terms occur only or more often in diagnostic corpus they show a high specificity, otherwise their specificity to the diagnostic area is lower. Evaluation and structuring. An independent evaluation was performed manually by two experts (a physician and a biostatistician with experience in SR). In cases of disagreements, consensus was established further to discussions. Each extracted term was examined, together with its distributions and frequencies across the corpora. Global inter-expert agreement was assessed with chance-corrected kappa statistics and with simple raw specific agreement indexes, which are the conditional probability, given one expert gives a result, that the other expert gives the same result [19]. Structuring was performed through a bottom-up approach: selected terms were categorized into categories and then subcategories, according to their semantics.

3. Results and Discussion Processing of diagnostic corpus led to extraction of 7,448 terms, among which 1,218 (16.3%) are already registered in MeSH, anf 6,230 are new terms. The acquisition on other corpora produced the following results: observational corpus provides 1,640 terms where 722 (44%) in MeSH; prognostic corpus provides 2,383 terms among which 531 (22.3%) in MeSH; therapeutic corpus provides 1,602 terms among which 590 (36.8%) in MeSH. Table 1: Excerpt of the extracted data. Terms E01 E05 N06 YATEA YATEA N06 YATEA YATEA

diagnosis roc curve prevalence diagnostic accuracy diagnostic performance confidence intervals characteristics curve clinical trials

Diagnostic Ftot Fmet 194 77 14 4 10 6 150 122 30 12 20 5 2 1 12 6

Fstu 117 10 4 28 18 15 1 6

Ntot Nmet 19 6 8 2 2 1 13 6 3 2 14 4 2 1 8 4

Nstu 13 6 1 7 1 10 1 4

Prog Ftot 13 2 3 10 0 7 0 4

Obs Ftot 27 0 11 0 0 3 0 8

Ther Ftot 6 0 0 0 0 8 0 38

Table 1 contains an example of the extracted terms together with their frequencies in various corpora. If an extracted term is also recorded in MeSH, we indicate in the first column its MeSH hierarchical tree (i.e., E, G or N), otherwise it is provided by YATEA. We then indicate frequencies of the extracted terms (frequency in diagnostic corpus Ftot, and

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separately in methodological documents Fmet and studies Fstu). We also indicate the number of diagnostic corpus documents in which these terms occurred (total number Ntot, and separately number of methodological documents Nmet and of studies Nstu). The last three columns indicate the frequencies of these terms in the three other corpora.Further to the expert evaluation, a set of 219 terms is selected. Among these, 26 (13%) are already registered in MeSH (E (n=11), G (n=2) and N (n=11) MeSH trees), while 193 are provided only by YATEA. The inter-expert agreement is NN. An additional set of 36 terms have been added by experts, which gives a total of 255 terms. The additional terms are often variations of the extracted terms (i.e. abbreviations: npv, ppv) or terms suggested by the extracted data (dor and cut point never occurred individually but within larger terms and have been added as individual entry). Within the initial set of 7,448 extracted terms, only 3% of these have been selected. The rejection rate is very important. Some of the rejected terms are indicated in lower part of table 1. Among the rejected terms we observe: (1) common errors usually observed with automatic term extraction methods due to tagging errors; (2) sequences non relevant to a terminology (journals, authors, ...); (3) too general terms (public health, confidence intervals, characteristics curve); (4) terms non specific to diagnostic studies (clinical trials). Specificity of the material needed for the task and current shortcomings of the automatic term extraction may explain such rejection rate. With this kind of data, where rate of selection is both globally low and heterogeneous between experts, inter-expert agreement kappa is low (0,106), although average positive (selection) and negative (rejection) agreements are respectively 0.14 and 0.84. Exploitation of such methods allows to construct a terminology where no existing semantic resources are available and to insure that this terminology will be relevant to the processing of real data. A low number of MeSH terms within the validated data indicates that diagnostic area is poorly covered by MeSH. If MeSH were to be enriched with such terms, the indexing of diagnostic studies would be more precise and would help realization of SRs. Next and final step of the work is dedicated to the structuring of the selected terms. Five levels of terms have been defined. Figure 1 shows the four higher levels corresponding to categories of terms. These four broad categories represent main aspects for diagnostic studies. Notice that nearly all the MeSH terms are positioned under the Test characteristics tree, which indicates again the necessity of such a resource.

Figure 1. Hierarchical tree of the terminology.

4. Conclusion and Perspectives We presented an experience in building a terminology of diagnostic studies within the EBM area. We exploited automatic methods for term extraction and for their alignment


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with an existing terminology (MeSH). Only small part of the acquired and validated terms is already recorded in MeSH. This indicates that MeSH may be enriched with some of the terms from the constructed terminology in order to provide assistance in indexing the diagnostic studies. The validated terms have also been structured, and the resulting semantic resource contains five hierarchical levels. We plan to exploit and evaluate this resource within the webservice dedicated to the automatic selection of literature [20]. Acknowledgments. This work is part of the ReSyTAL project, supported by a research grant from French PHRC, designed to facilitate the selection of relevant scientific literature as well as realization of diagnostic SRs.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

Haynes RB and Wilczynski NL. Optimal search strategies for retrieving scientifically strong studies of diagnosis from medline: analytical survey. BMJ 2005;330(7501):1162–3. Leeflang M, Scholten R, Rutjes A, Reitsma J, and Bossuyt P. Use of methodological search filters to identify diagnostic accuracy studies can lead to the omission of relevant studies. Clin Epidemiol 2006;59(3):234–40. Meade M and Richardson W. Selecting and appraising studies for a systematic review. Ann Intern Med 1997;127(7):531–7. Aphinyanaphongs Y, Tsamardinos I, Statnikov A, Hardin D, and Aliferis C. Text categorization models for high-quality article retrieval in internal medicine. J Am Med Inform. 2005;12(2):207–16. Cohen A, Hersh W, Peterson K, and Yen P. Reducing workload in systematic review preparation using automated citation classification. JAMIA 2006;13(2):206–19. Demner-Fushman D, Few B, Hauser S, and Thoma G. Automatically identifying health outcome information in medline records. JAMIA 2006;13(1):52–60. Kilicoglu H, Demner-Fushman D, Rindflesch T, Wilczynski N, and Haynes R. Towards automatic recognition of scientifically rigorous clinical research evidence. J Am Med Inform Assoc 2009;16(1):25–31. Condamines A and Rebeyrolle J. CTKB : A corpus-based approach to a terminological knowledge base. In: Proceedings of Computerm’98, Coling-ACL’98. 1998:29–35. Maedche A and Staab S. Mining ontologies from text. In: Dieng R and Corby O, eds, EKAW 2000. Giraldo G and Reynaud C. Construction semi-automatique d’ontologies à partir de DTDs relatives à un même domaine. In: Actes Ingénierie des Connaissances (IC). 28-30 mai 2002. Krivine S, et al. Construction automatique d’ontologies à partir d’une base de données relationnelles : application au médicament dans le domaine de la pharmacovigilance. In: IC 2009. Kamel M and Aussenac-Gilles N. Construction automatique d’ontologies à partir de spécifications de bases de données. In: IC 2009, 2009. Pisanelli D, Zaccagnini D, Capurso L, and Koch M. An ontological approach to evidence-based medicine and meta-analysis. In: MIE 2003, 2003:543–8. Zaveri A, Cofiel L, Shah J, et al. Achieving high research reporting quality through the use of computational ontologies. Neuroinformatics 2010;8(4):261–71. National Library of Medicine, Bethesda, Maryland. Medical Subject Headings, 2001. www.nlm.nih.gov/mesh/meshhome.html. Hamon T, Nazarenko A, Poibeau T, Aubin S, and Derivière J. A robust linguistic platform for efficient and domain specific web content analysis. In: RIAO 2007, Pittsburgh, USA. 2007. Schmid H. Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, Manchester, UK. 1994:44–9. Aubin S and Hamon T. Improving term extraction with terminological resources. In: FinTAL 2006, number 4139 in LNAI. Springer, August 2006:380–7. Cicchetti DV, Feinstein AR. High agreement but low kappa: II. Resolving the paradoxes. J Clin Epidemiol. 1990;43:551-558 Trinquart L, Fanet A, Grabar N, and Colombet I. A unique web service to facilitate the study selection process in systematic reviews. In: Joint Colloquium of the Cochrane & Campbell Collaborations, 2010.

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Representing Knowledge, Data and Concepts for EHRS Using DCM William GOOSSEN a,1 Lector ICT Innovations in Health Care at Windesheim, Zwolle, and director at Results 4 Care B.V., Amersfoort, the Netherlands a

Abstract. With the move towards next generations of Electronic Health Record Systems (EHRS), the focus changes from administrative and data retrieval and data entry system capabilities towards clinical functions. The representation of the clinical knowledge and evidence base into EHRS becomes an important asset for health care, with its own challenges. Clinician’s do want EHRS support but do not want to standardize care, they do want unified terminology and structured data entry but also free text. In addition, information modelers challenge each other for the best solution, and care pathways and other workflows seem to differ for each situation. Such diverging approaches add complexity to the already difficult situation around Information Technology in health care, the EHRS in particular. This paper argues that a change is necessary to adopt Detailed Clinical Modeling as a method to organize clinical knowledge, represent concepts and define data in such a manner that it allows for semantics to be exchanged without being trapped in a specific technology. DCM help to fulfill the requirements for the enter data once, reuse multiple times paradigm for EHRS. Keywords. concept representation, detailed clinical models, archetypes, Electronic Health Records, HL7 templates, information modeling

1. Introduction Next generation of Electronic Health Record Systems (EHRS) should fulfill many functional requirements of clinicians. Example EHRS functions that are increasingly becoming important are: structured data entry, easy data storage and retrieval, exchange of data for continuity of care, use of data for decision support, aggregation of data for quality indicators and epidemiology, and aggregation of data for billing etc. All these functions integrate with each other such that the knowledge required for each of these functions must be taken into account to properly represent the required clinical concepts in EHRS. This need for multipurpose representation of clinical concepts pinpoints to the most granular level of single data elements, their attributes, and their relationships. Moreover, to understand data that are exchanged, or properly compare groups of patients, a high level of standardization is required. At the same time, due to diversity of patients and increasing complexity of health care, a maximum flexibility is required in EHRS configuration for different domains. This cannot be achieved without a whole repertoire of health informatics standards.

1

Corresponding Author, Results 4 Care B.V. De Stinse 15 Amersfoort, the Netherlands. Email: [email protected].

W. Goossen / Representing Knowledge, Data and Concepts for EHRS Using DCM

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There are several approaches developed that attempt to fulfill these important EHRS functions. Each of these approaches begins with modeling efforts [1], and assumes an architectural framework [2], whether implicit or explicit. Clinical modeling examples in the literature and in practice include clinical elements [3], templates [4], care information models [5], clinical content models [6], clinical templates [7], archetypes [8], detailed clinical models [3, 9], and more. This approach is two level modeling, and it is carried out via disentangling the clinical data specification from the system technical functions [1]. Involving clinicians in such work is feasible, but usefulness is only apparent if a (international) system of governance is established [10]. This paper argues the case that adoption of conceptual level Detailed Clinical Modeling (DCM) is required to move to the next generation EHRS. DCM is both a method and a format to organize clinical knowledge, represent concepts, and define data elements in such a manner that it allows semantics to be exchanged without being trapped in a specific technology. DCM allows representing the semantics in a technology independent way and makes it feasible that next generation EHRS can be developed, and that existing healthcare information technology can interact with EHRS [3, 5, 7, 10, 11].

2. Clinical Modeling Benefits In a recent paper we reviewed a selection of the existing clinical modeling attempts listed above [11]. On the conceptual level, there is almost no difference in the representation of clinical knowledge in the form of data elements, relationships between data elements, attribute expression, and code binding [11]. However, there are differences on the use of a specific reference model and technology versus a more agnostic approach. In addition, there are differences in a top down (derived from a reference model) or bottom up approach (analysis of clinical phenomenon and afterwards link that to reference models). In addition, Blobel argues an architecture of health information technology is required to position these clinical models properly [2]. DCM starts with analyzing, sorting, and formalizing clinical knowledge on the fine-grained level of concepts. Next, the resulting material is structured and standardized on the level of individual and/or closely related data elements for clinical use. Doing this with conceptual modeling renders it possible to create and maintain a set of DCM independently of the technical implementation in which they are deployed. Hence, for an EHRS, clinicians are not completely dependent on vendors, or when a specific system is replaced, the clinical knowledge will remain available. Adding contextual knowledge and meta-information contribute to the overall usefulness of DCM for the different purposes for data use. These all are the core content of part – 2 – of international standard 13972 for DCM under development [12]. For a long term quality of DCM, it is important to engage clinicians, organize governance, enable access, and apply measures for patient safety. Methodologies that facilitate in the DCM work are the core of part – 1 – of the international standard [12].

3. Different Perspectives on Clinical Data The different purposes identified for use of clinical data, each require a careful analysis on detailed level of the requirements, in particular validity, relevance, and reliability of

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the data. Differences exist on problem, patient, sample, or population levels, and rules for data aggregation need to be taken into account. Hence, each purpose for data use has a specific set of attributes and constraints for the data entry, storage, processing, presentation, communication, aggregation, and so on. It is rarely possible to organize this for large data sets at once. However, when the big elephants are broken down in small portions, it becomes feasible to eat them. Thus, clinical data elements at the most ‘atomic’, or ‘small molecular’ granular levels are feasible to standardize from the different perspectives on data use [3, 5, 7, 10, 11]. DCM allows exactly doing that. Figure 1 illustrates overlap and differences in representing EHRS data for different purposes. This is the playground for DCM analysis and development.

Figure 1. Different purposes for data use requiring specific knowledge representation in EHRS.

Now a reasonable set of DCM is ready [3, 6], the diversity of patient populations can be addressed in full. We can deploy the same DCM in different clinical domains. This is where additional methods such as Domain Analysis Models [4] or Clinical Templates [7] are applied. In essence, these approaches define a clinical domain, such as diabetes care, skin assessment, care for a patient on a ventilator. It is obvious that there will be many data relevant and necessary in each domain. Some data overlap and other data differ from another domain. DCM would cover the small items like systolic and diastolic blood pressure, HbA1c value, Braden scale for pressure ulcer risk, among many others. Such DCM can be used in the domains via selecting from the DCM collection (repository) what a particular clinical group needs, creating DCM for what is absent, and combining and sometimes constraining the different DCM in the domain model or clinical template. Example constraints for diabetes domain model would be that the blood pressure must be measured in sitting position. Hence, the DCM has systolic and diastolic values and body position. Most domains will not use the latter.

4. From Clinical Data to Technical Implementation via Conceptual Modeling In order to achieve a technology independent representation of clinical knowledge, the DCM content, once sorted out, is modeled in generic information models. There are different options here, such as Unified Modeling Language (UML), Extended Markup Language (XML), or Web Ontology Language (OWL). Currently, most work in this area is done in a pragmatic way, using one of these representation methods. Figure 2 illustrates the three steps from clinical content via generic conceptual modeling to technical implementation. Moving from one-step to another will reveal what is unclear or not sufficiently specified. Hence, a feedback loop from each step to earlier steps


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improves the DCM quality and usability, but requires close interactions between clinicians, modelers, and technicians [13].

Figure 2. Three step modeling with DCM.

5. Core Components of a DCM Clinical Modeling work in the past decade has shown that there are several core components [3, 4, 5, 6, 7, 8, 9]. It is beyond the scope of this paper to fully list the components identified in current work on the DCM standard, but it is possible to summarize the most crucial ones [11, 12]. Table 1 shows the main DCM components. Table 1. Three core content areas of Detailed Clinical Models as in ISO daft 13972. Type of Knowledge Representation Clinical knowledge:

Data Element Specification:

Meta Data Specification:

Major Areas Addressed in DCM • Concept definition • Clinical population • Evidence base • Instruction for documentation • Interpretation • Data element • Data element definition • Data type • Unit or value set • Relationships between data elements • Unique code per data element • Detailed data model • Authors • Contact information • Versioning • Keywords • Endorsement / certification

6. Processes Around DCM In addition to the position and the core components of a DCM, three additional areas of concern have been identified and described [9, 11, 12]. The general opinion of the experts working on the DCM standard is that without clinician involvement and some arrangement to obtain endorsement from professional bodies, DCM will not be valid. In addition, clinical practice will continue to evolve, demands for data will probably increase, and challenges put to EHRS require that DCM can change over time. In that respect a large-scale governance structure will be required, similar to the major health classifications such as the International Classification of Diseases (ICD) and terminologies such as the Systematized Nomenclature for Medicine Clinical Terms (Snomed CT). Moreover, we would like to get access to DCM collections [9, 10]. Finally, a more recent evolving issue is that of ensuring patient safety in specifications

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for EHRS and other health care information technology [12]. Rule is to keep it simple and not too complex. DCM allows working on a fine-grained level, piece by piece.

7. Discussion and Conclusion It is likely that we will see no endpoint to the discussions and approaches to knowledge representation, concept modeling, and EHRS development. However, it is clear that the different quests for clinical data to accommodate different uses will go on and that EHRS fulfill a crucial role in addressing that requirement. This challenge is often expressed as the ‘enter once in EHRS and use multiple times’ paradigm. In this paper, we have argued that this challenge is not easy met and that it does require a high level of standardization in different knowledge areas. In particular, at the data element level, the specifications will have to be very precise and standardized. However, due to the many diverse DCM we can create, the flexibility to adapt to the diversity of patient populations and practice domains is present. Creating DCM of high quality and maintaining these for a long time requires quality criteria for the content, the modeling, and the methodologies. Hence, the standard 13972 currently under development at the International Standards Organization (ISO) [12] will be important to foster the clinical richness in DCM and deploying that in different EHRS. In the meantime, we see that DCM approaches do represent clinical knowledge, data elements and code binding such that the move to the multi functional next generation EHRS becomes feasible.

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Rector AL, Nowlan WA, Kay S, Goble CA, Howkins TJ. A Framework for Modelling the Electronic Medical Record. Methods Inf Med, 32 (1993), 109-119. Blobel B. Architectural Approach to eHealth for Enabling Paradigm Changes in Health. Methods Inf Med, 49(2) (2010), 123-134. Huff SM, Rocha RA, Coyle JF, Narus SP. Integrating detailed clinical models into application development tools. Medinfo 2004 Pt 2 11 (2004), 1058-1062. Health Level 7. Normative Edition of the HL7 Standards 2010. Ann Arbor, HL7 international. van der Kooij J, Goossen WTF, Goossen-Baremans ATM, Plaisier N. Evaluation of Documents that Integrate Knowledge, Terminology and Information Models. In: Park HA, et al, (Eds). Stud Health Technol Inform 122 (2006), 519-522. Center for Interoperable EHR CiEHR. Clinical Contents Manager. Seoul, Korea, Web documents. http://www.clinicalcontentsmodel.org/main.php. Visited Nov 26, 2010. Hoy D, Hardiker NR, McNicoll IT, Westwell P. A feasibility study on clinical templates for the national health service in Scotland. Stud Health Technol Inform. 129 (2007), 770-774. Beale T. Archetypes and the EHR. Stud Health Technol Inform. 96 (2003), 238-244. Goossen WTF. Using Detailed Clinical Models to Bridge the Gap Between Clinicians and HIT. In: De Clercq E, et al. (Eds). Collaborative Patient Centred eHealth. Proceedings of the HIT@Healthcare 2008. Amsterdam, IOS press (2008), 3-10. Garde S, Knaup P, Hovenga E, Heard S. Towards semantic interoperability for electronic health records. Methods Inf Med. 46 (3) (2007), 332-43. Goossen W, Goossen-Baremans A, M. van der Zel. Detailed Clinical Models: A Review. Healthc Inform Res. 16(4), (2010), 201-214. International Standards Organization. Draft materials ISO 13972 Health Informatics Quality Criteria and Methodologies for Detailed Clinical Models part 1 and part 2. Draft materials. Geneva, ISO. van der Zel M, Goossen W. Bridging the gap between software developers and healthcare professionals. Model Driven Application Development. Hospital Information Technology Europe, 3 (2), (2010), 2022.


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Ontology-Based Automatic Generation of Computerized Cognitive Exercises Giorgio LEONARDI a,c, Silvia PANZARASAb, Silvana QUAGLINIa Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy b Consorzio di Bioingegneria e Informatica Medica, Pavia, Italy c Dipartimento di Informatica, Università del Piemonte Orientale, Italy a

Abstract. Computer-based approaches can add great value to the traditional paperbased approaches for cognitive rehabilitation. The management of a big amount of stimuli and the use of multimedia features permits to improve the patient’s involvement and to reuse and recombine them to create new exercises, whose difficulty level should be adapted to the patient’s performance. This work proposes an ontological organization of the stimuli, to support the automatic generation of new exercises, tailored on the patient’s preferences and skills, and its integration into a commercial cognitive rehabilitation tool. The possibilities offered by this approach are presented with the help of real examples. Keywords. Ontology, cognitive rehabilitation, exercise adaptation

1. Introduction Cognitive rehabilitation is designed to reduce and/or compensate the impact of cognitive dysfunction in patients suffering from brain damage [1]. Traditional approaches require the patient to perform paper-based exercises and to undergo face-toface visits with specialists, in order to improve his/her attention, and cognitive and memory abilities. Computer-based applications can add great value to traditional methods, since they permit to involve the patient with multimedia features such as, for example, images, sounds and videos. A cognitive rehabilitation tool able to manage these new types of stimuli can create a new and effective experience to support the patient in the rehabilitation process, also proposing to him/her new varieties of exercises, impossible to achieve with paper-based approaches. The use of a knowledge base to organize the stimuli permits to exploit classifications, relationships and properties such as images and sounds to generate new exercises automatically. This ontological organization, in addition to organizing all the multimedia features used for the patient’s rehabilitation process, relieves the specialist of the need to generate byhand all the exercises to be scheduled for a particular patient. Using the stimuli ontology described in this paper, the specialist will have the only task to set up a template for every type of exercise, while the system will compose automatically the exercises by filling the templates with the appropriate stimuli, selecting and properly recombining them using the ontological classifications and the relationships defined. Furthermore, a system to classify the patient’s performance may use the ontology to generate exercises whose difficulty level is selected on the basis of the patient’s skills. To achieve the goals described, the stimuli ontology and the patient’s classification

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system have been integrated in the cognitive rehabilitation tool, built on top of the “EPrime” system, presented in [2].

2. Stimuli Ontology An ontology is defined as ‘‘an explicit specification of a conceptualization’’ [3], and is now gaining a specific role in Artificial Intelligence and other fields, such as knowledge engineering and many others, including knowledge management and organization [4]. An ontology is composed by classes (containing the concepts of our knowledge base), attributes (defining the intrinsic properties of a class) and relationships (defining semantic links between different classes). In our ontology, each class represents a stimulus, to be used in the exercises. The stimuli are grouped in taxonomies (hierarchies of concepts with the same common classification). The toplevel concepts define the main semantic categories (“Food”, “Animal”, “Dress”, “Habitation”, etc.). Each category contains sub-classes representing stimuli which are more specific respect to their main category. For example, in Fig. 1, which shows a part of the stimuli ontology implemented with the tool Protegè [5], “Pasta” is classified as a type of “Food”, while “Spaghetti” is a type of “Pasta” (and, in turn, a type of “Food”).

Figure 1. An excerpt of the stimuli ontology.

Attributes are associated to each class, to bind stimuli to the corresponding images, sounds and/or videos which will be shown to the patient (e.g. the picture and the whistle of a train). The relationships between the classes define the semantic links between the corresponding stimuli. In Fig. 1, two relationships are shown: the first one, called “mainIngredient”, binds a “Course” with the main “Food” it is composed of; the second relationship, called “ingredient”, binds the “Course” with all the other ingredients (selected from the “Food” taxonomy) needed to cook the course considered. For example, “SpaghettiWithTomatoSauce” has “Spaghetti” as its main ingredient (relationship defined in the left window), and “OliveOil”, “Onion”, “Tomato”, etc. as the other ingredients (relationship in the rightmost window). E-Prime can use this ontological structure to build new exercises automatically, as described in Section 3.


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3. Integration in the Cognitive Rehabilitation System The stimuli ontology has been integrated in [2] using a dedicated tool. Figure 2 shows the overall architecture of the cognitive rehabilitation tool (E-Prime by Psychology Software Tools), completed with the components for integrating the stimuli ontology.

Figure 2. The architecture of the system.

First of all, the stimuli ontology has been defined with the help of specialists in cognitive rehabilitation, through research in the literature and through the Internet. The Protégé editor has been used to formalize the ontology in a machine-readable format (using the XML language generated by Protégé-frames at the moment; restructuring in OWL [7] is a work in progress) and the formalized ontology has been integrated in the “TrialsDB” of E-Prime by means of a custom import tool. Thanks to the Ontologybased engine described in [2], E-Prime can use the imported stimuli ontology to generate new exercises using templates and configuration files defined by the therapists. This approach permits to define, edit and maintain the stimuli ontology using a graphical ontology editor (Protégé), while integrating the new versions of the ontology in E-Prime only when stable releases have been deployed.

4. Automatic Generation of Exercises In this section, we describe how the rehabilitation system uses the stimuli ontology to generate automatically two of the main exercises to be solved by the patient. In the first exercise, called “Find the correct category”, three images (and/or sounds) associated to three different stimuli belonging to the same category are shown on the top of the screen. On the bottom, three categories are listed. One is the correct answer, while the others are wrong. The exercise on the left of Fig. 3 shows images associated to the classes “SpaghettiWithTomatoSauce”, “Cheeseburger” and “ApplePie”. These images are found in the attribute “pictures” associated to the classes listed, which are obtained by choosing a category (“Course”) and selecting three sub-classes from this taxonomy. Considering the possible answers, “Course” will be the correct answer, while other two categories chosen randomly will represent the wrong answers (in this case, “Animal” and “Habitation”). In this way, the system can build many different exercises using this template and it is easy to verify if the patient provides the correct answer. The exercise on the center of Fig. 3 shows how the difficulty level can be changed automatically:

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Figure 3. Three exercises generated by the system.

the correct category can be chosen at any level of a selected taxonomy. The higher the level, the (potentially) easier will be for a patient to answer correctly. In this case, the categories (“First” as correct answer; “Second” and “Dessert” the wrong ones) are chosen at the first sub-level of the “Course” taxonomy. Probably it will be more difficult for the patient to recognize the difference between “First”, “Second” or “Dessert” courses, than to recognize the difference between “Course(s)”, “Animal(s)” or “Habitation(s)”. Thanks to this approach, the difficulty level of the exercises can change automatically as suggested by the patient classification system: a module of EPrime able to classify the patient’s performance, providing statistics about his/her ability to solve the exercises currently administered. In the second type of exercise, called “Select the main ingredient”, the system uses the relationship “mainIngredient” to show a course to the patient, and asks him/her what is the main ingredient of the course. In the example shown on the right of Fig. 3, the system selects a “Course” randomly (in this case “Cheeseburger”) and places its image on the top of the screen. The correct answer will be the “Food” “CalfMeat” (“Cheeseburger” “mainIngredient” “CalfMeat”), while the wrong answers are selected randomly among all the other classes in the “Food” taxonomy (which is the range of the “mainIngredient” relationship). The image associated to the “CalfMeat” is placed in a random position on the bottom of the screen; the other positions will contain the wrong answers. It is straightforward for the system to check the correctness of the patient’s answer, by verifying that, in this situation, only the stimuli “Cheeseburger” and “CalfMeat” are linked by the “mainIngredient” relationship. The examples described in this section demonstrate that the stimuli ontology allows the system to automatically generate new exercises, and to verify the correctness of the answers, without human intervention.

5. Discussion The use of ontologies for automatic quiz generation has been studied in the recent years [7, 8]. In this project, the stimuli ontology required to be structured in strict collaboration with the domain experts, to offer the best support for the generation of exercises for a delicate type of patient. For this reason, the control over categories, terminology and multimedia features associated to the stimuli are mandatory, because stimuli and categories must be easily recognizable by the patients solving the exercises. Furthermore, the level of categorization in the taxonomies and the network of relationships is designed to support E-Prime and the ontology-based engine to generate sound and intelligible exercises. Considering all these requirements, we initially did not consider general-purpose ontologies (for example the food ontology on the w3.org site) but we decided for a custom specialized solution built under the control of the domain


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experts. On the negative side, this approach could limit the number of stimuli which can be used by the system. As a work in progress, we are restructuring the stimuli ontology to obtain its formalization in OWL. Among the advantages offered by this format (use of a standard language, different levels of abstraction, use of meta-data information, etc.), it allows to import and join different ontologies to re-use them. We plan to study the use of our approach with ontologies imported from different domains and test if the new randomized exercises can be correctly built and solved by the patients. This approach raises some issue: from the technical point of view, switching to OWL means that all the concepts must be disjoint, to offer E-Prime a single solution for every exercise, while the reuse of reference ontologies must take care of at least two problems: 1) vocabulary and relationships network must be evaluated and approved by domain experts, and the exercises approved by the personnel in charge of the rehabilitation task, and 2) different languages are used for different countries. For example, this system has been used in an Italian hospital, therefore the ontology has been defined for Italian patients. The most of the reference ontologies, however, are defined in English, therefore a proper translation must be found or performed.

6. Conclusion Tele-medicine and tele-homecare may represent an appropriate approach for moving care delivery and rehabilitation from hospitals to home, and the use of a computerbased rehabilitation system allows this move. The multimedia features and the variety of the exercises can be considered as key points for the success of this type of system, since it can involve the patient improving the effectiveness of the treatment strategy. This work illustrates that an ontology-based approach permits the automatic generation of exercises for the rehabilitation of patients and the management of a wide range of (multimedia) stimuli. The preliminary tests show encouraging results, as half of the patients declare to prefer using this tool, rather than traditional paper-based exercises. Therefore, it may be considered as a means to create effective tele-homecare services.

References [1] [2]

[3] [4] [5] [6] [7] [8]

Christensen, A. Uzzel, BP. International Handbook of neuropsychological rehabilitation, Plenum Press, 1999. Quaglini, S. Panzarasa, S. Giorgiani, T. Zucchella, C. Bartolo, M. Sinforiani, E. Sandrini G.: OntologyBased Personalization and Modulation of Computerized Cognitive Exercises. Proceedings of the 11th Conference on Artificial Intelligence in Medicine AIME (2009), 240-244. Gruber, TR. A translation approach to portable ontology specification. Knowledge Acquisition, 5(1993), 199–220. Guarino, N. Formal ontology, conceptual analysis and knowledge representation, International Journal of Human-Computer Studies, 43(5/6)( 1995), 625–40. www.protege.stanford.edu http://www.w3.org/TR/owl-ref/ Zitko, B. Stankov, S. Rosić, M. Grubišić, A. Dynamic test generation over ontology-based knowledge representation in authoring shell, Expert Systems with Applications 36, 4 (May 2009), 8185-8196. Tsumori, S. Kaijiri, K. System Design for Automatic Generation of Multiple- Choice Questions Adapted to Students' Understanding, Proceedings of the 8th Int. Conference on Information Technology Based Higher Education and Training (2007), 541-546

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Creating a Magnetic Resonance Imaging Ontology Jérémy LASBLEIZab,1 Hervé SAINT-JALMESb, Régis DUVAUFERRIERa, Anita BURGUNa a Unité Inserm U936, IFR 140IFR 140, Faculté de Médecine b Laboratoire de Traitement du Signal et de l’Image; INSERM UMR642 Université de Rennes 1, France

Abstract. The goal of this work is to build an ontology of Magnetic Resonance Imaging. The MRI domain has been analysed regarding MRI simulators and the DICOM standard. Tow MRI simulators have been analysed: JEMRIS, which is developed in XML and C++, has a hierarchical organisation and SIMRI, which is developed in C, has a good representation of MRI physical processes. To build the ontology we have used Protégé 4, owl2 that allows quantitative representations. The ontology has been validated by a reasoner (Fact++) and by a good representation of DICOM headers and of MRI processes. The MRI ontology would improved MRI simulators and eased semantic interoperability. Keywords. MRI, MRI Simulator, OWL, ontology.

1. Introduction Magnetic Resonance Imaging is the most versatile diagnostic imaging technique. It can study T1, T2, diffusion, PH, temperature, spectroscopy… of tissues and of course make images. The vocabulary used by medical imaging constructors is very heterogeneous [1] and physical phenomena involved during MRI are very complex. So the MRI domain needs ontology to make the MRI community sharing the same concepts. To build our ontology we will take into account two MRI representations: MRI simulators and DICOM. The DICOM is an applicative representation with daily-use concepts. MRI simulators give a representation of complex physical phenomena that are involved in MRI and that are not describe in DICOM. The fusion of MRI simulators and DICOM concepts is needed to represent MRI examinations not only in an administrative way but in a useful way for radiologist interpretations.

2. Material and Methods 2.1. Analyzing DICOM [2] The DICOM standard is divided in different parts. The relevant part for MRI is C.8.13 « Enhanced MR Image ». It is a section of the standard part 3: « Information Object 1


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Definition ». All concepts of this part, and their DICOM tags, will be included in our ontology, thus will give a semantic interoperability to the ontology. In DICOM, there is a lack of definition for an ontology. We will fill this gap by domain expert definitions, thanks to MRI simulators analysis. 2.2. Analyzing MRI Simulators We decided to analyze two MRI simulators JEMRIS and SIMRI. SIMRI [3], is implemented in C language and is based on the Bloch equations. It enables simulations of 1D, 2D, and 3D images. Although simple, the user interface requires the use of C. The simulator is divided in different parts: Model (Virtual object): Each voxel of the virtual object contains a set of physical values that are necessary to compute the local spin magnetization vector with the Bloch equations. These values are the proton density and the two relaxation constants T1 and T2. MRI sequence: During an MRI experiment, the object is placed in a static magnetic field B0 and is excited by electromagnetic events of two types: RF pulses (B1 field) and magnetic field gradients. The acquisition of the object magnetization state is stored as a complex signal in the kspace to obtain the image. This part is divided in 4 parts: The free precession, precession with application of gradients (specified by its duration and the gradient magnitudes in the three spatial directions), signal acquisition (number of points to capture, bandwidth, readout gradient magnitude and position of this signal in the kspace), the application of RF pulses (specified by its duration, a flip angle and the rotation axis). RF inhomogeneity and gradient non-linearity are not simulated.The user can define the echo train and sequence parameters (repetition time, echo time, flip angle…). Chemical shift and susceptibility artefacts are modeled. JEMRIS [4-5], is a C++ software with XML tags. It uses an optimized library for numerical solutions equations needed to simulate complex RF-pulses. It can deal with multichannel Tx-Rx coil geometries and configurations, nonlinear gradients, chemical shift, reversible spin dephasing (T2*), susceptibility-induced off-resonance, temporal varying processes of the object (e.g., movement or flow), and concomitant gradient fields. The graphical user interface (GUI) is divided in three: one for interactively designing the MRI sequence, another for defining the coil configuration, and one for the setup and execution of the main simulator. The software is divided in 5 classes: sample (describes the physical properties of the object) signal (holds information about the MR signal) model (describes the functionality for solving the physical problem) coil (contains the code for spatially varying RF transmission and signal reception), sequences. The sequence loop is represented as a left-right ordered tree with loops (Fig1). The xml language has been used to serialized C++ objects, describing the different steps of each sequence. The management of time interval has also been taken into account and formalised. The different modules interact with each other.

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Figure1. Echo Planar Imaging sequence schema in JEMRIS [4] yellow =loops , blue = pulses, green = intervals.

2.3. Using Protégé 4, owl2, Ontology Validation To build our ontology, we will use Protégé [6], which is a free, open source ontology editor and knowledgebase framework and the owl language. In our case, the domain has a lot of quantitative informations so we choose owl2, which allows us to define quantitative data properties. First of all, we have taken into account concepts from DICOM and secondly we have added concepts from MRI simulators. We will use an ontology classifier FACT++ to check the ontology consistency. The ontology will be validated by the analysis of 10 MRI examination DICOM headers, extracted with OSIRIX [7] and the possibility to write sequences with the ontology.

3. Results 3.1. Ontology Taxonomy The main classes of ontology taxonomy are: Object of the study: Defined by its size, voxels size, properties (T1, T2, Proton Density, Diffusion, Contrast enhancement cinetic), T2*, movements (general and flux) Device: Magnet (intensity, shape, kind) coil (receiver coil, transmitter coil, multielement coil, region) Gradient (magnetic field, slice selection, diffusion…) Sequence, from this point our vision is different from JEMRIS. Actually, the representation of loop in a vertical way (fig.1) of physical events that are horizontal (dependant to time) and independent cannot be included in an ontology. Therefore we have divided sequences in elementary events: radiofrequency pulse, slice selection gradient, readout gradient… according to SIMRI description of events.


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The signal acquisition modeled, has to be formalised by a mathematical way thanks to Bloch equations resolution as in the two softwares. The formula will be integrated in the ontology. Acquisition results will be divided in: image, quantitative result… Organisation of sequences in taxonomy is a difficult management. An article [8], written in a didactical goal, has organized sequences with their technical characteristics and with loops. Taxonomy doesn’t have loop and the problem is that sequences can be a mix of different techniques that can’t be organised in taxonomy. The solution we have chosen is to classify sequence according to their goals. This solution is intuitive for clear goal: diffusion, angiography image… but less obvious for contrast sequences. So we have chosen to start with a general taxonomy of sequences (Fig.2), adding to each of them the Weighting of final images: T1Weighted, T2Weighted, DPWeighted and T2*Weighted. Constructor acronyms of sequences have been added as synonyms of sequence name.

Figure 2. Contrast sequence Taxonomy

Acquisition parameters are divided in two essential parts: parameters modifying image geometry and parameters modifying image contrast. The ontology relations are: Different kinds of relations between concepts will be defined: General: Has_a , Has_Parameters… ; Quantitative : Has_Value, Has_Unit… Owl2 permits quantitative representation of classes. The relations between classes are then: A Has_Modifyer B, A Increase_When_Decrease B, A Decrease_When_ Increase B will permit to describe variations of parameters. 3.2. Ontology validation With the concepts present in the ontology we can define events that happen during MRI experiences, for examples:

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Spin echo T2 weigthed sequence : Spin_Echo_T2W has_modifier some ((TR and (Has_Unit some milisecond) and (Has_Value some float [>=2000])) and (TE and (Has_Unit some milisecond) and (Has_Value some float [>80]))) radiofrequency pulses of Spin Echo sequence: Spin_Echo Has_Parameter some Radiofrequency_Pulse and ( RadioFrequency_Pulse Has_a Flip_Angle ((Flip_Angle Has_Value value =90) or (Flip_Angle Has_Value value =180)). We extract DICOM headers of 10 MRI examinations with OSIRIX Métadonnées. The analysis shows that concepts of DICOM headers are well represented in the ontology. The problem is that MRI constructors don’t share the same DICOM tags for the same concept.

4. Discussion There is only one work about MRI and ontology. It concerns brain functional MRI [9] and are interested in all the process and not only MRI. However it has already shown the need of ontology in the domain. JEMRIS have also, by using XML, shown the interest of web semantic in physical process description. DICOM also need to be improved with definitions and rules that ontology could define. Our ontology can increase the semantic interoperability in MRI. An ontology has already be implemented on a PACS in that goal [10] but not for MRI examinations.

References B. Gibaud, The quest for standards in medical imaging. Eur J Radiol. May 31, 2010. Digital Imaging and Communication in Medicine: DICOM web site, available from: http://medical.nema.org/. Access January 2011. [3] H. Benoit-Cattin, G. Gollewet, B. Belaroussi, H. Saint-Jalmes, C. Odet, The SIMRI project : a versatile and interactive MRI simulator, Journal of Magnetic Resonance, Vol. 173, pp. 97-115, 2005. [4] http://www.jemris.org, Access January 2011. [5] T. Stöcker, K. Vahedipour, D. Pflugfelder, N. Jon Shah. High-performance computing MRI simulations. Magnetic Resonance in Medicine, 64 (1), 186 – 193, 2010. [6] http://protege.stanford.edu, Access January 2011. [7] http://www.osirix-viewer.com/ , Access January 2011. [8] GE. Boyle, M. Ahern, J. Cooke, NP. Sheehy, JF. Meany, An Interactive Taxonomy of MR Imaging Sequences, RadioGraphics November-December vol. 26 no. 6 e24, 2006. [9] T. Nakai, E. Bagarinao, Y. Tanaka, K. Matsuo, D. Racoceanu, Ontology for FMRI as a biomedical informatics method. Magn Reson Med Sci. 2008;7(3):141-55. Review. [10] DL. Rubin, P. Mongkolwat, V. Kleper, K. Supekar and DS. Channin, Medical Imaging on the Semantic Web: Annotation and Image Markup. In: 2008 AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford University, 2008. [1] [2]


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Validation of the openEHR Archetype Library by using OWL Reasoning Marcos MENÁRGUEZ-TORTOSAa,1 and Jesualdo Tomás FERNÁNDEZ-BREISa a Departamento de Informática y Sistemas, Facultad de Informática, Universidad de Murcia, CP 30100, Murcia, Spain

Abstract. Electronic Health Record architectures based on the dual model architecture use archetypes for representing clinical knowledge. Therefore, ensuring their correctness and consistency is a fundamental research goal. In this work, we explore how an approach based on OWL technologies can be used for such purpose. This method has been applied to the openEHR archetype repository, which is the largest available one nowadays. The results of this validation are also reported in this study. Keywords. Archetypes, openEHR, Ontology, Reasoning

1. Introduction Domain knowledge based on archetypes plays a fundamental role for the achievement of semantic interoperability of Electronic Health Record (EHR) systems [1]. This means that archetypes should be the clinical knowledge unit exchanged by clinical systems in order to process the clinical data of the patients. Consequently, the quality and accuracy of archetypes is a crucial issue. Archetypes need to be optimally designed for their purpose, and considered trustworthy within their intended communities of use. This requires sound methodologies for designing archetypes, and rigorous and robust processes for validating them against its clinical evidence base. Quality criteria, governance practices for archetype development and editorial policies for certifying the quality of libraries of archetypes were defined by the Q-REC project (http://www.eurorec.org/RD/pastProject_Q-REC.cfm). However, the development of large libraries of archetypes is still relatively new and only openEHR (http://www.openehr.org) has a library with an interesting size for applying quality criteria and methods. In [2], the requirement of formal methods for validating the design and content of archetypes has been identified. So far, few archetype-authoring tools implement techniques for assuring the quality of archetypes. The most significant case is the LinkEHR editor [3], which defines a formal framework for archetype validation. There, archetype constraints are expressed in an algebraic formalism and operations supporting archetype validation are defined and implemented. However, the drawback of this proposal is the absence of a knowledge-based representation of archetypes to perform semantic activities. This is a common issue in archetype editing tools since 1


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they represent archetypes by using the Archetype Definition Language (ADL), which has a syntactic orientation. In this paper, we will not focus on the evaluation of the clinical correctness and usability of the archetypes but on using formal semantic methods for checking their technical correctness. A knowledge-based representation of archetypes capable of supporting validation and quality assurance would certainly be very useful for several reasons. First, knowledge models would be used for a proper representation of clinical knowledge, and this would facilitate the development of efficient knowledge management methods. Second, the combination of advanced semantic models with reasoning techniques would certainly reduce the effort required for implementing the quality assurance and validation methods. In this work, we use the Web Ontology Language (OWL) (http://www.w3.org/TR/owl2-syntax/), which is the W3C standard for the exchange of semantic content on the web. In particular, we use its description logics flavor, OWL-DL. Thus, in this work, an OWL-based method for checking the consistency of archetypes is presented. The possibilities and limitations of the approach will be illustrated through its application to the openEHR archetype library, thus the errors found in such library will be reported.

2. Methods 2.1. Semantic Representation of Archetypes Archetypes are detailed and domain-specific definitions of clinical concepts in the form of constrained combinations of the entities of a reference model in a tree-like structure [4]. Concepts in archetypes are characterized by the number of instances that can be part of the association they belong to. In addition, multivalued associations between concepts may be restricted in different ways. First, the cardinality of the association can be constrained by a range. Second, instances might be ordered according to the position of the definition of their concepts in the association. Finally, repeated instances can be allowed or not. An archetype can be defined as the specialization of another one. An archetype concept is defined as the specialization of an entity of the reference model or a concept in the parent archetype. The definition is based on constraints applied to attributes of such entity. Specialization does not mean reuse of the definitions as in object-oriented modeling, but it is a compliance relationship. In this way, if an archetype B specializes an archetype A, then all EHR extracts that are compatible with the archetype B must also be compatible with the archetype A. In addition to the above constraints, an archetype specialization might replace the type of a concept by a compatible type. Our OWL representation of the openEHR reference model was achieved by following the rules proposed by the OMG in the Ontology Definition Metamodel specification (ODM) (http://www.omg.org/docs/formal/09-05-01.pdf). Each concept is defined in our representation by means of an OWL class, and its constraints are defined using OWL-DL axioms. Concept identity is associated with the node id, which is used in the archetype definition to bind concepts and ontological definitions. The concepts in specialized archetypes might include additional annotations that guide the validation process. Those annotations indicate the name of the OWL class in the parent archetype that is being specialized, if any. That binding is based on the concept identifier.


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An example is shown next. Figure 1 shows the first definitions of the archetype CLUSTER.inspection.v1. The upper part corresponds to the definition in ADL and the lower one corresponds to the definition in Manchester OWL Syntax (http://www.w3.org/TR/owl2-manchester-syntax/). An inspection is an unbounded cluster of unordered data items. It contains an optional cluster of normal statements. Each concept is defined in OWL by means of equivalency axioms. The constraints on multivalued associations are also translated into one class. CLUSTER[at0000] matches { -- Inspection items cardinality matches {0..*; unordered} matches { -- Normal statements CLUSTER[at0001] occurrences matches {0..1} matches { ...

Class: CLUSTER_at0000 EquivalentTo: CLUSTER and ARCHETYEPED_CLASS and (id value "at0000") and (op_items only COLLECTION_CLUSTER_at0000_items) Class: COLLECTION_CLUSTER_at0000_items EquivalentTo: COLLECTION and (ordered value false) and (id value "COLLECTION_CLUSTER_at0000_items") and (elements max 1 CLUSTER_at0001) Figure 1. Excerpt of the archetype CLUSTER.inspection.v1 and its OWL representation

The archetype CLUSTER.inspection_tympanic_perforation.v1 specializes the previous archetype. It defines the concept normal statements in a different way, since an unbounded number of declarations is allowed. Figure 2 depicts an excerpt of that archetype and the OWL definition of the multivalued association. CLUSTER[at0000] matches { -- Inspection items cardinality matches {0..*; unordered} matches { -- Normal statements CLUSTER[at0001] occurrences matches {0..*} matches {

Class: COLLECTION_CLUSTER_at0000_items EquivalentTo: COLLECTION and (ordered value false) and (id value "COLLECTION_CLUSTER_at0000_items") Figure 2. Excerpt of the archetype CLUSTER.inspection_tympanic_perforation.v1

2.2. Detecting Inconsistent Specializations Using OWL Reasoners The detection of inconsistencies in specializations is a major challenge in archetype edition. An archetype is correct if the set of constraints defined over the reference model and the parent archetype is valid. The specialization of archetypes does not imply inheritance but the definitions in the specialized archetype have to be consistent with the parent's ones. The semantics of archetype specialization is that the OWL semantics of the parent archetype subsumes the one of the specialized archetype. OWL reasoners allow us to find incorrect constraints over the reference model. Thereby, a concept is wrongly defined if the derived OWL class is unsatisfiable. That is, the set of instances of such concept does not conform to the reference model. OWL reasoners infer subclass and equivalent axioms between classes. In this way, checking

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the correctness and consistency of a specialization consists on checking whether that subsumption is inferred. In the previous example, the specialization is not subsumed by the parent archetype because the specialized archetype allows any number of normal statements, that is, CLUSTER[at0001]. The basic method does not provide much information about the causes of the inconsistency. Our solution to this issue was isolating the errors. Our OWL representation permits the identification of the classes that violate the definition of the parent archetype. In our work, the precise identification of inconsistencies is based on the definition of additional support classes that allow the isolation of each archetype constraint. Figure 3 shows the representation of the order constraint for the multivalued association items of the concept inspection. Class: ORDER_COLLECTION_at0000_items EquivalentTo: ORDER and (id value "COLLECTION_at0000_items") and (order_value value "false") Figure 3. Example of support class for precise error identification

3. Results Our method has been implemented in the tool Archeck that is available at http://miuras.inf.um.es/archeck. Consistency errors are reported precisely by concept and attribute in the archetype definition. The tool has been implemented in Java and makes use of the openEHR Java tools (http://www.openehr.org/projects/java.html). Ontologies are processed with the OWL API (http://owlapi.sourceforge.net/) and we have used the reasoners Pellet (http://clarkparsia.com/pellet) and Fact++ (http://owl.man.ac.uk/factplusplus/) for our validation experiment. Finally, the transformation of the reference model to OWL based on ODM specification has been automated so can be applied to other reference models such ISO 13606. Our validation experiment has used the archetypes available in the openEHR repository (http://www.openehr.org/svn/knowledge/archetypes). The complete results are available at the previously mentioned website. Our analysis reported 12 inconsistent archetypes, and all of them are wrong specializations. The most common error is due to the incorrect definition of the occurrence constraint, which happens in 11 archetypes, including the running example. Another sort of inconsistency is also present in the archetype CLUSTER.inspection-tympanic_perforation.v1. CLUSTER[at0022] contains a DV_TEXT, but its parent concept allows only DV_CODED_TEXT. In addition to this, Fact++ was faster than Pellet, since both average processing times per archetype were 346 and 1160 ms, respectively. Some discussion about limitations of the approach comes next. When an optional concept has a maximum occurrence constraint, then that might be omitted in the specialization. OWL reasoners raise a validation error in such situations. To overcome this limitation, the maximum occurrence constraint of optional concepts in the parent archetype is included in descendant ones, if undefined. This modeling decision also permitted solving the problem caused when a subclass axiom is inferred instead of an equivalency one between two concepts related in archetype specialization. This modifies slightly the semantics of the specialized archetype, but it does not affect the process of detecting inconsistencies.


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Archetypes may include abstract or general concepts, which can be specialized by applying archetype modelling practices, such as the ones proposed by openEHR (http://www.openehr.org/wiki/display/spec/openEHR+Templates+and+Specialised+Ar chetypes). For instance, node identifiers in concept specializations should start with the parent node identifier, e.g. at0001.1 specializes at0001. This is considered in our method, although archetypes that do not follow such practice can be structurally correct. Our method keeps the identifier of the parent node in an annotation in the specialized concept.

4. Conclusions In this work, we have proposed a knowledge-based representation of archetypes able to validate their definitions. We propose a representation of archetypes as OWL classes, therefore clinical information and knowledge contained in EHR extracts might be semantically exploited. In this work, only some structural constraints have been addressed, since the current versions of Fact++ and Pellet do not provide mechanisms for representing and implementing some axioms, especially constraints on some primitive types. The approach has been applied to the openEHR archetype repository, which is the largest available repository. The overall time performance of the process is acceptable. The tool has proved to be useful since a number of archetypes has found inconsistent in the openEHR repository. All the inconsistencies found in the repository are due to specialization errors. Archetypes comply with the reference model because the authoring tools guarantee this issue. This method might be interesting not only for validating archetypes but also for finding and analyzing bad archetype modeling practices, such as node identifiers in concept specializations. Finally, we are working on processing the bindings of archetype concepts to clinical terminologies such as SNOMED-CT and extending the approach to ISO 13606. Expressing archetypes and terminologies in the same formalism will make possible the automatic classification of clinical archetypes and facilitate the semantic interoperability in EHR. Acknowledgements: This work has been possible thanks to the Spanish Ministry of Science and Innovation through grant TSI2007-66575-C02-02 and TIN2010-21388-C02- 02.


[4]

European Commission, Semantic interoperability for better health and safer healthcare deployment and research roadmap for Europe. ISBN-13: 978-92-79-11139-6, 2009. Kalra D. EHR archetypes in practice: getting feedback from clinicians and the role of EuroRec. In: eHealth Planning and Management Symposium, 2007. Maldonado JA, Moner D, Bosca D, Fernández-Breis JT, Angulo C, Robles M.: LinkEHR-ED: A multireference model archetype editor based on formal semantics. International Journal of Medical Informatics 78(8) (2009) 559–570. Beale T. Archetypes: Constrained-based Domain Models for future-proof Information Systems. In Eleventh OOPSLA Workshop on Behavioral Semantics: Serving the Customer, 2002.

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Grouping pharmacovigilance terms with semantic distance Marie DUPUCHab, Magnus LERCHc, Anne JAMETbd, Marie-Christine JAULENTab, Reinhard FESCHAREKe, Natalia GRABARf a Université Pierre et Marie Curie - Paris6, Paris, F-75006 France b INSERM, U872 eq. 20, Paris, F-75006 France c Consulting & Coaching, Berlin, Germany d HEGP, AP-HP, Paris, France e CSL Behring GmbH, Marburg, Germany f CNRS UMR 8163 STL, Université Lille 3, France Abstract. Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. Besides other methods, statistical algorithms are used to detect previously unknown ADRs, and it was noted that groupings of ADR terms can further improve safety signal detection. Standardised MedDRA Queries are developed to assist retrieval and evaluation of MedDRA-coded ADR reports. Dependent on the context of their application, different SMQs show varying degrees of specificity and sensitivity; some appear to be over-inclusive, some might miss relevant terms. Moreover, several important safety topics are not yet fully covered by SMQs. The objective of this work is to propose an automatic method for the creation of groupings of terms. This method is based on the application of the semantic distance between MedDRA terms. Several experiments are performed, showing a promising precision and an acceptable recall. Keywords. Natural Language Processing, Medical informatics, Drug safety, Pharmacovigilance, Signal detection, Drug toxicity, Semantics, Terminology

1. Introduction Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. ADRs are coded with terms from dedicated terminologies, e.g., WHO-ART (World Health Organization Adverse Reaction Terminology) and MedDRA (Medical Dictionary for Regulatory Activities). Safety signal detection – i.e., the detection of previously unexpected potentially causal associations between drugs and ADRs – depends on the quality and specific features of ADR coding. Besides traditional pharmacovigilance methods, statistical algorithms are increasingly utilized to detect signals in large safety databases [1, 2]. To improve signal detection, these methods benefit from groupings of related ADRs [3]. Indeed, the use of very specific terms for coding ADRs may cause a dilution of signals [4]. Thus, various hierarchical levels of MedDRA (PT, HLT, SOC) and manually built SMQs (Standardised MedDRA Queries) have been used for signal detection. The PT (Preferred Term) level - which is most often used in quantitative signal detection - corresponds mainly to specific ADRs, while HLTs (High Level Terms) and SOCs (System Organ Classes) are hierarchical levels above the PT level.

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As for the SMQs, their objective is to link terms relevant to a medical condition. SMQs are designed by a group of experts, who start with the scientific definition of the medical condition of interest, followed by manual identification of relevant MedDRA terms [5]. This is a labour-intensive task. Evaluation studies of the SMQs have demonstrated that SMQs often present the highest sensitivity [6, 7], but can be overinclusive [7] and, because the reports found might lack specificity, their evaluation can be time-consuming. Finally, relevant PTs may be missing in SMQs [7], and several serious safety topics are not yet addressed. In order to ease and systematize the process of creation of term groupings, automatic methods can be applied. One existing work proposes hierarchical groupings of ADRs [8], but this approach does not necessarily respect medical reasoning. For instance in renal diseases, in addition to terms such as Acute nephritis and Insufficiency renal, which have a hierarchical relation among them, it can be also relevant to consider terms related to laboratory results or medical procedures. Semantic distance may lead to the creation of groupings which respect medical reasoning significantly better: it has previously been applied to subsets of terms from MedDRA [9] and WHOART [10]. In the WHO-ART related work [10], the obtained groupings demonstrated several types of relations: synonyms, antonyms, associated symptoms, abnormal laboratory tests ... However, no evaluation has yet been performed comparing systemgenerated with existing MedDRA groupings (SMQs, HLTs, SOCs). Semantic distance has also been applied to other biomedical terminologies (Gene Ontology [11], MeSH and SNOMED CT[12], and UMLS [13]), with a manual rating and evaluation of pairs of terms. We propose a better adaptation of the semantic distance approaches for the creation of ADR term groupings. The whole set of MedDRA terms is used, and special attention is paid to compare the obtained groupings with reference SMQs, which have become a widely accepted standard in pharmacovigilance organizations.

2. Material and Method Material. Material used is issued from MedDRA 13.0 [14]: ontoEIM and SMQs. The ADR ontology ontoEIM [8] has been created thanks to the projection of MedDRA on SNOMED CT (SNCT) [15] through the UMLS [16]. Only 46% of MedDRA terms are aligned. The terminological representation of MedDRA terms is enriched: their structure is improved and becomes parallel to the structuring in SNCT, and terms receive formal definitions (on four SNCT axes: morphology, topography, causality and expression). Our second material, SMQs, are groupings of MedDRA terms related to a given medical condition (e.g., Acute renal failure). SMQs, consisting of MedDRA PTs and LLTs, are created to assist users in searching ADR reports related to a medical condition. Currently, 84 SMQs have been released. In our experiments, ontoEIM is the source we use to create groupings of ADR terms, while SMQs in their broad version are used as gold standard for the evaluation. Method. Semantic distance between terms is often computed within terminologies. It depends on the number of edges (or the shortest path) between two terms (e.g., four edges between the terms Abdominal abscess and Pharyngeal abscess in Fig. 1), although other factors may be taken into account. We present here the main step of the method, relative to the computing of semantic distance [17] between MedDRA PT and LLT terms through the ontoEIM. We use either a) the ADR terms only (which belong mainly to the Clinical disorder axis D), or b) ADR terms and their formal definitions.

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Within the formal definitions, we use elements provided by two axes: morphology M (kind of the abnormality) and topography T (anatomical localization). These axes are often involved in the definition of ADRs [18] and they are also frequently represented in ontoEIM, as in this example for terms Abdominal abscess and Pharyngeal abscess defined as follows : – Abdominal abscess: M = Abscess morphology, T = Abdominal cavity structure – Pharyngeal abscess: M = Abscess morphology, T = Neck structure The shortest paths sp are computed between these two terms (axis D) and between their formal definitions (axes T and M). The weight of the edges is set to 1, and the value of each shortest path corresponds to the sum of weights of all its edges. For this pair of terms we obtain the following sp values: spD =4, spT=10 and spM=0.

Figure 1: The shortest paths sp between Abdominal abscess and Pharyngeal abscess computed on the three axes: clinical disorder (D), topography (T) and morphology (M).

Semantic distance is computed which allows to generate a semi-matrix and to apply an ascendant hierarchical classification for the creation of groupings of terms. The minimal threshold is set to 2. We perform several experiments in which we evaluate: one axis (D) vs three axes (D, M, T); all terms in SMQs vs only terms aligned with SNCT; the best grouping for a given SMQ vs merged groupings. Groupings are compared with 9 SMQs (Acute renal failure, Agranulocytosis, Anaphylactic reaction, Cytopenia, Gastrointestinal haemorrhages, Peripheral neuropathy, Rhabdomyolysis, Severe cutaneous adverse reaction, Thrombocytopenia). Evaluation is performed with three classical measures: precision P (number of relevant grouped terms as a percentage of the total number of the grouped terms), recall R (number of relevant grouped terms as a percentage of the number of terms in the corresponding SMQ) and F-measure F (the harmonic mean of P and R).

3. Results and Discussion Figure 2 shows our results – mean, min and max values for precision, recall and Fmeasure - obtained from eight experiments. Four experiments are performed with the best grouping for each SMQ, i) using one axis with the complete set of SMQ terms (1a-bc) or with the aligned terms only (1a-ba), ii) using three axes with the complete set of SMQ terms (3a-bc) or with the aligned terms only (3a-ba). Four more experiments are similar to the above, but are performed with merged groupings. The first four experiments show a promising precision, whereas recall and F-measure are low. Although unsatisfactory for recall and F-measure, such precision nevertheless seems to meet the expectations of pharmacovigilance experts looking for highly specific groupings. The four additional experiments, where we merged the n-best


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groupings for each SMQ together, were expected to increase the overall performance. As the graphs in Figure 2 show, we can indeed improve the recall with only small deterioration of precision. The F-measure is also improved. When we take into account only one axis (D) the performance is always better than using three axes (D, M, T). This seems to be due to the incompleteness of the available formal definitions. Another factor influencing the performance is related to the use of the complete set of SMQ terms vs the reduced set of only aligned SMQ terms. With the reduced set of terms, we observe a positive effect on the evolution of recall and F-measure (number of terms to be found is reduced), although we observe a negative effect on precision. Additionally, we indicate the min and max values for the three measures. The min-max intervals are visibly very large, which means that there is considerable performance variability for the different SMQs, and that probably various strategies should be used to achieve optimal results for all medical conditions of interest.

Figure 2: Mean, min and max values for precision, recall and F-measure.

4. Conclusion and Perspectives The proposed method applies the semantic distance to the creation of groupings of ADR terms. Such groupings, especially when they show a high specificity, may be a useful tool for the detection of signals in pharmacovigilance database. The method may also be helpful during the creation of new or improvement of existing SMQs. Furthermore, it could be used to create groupings representing the same medical concept in different terminologies. This in turn, would enable researchers to apply the same term groupings to safety databases independent of the terminology used for ADR coding. In our work, several experiments have been performed to compare systemgenerated term groupings with SMQs in their broad version. Future experiments will include comparison with the narrow versions of SMQs. A novel aspect of our work, which consists of the merging of n-best groupings, allows to improve recall without a significant deterioration of precision. Future studies may lead to an adjustment of thresholds and variables (edge weights, coefficients of axes) and to the identification of other factors which influence the quality of groupings. Besides, methods provided by Natural Language Processing may enrich and improve the groupings. Acknowledgments. This work was partly supported by funding from the European Community's Seventh Framework Programme (FP7/2007-2013) for the Innovative Medicine Initiative (IMI) under Grant Agreement [1150004]. The research leading to these results was conducted as part of the PROTECT consortium (Pharmaco-epidemiological Research on Outcomes of Therapeutics by a European ConsorTium, www.imi-protect.eu) which is a public-private partnership coordinated by the European Medicines Agency. Authors are thankful to other participants of this task (C. Bousquet, O. Caster, G. Declerck, R. Hill, A. Kluczka, X. Kurz, N. Noren, V. Pinkston, E. Sadou, J. Souvignet, T. Vardar), but views expressed are those

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of the authors only.

References [1]

Bate A., Lindquist M., Edwards I., Olsson S., Orre R., Lansner A. & De Freitas R. (1998). A bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol, 54(4), 315– 21. [2] Meyboom R., Lindquist M., Egberts A. & Edwards I.(2002). Signal selection and follow-up in pharmacovigilance. Drug Saf, 25(6), 459–65. [3] Hauben M. & Bate A. (2009). Decision support methods for the detection of adverse events in postmarketing data. Drug Discov Today, 14(7-8), 343–57. [4] Fescharek R., Kübler J., Elsasser U., Frank M. & Güthlein P. (2004). Medical dictionary for regulatory activities (MedDRA): Data retrieval and presentation. Int J Pharm Med, 18(5), 259–269. [5] CIOMS (August 2004). Development and Rational Use of Standardised MedDRA Queries (SMQs): Retrieving Adverse Drug Reactions with MedDRA. Report of the CIOMS Working Group, CIOMS. [6] Mozzicato P.(2007). Standardised MedDRA queries: their role in signal detection. Drug Saf, 30(7), 617–9. [7] Pearson R, Hauben M, Goldsmith D, Gould A, Madigan D, O’Hara D, Reisinger S, Hochberg A.(2009). Influence of the MedDRA hierarchy on pharmacovigilance data mining results. Int J Med Inform, 78(12), 97–103. [8] Alecu I., Bousquet C., Jaulent MC. (2008). A case report: using SNOMED CT for grouping adverse drug reactions terms. BMC Med Inform Decis Mak, 8(S1), 4. [9] Bousquet C., Henegar C., Louët A., Degoulet P. & Jaulent M. (2005). Implementation of automated signal generation in pharmacovigilance using a knowledge-based approach. Int J Med Inform, 74(7-8), 563–71. [10] Iavindrasana J., Bousquet C., Degoulet P. & Jaulent M.(2006). Clustering WHO-ART terms using semantic distance and machine algorithms. In AMIA Annu Symp Proc, p. 369–73. [11] Lord PW, Stevens RD, Brass A & Goble CA. (2003). Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19(10): 12751283 [12] Caviedes JE, Cimino JJ. (2004). Towards the development of a conceptual distance metric for the UMLS. Journal of Biomedical Informatics 37:77-85 [13] Al-Mubaid H, Nguyen HA. (2009). Measuring semantic similarity between biomedical concepts within multiple ontologies. Trans. Sys. Man Cyber Part C, 39(4):389--398 [14] Brown E., Wood L. & Wood S. (1999). The medical dictionary for regulatory activities (MedDRA). Drug Saf., 20(2), 109–17. [15] Stearns M., Price C., Spackman K. & Wang A. (2001). SNOMED clinical terms: overview of the development process and project status. In AMIA, p. 662–666. [16] NLM (2008). UMLS Knowledge Sources Manual. National Library of Medicine, Bethesda, Maryland. www.nlm.nih.gov/research/umls/. [17] Rada R., Mili H., Bicknell E. & Blettner M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on systems, man and cybernetics, 19(1), 17–30. [18] Spackman K. & Campbell K. (1998). Compositional concept representation using SNOMED: Towards further convergence of clinical terminologies. In AMIA 1998, p. 740–744.


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The Archetype-Enabled EHR System ZKARCHE – Integrating the ISO/EN 13606 Standard and IHE XDS Profile Michael KOHLER,a,1 Christoph RINNERa, Gudrun HÜBNER-BLODERb, Samrend SABOORb, Elske AMMENWERTHb, Georg DUFTSCHMIDa a Section for Medical Information Management and Imaging Center for Medical Statistics, Informatics and Intelligent Systems Medical University of Vienna, Austria b UMIT-University for Health Sciences, Medical Informatics and Technology Hall in Tirol, Austria

Abstract. The EHR system ZK-ARCHE automatically generates forms from ISO/EN 13606 archetypes. For this purpose the archetypes are augmented with components of the reference model to achieve so-called “comprehensive archetypes”. Data collected via the forms are stored in a list which associates each value with the path of the corresponding comprehensive archetype node coded as W3C XPath. From this list archetype-conformant EHR extracts can be created. The system is embedded with the IHE XDS profile to allow direct data exchange in an environment of distributed data storage. Keywords. EHR, ISO/EN 13606, archetype, form generation, archetype-conform EHR extract

1. Introduction The project EHR-ARCHE 2 aims to support health care providers in finding those contents within electronic health records (EHR), which are relevant for their respective information needs, in consideration of the ever growing Information overload from chronically ill. It emanates from an IHE XDS [1] based distributed data storage architecture with a central metadata component. The EHR data are represented as fullystructured ISO/EN 13606 EHR extracts. The ISO/EN 13606 standard [2, 3] is based on the dual model approach, which means that the representation of the EHR data are described by a reference model (RM) and a set of archetypes (AT) [4]. In this paper we describe the EHR system ZK-ARCHE, which serves as the data source within EHR-ARCHE’s IHE XDS environment. Its purpose is to support the fast and convenient creation of archetyped EHR extracts, as well as their provision to the XDS-repository and registration within the XDS-registry.

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Corresponding Author: Spitalgasse 23, 1090 Vienna, Austria, [email protected]. See http://www.meduniwien.ac.at/msi/arche/

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2. Method The functionality of the system can be grouped in three main steps (see Figure 1): (a) After the user selects an archetype from the archetype repository, the system automatically generates a corresponding data collection form. (b) Data collected by means of the form are stored as archetyped EHR extracts. (c) The latter can then be transferred to the repository and registered. This step is supported by automatically retrieving the required IHE XDS metadata from the EHR extracts.

Figure 1. Functionality of the EHR system ZK-ARCHE

We developed the ZK-ARCHE system according to the classic Model-ViewController (MVC) [5] pattern. In the following we describe how the different tasks are split up between the model, the view and the controller. 2.1. Model The model is an instance of the Archetype Object Model (AOM). ATs only contain those attributes of RM classes, which they constrain, i.e. they represent a “differential view” of the RM. When processing an AT we therefore have to additionally consider the RM. As suggested in [6] we use a so-called “Comprehensive AT” for this purpose, which augments the AT with the mandatory attributes of the RM classes that are not constrained by the AT. In the Comprehensive AT all referred ATs (slots) are included and augmented like the referring AT. To ensure unambiguous node-IDs, the node-IDs of the referred ATs are extended with the ID of their AT as prefix. Predefined data (e.g. Unified Code for Units of Measure Object Identifier) are filled in and every node of the Comprehensive AT is associated with a relative W3C XPath.

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2.2. View The model is visualized as a data collection form to allow user input. The structure and input options of the form are generically derived from the model. The RM classes referred to within the model are transformed to form widgets as follows: • The COMPOSITION represents the view’s root class and corresponds to the whole form. • SECTIONs are displayed as individual pages within a tab-box. • ENTRYs and CLUSTERs group their sub-elements as defined by the AT. • For the data values held by ELEMENTs we support entry fields of data types date, time, number, text including selection lists, and boolean. Data values that are not entered by the user (e.g., fixed values prescribed by the archetype or system-provided metadata such as instance identifiers of RECORD_COMPONENTs) are automatically completed and cannot be edited in the form. • If the comprehensive AT prescribes an occurrence > 1 for a node, the corresponding widget may be dynamically duplicated via a button in the form. All data are internally held in a list of key-value pairs (see Table 1). Each value included in the EHR extract is associated with a key that consists of the absolute W3C XPath of the AT-node holding the value. Emanating from this list it is possible to create a complete AT-conformant EHR extract; no additional information such as the Comprehensive AT is required. This technique is therefore also appealing for integrating archetypes into existing EHR systems. It was also successfully applied in [7]. Table 1. Sample entries in the key-value list, which holds the data to be stored in the EHR extract. The creation time of the EHR extract (1st row) is generated by the system. The service start time (2nd row) and heading of the lab findings SECTION (3rd row) are prefilled by the system respective by the AT and may be adapted by the user. Key

Value

/EHR_EXTRACT/time_created[@xsi:type='TS']/time

2011-0215T14:23:00Z

/EHR_EXTRACT/all_compositions[archetype_id='CEN-EN13606COMPOSITION.discharge_summarization_note.v1/at0000' and @xsi:type='COMPOSITION'][1]/session_time[@xsi:type='IVL']/low[@xsi:type='TS'] /time /EHR_EXTRACT/all_compositions[archetype_id='CEN-EN13606-COMPOSITION. laboratory_report.v1/at0000' and @xsi:type='COMPOSITION'][1]/content [archetype_id='CEN-EN13606-SECTION.Laboratory_findings.v1/at0000' and @xsi:type='SECTION'][1]/name[@xsi:type='SIMPLE_TEXT']/originalText

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Laboratory findings

2.3. Controller The controller creates the model for a selected AT and derives the view from the model. Documents collected via the view are converted to the key-value list, from which the AT-conformant EHR extract can be directly generated. The EHR extract can either be stored locally or in an IHE XDS repository. When storing the document in the IHE XDS repository, 23 metadata required for registering the document are retrieved from the EHR extract and the EHR system. Some of these metadata, e.g., the EHR system

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ID and the document ID, are set by the system or derived from the AT, e.g., classCode, language. Others, e.g., the service start and stop time, are entered by the user.

3. Results ZK-ARCHE was implemented as a web application in Java using the Archetype Definition Language (ADL)-Parser 3 of the openEHR foundation and the ZK Framework4. As client it only requires a web-enabled browser without any plugins. For the communication to the IHE-XDS environment the “Sense” infrastructure from ITH icoserve [8] was used. Within the EHR-ARCHE project we developed 128 ATs for the domain of diabetes treatment [9]. Hereby the ZK-ARCHE system provided valuable assistance by allowing the physicians involved in the AT design process to visualize each draft of an AT as data collection form on the fly. Twelve of the 128 ATs are of type COMPOSITION and include the other ATs via slots. They are the starting point of the form generation process. The largest AT contains 119 slots. It results in a form of initially 745 input fields, which may be dynamically extended (e.g. by adding further table rows). This AT consists of 22.122 lines of code in the ADL. The resulting Comprehensive AT has 35.998 lines of code, and thus enlarges the AT by 63%. The creation of the corresponding form takes 5 sec on an Intel Core 2 Quad Q9400 computer. Besides numerous test documents we also created 29 documents based on real anonymised patient information. They were all successfully stored as ATconformant EHR extracts and uploaded into the IHE XDS environment.

4. Discussion In [10] a method for the automatic creation of forms from openEHR ATs is described. It does not, however, address how to create AT-conformant EHR extracts from the collected data. Under the name of Opereffa an open source application is developed, which allows forms to be generated from openEHR ATs [11]. In [7] and [12] approaches of integrating openEHR ATs in existing EHR systems are presented. The tool LinkEHR [6] allows existing data to be mapped to ATs, to transform them into AT-conformant data. It supports the ISO/EN 13606, openEHR and HL7 Clinical Document Architecture (CDA) data models. However, automatic generation of forms for ATs is not in the focus of this tool. EHRflex [13] creates forms from ISO/EN 13606 ATs. Although it follows a slightly different approach, it provided helpful evidence on the implementation of our forms. Our ZK-ARCHE System extends the before mentioned tools and systems with its embedding into an IHE XDS environment. In [14] a health information framework is described, which integrates a commercial EHR system into an IHE XDS architecture. It supports the exchange of free text hospital discharge letters embedded in CDA documents. For the creation of the Comprehensive AT some assumptions had to be made to simplify the implementation. Optional attributes of the RM, which are not constrained by the AT, are not included in the Comprehensive AT. Slots may only be filled with a 3 4

http://www.openehr.org/projects/java.html http://www.zkoss.org/


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single AT. ELEMENT nodes with unspecified data type in the AT (matches {*}) are interpreted as data type SIMPLE_TEXT. Without these assumptions the Comprehensive AT would further grow in relation to the AT. Because of the direct derivation of the form from the Comprehensive AT, the form usability depends on the modeling of the AT. Complex structures in the AT result in an equally complex form. This problem could be solved by adding a GUI design tool to the system, which allows the generated forms to be manually edited. Alternatively, an intermediate layer for describing the visualization of an AT could be added, similar to the description of an archetyped EHR extract’s visualization such as presented in [15]. Acknowledgements. The project EHR-ARCHE is funded by the Austrian Science Fund (Fonds zur Förderung der wissenschaftlichen Forschung FWF), Project number P21396.

References [1] [2] [3] [4]

[5] [6] [7]

[8] [9]

[10]

[11] [12] [13] [14] [15]

Integrating the Healthcare Enterprise (IHE), IT Infrastructure Technical Framework, vol. 1 (ITI TF-1, chapter 10), vol. 2 (ITI TF-2, chapter 3.14 and Appendix L), I. t. H. E. (IHE), Editor. 2007. European Committee for Standardization, EN 13606 Electronic healthcare record communication. 2007. International Organization for Standardization, ISO 13606 Electronic health record communication. 2008. Beale T. Archetypes, Constraint-based domain models for future-proof information systems. In Eleventh OOPSLA Workshop on Behavioral Semantics: Serving the Customer. 2002. Seattle, Washington, USA: Northeastern University, Boston. Reenskaug T. Models-views-controllers. 1979, Technical note, Xerox PARC. Maldonado JA, Moner D, Boscá D, Fernández-Breis JT, Angulo C, Robles M. LinkEHR-Ed: A multireference model archetype editor based on formal semantics, Int J Med Inform 78(2009), 559-70. Chaloupka J. Automated integration of archetypes into electronic health record systems based on the Entity-Attribute-Value model, in Section for Medical Information Management and Imaging. 2009, Diploma thesis, Technical University of Vienna: Vienna. ITH icoserve. sense - smart eHealth solutions. 2010; Available from: http://www.ithicoserve.com/loesungen/sense-smart-ehealth-solutions/uebersicht/. Rinner C, Kohler M, Hübner-Bloder G, Saboor S, Ammenwerth E, Duftschmid G. Creating ISO/EN 13606 Archetypes based on Clinical Information Needs. In Accepted at EFMI Special Topic Converence STC 2011. 2011. Laško, Slovenia. Schuler T, Garde S, Heard S, Beale T. Towards Automatically Generating Graphical User Interfaces from openEHR Archetypes, in Ubiquity: Technologies for Better Health in Aging Societies, Hasman A, Haux R, VanderLei J, DeClercq E, FHR, eds. France, 2006, I O S Press: Amsterdam. p. 221-226. Arikan S, Shannon T, Ingram D. Opereffa. 2009; Available from: http://opereffa.chime.ucl.ac.uk/introduction.jsf. Chen R, Klein GO, Sundvall E, Karlsson D, Ahlfeldt H. Archetype-based conversion of EHR content models: pilot experience with a regional EHR system, BMC Med Inform Decis Mak 9(2009), 33. Brass A, Moner D, Hildebrand C, Robles M. Standardized and flexible health data management with an archetype driven EHR system (EHRflex), Stud Health Technol Inform 155(2010), 212-8. Alves B, Muller H, Schumacher M, Godel D, Abu Khaled O. Interoperability prototype between hospitals and general practitioners in Switzerland, Stud Health Technol Inform 160(2010), 366-70. van der Linden H, Austin T, Talmon J. Generic screen representations for future-proof systems, is it possible? There is more to a GUI than meets the eye., Computer Methods and Programs in Biomedicine 95(2009), 213-26.

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Using a Logical Information Model-Driven Design Process in Healthcare Yu Chye CHEONGa1,, Linda BIRDa, Nwe Ni TUNa, Colleen BROOKSa a MOH Holdings Pte Ltd, Singapore

Abstract. A hybrid standards-based approach has been adopted in Singapore to develop a Logical Information Model (LIM) for healthcare information exchange. The Singapore LIM uses a combination of international standards, including ISO13606-1 (a reference model for electronic health record communication), ISO21090 (healthcare datatypes), SNOMED CT (healthcare terminology) and HL7 v2 (healthcare messaging). This logic-based design approach also incorporates mechanisms for achieving bi-directional semantic interoperability. Keywords. Logical Information Model, Semantic Interoperability, Healthcare Standards, Messaging

1. Introduction Most clinical applications can send or receive point-to-point messages using standards, such as HL7 version 2. However, for two or more clinical systems to share healthcare data unambiguously, the structure, the (reference) terminology and the semantics must all be agreed upon. This is a requirement for truly shareable Electronics Health Records (EHRs) and downstream functionality such as clinical decision support and care planning that relies on semantic interoperability. The current lack of message standardisation in Singapore is hindering information sharing between healthcare clusters, sectors and facilities. HL7 v2 is the current de facto standard for healthcare messaging in Singapore – however, there are numerous different HL7 v2 message profiles being used, and widespread use of local extensions and locally defined Z-segments. As a result, national information exchange, querying and conformance quality testing has been difficult. These challenges are further exacerbated by disconnected terminology sets, which differ in their degree of precoordination due to differing local interfaces and information structures. To achieve bi-directional semantic interoperability [1] within this multi-profile environment, each clinical system must be able to produce and consume every message variation. Each system may therefore need to support dozens of interfaces to other systems. To address these interoperability issues, a logical information model is needed to harmonize (reference) terminology, semantics and structure. The Singapore Logical Information Model is a critical enabler for national initiatives such as the National Electronic Health Record (NEHR) system [2], which aims to consolidate distributed information from various institutions into a single electronic health record for each patient. 1

Corresponding Author: {yuchye.cheong, linda.bird, nweni.tun, colleen.brooks}@mohh.com.sg

Y.C. Cheong et al. / Using a Logical Information Model-Driven Design Process in Healthcare

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2. Method The Singapore Logical Information Model (LIM) is an implementation-independent information model for healthcare data exchange. The LIM is based on a standardsbased Logical Reference Model (LRM) and includes a set of ‘archetypes’, or reusable building blocks of clinical information. These archetypes can be further constrained into ‘templates’ to meet specific use cases. The LIM defines the structure, reference terminology and clinical content of healthcare data exchanges. The LIM can be expressed in a machine-readable format that can be used to generate a variety of artefacts such as exchange format specifications, conformance validation software, user interfaces and human readable documentation. The LIM’s novel use of ‘design pattern’ constructs support a diversity of pre-coordination approaches used by clinical systems to populate their messages using native interface terms. The process of developing the LIM and resulting artefacts is shown below in Figure 1.

Figure 1: LIM Design Process

Firstly, a Logical Reference Model (LRM) was developed to provide both modelling integrity and flexibility. It incorporates the following international standards: • ISO 13606-1 [3]: A profile of the ISO 13606 reference model is used, in which certain attributes were removed due to a lack of a tangible use case in our local context and to reduce modelling complexity. Some ISO 13606-1 constraints were also relaxed in the LRM - for example, some mandatory constraints were changed to optional, where existing clinical systems could not support ISO 13606’s record-keeping metadata requirements, (e.g. AUDIT_INFO.committer [0..1]), or where a standard default value has been defined for Singapore (e.g. RECORD_COMPONENT.synthesised: default=”FALSE”). Other changes made to ISO13606-1 include the extension of FUNCTIONAL_ROLE to allow a Participation_Type and Participation_Time, and the extension of IDENTIFIED_ENTITY to support Singapore-specific demographic requirements.

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•

ISO 21090 data types [4]: A profile of the ISO 21090 data types is used, in which some datatypes (e.g. MO) were excluded, and the HXIT attributes were removed (except for validTimeLow and validTimeHigh required for II).

Besides ISO 13606, Singapore also evaluated the HL7 Reference Information Model (RIM) as the basis for the LRM. The HL7 v3 RIM artefacts (e.g. DIMs, CIMs and CMETs) require a high level of technical skill to interpret, thereby inhibiting widespread and effective clinician validation. There is also an overlap in the semantics of the RIM and SNOMED CT, which can lead to ambiguities. In view of these issues, it was decided that the RIM should not form the basis for the LRM. Secondly, a Logical Information Model (LIM), conforming to the LRM, was developed for Singapore’s healthcare information exchange. The requirements analysis for the LIM was based on two main approaches: • An evidence-based approach involved the analysis of existing healthcare information exchange. All relevant message profiles (primarily HL7 v2) in Singapore were fully documented in a consistent format, and validated against several million messages in conjunction with local implementation groups. Message types such as ADT (Admission/Discharge/ Transfer), Pharmacy Order and Laboratory Results were covered. A number of local message profiles exist for each of these message types, each using a surprising diversity of representations for the same or similar semantics. • A clinician-driven approach to gathering requirements for the NEHR and Discharge Summary documents. The LIM was developed as a set of reusable, clinical ‘archetypes’ for each ENTRY that needed to be exchanged (e.g. ‘Problem/Diagnosis’, ‘Pharmacy Order’). Archetypes were initially developed based on modelling the clinical semantics of the data that was currently being exchanged, rather than modelling the ‘intended’ meaning of the HL7 v2 message. In many cases, this resulted in a single HL7 v2 field being mapped to two different LIM elements (where the meaning of data included in this field differed between existing profiles), and two different HL7 v2 fields being mapped to the same LIM element (where the meaning of data used in a field of one profile was actually the same as that used in a different field of another profile). For each LIM element, mappings to the relevant local message profiles were developed to provide traceability back to the source requirement. The constraints defined on each LIM element were the lowest-common-denominator of all existing message profiles. For example, if the cardinality of a particular element was mandatory in one local profile, but optional in another, then the LIM element cardinality was set to optional, to cater for all existing information requirements. Record-keeping metadata was mapped to, and supported by, the LRM attributes. The LIM supports the binding of elements to both the national ‘reference terminology’ and various ‘interface terminologies’ used within local clinical systems. To support the diversity of pre-coordination allowed in clinical interface terms, ‘design patterns’ (DP) were introduced, based on the SNOMED CT concept model [5][6]. These design patterns allow more than one split between the information model and the terminology model to be represented, and then normalised for consistent, national querying. The approach used to normalise the interface terms is shown in Figure 2. A reverse process is also being developed to take the normalised terms and convert them back into a system-specific structure to enable bi-directional semantic interoperability.


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Figure 2. Use of Design Patterns

Thirdly, a series of use case-specific Templates were developed for each message or document type, as a set of constraints on the LIM. Templates have been developed for two main purposes: • To represent the mapping from an existing messaging profile to the LIM • To represent the set of elements and constraints that forms the national standard for a given message type – referred to as the National Data Definition Specification (NDDS). Each NDDS accommodates all data currently being exchanged for a given message type, and all anticipated future requirements. Lastly, from each NDDS one or more format-specific National Data Exchange Specifications (NXDS) are generated. These NXDSs include guidance on how each LIM element in the associated NDDS is mapped into the specific exchange format. NXDSs for two exchange formats have been developed – namely: • Logical XML (LXML): This exchange format has been developed as a direct XML serialisation of each LIM-based NDDS (called NXDS-LXML). This enables the exchange specification and conformance testing software to be generated in a completely automated way from the clinician-validated requirements, represented in the LIM. Use case-specific XML tag names have been used to make implementation easier, and enable simple conformance compliance testing to be achieved using XML schema. However, to minimise the maintenance costs arising from changing business requirements, and provide a future-proof capability, the LXML is developed by extending the record-keeping components of the ISO 13606 reference model XML schema. This enables a pair of simple XSLT transforms to be written which takes any LXML instance and converts it to/from a generic ISO 13606-1 XML schema. • HL7 v2: An HL7 v2.3.1 [7] NXDS specification (called NXDS-HL7 V2) has been developed for each NDDS. These national HL7 v2 profiles include Singapore-specific cardinalities, constraints and value domains. The HL7 v2 NXDSs minimise information loss from the NDDSs by including those entries

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that do not fully map to standard HL7 v2 segments, into additional structured OBX and NTE segments (also referred to as ‘archetyped v2’). This approach allows additional information to be included in the HL7 v2 messages, while still maintaining conformance to the standard message segment tables.

3. Results and Discussion The LIM currently supports the generation of 6 main NDDSs – ADT, Pharmacy Order, Pharmacy Dispense, Laboratory Results, Radiology Results and ACIDS (Acute Care Inpatient Discharge Summary) – and 12 NXDSs (HL7 v2.3.1 and LXML for each NDDS). Variations to these message types (including smaller, constrained versions tailored to the NEHR requirements) can be achieved with little additional effort. The above LIM-based design approach has initially been implemented on an extremely small tooling budget. The LIM has been documented in the form of a spreadsheet, in which each ‘archetype’ is represented on a separate worksheet (using a predefined definitional format), and each ‘template’ is represented using a column of this worksheet (to document each template constraint against the associated data components). NDDSs are generated by auto-filtering the rows of the spreadsheets, based on the appropriate template constraints, HL7 v2 NXDSs are generated through manual mappings, and LXML NXDSs are generated by manually serialising the NDDSs into XML schema. The intention, however, is to transition to a comprehensive and highly automated tooling suite to fully realise the benefits of the above approach. We plan to implement terminology normalisation and denormalisation algorithms over the LIM’s design patterns, and a query language over the LIM semantics, which can be transformed to system-specific queries over multiple heterogeneous data sources. In conclusion, we believe that the establishment of the LIM is a critical step in achieving bi-directional semantic interoperability in Singapore, and ultimately achieving greater clinical safety in the interchange of healthcare information.

References Stroetmann VN, (Ed.), Kalra D, Lewalle P, Rector A, et al. Semantic Interoperability for Better Health and Safer Healthcare, SemanticHEALTH Report, European Communities, 2009. [2] Singapore National Electronic Health Record System [3] ISO 13606-1. Electronic Health Record Communication - Part 1: Reference Model, 2008. [4] ISO 21090. Harmonized Datatypes for Information Interchange, 2009. [5] IHTSDO. SNOMED Clinical Terms User Guide: January 2010 International Release, 2010. [6] Spackman KA. Expressions and Context Patterns, IHTSDO, 2008. [7] Quinn J, (Tech. Chair). HL7 v2.3.1 Final Standard, 1999. [1]


809

SNOMED CT Implementation: Implications of Choosing Clinical Findings or Observable Entities a

Anne Randorff RASMUSSENa,1, Kirstine ROSENBECK a Department of Health Science and Technology, Medical Informatics, Aalborg University, Denmark

Abstract. Internationally, it is a priority to develop and implement semantically interoperable health information systems.[1] One required technology is the use of standardised clinical terminologies. The terminology, SNOMED CT, has shown superior coverage compared to other terminologies in multiple clinical fields. The aim of this paper is to analyse SNOMED CT implementation in an Electronic Health Record (EHR). More specifically, differences and consequences of applying clinical findings (CFs) as an alternative to observable entities (OEs) is analysed. Results show that CFs represents the content of the templates with better coverage, with more parent concepts and with a higher degree of fully defined terms than the OEs. We discuss the possibility to further evaluate the observable entity hierarchy to overcome a potential overlapping use of the two hierarchies. Keywords. Clinical terminology, Implementation, SNOMED CT, Observable entity, Clinical finding, Electronic Health Record

1. Introduction Multiple definitions of identical concepts are a challenge in data communication in health care. Use of standardised clinical terminologies has the potential to ensure unambiguous data definition. This is a prerequisite in achieving semantic interoperability between health information systems. There exists numerous clinical terminologies, but SNOMED CT has shown to be superior regarding coverage in multiple clinical fields.[2,3] Therefore, SNOMED CT is chosen as the point of departure in this study. SNOMED CT is maintained and refined by the International Health Terminology Standardisation Organisation (IHTSDO). The organisation has published strategies and rules for the implementation of SNOMED CT to unify future implementation [4]. However, these are mostly theoretical as only few SNOMED CT implementation projects are documented.[5] Inevitably, there will be deviations between the way SNOMED CT is implemented in real-life projects and the theoretical recommendations. These deviations are important to report, since they increase knowledge on possible implementation strategies for SNOMED CT. To support this, Alan Rector has argued that the goal of clinical terminologies is implementation in clinical information systems. In addition, he doubted that all terms currently part of SNOMED CT was actually 1

Anne Randorff Rasmussen, Fr. Bajers Vej 7 C2-, DK-9220 Aalborg Ø, [email protected].

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A.R. Rasmussen and K. Rosenbeck / SNOMED CT Implementation

operational: “It is a significant clinical task to find out what situations the term is intended to cover which might actually be recorded in an operational record”.[6] This study is based on implementation of SNOMED CT in an EHR-system in the Northern Jutland Region in Denmark. The terminology is implemented alongside the configuration the EHR-system. Our point of departure is two locally designed clinical templates “nursing status” and “physical examination”. As they are clinical notes, a structured narrative approach was chosen. Structured narratives combine the familiarity, ease of use and freedom of expression of the narrative with the ability to browse data based on the gross structure represented by sections, fields and paragraphs.[7] In the specifications provided by IHTSDO, it is stated that the OE hierarchy in SNOMED CT should be used for coding sections, fields and paragraphs. “Concepts in this hierarchy can be thought of as representing a question or procedure which can produce an answer or a result.” 2 However, when mapping expressions from the respective templates to SNOMED CT, a lack of quality and comprehensiveness was found in the OE hierarchy. The aim of this paper is to systematically analyse the implications of applying CFs as an alternative to OEs when configuring the “nursing status” and “physical examination” templates.

2. Method

Figure 1. Overview of the method applied to compare OEs and CF in this study

The method applied is illustrated in Figure 1. Templates that represent two clinical domains are included in this study to achieve expressions with varied characteristics. The data set consists of a total of 34 clinical expressions: Physical examination (22 expressions) and Nursing status (12 expressions). 8 cases of compounded terms exist in the data set, e.g. 'skin and mucosa finding’, and ‘respiration and circulation’. 7 of these are found in ‘Nursing status’. The clinical expressions were mapped to SNOMED CT OEs and CFs respectively. When mapping the compounded terms we initially strived to find a pre-coordinated concept covering both expressions, otherwise post-coordination by combination is used to represent the expressions. The analysis framework was developed to systematically evaluate the usefulness of a set of SNOMED CT concepts. In the research literature there are rather few 2

http://www.ihtsdo.org/snomed-ct/snomed-ct0/snomed-ct-hierarchies/observable-entity/#c1513


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methods for analysing SNOMED CT. An exception is [8] where an informationcontent measure is developed. This measure is based on the analysis of the parents, pathways and branches of SNOMED CT. However, we want to analyze retrieval and reuse potential, therefore the exact measure of [8] is not applicable, but our approach is similarly based on these core-characteristics of SNOMED CT. In the analysis, we compared and assessed the potential of each hierarchy to represent the clinical expressions. The analysis is conducted within the following areas; content coverage, level of granularity and concept definition. These are described in details below. The content coverage is analyzed to assess whether concepts in SNOMED CT are able to represent clinical expressions. Also the use of pre- and postcoordinated concepts is stated. The level of granularity is examined, defined as the level of detail associated with each concept. Hence, the number of parent concepts is measured, as shown in Figure 2. This measure is chosen, as it expresses the potential of the concept to be used for data retrieval purposes, as search strategies can be based on either one of the parents or the concept itself. The parents make it possible to retrieve data based on a more granular level based on inherited meaning only. A Wilcoxon signed-rank test is performed to assess whether there is a significant difference between the number of parent concepts in the CF and OE hierarchy. This test is done contrary to a paired t-test, as we cannot assume normal distribution. B) A)

Figure 2 A) 4 parent nodes and B) 8 parent nodes. Identical parent concepts are only included once

The concept definition is examined, defined as whether the concept is primitive or fully defined. A concept is primitive when its logic definition does not sufficiently express its meaning. Further, primitive concepts do not have the defining relationships needed to computably distinguish them from their parent or sibling concepts.[9] For fully defined concepts, aggregated data can be based on characteristics that are stated by other expressions than the inherited meaning.

3. Results The results of analysing the content coverage, level of granularity and concept definition is presented in following tables; The content coverage is shown in Table 1. A coverage of 100% for the CF and 94 % for OE is achieved. Post-coordination is used more frequently to represent the clinical expression in OE than in CF and more pre-coordinated concepts of the compounded expressions was found for the CF hierarchy than in the OE. In CF the

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compounded expressions 'Skin and mucous membranes' and 'sleep and rest' exist. In the OE hierarchy these expressions exist as separate concepts only. The tables (Table 2a and Table 2b) show results of analysing level of granularity and concept definition for each expression. Table 1. SNOMED CT coverage for the clinical expressions in nursing status and physical examination. Nursing Status and Physical Examination CF OE

PreCoordination 31 (91%) 25 (74%)

PostCoordination 3 (9%) 7 (20%)

Total 34/34 (100%) 32/34 (94%)

functional perf. and activity

Nutrition

Defecation

Micturition

Respiration

Circulation

Skin

Mucosa

Pain / sensation

Sleep (and rest)

Rest

Psychosocial

Cognitive funct.

Communication

Value belief

Sexuality

Reproduction

5

5

16

16

4

4

4

4

4

7

7

5

5

4

5

7

4

CF

5

5

7

12

4

4

6

4

5

6

0

7

7

4

6

5

5

OE

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

CF

1

0

1

1

0

0

0

0

1

1

-

1

0

1

1

1

1

Clinical expressions NoP

OE

Def

Table 2a Results assessing the number of parents (NoP) and definition for nursing status.

6

0

5

4

4

14

5

5

3

6

OE

1

1

1

1

1

1

1

1

1

1

1

1

1

-

-

1

1

1

1

-

1

1

1

CF

1

1

1

0

0

0

0

0

0

0

1

0

0

1

1

0

0

0

0

0

0

1

0

NoP Def

Skin

4 7

Neurological

6 6

Limb

4 7

Beck structure

0

13 10

Rectum

0

4

Urogenital

4

7

Breast

5

4

Abdominal

Respiratory ausc.

5

6

Endocrine Lymphoid system Truncus

6

8

Neck

8

9

Nose

6

10

Ear

4

4

Oral cavity

4

9

Eye/Vision

7

5

Skull

5

6

Mental state 4

5

Head and neck

5

4

Physics

4

CF

General

OE

Clinical expressions

Cardiac ausc.

Table 2b Results assessing the number of parents (NoP) and definition for nursing status.

The Wilcoxon signed-rank test shows a significant difference in the number of parent concepts for the two hierarchies with p=0.031. The average number of parents for the concepts in OE is 5.15 and for CF 6.33, looking at the concepts for the physical examination only, the difference in number of parents increase. This means that for the whole dataset and especially for the physical examination, the CF hierarchy has more granulated concepts than the OE hierarchy. A similar difference is obtained when comparing the level of definition for each concept. All OEs are primitive, whereas 56% of CF is fully defined. Also, it is observed that the proportion of fully defined concepts is higher if we look at the physical examination alone. 4. Discussion In this study a comparison was performed by mapping concepts from two clinical templates to concepts from the OE and the CF hierarchies. The aim was to investigate whether the CF contribute with a higher quality and comprehensiveness than the OEs.


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Existing literature lacks focus on the usage of the specific SNOMED CT hierarchies. The main objective in the scientific literature is to investigate the potential of SNOMED CT to cover the content of different clinical domains.[5] The results show that the needed concepts can be found in both the OE hierarchy (94%) and CF hierarchy (100%), which potentially can induce ambiguous encoding. The problem is not merely redundant concepts, but that two hierarchies can be used interchangeably. IHTSDO suggest that each hierarchy has a certain purpose, but our study and a study by Lee et al. suggest that the stated purposes are not clear enough to allow consistent mapping. [10] Lee et al. find overlaps between e.g. the “clinical finding” and “morphologic abnormality” hierarchies when mapping a palliative care dataset. To keep the mapping consistent, they introduce guidelines. However, local guidelines cannot handle terminology inconsistencies between organisations. Therefore, in time, improving the consistency of SNOMED CT itself would be preferable. Improving the consistency to avoid redundant use of hierarchies is not a simple task. In our study, it is suggested that the parameters: content coverage, granularity and definition might be useful in determining which hierarchy reaps most benefits in terms of retrieval and reuse purposes. Using these parameters and the “nursing status” and “physical examination” datasets, the CF hierarchy is superior to the OE hierarchy. However, these are only two clinical examples and even among these there are differences in the results. To the authors knowledge similar studies examining the same dataset using two different SNOMED CT hierarchies is not available. Therefore, more studies are needed on the implementation of SNOMED CT with the focus of analysing the usage of the hierarchies. Acknowledgement. This research is part of our PhD-studies that are co-financed by Region Northern Jutland, CSC Scandihealth and Trifork A/S.

References [1]

Garde S, Knaup P, Hovenga EJS, Heard S. Towards Semantic Interoperability for Electronic Health Records. Methods Inf.Med. 2007;3:332. [2] Brown SH, Rosenbloom ST, Bauer BA, et al. Direct Comparison of MEDCIN® and SNOMED CT® for Representation of a General Medical Evaluation Template. AMIA.Annu.Symp.Proc. 2007:75. [3] Chute CG, Cohn SP, Campell KE, Oliver DE, Campell JR. The content coverage of clinical classifications. For The Computer-Based Patient Record Institute's Work Group on Codes & Structures. JAMIA 1996;3(3):224. [4] International Health Terminology Standards Development Organisation. IHTSDO. Available at: http://www.ihtsdo.org/. Accessed 11/18, 2009. [5] Cornet R, de Keizer N. Forty years of SNOMED: a literature review. BMC Med Inform Decis Mak 2008 Oct 27;8 Suppl 1:S2. [6] Rector AL. Clinical terminology: why is it so hard? Methods Inf Med 1999 Dec;38(4-5):239-252. [7] Johnson SB, Bakken S, Dine D. An Electronic Health Record Based on Structured Narrative. Journal of the American Medical Informatics Association :JAMIA 2010 21:54. [8] Cornet R. Information-content-based measures for the structure of terminological systems and for data recorded using these systems. Stud.Health Technol.Inform 2010:1075. [9] IHTSDO. SNOMED Clinical Terms. User Guide. 2010 January. [10] Lee DH, Lau FY, Quan H. A method for encoding clinical datasets with SNOMED CT. BMC Med Inform Decis Mak 2010, 10:53

814


What is the Coverage of SNOMED CT® on Scientific Medical Corpora? a

Dimitrios KOKKINAKISa1 Centre for Language Technology, Department of Swedish Language, the Swedish Language Bank, University of Gothenburg, Gothenburg, Sweden

Abstract. This paper reports on the results of a large scale mapping of SNOMED CT on scientific medical corpora. The aim is to automatically access the validity, reliability and coverage of the Swedish SNOMED-CT translation, the largest, most extensive available resource of medical terminology. The method described here is based on the generation of predominantly safe harbor term variants which together with simple linguistic processing and the already available SNOMED term content are mapped to large corpora. The results show that term variations are very frequent and this may have implication on technological applications (such as indexing and information retrieval, decision support systems, text mining) using SNOMED CT. Naïve approaches to terminology mapping and indexing would critically affect the performance, success and results of such applications. SNOMED CT appears not well-suited for automatically capturing the enormous variety of concepts in scientific corpora (only 6,3% of all SNOMED terms could be directly matched to the corpus) unless extensive variant forms are generated and fuzzy and partial matching techniques are applied with the risk of allowing the recognition of a large number of false positives and spurious results. Keywords. SNOMED CT; Scientific Medical Corpora; Quality Assessment; Term Validation; Term Variation; Term Mapping

1. Introduction Term variation is considered an obstacle to systematic knowledge acquisition and to many NLP applications [1]. The aim of this work is to develop and apply techniques for automatically mapping structured concepts from the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) to unrestricted medical texts to evaluate the validity and reliability of the terminology content [2]. The textual material used in this work is based on large samples of scientific medical corpora, covering a broad spectrum of medical subfields and is not limited to clinical data. The corpus is used as a test bed for exploring and measuring coverage and quality related to the concept instances. Our approach aims to give an empirical indication of the quality of terms and identify potential problems or shortcomings related to the choice of terminological forms in the resource. Therefore, it applies a number of processing steps that intend to overcome most of the potential limitations and deficiencies identified, e.g. by generating term variants and alternative surface realizations of concepts. Each generated variant found in the corpora is linked to its recommended form via its unique 1

Corresponding author: Dimitrios Kokkinakis, Centre for Language Technology, Dept of Swedish, the Swedish Lang. Bank, Box 200, 405 30 Gothenburg, Sweden; E-mail: [email protected].

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concept id-number as stated in SNOMED CT and can be queried on-line: .

2. Background There have been a number of studies described in the literature to measure the coverage of SNOMED CT with respect to textual samples in different medical/clinical subdomains and diagnosis/problem lists and also to devise ways to augment its content [3]. A characteristic of these studies has been the high percentage of agreement or coverage obtained between the terminological resource and the textual data. In [4] it is shown that the majority of entries in diagnosis/problem lists were found in SNOMED CT (88.4%), while of the 145 missing terms, only 20 represented significant concepts missing, resulting in concept coverage of 98.5%. In the work by [5] it is emphasized that SNOMED-CT has promise as a coding system for clinical problems. In [6] 85% of the clinical significant information was captured, while the results in [7] showed that of the 4996 problems in a test set, SNOMED CT could correctly identify 4568 terms. [8] describe a system combining vector space and the regular expression modules and a top precision on recognizing SNOMED terms of 82.3%. Finally [9] by using Case Report Forms discuss that most of the core clinical concepts were covered (88%); however, far fewer of the concepts were fully covered (that is, where all aspects of the text item could be complete without post-coordination; 23%). In addition, the majority of the concepts (83%) required post-coordination to better capture complex clinical concepts.

3. Materials and Methods 3.1. Scientific Medical Corpora A large Swedish scientific medical corpus is used as a reference for measuring the coverage and quality related to the concept instances of the Swedish translation of SNOMED CT. The corpus comprises the electronic archives of the Swedish Medical Association Journal, Läkartidningen, (), one of the most reliable sources for comprehensive and up-to-date scientific medical knowledge in Swedish. The material covers a broad spectrum of medical subfields, including special issues on different topics such as Sexually Transmitted Diseases; Oncological Medicines and Medical Ethics. Since 1996 the archive’s content exists in digital format, including XML-annotated versions. Table 1 shows some characteristics of the corpus which currently comprises 28,113 different articles and approx. 26,5 million tokens. Table 1. Corpus characteristics Publ. Year 1996 1997 1998 1999 2000 2001 2002

Articles 2345 2116 2089 1779 1908 1940 2159

Tokens 2,050,000 2,007,000 2,223,000 2,096,000 2,027,000 2,122,000 2,044,000

Publ. Year 2003 2004 2005 2006 2007 2008 2009

Articles 2151 2201 1803 1941 2004 1908 1769

Tokens 1,784,000 1,867,000 1,535,000 1,615,000 1,676,000 1,782,000 1,735,000

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3.2. SNOMED CT® (Swedish) SNOMED CT is a large and systematically organized computer processable collection of health and social care terminology. It is also a common computerized language, a so called compositional concept system in which concepts can be specialized by combinations with other concepts, e.g. by post-coordination [10] which describes the representation of a clinical meaning using a combination of two or more concept identifiers. According to the international release of July 2008, SNOMED CT includes more than 315,000 active concepts (for English), organized into 19 top-level hierarchies, containing over 806,000 English language descriptions and more than 945,000 logically-defining relationships. The first Swedish release of April 2010, provided by the Swedish National Board of Health and Welfare (Socialstyrelsen, ), included 278,000 concepts; disorders being the largest group with >63,000 concepts followed by procedures with >48,000 concepts. 3.3. SNOMED CT Pre-Processing Three types of pre-processing have been taken place. All terms have been tokenized, converted to low case, while all homonyms, that is terms that happens to have the same surface form with another term that possibly belongs to some other hierarchy, have been merged into a single term with all individual identifiers joined. For instance, the term blodprov ('blood sample') belongs to either Specimen#119… or Procedure#396…; according to the previous discussion, a new merged terms has been created, with all of its characteristics preserved: blodprov# Specimen#119…# Procedure#396…. Moreover 3,8% of the SNOMED terms are over 10 tokens long and were not used since they are not suitable for automatic mapping using the methodology followed here. 3.4. Generation of Term Variants Even within the same text, a term can take many different forms. [11] discuss that a term may be expressed via various mechanisms including orthographic variation, usage of hyphens and slashes, lower and upper cases, spelling variations, various Latin/Greek transcriptions and abbreviations. Some of the many possible variation types are further described in [2: 161-219]. This rich variety for a large number of term-forms is a stumbling block for many applications, as these forms have to be recognized, linked and mapped to terminological and ontological resources; for a review on normalization strategies see [12]. Moreover, a number of necessary adaptations of the terminological content have to take place in order to produce a format suitable for text processing, for instance indexing. This is a necessary step, since many term occurrences cannot be identified in text if straightforward dictionary/database lookup is applied. We provide here an outline of various ways we have implemented to deal with term variation: morphological: such as the generation (or programmatic identification) of inflection and derivational patterns, e.g. plural and participle forms etc. structural variations: capture the link between a term, e.g. a compound noun and a noun phrase containing a right-hand prepositional phrase, such as skin neoplasm vs. neoplasm of/in/on the skin. Note that compounds in Swedish are written as a single word, i.e. hudtumör (‘skin neoplasm’) which implies that compound segmentation is taking place to distinguish head and modifier(s).


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compounding: the inverse of the previous; a noun phrase containing a right-hand prepositional phrase, or a two word term, is re-written as a single-word compound, e.g. glomerulär filtration (‘glomerular filtration’) becomes glomerulusfiltration and tumör i tibia (‘tumor of tibia’) becomes tibiatumör. splitting: a single-word compound is splitted into its head and modifier(s). This way we also capture a number of spelling mistakes, i.e. splitted compounds that should have been written as a single word; e.g. synovialled ('synovial joint') and synovial led. modifications, orthographic variation, substitutions and types of exclusions: these are transformations that associate a term with a variant in which the head word or one of its argument has an additional modifier, hyphenation, e.g. b cell vs. b-cell; substitution of Arabic to Roman numbers, e.g. NYHA type 2 vs. NYHA type II; deletion of embedded acronyms or parts of lengthy multiword terms (function words, punctuation), e.g. diabetes mellitus type 1 vs. diabetes type 1 vs. type 1 diabetes coordination: transformation that associates >2 terms with a composite variant. Sometimes such entities are coordinated by their heads, e.g. interleukin-1 och -6 actually interleukin-1 och interleukin-6 (‘interleukin-1 and 6’) and sometimes by their arguments, e.g. hjärt- och njursvikt actually hjärtsvikt och njursvikt (‘heart and kidney failure’). Using compound segmentation we try to associate a head or modifier of a segmented form to its elliptic counterpart. partial matching: related to the previous, by applying automatic compound segmentation on all text tokens not already captured by SNOMED we try to match subparts of words, e.g. insulinnivå (‘insulin level’); here the compound word has been segmented to insulin+nivå. This way we can capture at least a part, here insulin, that either appears in the head or modifier position or both, but no occurrence of the compound as such is present in the terminology. acronyms: recognized using various regular expression patterns see [13] (near) synonyms: manually added and flagged as new. For instance, for läkemedel (‘drug’) we have added the near synonyms preparat and farmaka. spelling variants and fuzzy matching: so far we have been restrictive to fuzzy matching due to the risk of capturing a lot of false positives; spelling variants have been semi-automatically added though, e.g. koloskopi vs. coloskopi. 3.5. Filtering of Term Variants A small number of existing terms, as well as a number of some of their generated variants, are problematic with respect to ambiguity with the general vocabulary and have been either completely removed from the term list or filtered out after annotation. The first group consists of terms of length 1-2 characters (208 terms), e.g. ja, -3 and II. The second group consists of terms of length 3-5 characters which we have manually inspected and some, predominantly qualifiers, removed, since they are also common in the general vocabulary, e.g. eller (‘or’), man, dollar and under.

4. Results and Discussion The total number of SNOMED annotations obtained, including the term generation and filtering process, were 2,783,216, this number corresponds to 7,86% (or 20114 unique terms) of the SNOMED terms could be identified in the corpus. The baseline, i.e. the SNOMED terms matched in the corpus without any processing, were less than half,

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1,057,235 this figure implies that 6,3% of the SNOMED terms could be found in the corpus by a direct match approach. Another large group of terms as probably expected were inflected forms, 683,206. Out of all the annotations, 28,4%, were partial ones, e.g. laktatacidos (‘lactic acidosis’) is matched as +, here both parts are in SNOMED but not the compound itself. We have also manually and in detail examined the results of 20 randomly chosen, annotated articles in order to get an indication of what types of terms have been left unrecognized and whether they were any ambiguous terms recognized, despite the filtering process described earlier. 1279 annotations were obtained (using the enhanced SNOMED; 36% partial matches); 48 potential terms were left unmatched, not in SNOMED, (e.g. otorré; mitokondrier, nucleus accumbens [the synonym accumbenskärna exists though]). >8 were wrong due to ambiguity (e.g. ‘body’ was referring to a person Kropp). In general, several problems have to do with the existence/mixture of laymen forms and anglicisms. Neither stemming nor coreference (e.g. “…chromosome 17. This chromosome is…”) were used. Stemming usually results into conflated ambiguous terms. Perhaps such processing could have increased the coverage a bit more with the risk of a large number of false positives. Currently the Swedish SNOMED CT does not contain synonymic term variants just recommended ones. To be a useful resource for practical applications it is required to be enhanced with synonyms, tightly integrated with existing recommended terms. Obviously, applications using SNOMED CT should also provide appropriate mechanisms for coping with text and term variation and disambiguation. Our results showed that simple means can enhance the recognition of term variants that otherwise would have been neglected during the automatic processing.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Nenadié G, Ananiadou S, Mcnaught J. Enhancing automatic term recognition through recognition of variation. The 20th Conf. on Computational Linguistics - COLING. Switzerland. (2004). Jacquemin C. Spotting and Discovering Terms through Natural Language Processing. MIT Press. 2001. Patrick J, Wang Y, Budd P. An automated system for conversion of clinical notes into SNOMED clinical terminology. ACSW '07 5th Australasian symposium on ACSW frontiers - Volume 68. 2008. Wasserman H, Wang I. An applied evaluation of SNOMED CT as a clinical vocabulary for the computerized diagnosis and problem list. AMIA Annu Symp. (2003):699-703. Penz JF, et al. Evaluation of SNOMED coverage of Veterans Health Administration terms, Stud Health Technol Inform. (2004) 107(Pt 1):540-4. Lussier YA, Shagina L, Friedman C. Automating SNOMED coding using medical language understanding: a feasibility study. Proc AMIA Symp., (2001), 418–422. Elkin PL, et al. Evaluation of the Content Coverage of SNOMED CT: Ability of SNOMED Clinical Terms to Represent Clinical Problem Lists. Mayo Clin Proc. 81(6), (2006), 741-748. Ruch P, Gobeill J, Lovis C, Geissbühler A. Automatic medical encoding with SNOMED categories. BMC Medical Informatics and Decision Making, 8 (Suppl 1), (2008). Richesson RL, Andrews JE, Krischer JP. Use of SNOMED CT to represent clinical research data, JAMIA. 13(5) (2006), 536-46. Spackman K, Gutai J. Compositional Grammar for SNOMED CT Expressions in HL7 V. 3. 2008. Tsujii J, Ananiadou S. Thesaurus or Logical Ontology, Which One Do We Need for Text Mining? J. of Language Resources and Evaluation. (2005) 39:1, 77-90. Krauthammer M, Nenadic G. Term identification in the biomedical literature. J Biomed Inf. 37(6):51226. 2004. Kokkinakis D, Dannélls D. Recognizing Acronyms and their Definitions in Swedish Medical Texts. 5th Languages Resources and Evaluation (LREC). Genoa, Italy. Pp. 1971-1974. 2006.


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Assisting the Translation of the CORE Subset of SNOMED CT Into French Hocine ABDOUNEa, Tayeb MERABTIb,c, Stéfan J. DARMONIb,c, Michel JOUBERTa,1 a LERTIM, Faculty of Medicine, University of Aix-Marseille 2, France b CISMeF, Rouen University Hospital, France c TIBS, LITIS EA 4108, Institute of Biomedical Research, University of Rouen, France Background: the Core Subset of SNOMED CT is part of the UMLSCore Project dedicated to study problem list vocabularies. SNOMED CT is not yet translated into French. Objective: to propose an automated method to assist the translation of the CORE Subset of SNOMED CT into French. Material: the 2009 AA versions of the CORE Subset of SNOMED CT and UMLS; use of four French-language terminologies integrated into the UMLS Metathesaurus: SNOMED International, ICD10, MedDRA, and MeSH. Method: an exact mapping completed by a close mapping between preferred terms of the CORE Subset of SNOMED CT and those of the four terminologies. Results: 89% of the preferred terms of the CORE Subset of SNOMED CT are mapped with at least one preferred term in one of the four terminologies. Discussion: if needed, synonymous terms could be added by the means of synonyms in the terminologies; the proposed method is independent from French and could be applied to other natural languages. Keywords. Problem lists, SNOMED CT, UMLS, Translations

1. Introduction Weed first introduced and has since popularized the concept of the problem-oriented medical record [1]. The problem-oriented record consists of four essential elements: the data base, problem list, detailed plans, and structured progress notes dealing with each of the identified problems. Problem lists data are often used to drive functions other than clinical documentation, e.g. generation of billing codes, supporting clinical research and quality assurance. In an ideal world, everybody should use a single, standardized problem list vocabulary. In reality, most institutions use their own local vocabularies. The U. S. National Library of Medicine (NLM) started the UMLS-CORE Project to study problem list vocabularies [2]. The Unified Medical Language System (UMLS) is a valuable resource for terminology research. CORE stands for Clinical Observations Recording and Encoding, a mnemonic referring to the capture and codification of clinical information in the summary segments of the medical record such as the problem list, discharge diagnosis and reason for the encounter. The UMLS-CORE 1 Corresponding author : Michel Joubert, Lertim, Faculté de Médecine, Université de la Méditerranée, 27 boulevard Jean Moulin, 13005, Marseille, France

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Project has two goals: 1) to study and characterize the problem list vocabularies of large health care institutions in terms of their size, pattern of usage, mappability to standard terminologies and extent of overlap, and 2) to identify a subset of UMLS concepts that occur with high frequency in problem lists to facilitate the standardization of problem list vocabularies. A CORE Problem List Subset was derived based on datasets from several institutions. The most frequently used terms, about 14’000 in all, represented about 95% of the usage volume in each institution. These were mapped to 6’800 UMLS concepts, which formed the basis of the UMLS-CORE Subset. SNOMED CT covers a high percentage (81%) of the identified UMLS-CORE concepts [3]. Our aim is to propose an automated method to assist a translation of the CORE Subset of SNOMED CT (shortly, CORE Subset in what follows) into French. This study follows a work related to an assistance of an automated translation of SNOMED CT into French [4]. The translation of SNOMED CT is currently being performed in Canada by the Infoway institution in accordance with the IHTSDO organization [5].

2. Material 2.1. Unified Medical Language System The UMLS project launched by the NLM integrates health terminologies in a single Metathesaurus [6]. To date, the UMLS Metathesaurus contains a hundred terminologies. More specifically, within the Metathesaurus we will be using: the MRCONSO table, which lists all the concepts incorporated in the UMLS with no duplication and in which each concept is attributed a unique identifier (CUI), and the MRREL table which describes explicit relationships, if any, between concepts in the original terminologies. Within MRREL, we only use the following explicit mappings: primary_mapped_to/from, mapped_to /from, other_mapped_to/from [7]. We worked with the 2009 AA version of UMLS. Our mappings operate exclusively on preferred terms (PTs) of each French-language terminology: SNOMED International (107’900 PTs), ICD10 (9’306 PTs), MeSH (25’186 PTs), et MedDRA (18’209 PTs). 2.2. CORE Subset of SNOMED CT SNOMED CT is a hierarchical structure of concepts. It contains 310’074 terms in the 2009 version integrated into UMLS. These terms are organized along axes. The most representative axes are: disorder (73’006 terms), procedure (53’119 terms), finding (33’626 terms). CORE Subset version 2009AA is a set of SNOMED CT concepts which represent the most frequently used (14’000 terms) in the databases of the institutions studied by the NLM [3]. These terms have been mapped by NLM to 6’800 UMLS concepts, and more than 5’000 to SNOMED CT concepts. They are principally distributed along the following axes: disorder (3’794 concepts), finding (752 concepts), procedure (396 concepts).


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3. Method The mapping method is as follows: suppose two terms t1 and t2 of two different terminologies, suppose CUI1 and CUI2, the respective projections of t1 and t2 in the Metathesaurus, then t1 and t2 are mapped if: 1) CUI1=CUI2 (in MRCONSO), this corresponds to an exact mapping, and/or 2) there is an explicit mapping between CUI1 and CUI2 (in MRREL). The algorithm is run sequentially, all the possible mappings, exact and explicit, are tried to align each couple of terms. When an explicit mapping relationship exists (e.g. SNOMED CT to ICD-9-CM [8]) between two concepts, CUI1 and CUI2, it is likely that all terms designating CUI2 can be mapped to terms designating CUI1, whatever the terminologies and whatever the language in which they are formulated. In other words, explicit mappings between two terminologies can be “reused” for other terminologies by means of the UMLS concept structure [9].

4. Results Table 1 shows the contribution of each of the four French-language terminologies with regard to the three most representative axes of the CORE Subset. For instance, 3’277 terms of SNOMED International map disorder concepts of the CORE Subset. They represent 86% of the 3’794 terms of the CORE Subset of this axis. Table 1: Contribution in number and percentage for each terminology by axis in the CORE Subset. Terminologies SNOMED Int. ICD10 MeSH MedDRA

Disorder 3,277

Finding /

86%

522

Procedure /

69% 2,733

/

72%

262

477

/

364

/

7 / 2%

63% 2,151

/

57%

48% 2,505

66%

/

/

66%

118

/

162

/

30% 495/

66%

41%

Table 2 shows the the number of PTs of the union of the four French-language terminologies mapped to CORE Subset PTs (concepts) with regard to the three studied axes. For instance, the disorder axis shows 3’463 of the union of terminologies mapped to 3’794 CORE Subset concepts, they represent 91% of them. In the end, the method allows the translation of 89% of CORE Subset terms along these three axes. Table 2: Number of PTs in the union of French-language terminologies aligned by axis with PTs of the CORE Subset. # of PTS of French # of PTs of % of PTs of Axes Terminologies the CORE Subset the Core Subset Disorder

3, 463

3, 794

91%

Finding

632

752

84%

Procedure

291

396

73%

Total

4,386

4,942

89%

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5. Discussion and Conclusion Table 1 shows that the contribution of SNOMED International for translating terms is about 80% of the terms of CORE Subset along the three axes, and that ICD10 contribution is 63%. These results may be explained by the fact that 91% of SNOMED International terms are integrated into SNOMED CT, and that 87% of ICD10 terms are also integrated into SNOMED CT [4]. Considering the three axes in Table 2 (disorder, finding, and procedure), it is possible to propose at least one proposal for the translation of 4’386 of the 4’942 CORE Subset terms, that means 89%. Terminologies are integrated into the UMLS Metathesaurus by experts by means of exact and explicit mappings. Then we can expect that terms of different terminologies referring to a same biomedical concept are attached to a same Metathesaurus concept. So, the mapping we operate does not need validation in our mind, because they have been made previously. With the intent of improving the assistance of the translation of CORE Subset, we would like to propose a set of French-language terms and of synonyms to an original English set. This proposal is based on the construction of the UMLS Metathesaurus itself: the Metathesaurus is a terminology integration system, in which synonymous terms from various terminologies are clustered into concepts, allowing for seamless mapping between terms from different terminologies through a UMLS concept [10, 11]. For instance, The CORE Subset concept “acute myocardial infarction” is translated to infarctus aigu du myocarde in the French ICD10, and into the same MedDRA PT with synonyms “acute myocardial infarction, unspecified site”, “acute myocardial infarction, unspecified site, episode of care unspecified” (expressed in English). Let remark that this concept is not mapped to MeSH. Moreover, synonymy is a symmetric relationship between terms. In order that transitivity can be applied: a synonymous term of another term is considered a synonym of the synonyms of the latter synonym. Hence, it is possible to build a set of terms for a term made of preferred terms originating from different terminologies and via synonyms in these terminologies. As such, MeSH can largely contribute thanks to its 97’000 synonyms, not counting more than 20’000 French synonyms added by the CISMeF team (Rouen University Hospital, France), not yet integrated in the French translation of MeSH. As previously proposed for assisting the translation of SNOMED CT into French [4], our method could be improved by exploiting hierarchical relationships within some terminologies and propose more generic terms for the translation of more specific ones when exact and explicit mappings are not successful. This refinement seems promising but collides with two difficulties: 1) it requires a human expertise to validate a translation proposal, and 2) some research studies have shown the possible confusion that may occur in some terminologies in the interpretation of hierarchical relationships, notably between IS_A and PART_OF relationships [12, 13]. Moreover this kind of inheritance due to hierarchies does not apply to the concept of synonymy described above. The automated method we propose for assisting the translation of the CORE Subset terms is not dependent on French, since it works at a conceptual level and not at a lexical one. Hence, it can be reused for another natural language than French, on condition that terminologies in this language are sufficiently integrated in the Metathesaurus.


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Acknowledgements: The authors thank the National Library of Medicine of the United States who provided them with the UMLS knowledge sources and the CORE Subset of SNOMED CT. The authors are also grateful to Richard Medeiros, Rouen University Hospital Medical Editor, for editing the manuscript.

References [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10] [11] [12] [13]

Weed LL. Medical records that guide and teach. N Engl J Med 1968; 278: 593-600 and 652-7. Fung KW, Mc Donald C, Strinivasan S. The UMLS-CORE project: a study of the problem list terminologies used in large healthcare institutions. JAMIA 2010; 17(6): 675-80. The CORE Problem List Subset of SNOMED CT. http://www.nlm.nih.gov/research/umls/Snomed/core_subset.html Joubert M, Abdoune H, Merabti T, Darmoni S, Fieschi M. Assisting the translation of SNOMED CT into French using UMLS and four representative French-language terminologies. Proc. AMIA Annu Symp 2009; 2009:291-5. Canada Health Infoway. http://www.ihtsdo.org/members/ca00/ National Library of Medicine. UMLS Metathesaurus. http://www.nlm.nih.gov/pubs/factsheets/umlsmeta.html Fung KW, Bodenreider O. Utilizing the UMLS for semantic mapping between terminologies. Proc AMIA Annu Symp. 2005: 266-270. Imel M. A closer Look: The SNOMED Clinical Terms to ICD-9-CM Mapping. Journal of AHIMA 2002; 73; 66-69. Bodenreider O, Nelson SJ. Beyond synonymy: Exploiting the UMLS Semantics in Mapping Vocabularies. Proc AMIA Annu Symp 1998: 815-9. McCray AT, Nelson SJ. The Representation of meaning in the UMLS. Methods Inf Med 1995; 3:193201. Bodenreider 0. Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support. Yearb Med Inform, 2008: 67-79. Cimino JJ, Min H, Perl Y. Consistency across the hierarchies of the UMLS Semantic Network and Metathesaurus. J Biomed Inform 2003; 36: 450-61. Ceusters W, Smith B, Kumar A, et al. Mistakes in medical ontologies: where do they come from and how can they be detected? Stud Health Technol Inform 2004; 102: 145–63.

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Recording Associated Disorders Using SNOMED CT a

Ronald CORNETa, Nicolette F de KEIZER a Department of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands

Abstract. Multidisciplinary communication about patients with multiple and often interrelated diseases is of utmost importance to guarantee high quality of care. In this paper we focus on storing into the electronic medical record patients’ disorders which are associated with each other, taking into account the role of SNOMED CT. The objectives of this paper are to design and discuss possibilities to appropriately record the associations between two disorders as defined in SNOMED CT and to get insight into the use of the relationship “associated with” in SNOMED CT and its consequences for data reuse. Our study showed that textual and concept-based reproducible recording of reusable data is hampered due to incorrect or incomplete modeling of associations between disorders in SNOMED CT. A possible solution for this is to record constituting characteristics of concepts directly into the record, instead of only being represented in the terminology. Further research on binding of information models and terminologies is needed. Keywords. Terminological system, SNOMED CT, electronic medical record, semantic interoperability

1. Introduction With the aging population more and more patients have multiple, chronic and often interrelated diseases. To streamline diagnostic and treatment activities good communication is required between the care providers that are involved from different disciplines. These care providers will have different clinical perspectives, for example a neurologist might describe a patient as having a ‘diabetic neuropathic arthropathy’, while the same situation is described by a diabetologist as ‘Type II diabetes mellitus with neuropathic arthropathy’ and as ‘arthropathy associated with a neurological disorder’ by an rheumatologist. A semantically interoperable electronic medical record (EMR), i.e. a medical record in which the meaning of the data can be exchanged and understood across the borders of systems, clinical contexts, and users, should support interdisciplinary communication. Terminological systems which explicitly define medical concepts are essential to realize this. SNOMED CT is considered to be a comprehensive clinical healthcare terminological system that can be used as the foundation for EMRs and other applications. Due to its separation of concepts and descriptions, each unique concept can be described by multiple synonymous terms which support the use across the borders of medical specialties. SNOMED CT provides formal definitions for its

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concepts using IS A relationships and attribute relationships. Relationships provide a formal way to reflect the semantics of a concept. In this paper we focus on recording patient’s disorders which are associated with each other, taking into account the role of SNOMED CT. We analyze whether situational descriptions recorded from different clinical perspectives, as in the example above, convey the same meaning and can be used interchangeably when using the data for retrieval and aggregation. The first objective of this paper is to discuss and compare three ways of representation to appropriately record the associations between two disorders as defined in SNOMED CT. The second objective is to get insight into the consistent or inconsistent use of the relationship associated with in SNOMED CT and the consequences for reasoning with concepts that are defined by this relationship.

2. Material and Methods 2.1. Representation of Information in a Patient Record When representing information in a patient record, three possibilities can be distinguished: textual, concept-based, and instance-based representation, i.e., referring to each of the corners of the semiotic triangle [1]. Textual representation refers to information that can only be (humanly) interpreted based on the description of a concept. For example, in SNOMED CT, diabetes mellitus type 1 and type 2 are defined as children of diabetes mellitus, without defining explicitly the difference between the genus (diabetes mellitus) and the species (type 1 and type 2) or among the species. There are various reasons why no explicit definition of the difference is given. It may either be an error of omission (i.e., the difference can be made explicit, but is lacking), or a limitation of the concept model or representation (i.e., no attributes exist in the terminology to adequately describe the difference), or the concept is a so-called natural kind for which no explicit difference can be specified [2]. Concept-based representation refers to information that is explicitly represented as part of a definition of a concept. For example, diabetes mellitus is defined as having a finding site which is an endocrine pancreatic structure. Creating an instance of the concept diabetes mellitus in a patient record does not provide explicit reference to an instance of an endocrine pancreatic structure. Whereas this may not be necessary in most cases, it may be relevant in some others. With concept-based representation it is not possible to make explicit for example whether different disorders refer to the same instance of “endocrine pancreatic structure” or to other instances thereof. Instance-based representation enables to make the above distinction. If a patient with diabetes gets a pancreas transplant, then it may be relevant to distinguish disorders of the original pancreas from disorders related to the transplanted pancreas. This can be realized by creating appropriate instances in the medical record [3]. 2.2. Methods and Analyses The July 2010 release of SNOMED CT was used. In this release, 292,073 active concepts are defined, for which 760,950 English-language preferred and synonymous descriptions are provided. The concepts are defined using a total of 1,210,095 relationships, which are is_a relationships and attribute relationships such as finding site. The concepts, descriptions and relationships have been imported into an

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MSAccess database. SNOMED CT contains 64,162 active disorder concepts, i.e., concepts of which the fully specified name ends with “(disorder)”. We focused on attribute relationships between disorders and specifically on the relationship associated with and its subtypes due to and after. Therefore, we selected all active SNOMED CT disorders including associated with, due to or after in their description (textual representation) to evaluate the adequacy of its concept-based representation, i.e. whether a formal definition describing the association is present, comparable to [4]. Furthermore, queries were created to extract active disorder concepts that are defined with associated with, due to, and after relationships, and to analyze the amount of the types of disorders they interrelate.

3. Results 3.1. Impact Analysis The importance of proper use of terminological systems lies in the possibility of aggregating information at different levels of detail. This can be taxonomic reasoning (DM type II is a disorder of the endocrine system), partonomic reasoning (endocrine pancreatic structure is part of the pancreas) or syndromic reasoning (tetralogy of Fallot involves among others ventricular septum defect, pulmonic valve stenosis). The current modeling in SNOMED CT focuses on supporting these kinds of reasoning, by supporting at least 3 ontological commitments [5]. However, the modeling provides no proper solution for reasoning with associations between disorders. From the example in the introduction, if a neurologist records “diabetic neuropathic arthropathy (201724008)” one can infer that the patient has 3 related disorders: arthropathy, neurological disorder and diabetes mellitus. However, in SNOMED CT it is only a type of arthropathy. Neurological disorder and diabetes mellitus are referenced by means of the associated with relation. Although this makes sense (as arthropathy is not a kind of diabetes mellitus) it hampers reuse of data. For example, selecting patients with some kind of diabetes will not include patients with a diabetic neuropathic arthropathy. The reuse is further hampered by the fact that a single clinical situation is represented by multiple concepts in SNOMED CT that represent different clinical perspectives. Due to the way in which these concepts are modeled in SNOMED CT, they result in different inferences. In the situation described above a diabetologist may record this situation as “Type II diabetes mellitus with neuropathic arthropathy (314904008)”. The definitions of the two concepts (201724008 vs. 314904008) in SNOMED CT are so different that their most specific common ancestor is “Disorder of body system”. In SNOMED CT, this could be resolved by defining concepts so that associated disorders are defined as parents rather than via an associated with attribute. However, this would for example render an arthropathy as a kind of diabetes, rather than a related disorder, which is undesirable. Therefore, a solution needs to be found in the way in which this information is recorded in the EMR. 3.2. Text-Based, Concept-Based, and Instance-Based Representation Clearly, text-based representation is insufficient for reproducible retrieval and aggregation of patient information. The analysis above shows that simple concept-


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based representation also impedes reproducibility. A possible solution for this is to record constituting characteristics of concepts directly into the record, instead of only being represented in the terminology. In the above example, 3 distinct disorders that the patient has should be recorded: − The patient has diabetes mellitus type II − The patient has arthropathy − The patient has disorder of nervous system In addition the associations between the disorders should be recorded: − The disorder of nervous system is associated with the diabetes mellitus type II − The arthropathy is associated with the disorder of nervous system − The arthropathy is associated with the diabetes mellitus type II In this way, the information that the SNOMED CT concept represents is preserved, but presented in a way that is independent of clinical perspective. The perspective is provided when the information is retrieved, i.e., it can be regarded diabetes mellitus type II which has an associated arthropathy, but also as an arthropathy which is associated with diabetes mellitus. Ideally, one does not only record the type of disease, but also explicitly identify it, i.e., create explicit reference to an instance. This instance can be referred to in other clinical situations involving the same disease, e.g. when a patient is later diagnosed with diabetic nephropathy it will refer to the representation of the same instance of diabetes mellitus. 3.3. Textual vs. Concept-Based Representation of Associated Disorders in SNOMED CT In total 2,804 active disorder concepts are described by a fully specified name that contains ‘associated with’, ‘due to’ and/or ‘after’. Of these 35% (n=969) are formally defined by associated with, due to and/or after relations. The targets of these relations are in majority disorders (for 780 concepts) and procedures (for 142 concepts). In total 2,981 unique source disorder concepts and 674 different target disorder concepts are formally interrelated via associated with (n=1,011), due to (n=1,551) and/or after (n=718) relations. The three most frequently used target disorder concepts of the associated with, due to and/or after relations are hypersensitivity reaction, traumatic injury and diabetes mellitus. The overlap between concepts with textual representation and with concept-based representation is only about 26% (n=780). The 2,981 concepts involving association in their definition constitute 4.6% of all active disorder concepts in SNOMED CT.

4. Discussion and Conclusion In this paper we describe the impact of the way in which associations between disorders are modeled in SNOMED CT, and the extent to which such associations are used in SNOMED CT. Over 4.5% of all active disorder concepts in SNOMED CT involve a formally represented association with another disorder, with a small overlap of concepts which have a textual representation of association. This, together with the fact that more and more patients suffer from multiple and interrelated diseases,

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supports the relevance of the subject of this paper. Our study showed that reproducible recording of reusable data is hampered by (incomplete) modeling of associations between disorders in SNOMED CT and by the lack of adequate inferencing procedures. First, many (65%) of all active disorder concepts described by a fully specified name that contains ‘associated with’, ‘due to’ and/or ‘after’ lack a formal relationship describing that association. Although this can in part be explained by the use of other relations representing this association, e.g., causative agent, still a larger part of these associations is not represented formally. Second, we show that multiple SNOMED CT concepts, which cannot be inferred as equivalent, can describe a single clinical situation from a different clinical perspective. A limitation of our study is that the analysis performed is only based on the descriptions and the formal concept definitions containing ‘associated with’, ‘due to’ and/or ‘after’. It reveals that there are other comparable textual descriptions for associations, e.g., ‘complication’, or ‘secondary’, which are not taken into account in this study, and conversely, the textual descriptions may have been represented by other relationships, as pointed out above. Furthermore, in the analysis of the formal definitions, inherited properties may have been disregarded. The proposed representation of associated diseases in the EMR requires further analysis and research. As discussed by Rector in [6], some concepts represent situations rather than disorders. These concepts can be questioned to be relevant in a reference terminology and better fit in an interface terminology. However, concepts that involve associated diseases may represent actual disorders and hence belong to a reference terminology. It should be possible to make the distinction explicit whether concepts represent disorders or situations. The way in which concepts should be stored in the patient record (e.g., concept-based or instance-based) should also be made explicit. This also requires investigation on the binding of information models and terminologies and on the use of advanced logic-based reasoning. Only then we will reach a situation in which data in the EMR can be appropriately aggregated and reused. Acknowledgments: The authors serve as member of the International Health Terminology Standards Development Organisation Technical Committee (RC) and Content Committee (NdK).

References [1] [2] [3] [4] [5] [6]

Campbell KE, Oliver DE, Spackman KA, Shortliffe EH. Representing thoughts, words, and things in the UMLS, J Am Med Inform Assoc. 5(5) (1998), 421-31. Cornet R, Abu-Hanna A. Auditing description-logic-based medical terminological systems by detecting equivalent concept definitions, Int J Med Inform. 77(5) (2008), 336-45. Ceusters W, Smith B. Tracking referents in electronic health records, Stud Health Technol Inform. 116 (2005), 71-76. Mougin F, Bodenreider O, Burgun A. Looking for Anemia (and Other Disorders) in SNOMED CT: Comparison of Three Approaches and Practical Implications, AMIA Annu Symp Proc. 2010:527-31. Schulz S, Cornet R, Spackman KA. Consolidating SNOMED CT’s Ontological Commitment, Applied Ontology 6(1) (2011), 1-11. Rector AL. What’s in a code? Towards a formal account of the relation of ontologies and coding systems, Stud Health Technol Inform. 129(Pt 1) (2007), 730-4.


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SNOMED CT’s RF2: Is the Future Bright? a

Werner CEUSTERSa,1 New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, USA

Abstract. SNOMED CT’s new RF2 format is said to come with features for better configuration management of the SNOMED vocabulary, thereby accommodating evolving requirements without the need for further fundamental change in the foreseeable future. Although the available documentation is not yet convincing enough to support this claim, the newly introduced Model Component hierarchy and associated reference set mechanism seem to hold real promise of being able to deal successfully with a number of ontological issues that have been discussed in the recent literature. Backed up by a study of the old and new format and of the relevant literature and documentation, three recommendations are presented that would free SNOMED CT from use-mention confusions, unclear referencing of real-world entities and uninformative reasons for change in a way that does not force SNOMED CT to take a specific philosophical or ontological position. Keywords. SNOMED CT, RF2, change management, meaning

1. Introduction SNOMED CT is a clinical reference terminology designed to enable electronic clinical decision support, disease screening and enhanced patient safety. It was first released in 2002 following the merger of SNOMED-RT and Clinical Terms Version 3. In 2010, the International Health Terminology Standards Development Organization (IHTSDO) announced the future distribution of SNOMED CT under a new format called 'RF2' [1] of which more detail became officially available with the January 2011 version [2-4]. The RF2 format is claimed to offer greater flexibility and more explicit and comprehensive version control than RF1 with new features for configuration management thereby accommodating evolving requirements without a need for further fundamental change in the foreseeable future [4]. One such feature is that RF2, through the introduction of a new hierarchy called the ‘SNOMED CT Model Component’ [2] which includes the existing Concept Model, allows SNOMED CT to be described in terms of its own structure thereby reducing, so it is hoped, the burden and costs incurred by content developers, implementers and release centers while at the same time improving product functionality and quality. The current documentation of RF2 is marked by a focus on making language- and realm extensions as well as mappings towards other terminologies more manageable. It introduces in addition a number of merely cosmetic changes to the existing history mechanism. But at first sight, it seems also to hold much promises to deal with a number of issues concerning the ontological underpinnings of SNOMED CT that have been reported upon in the literature such as, 1

Corresponding Author: Werner Ceusters. Ontology Research Group, New York State Center of Excellence in Bioinformatics & Life Sciences, University at Buffalo, 701 Ellicott street, Buffalo NY 14204, USA; E-mail: [email protected].

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for example, the underspecification of reasons for change [5], the (in)adequacy of SNOMED’s intensional and extensional definitions [6], its still incoherent ontological commitment [7], and the ambiguities and conflations in its conceptual structures and in its treatment of terms proposed as ‘synonyms’ [8]. The goal of the work reported on here was to assess whether RF2 represents an opportunity to resolve these issues whether immediately or in the foreseeable future.

2. Methods SNOMED CT’s documentation and its Concept Model as reflected in the Linkage Attributes were studied for all releases from January 2002 to July 2010. To assess the evolution of the Concept Model, we generated from the relationship tables included in each version a graph representing the relationships actually used in linking conceptIDs from one hierarchy to conceptIDs from the same or another hierarchy, thereby keeping track in each version of the number of times a specific relation, e.g. ‘USING DEVICE’ was used in relation to the status, e.g. ‘current’, ‘ambiguous’, etc., between specific hierarchies. As an example, the relationship ‘Computerized tomography guided biopsy of brain (procedure) METHOD Biopsy – action (qualifier value)’ in version V would increment the occurrence count of the 5-tuple ‘procedure – (0) METHOD qualifier value – (0)’ for version V where ‘0’ indicates the status ‘current’. For each tuple, 10 examples of relationships for further inspection – specifically those that revealed astonishing results such as ‘substance (2) SAME AS procedure (0)’ – were selected to find commonalities in the underlying causes for error and of assessing to what extent they relate to the issues described in the introduction. Finally, the new Model Component hierarchy was investigated to see whether it could be expanded with additional entries capable of either solving the issues, or if not, making them explicit.

3. Results: Three Recommendations The data upon which our analysis and recommendations are based can be downloaded from [9]. They indicate that many problems can be traced back to underlying causes: (1) a mixing of object and meta-language and use-mention confusions, (2) unclarity about what some conceptIDs exactly denote, and (3) use of ambiguous and uninformative codes for the reasons why concepts are inactivated. Unfortunately, the documentation of RF2 is not yet explanatory enough and lacks clearly worked out examples to assess for each issue identified whether it can be resolved by merely introducing new Model Component entries and associated data types or whether other measures are required as well. Our first – and by far not exhaustive – proposal is therefore formulated in terms of the following three recommendations which experts in RF2 can then implement more adequately in the new format they have designed: 1. do not make double use of the ConceptID as an identifier for the concept and an identifier for the Concept Component; 2. add to each Concept Component a field that indicates to what broad category the intended referent of that concept belongs; 3. expand the Concept Inactivation Value sub-hierarchy with concepts that reference whether a change in SNOMED CT is motivated by (1) a change in


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reality, (2) the SNOMED CT authors’ or users’ understanding of reality as reflected in the advance of the state of the art in the biomedical domain, or (3) a mistake that is strictly internal in SNOMED CT as an information artifact [10].

4. Discussion SNOMED CT is in its Technical Reference Guide described as ‘a concept-based terminology which means that each medical concept is uniquely identified and can have multiple descriptions’. Readers are further told that ‘concepts are related to each other by hierarchical relationships’ and that ‘relationships are also defined to describe additional attributes of concepts’ [11]. Until the January 2010 version, SNOMED CT’s authors defined a concept as ‘a clinical idea to which a unique ConceptId has been assigned ’ thereby further specifying that ‘each Concept is represented by a row in the Concepts Table’ [12]. In 2010, in line with earlier critiques about the ambiguities concept-based systems in general suffer from [13], the glossary of the Technical Reference Guide marks the word ‘Concept’ as ‘an ambiguous term. Depending on the context, it may refer to: a clinical idea to which a unique ConceptId has been assigned; the ConceptId itself, which is the key of the Concepts Table (in this case it is less ambiguous to use the term “concept code”); the real-world referent(s) of the ConceptId, that is, the class of entities in reality which the ConceptId represents (in this case it is less ambiguous to use the term “meaning” or “code meaning”)’ [14]. However, merely pointing this out, however true it might be, does not yet solve the problem. For one could still read in the same document, for example, that a SNOMED CT term is ‘a text string that represents the Concept’. So what is it then that is represented by a term: (1) the clinical idea, (2) less likely, but nevertheless in line with the expressed ambiguity – the ConceptId, or (3) the real-world referent(s)? The same question must then be asked for the several hundred occurrences of the word ‘concept’ throughout the SNOMED CT documentation. In some cases, readers can infer from the context which meaning is intended, but in most cases, only the SNOMED CT authors can provide the answer by rewriting the entire documentation. Unfortunately, as inspection reveals, it is very hard for readers and even for SNOMED CT authors, to disambiguate on the basis of the minimal context provided in sentences in which the word ‘concept’ appears between concept as clinical idea and concept as meaning, i.e. as real-world referent. This is not only because clinical ideas are real-world entities themselves – although of a different nature than, for example, persons, viruses and surgical procedures, and some being such that they are about other real-world entities while others are about nothing at all [8] – but also because SNOMED CT authors have not yet made it clear what sorts of real-world entities their concepts represent: denoting real-world entities unambiguously requires ontological commitment and it has been shown that SNOMED CT is incoherent in this respect [7]. Relying on ‘meaning’ unfortunately doesn’t help much. According to SNOMED CT’s glossary definition for ‘concept’ discussed above, the meaning of a concept(Id) would correspond to what Frege referred to as the ‘Bedeutung’ (‘reference’, ‘extension’) of a term [15]. However, in the User Guide, it is specified that ‘a “concept” is a clinical meaning identified by a unique numeric identifier (ConceptId) that never changes. The concepts are formally defined in terms of their relationships with other concepts. These logical definitions give explicit meaning which a computer

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can process and query on’ [16]. Here, the word ‘meaning’ corresponds rather to Frege’s ‘Sinn’ (‘sense’, ‘intension’) [15]. And finally, in the SNOMED-CT Editorial Guide, a document that became part of the official documentation only since the latest release (although parts of it existed earlier in the form of drafts for comments), SNOMED CT is described as a ‘terminological resource’ which ‘consists of codes representing meanings expressed as terms, with interrelationships between the codes to provide enhanced representation of the meanings’ [17]. As a result, the reader is not only left with the question what sort of meaning is discussed each time the word ‘meaning’ is used – the Editorial Guide is indeed more about ‘meanings’ than ‘concepts’ – but also what actually is represented in SNOMED-CT: (1) clinical ideas – in people’s minds or concretized in writings, software programs and presentations, respectively called L2 and L3-entities in [8], (2) a broader group of real-world referents that includes not only tangible entities such as patients and knives but also the processes in which the latter participate and the forces they undergo, or (3) ‘meanings’. Without a clear answer to these questions, an answer that might be different for each individual occurrence of the word, SNOMED CT users will make interpretations in different ways, thereby rendering their data mutually incompatible. It will be difficult also to grasp, yes, the meaning of statements such as ‘The meaning of a Concept does not change [emphasis added]’, when immediately followed by the sentence ‘If the Concept’s meaning changes because it is found to be ambiguous, redundant or otherwise incorrect, the Concept is made inactive [emphasis added]’ [11]. For the same reason, probably, it has escaped the attention of the SNOMED CT authors that relationships of the sort ‘event MAY BE navigational concept’, ‘person MOVED TO namespace concept’ and, indeed ‘physical object IS A inactive concept’ do not have the same sort of meaning as ‘procedure METHOD physical object’ [9]. The former are statements about the concepts as representational units in SNOMED CT itself (i.e. meta-language statements), while the latter is a statement about the referents of these concepts (an object-language statement). The problem arises because SNOMED CT does not assign, in contrast to entries in the Description and Relationships Table, a separate component ID to an entry in the Concept Table.

5. Conclusion The three recommendations, despite being very modest, address the issues sufficiently. The first solves the object-/metalanguage confusion. The second solves the problem of what sort of entity in each individual case is referenced by a conceptId. Potential values for the proposed field can be based not only on the L1/L2/L3 distinction [8] – roughly: first-order entities that are not about anything (e.g. person, scalpel) / beliefs, desires, intentions whether about something (e.g. a diagnosis) or about nothing (e.g. some psychotic beliefs) / and information artifacts such as staging scales, guidelines, and, indeed, SNOMED CT itself – but also on whether a universal or defined class is referenced [18], and potentially even on the putative ‘possibilia’ and ‘non-existing entities’ [19] endorsed by terminology and ontology developers who do not wish to be hampered by the complexity of Ontological Realism [20]. By doing so, SNOMED CT can even maintain a philosophically rather neutral position even though a clear shift towards OBO Foundry compatibility is observable. And finally, the rather ad hoc motivation for inactivating concepts is catered for by our third recommendation.


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Acknowledgements: The work described was funded in part by grant R21LM009824 from the National Library of Medicine. The content of this paper is solely the responsibility of the author and does not necessarily represent the official views of the NLM or the NIH.


[4]

[5]

[6]

[7]

[8]

[9] [10]

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International Health Terminology Standards Development Organisation. SNOMED Clinical Terms® Technology Preview Guide - January 2010 International Release (US English) 2010. International Health Terminology Standards Development Organisation. SNOMED CT® Release Format 2.0 Reference Set Specifications - Version 1.0a (January 2011 International Release) 2011. International Health Terminology Standards Development Organisation. SNOMED Clinical Terms® Release Format 2.0 Data Structures Specification - Version 1.0a (January 2011 International Release) 2011. International Health Terminology Standards Development Organisation. SNOMED CT® Release Format 2.0 Guide for Updating from RF1 to RF2 - Version1.0a (January 2011 International Release) 2011. Ceusters W, Spackman KA, Smith B, editors. Would SNOMED CT benefit from Realism-Based Ontology Evolution? American Medical Informatics Association 2007 Annual Symposium Proceedings, Biomedical and Health Informatics: From Foundations to Applications to Policy; 2007 November 1014; Chicago IL: American Medical Informatics Association. Mougin F, Bodenreider O, Burgun A. Looking for Anemia (and Other Disorders) in SNOMED CT: Comparison of Three Approaches and Practical Implications. AMIA Annual Symposium Proceedings. 2010:527-31. Schulz S, Cornet R. SNOMED CT's Ontological Commitment. In: Smith B, editor. ICBO: International Conference on Biomedical Ontology. Buffalo NY: National Center for Ontological Research; 2009. p. 55-8. Ceusters W, Smith B. A Unified Framework for Biomedical Terminologies and Ontologies. In: Safran C, Marin H, Reti S, editors. Proceedings of the 13th World Congress on Medical and Health Informatics (Medinfo 2010), Cape Town, South Africa, 12-15 September 2010. Amsterdam: IOS Press; 2010. p. 1050-4. Ceusters W. Additional Data for MIE2011; www.referent-tracking.com/CeustersMIE2011AddData.zip. 2011. Ceusters W. Applying Evolutionary Terminology Auditing to SNOMED CT. American Medical Informatics Association 2010 Annual Symposium (AMIA 2010) Proceedings. Washington DC2010. p. 96-100. International Health Terminology Standards Development Organisation. SNOMED CT® Technical Reference Guide - January 2011 International Release - (US English)2011. The International Health Terminology Standards Development Organisation. SNOMED CT® Technical Reference Guide – July 2009 International Release2009. Smith B. Beyond concepts: ontology as reality representation. Proceedings of the third international conference on formal ontology in information systems. Amsterdam: IOS Press; 2004. p. 73-84. International Health Terminology Standards Development Organisation. SNOMED CT® Technical Reference Guide - July 2010 International Release (US English)2010. Frege G. Über Sinn und Bedeutung. Zeitschrift für Philosophie und philosophische Kritik. 1892;100:25-50. International Health Terminology Standards Development Organisation. SNOMED Clinical Terms® User Guide - January 2011 International Release - (US English) 2011. International Health Terminology Standards Development Organisation. SNOMED CT® Editorial Guide - January 2011 International Release - (US English) 2011. Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology. 2010;5(3-4):139-88. Ceusters W, Elkin P, Smith B. Negative Findings in Electronic Health Records and Biomedical Ontologies: A Realist Approach. International Journal of Medical Informatics. 2007 March;76:326-33. Lord P, Stevens R. Adding a Little Reality to Building Ontologies for Biology. Plos ONE. 2010;5(9):e12258.

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Serious Adverse Event Reporting in a Medical Device Information System Fabrizio PECORAROa and Daniela LUZIa1 Institute for Research on Population and Social Policies (IRPPS), National Research Council, Rome, Italy

a

Abstract. The paper describes the design of a module that manages Serious Adverse Events (SAEs) reporting within a Clinical investigation on Medical devices. This module is integrated in a Medical Device Information System (MEDIS) that collects data and documents exchanged between applicants and the National Competent Authority during the clinical investigation lifecycle. To improve information sharing among different stakeholders and systems MEDIS design and developed were based on the HL7 v.3 standards. The paper provides a conceptual model on SAEs based on HL7 RIM that underlines Medical Device characteristics. Keywords. Medical Device, Clinical Investigation, HL7, Serious Adverse Event

1. Introduction Serious adverse event reporting in Clinical Investigations (CIVs) encompasses an intensive and long-standing interaction between different stakeholders acting in different environments, producing and using different types of data according to specific aims: CIVs’ sponsors, investigators, human research ethics committees, National Competent Authorities (NCAs), clinical trial monitors, patients. National laws, European directives as well as Good medical practice and guidelines provide the legal framework for CIVs on both pharmaceutical products and Medical Devices (MDs) identifying responsibilities of the parties concerned, defining adverse events (AEs) and serious adverse events (SAEs), requiring specific information on severity, causality and action taken to safeguard patient safety. Although there are an increasing number of applications that support CIV management (electronic data capture, patient recruitment, site management, etc.), only a limited number is devoted to SAE reporting and monitoring. They are generally developed to facilitate the communication among investigators within hospitals or networks dealing with many CIVs at a time [1,2]. Usually, SAE reporting still relays on paper-based communication and this is especially true in the MD domain. Moreover, it is worth noting that SAEs on MDs require additional data, compared to those related to investigational drugs: specification whether SAEs depend on device malfunction, failure, misuse and reporting whether subjects others than patients (e.g. operators or caregivers) are involved in SAEs.

1

Corresponding author: Daniela Luzi: Institute for Research on Population and Social Policies (IRPPS), National Research Council, Via Palestro 32, 00185, Rome, Italy; E-mail: [email protected].

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In our vision SAEs reporting should be embedded within NCA’s information systems that support regulatory submission of CIV proposals and monitor the entire lifecycle of CIV performance. In this way SAE reporting could a) benefit from information already available (detailed MD description, clinical protocol, investigational sites, etc.), b) become a landmark to exchange information with other stakeholders, including other NCAs where a CIV takes place, c) enhance the process of analysis and monitoring of similar and/or related SAEs. These advantages can assure patient safety through a timely and widespread diffusion of this information. Moreover, the importance of information sharing in this domain makes it crucial to develop interoperable information systems based on standardized clinical data. The paper describes a Medical Device information system (MEDIS) developed by the National Research Council focusing in particular on the design and development of a module that manages SAE reporting. Taking the above-mentioned requirements into account, MEDIS was designed and developed to interoperate with other systems in particular with other NCA registries and with the European Databank on Medical Device (EUDAMED). For this reason MEDIS design was based on Health Level 7 (HL7) v.3 standards [3]. Paragraphs 2 and 3 describe respectively the main issues concerning SAE reporting and a brief overview of standardization initiatives, while paragraph 4 describes the SAE conceptual model and motivates HL7 adoption providing the related Refined Message Information Model (RMIM).

2. Main Issues in SAE Reporting in Clinical Investigations Risk management is one of the main concerns in the development of MDs that starts in the product’s design phase and is continuously verified by both manufacturers and regulatory authorities when a MD is placed on the market. When a manufacturer proposes a CIV on MD, a detailed risk analysis document is a pre-requisite for a CIV approval as it determines levels of probability as well as degree of severity of identified risks that may occur. Both in CIVs and post market surveillance adverse event reporting and its evaluation represent one of the most important means to test MD efficacy and safety, provided that collected data are comparable. Differently from adverse events reported in the framework of surveillance systems, all adverse events occurred during CIVs are collected in Case Report Forms (CRFs) by the manufacturer enabling their analysis. Only recently can NCAs partially achieve this task thanks to the recent enforcement of MEDDEV 2.7/3 [4] that established a common set of data to be exchanged when a SAE occurs. However, the “cumulative overview” provided by the MEDDEV template based at the most on sending excel forms, together with a limited emphasis on the necessity of establishing automatic procedures to exchange data efficiently risk to reduce SAE reporting to a simple notification. The improvement of electronic methods to detect and diffuse SAE information in an integrated environment can enhance NCA’s role in safeguarding patient safety [5]. Under this perspective the MEDIS system intends to contribute to development of a CIV infrastructure that facilitates data sharing including SAE reporting within the information gathered in a national registry. For these reasons, a specific module of SAE reporting was developed within the MEDIS system supporting 1) applicants in SAEs reporting activity (both initial and final report) providing a set of forms related to the description of SAE (severity, causality, MD tracking information), subjects involved and action taken for each subject; 2) NCA in monitoring SAEs occurred at national

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level, and managing communications (e-mail and other required documents) exchanged during the SAE lifecycle.

3. Standardization Initiatives Although MDs are increasingly used in the daily medical practise, data modelling and standardization are at initial state. Efforts toward interoperability and standardization are carried out mainly by the Clinical Data Interchange Consortium (CDISC) and HL7. However, CDISC is focused on data standardization related to clinical trials on pharmaceutical products. Only recently a SDTM (Study Data Tabulation Model) Device sub-team has been formed with the aim of developing a domain that describes information (properties and characteristics) usable to capture data and metadata collected by manufacturers during CIVs on different types of MDs, such as implantable, imaging and diagnostic MDs [6]. Within HL7 interoperability initiatives some models were released focusing only on particular aspects of this domain [3]. The combination of HL7 methodology and CDISC data model has been used to develop a CIV representation within the BRIDG project (Biomedical Research Integrated Domain Group) [7,8] that however does not address the characteristics of MD domain.

4. Serious Adverse Event Domain Analysis Model (DAM) According to the HL7 v.3 methodology figure 1 depicts the portion of the MEDIS DAM [9] modelling SAE reporting using UML class diagram notation.

Figure 1. Serious Adverse Event Domain Analysis Model.

The Act class Serious Adverse Event describes the main information about SAEs. It is related to one or more Assessment Results that contain the description of the assessment performed by each SAE Evaluator. An evaluator represented by the stereotype Participation can be either the applicant (Applicant Environment) of the CIV

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or the Principal Investigator belonging to the Investigation Centre where the SAE took place. Both of them are represented by the stereotype Role and related to the Entity Organization. Each Report has an Author (Participation) that in MEDIS is a Person (Entity) who has the right to access the system. Finally, a Serious Adverse Event is also described by: the Location (Participation) where the SAE occurred; the Medical Device (Role) that is Deployed (Participation) in the SAE and the Subject (Participation) who experienced the SAE. A Subject is a Person who can be either the Patient who is using the MD or the Care Giver that works with it, represented by the stereotype Role. Each Subject is connected with the class Action Taken that reports the medical procedures to mitigate effects of a SAE. This class is related to the class Report. Compared with SAE information required for pharmaceutical clinical trials, MD domain described by the MEDIS DAM includes additional information as required by MD directives and guidelines related to SAE reporting [4,10]. It adds the notation of different subjects who might be involved in a SAE (patients, users), identification of the causality (SAE related to the investigational device and/or procedure in deploying it), and data on the decisions taken by the NCA that for example might interrupt a CIV and/or asks for further information.

Figure 2. Serious Adverse Event Refined Message Information Model.

The RMIM (fig. 2) derived from the DAM gives grounds of the adoption of HL7 as well as of the application of its conceptual model in the domain of SAE reporting between an applicant of an approved CIV and the relevant NCA. In this context the information that according to regulations identifies whether the adverse is a reportable one, establishes causality and severity grades progressively evaluated by both investigators and applicants, is represented by the process of gathering data related to the event to be notified. The often-criticized ambiguity [11,12] of HL7 definition of the Act class that can be specialised either as an action (such as Observation or Procedure) or as a Structured document (such as ContextStructure and Document) is implicitly disambiguated by the class code attribute as used in the Act Report (classCode=DOC).

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Based on the definition of the Act class (see § 1.3 and 3.1.1.1 of [13]), we consider the Act Report as a container of documented actions. The double interpretation of HL7 Act class is confirmed by the attribute statusCode, that tracks creation, updating and versioning of the document. Moreover, the relationship Participation-Act identifies the responsible actor who is the legal authenticator of the Report. This is a crucial aspect also in the context of registry managed by a NCA that has to identify the attributability (authored and signed) of the information reported [14]. Similar uses of the Act Document have been already balloted in the domain of regulated studies to collect data and audit trail information about the experimental units involved in a clinical study (see Regulated Studies Domain of [3]).

5. Conclusion The paper presents the methodology used to design SAE reporting activities within an NCA information system to submit and monitor CIV on MDs at national level. This allows users to increase consistency in SAE data, reduce time of reporting, track status of SAE lifecycle as well as facilitate the analysis of reported events. The necessity of sharing information among different stakeholders as well as of systems’ interaction led us to choose HL7 v. 3 standards to design MEDIS system. This contributes to improve the use of standards in a relatively new and expanding domain of clinical research. Acknowledgements. This study was supported by the Italian Ministry of Health through the MEDIS project (MdS-CNR collaboration contract n° 1037/2007).

References [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

London JW, Smalley KJ, Conner K, Smith JB. The automation of clinical trial serious adverse event reporting workflow, Clinical Trials 6 (2009), 446-454 Mitchell R, Shah M, Ahmad S, Rogers AS, Ellenberg JH. A unified web-based Query and Notification System (QNS) for subject management, adverse events, regulatory, and IRB components of clinical trials, Clinical Trials 2 (2005), 61-71 Health Level Seven, v3. http://hl7.org/v3ballot/html/welcome/environment/index.htm MEDDEV 2.7/3. Guidelines on Medical Devices – Clinical Investigations: Serious Adverse Event Reporting. December 2010. Murff HJ, Patel VL, Hripcsak G, Bates DW. Detecting adverse events for patient safety research: a review of current methodologies. Journal of Biomedical Informatics 36 (2003), 131-143 Smoak C. CDISC for the Medical Device and Diagnostic Industry: an Update, (2009) Available at: http://www.wuss.org/proceedings09/09WUSSProceedings/papers/cdi/CDI-Smoak.pdf Fridsma BD, Evans J, Hastak S. Mead CN, The BRIDG project: a technical report. JAMIA 15 (2007), 130-137 BRIDG project (Biomedical Research Integrated Domain Group). Available at http://bridgmodel.org/ Luzi D, Pecoraro F, Ricci FL, Mercurio G. A medical device domain analysis model based on HL7 Reference Information Model. In: Proceeding of MIE 2009, IOS Press, (2009) 162-166. ISO 14155. Clinical investigation of medical devices for human subject. Draft version 2011 Vizenor L. Actions in health care organizations: an ontological analysis. In: Proceedings of MEDINFO 2004 11 (2004), 1403-1407. Smith B, Ceusters W. HL7 RIM: An Incoherent Standard. In: Hasman A, Haux R, Lei Jvd, Clercq ED, Roger-France F, eds. In: Proceedings of MIE 2006. Amsterdam, IOS Press, (2006) 133-138. Health Level Seven Reference Information Model, hl7.org/v3ballot/html/infrastructure/rim/rim.html Rector A, Nolan W, Kay S. Foundations for an electronic medical record. Methods of Information in Medicine 30 (1991); 179-186.


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Metadata - an International Standard for Clinical Knowledge Resources a

Gunnar O KLEINa1 Dept of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Sweden

Abstract. This paper describes a new European and International standard, ISO 13119 Health informatics – Clinical knowledge resources - Metadata that is intended for both health professionals and patients/citizens. This standard aims to facilitate two issues: 1) How to find relevant documents that are appropriate for the reader and situation and 2) How to ensure that the found knowledge documents have a sufficient or at least declared quality management? Example of use is provided from the European Centre for Disease Control and Prevention. Keywords. metadata, decision support systems, clinical knowledge resource, standard, ISO 13119

1. Introduction The internet is rapidly changing the way we access medical knowledge. Health professionals use web based knowledge sources and digital documents are provided from databases and via e-mail. Also the patients/citizens turn to the internet for advice. The European Commission has published a set of quality criteria for health related websites [1] as one way of establishing trust in web resources. A trust-mark indicating a “minimum” level of trustworthiness requires: • a set of quality requirements. This might be very difficult to agree on as relevant for all contexts. • third party control by governmental bodies or professional associations of all possible documents to receive the mark. • reliance on a self-declaration by the issuer in which case the user of the information has no real guarantee that the criteria are met even if the mark is there. • Instead of reviewing the actual content of the medical knowledge resources, we can define processes behind their development. Health authorities in many countries and in co-operation with the Commission have considered the possible requirement for legislation and control procedures, but generally the conclusions have been that rather than trying to ban bad quality information, one should facilitate for the citizens as well as for the health professionals to find the type of information they request where quality criteria behind a knowledge resource are easily accessible. One feasible and important approach is to establish a set of metadata for each knowledge resource to describe the content and the procedures behind its production. 1

Corresponding author. Gunnar Klein, 177 77 Stockholm, Sweden, E-mail: [email protected]

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G.O. Klein / Metadata – An International Standard for Clinical Knowledge Resources

In this paper the development of a standardised set of metadata is described. The following issues are addressed: What are the possible uses of a standardised set and What are the basic principles of the new standard in the field?

2. Materials and Methods This study is based on the work of the European and International standards organisations during the years 2000-2010. This started with a literature review on the use metadata for various health care purposes and the general development of metadata for intersector use, the Dublin Core and various initiatives to propose metadata especially for clinical guidelines. The development of the first draft and discussions of the standardisation working groups was followed by extensive and repeated international review and comments with suggestions for improvements from many nations. The author was the project leader of the standardization project started in CEN that led to the publication of the CEN/TS 15699:2009 [2] and further enhanced in ISO to the ISO 13119 [3] also to be published as a European standard.

3. Results 3.1. The Scope of the Metadata Standard This standard defines a number of metadata elements that describe documents containing medical knowledge, primarily digital documents provided as web resources, accessible from databases or via file transfer, but can be applicable also to paper documents, e.g. articles in the medical literature. It is based on the ISO 15836:2009 Information and documentation- Metadata – The Dublin Core metadata element set [4]. The metadata should: • support unambiguous and international understanding of important aspects to describe a document, e.g. purpose, issuer, intended audience, legal status and scientific background • be applicable to different kinds of digital documents e.g. recommendation from consensus of a professional group, regulation by a governmental authority, clinical trial protocol from a pharmaceutical company, scientific manuscript from a research group, advice to patients with a specific disease, review article • be possible to present to human readers including health professionals as well as citizens/patients • be potentially usable for automatic processing e.g. to support search engines to restrict matches to documents of a certain type or quality level 3.2. Characteristics of the Metadata Element Set In the element descriptions below, each element has a descriptive label intended to convey a common semantic understanding of the element, as well as a unique, machine- understandable, single-word name intended to make the syntactic specification of elements simpler for encoding schemes.


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Each element is optional and repeatable. Metadata elements may appear in any order. The ordering of multiple occurrences of the same element (e.g. Creator) may have a significance intended by the provider, but ordering is not guaranteed to be preserved in every system. To promote global interoperability, a number of the element descriptions suggest a controlled vocabulary for the respective element values. The Dublin Core set assumes that different domains develop where necessary controlled vocabularies as specialisations of the content of the general purpose metadata element set and adding other metadata elements as required. This standard is such a specialisation for the medical knowledge domain. 3.3. Metadata Groups The metadata elements are grouped purely for human navigational purposes: • Resource form • Intended use • Subject and Scope • Identification and source • Quality control The total number of metadata element tags in this standard is 150. 3.4. Examples of Specialisations In some areas the standard contains an enumerated list of specialisations to be used for the content under some metadata elements. 3.4.1. Healthcare Specialization for Type One example is for the element Type defined by Dublin Core: Definition: Nature or genre of the content of the resource. The following Types are from the Dublin Core 2009: Text, MovingImage, StillImage, Sound, Dataset, InteractiveResource, Software, Device. Table 1: The following terms may be used to describe Type.Text in health care:

Journal_article Book_chapter Book Report Abstract Patient_information FAQ Algorithm Clinical guideline Policy-strategy Information_standard

Teaching_material Computable clinical information model Terminological_resource Metainformation Case_report Proposal Event Service_description Product_information Critically_appraised_topic

Known_uncertainty Observational_study Qualitative_study Randomised_controlledtrial Research_study Review Systematic_review Structured_abstract Care_pathway

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3.4.2. Example of Healthcare Specialization for Situation This defined as: Description of the situation where the knowledge is intended to be used (HC). This can also be understood as the intended role of the knowledge resource. Healthcare specific specialisation: • Clinical_guidance • Self_guidance • Supporting_software • Research_protocol • Background_knowledge 3.5. Overview of the Metadata Classes Figure 1 shows an overview of the classes.

Figure 1. Overview of the Metadata elements for Clinical Knowledge Resources.

3.6. A First User of the New Standard - ECDC The European Centre for Disease Control and Prevention, ECDC, which is a rather new European Union agency that is mainly active in the surveillance and prevention of communicable disease has an ambitious programme for knowledge management that shall serve not only its internal staff an specially commissioned experts but also the member states of the European union with their national agencies for control of communicable diseases. This organisation was looking for their own implementation of the Dublin Core metadata standard when they were approached and studied the new standard already at the draft stage. They have now implemented its use as a routine part of their work together with other strategies for knowledge retrieval in an Enterprise wide search system.

4. Discussion A system of metadata tags, the names of the elements can have many different uses. The first uses have been within larger organisations that have a need to ensure that their documents, the most common form of a knowledge resource, can be found using automatic retrieval methods. If metadata are assigned in a consistent and well


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structured way to each document, this can ensure complete retrieval of all relevant documents meeting the search profile whereas various other ways indexed or not using the core content of a document without any metadata can usually not ensure that all relevant documents are found. The other major feature of a good metadata based system is to exclude irrelevant documents because of a much more specific search profile. With the explosive growth of various document resources, this becomes more and more important. It is something a clinician is frequently experiencing searching knowledge on various dedicated medical knowledge sites. It is of course also a common problem for the general public using general purpose search engines as Google on the general world wide web. It should be emphasized that there is no requirement in the standard to use all of the metadata elements available. This is a set of optional elements and typically a publisher of a type of knowledge resource only uses a small subset. If required it is possible to extend the set by additional elements. For some elements there is very detailed guidance provided where there was some good justification to propose details. In other areas the users will have to develop their own guidance documents if consistency is to be achieved. There is also another use of metadata that is not related to retrieval but for the user of the resource to be able to understand what the resource is, its intended use, source and possibly quality control. The latter is achieved largely through reference to the Grade system for clinical guideline documents, which is also acknowledged by the WHO [5]. Acknowledgments: This study was supported by the European Union Network of Excellence: Semantic Mining. During the first years of this work the author was co-operating with Dr Anders Thurin, Göteborg, Sweden. His important contributions are gratefully acknowledged together with many other experts of CEN/TC 251/WG 2 and ISO/TC 215/WG 3.

References [1] [2] [3] [4] [5]

The European Commission, COM(2002) 667, eEurope 2002: Quality Criteria for Health related Websites. CEN/TS 15699:2009. Health informatics – Clinical knowledge resources – Metadata. The European Committee for Standardization, Brussels, (2009). ISO/DIS 13119:2011. Health informatics – Clinical knowledge resources – Metadata. International Organization for Standardization, Geneva, (2011). ISO 15836:2009 Information and documentation- Metadata – The Dublin Core metadata element set. International Organization for Standardization, Geneva, (2009). H.J. Schünemann, A. Fretheim, A.D. Oxman. Improving the use of research evidence in guideline development: 9. Grading evidence and recommendations. Health Research Policy and Systems 4 (2006), 21.

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Comparing Existing National and International Classification Systems of Surgical Procedures with the CEN/ISO 1828 Ontology Framework Standard Jean M. RODRIGUESa,b,c,1, Ann CASEYd , Cédric BOUSQUETa,b, Anand KUMARa, Pierre LEWALLEa, Béatrice TROMBERT PAVIOTa,b. a University of Saint Etienne, CHU, Department of public health and medical informatics, Saint Etienne, France b INSERM UMR 872 Eq 20, Paris, France c WHO collaborating center for International Classifications in French Language, Paris, France d Royal College of Nursing of United Kingdom, London, England

Abstract: Among different standardization strategies for biomedical terminologies the European Standard Body CEN TC 251 followed by ISO TC 215 have stated that it was not possible to convince the different European or international member states using different national languages to agree on a reference clinical terminology or to standardize a detailed language independent biomedical ontology. Since 1990 they have developed since an approach named the Categorial Structure that standardises only the terminologies’ model structure. The methodology for the Categorial Structure development and a comparison of the different existing classification systems based on this ontology framework is presented as a step towards increased interoperability between biomedical terminologies through conformity to a minimum set of ontological requirements. Keywords: Standard; Biomedical terminology; Categorial Structure; Ontology;

1. Introduction There is a growing need to compare data produced at national and international levels on a range of shared concerns relating, for instance to population based indicators, Electronic Health Record safety, OECD (Organisation of Economic Cooperation and Development), trans border migration of population, case mix and procedure payment . Clinical terminology systems, classifications and coding systems that are drawn upon to that end have unfortunately been developed using independent, divergent or uncoordinated approaches which have produced non reusable systems with overlapping fields for different requirements. For some decades, several broad pre-coordinated or compositional systems have been proposed to users targeting different goals for example UMLS (Unified Medical Language System) [1], LOINC [2] for clinical 1

Corresponding author: JM Rodrigues, CHU de St Etienne, SSPIM, Chemin de la Marandière, 42 270 Saint Priest en Jarez, France, E-mail : [email protected]

J.M. Rodrigues et al. / Comparing Existing National and International Classification Systems

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laboratories, DICOM SDM [3] for imaging or SNOMED CT [4]. At the same time most of developed countries have continued to maintain, update and modify their own coding systems for procedures and their national adaptations of ICD, in order to manage and to fund their health care delivery. Significant efforts have been made for example in Australia with ACHI (Australian Classification of Health Interventions) and ICD10 AM [5], in Canada with the Canadian Classification of Health Interventions (CCI) [6] developed by the Canadian Institute for Health Information (CIHI) and in France with CCAM (Classification Commune des Actes Médicaux) [7]. Standardisation in health informatics started in the US with the HL7 user group. The European Standard Body CEN TC 251 WG2 (Comité Européen de Normalisation Technical Committee 251 Working Group 2) and later the International Organisation for Standardization (ISO) TC 215 WG3 elaborated and developed a standard approach for biomedical terminology named Categorial Structure. We outline that ontology framework and the latest standard on terminologies of surgical procedures currently pending final approval [8] in section 2 below: in section 3 we compare major national and international classifications of surgical procedures in the light of that standard. Finally we discuss the role that standard could play not only to support the comparison of classifications and coding systems of surgical procedures but, to facilitate their harmonization towards a more complete semantic interoperability based on a shared biomedical ontology.

2. CEN/ISO Categorial Structure Standard Approach 2.1. Definition The CEN Categorial Structure was defined within some linguistic variations [9], as a minimal set of health care domain constraints to represent a biomedical terminology in a precise health care domain with a precise goal to communicate safely. It is a definition of a minimal semantic structure or ontology framework describing the main properties of the different artefacts used as terminology (controlled vocabularies, nomenclatures, reference terminologies, coding systems and classifications): a model of knowledge restricted to; 1) a list of semantic categories; 2) the goal of the Categorial Structure; 3) the list of semantic links between semantic categories authorised with their associated semantic categories; 4) the minimal constraints allowing the generation and the validation of well formed terminological phrases. Any biomedical terminology artefact claiming conformance to the standard shall attach with the data sent the Categorial Structure of the terminology used. The Categorial Structure shall satisfy the four constraints but can add more constraints. 2.2. Specifications for Terminologies of Surgical Procedures 1. 2.

The main semantic categories are Human Anatomy, Deed, Interventional Equipment and Lesion . The semantic links are has_object, has_site, has_sub_surgicaldeed, has_means 2.1. has_object is authorised between Deed and Human anatomy or Interventional Equipment or Lesion

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3.

2.2. has_site is authorised between Interventional equipment or Lesion and Human anatomy 2.3. has_means is authorised between Deed and Human anatomy, Interventional equipment or Lesion 2.4. has_sub_surgicaldeed is authorised between Deed and Deed The minimal constraints required 3.1. A Deed and has_object shall be present 3.2. Human anatomy shall always be present either with a has_object or with a has_site 3.3. Use of Lesion shall be restricted to macroscopic lesion and to cases where it allows to differentiate the procedure from procedures using the same deed and the same human anatomy; 3.4. When has_sub_surgicaldeed is used the Deed on the right side of the semantic link must be conform to the rules 3.1, 3.2 and 3.3.

3. Comparison of Existing Classification Systems of Surgical Procedures One goal of the standard is to support comparisons between existing classification systems of surgical procedures. During the development of the standard, new national and international surgical procedures classification systems were developed, some claimed conformance with the European standard initially specified in 1995. The comparison of their conformance to the standard was undertaken firstly to assess whether or not the most advanced systems met or nearly met the requirements of the standard and to identify whether at the level of the ontology framework, the various systems are as different as they appear to be in their terminology part. The systems were mapped to the standard specifications described above by a Task Force of TC 215 WG 3 and reviewed by each organisation or country experts. At the international level the selected systems were IHTSDO SNOMED CT (procedures only) [10] and WHOFIC ICHI [11]. Five national existing systems were selected: Australia ACHI [12], Canada CCI [13], France CCAM [14], Japan Surgical Society procedures codes [15] and USA ICD 10 PCS [16].

4. Discussion Table 1 show that the selected international and national classifications or terminology systems of surgical procedures are based on the semantic categories of the standard with some restrictions for the category Lesion. This is characterized only by SNOMED CT and the Japan Surgical Society system although the other systems may use some Lesion value sets without specifying a semantic category. For the semantic links all the studied systems use has_object but only 4 out of 7 (SNOMED CT, ICHI, CCAM and the Japan Surgical Society) are based on all the semantic links. Only SNOMED CT and ICHI explicitly define the list of domain constraints. None of the systems prescribe the list of minimal domain constraints. From this comparison it can be said that the most recently developed international and national terminologies and classification systems of surgical procedures are based on the CEN/ISO 1828 standard semantic categories. Only 4 out of 7 are based on all


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the specified semantic links and only 2 out of 7 explicitly prescribe the list of domain constraints with none prescribing a minimal list. On the path to increasing semantic interoperability to level 2 (understanding the terms with the meaning of the sender) conformance to the EN/ISO 1828 ontology framework standard is an opportunity which has started to be used by the most advanced classification systems [17] and the international ICHI initiative. This use will be completed by explicitly associating the Categorial Structure ontology framework to biomedical terminologies exchanges protocols. This step will ease the development of a full shared biomedical ontology based on an upper level ontology needed to reach the level 3 of semantic interoperability when the receiver or final user can process the data as safely as he can do with his own terms and meaning. Acknowledgements. We wish to thank our partners in the CEN TC251 WG2 and ISO TC 215 WG 3 and the convenors Hendrik Olesen, Ann Harding, Magnus Fogelberg, Chris Chute and Heather Grain and terminology and biomedical ontology experts namely Alan Rector from University of Manchester and Barry Smith from the University at Buffalo. Special thanks to our partners in the GALEN program and namely Pieter Zanstra, Egbert van der Haring, Robert Baud, Jeremy Rogers.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

[10] [11] [12] [13] [14] [15] [16] [17]

McCray AT, Nelson SJ. The representation of meaning in the UMLS. Methods Inf Med 1995;34(12):193-201. Logical Observation Identifiers Names and Codes(LOINC). See :http://www.loinc.org/ DICOM see http://www.xray.hmc.psu.edu/dicom/dicom_home.html SNOMED Clinical Terms. College of American Pathologists.see http://www.snomed.org/ National Centre for Classification in Health see http://www3.fhs.usyd.edu.au Canadian Classification of Health Interventions http://secure.cihi.ca/cihiweb/dispPage.jsp Agence Technique de l’Information Hospitalière see http://www.sante.atih.gov.fr prEN ISO 1828. Health informatics – Categorial Structure for classifications and coding systems of surgical procedures. Rodrigues J-M, Kumar A, Bousquet C, Trombert B. Standards and Biomedical Terminologies: The CEN TC 251 and ISO TC 215 Categorial Structures. A Step towards increased interoperability. In:S K Andersen et al. (Eds.) MIE 2008 Proc. IOS Press, 2008; pp. 735-740. Snomed CT see http://www.nlm.nih.gov/research/umls/Snomed/snomed_main.html ICHI project plan version 2.2 .R Madden .In Proc WHO-FIC Annual meeting Toronto 16-22 Octobre 2010 Australian Classification of Health Interventions (ACHI) see: www.ncch.com.au Canadian Classification of Health Interventions (CCI) see: http://secure.cihi.ca/cihiweb/dispPage.jsp ?cw_page=codingclass_cciover_e French Classification des Actes Medicaux (CCAM) see: http://www.ameli.fr/fileadmin/user_upload/ documents/CCAMV23.pdf Japan Surgical Society Procedure codes Procedure Coding System (USA) (PCS): see: www.cms.hhs.gov/ICD9ProviderDiagnosticCodes/ 08_ICD10.asp Rodrigues J-M, Rector A, Zanstra P, et al. An ontology driven collaborative development for biomedical terminologies: from the French CCAM to the Australian ICHI coding system. Stud Health Technol Inform. 2006;124:863-8.

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APPENDIX Table 1. Comparison of selected international and national classifications or terminology systems of surgical procedures using ENISO1828 standard

ENISO 1828 Categorial SNOMED CT structure Category Deed Category Human Anatomy Category Lesion Category Interventional Equipment

Method action

Procedure site direct Direct morphology Direct device Procedure site Semantic link direct hasSite Procedure site indirect Using device Using access device Semantic link hasMeans Indirect morphology Indirect device Semantic link Access, HasSubApproach surgicalDeed List of domain Yes constraints List of minimal No domain constraints Semantic link hasObject

ICHI

CCAM

CCI

ACHI

action

Action axis 2 and 3

Field 3

Axis 3

Axis 1 and 3

Acts

Field 2

Axis 1

Axis 2 and 4

Organ/area

-

-

-

Lesion

Field 5

-

Axis 6

Instruments or device

Anatomical structure Target Body Anatomical (body structure site axis 1 structure) Morphological abnormal structure Device

Japan Surgical Society Procedure Code

ICD10 PCS

Device

Axis4 technique

hasObject

hasObject

hasSite

hasSite

hasSite

-

hasSite

Secondary organ/area

hasMeans

hasMeans

-

-

-

Approaching method/ device

-

Acces, Approach

-

-

-

Sequence of acts

Yes

No

No

No

No

No

No

No

No

No

No

No

hasObject hasObject hasObject

Target


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Model Driven Development of Clinical Information Sytems using openEHR Koray ATALAG a,1, Hong Yul YANG a, Ewan TEMPERO a, Jim WARREN a,b a Department of Computer Science, bNational Institute for Health Innovation The University of Auckland, Auckland, New Zealand

Abstract. openEHR and the recent international standard (ISO 13606) defined a model driven software development methodology for health information systems. However there is little evidence in the literature describing implementation; especially for desktop clinical applications. This paper presents an implementation pathway using .Net/C# technology for Microsoft Windows desktop platforms. An endoscopy reporting application driven by openEHR Archetypes and Templates has been developed. A set of novel GUI directives has been defined and presented which guides the automatic graphical user interface generator to render widgets properly. We also reveal the development steps and important design decisions; from modelling to the final software product. This might provide guidance for other developers and form evidence required for the adoption of these standards for vendors and national programs alike. Keywords. EHR, HIS, openEHR, interoperability, GUI.

1. Introduction In this paper, we present the development methodology of a .Net/C# desktop application (GastrOS) for endoscopy reporting which is driven by openEHR models. The main drivers of such a model driven software development are: 1. Transfer of domain knowledge from healthcare professionals into software is ineffective using traditional development process where technical professionals need to capture and transform this into code. Put simply, software can be as good as this hand-over [1]. Two-level modelling technique in openEHR, essentially a model driven approach, allows clinicians to engineer knowledge using high-level tools which is then fed into the technical environment and consumed readily. This ensures the requirements are correct, complete and collected in a timely fashion. 2. The main challenge in achieving semantic interoperability lies in the nontechnical domain and has to do with establishing common language, sharing data set definitions and creating computable information and knowledge artefacts [2]. openEHR defines methods and processes which meets these requirements. 3. The main determinant of software cost is the maintenance phase [3]. Healthcare is no exception. Redevelopment due to modifications includes 1

Corresponding Author: Koray Atalag, MD, PhD. Department of Computer Science, The University of Auckland, Private Bag 92019 Auckland 1142, New Zealand; E-mail: [email protected].

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redesign, coding, testing and deployment, which is very costly. Therefore being able to introduce these changes by modelling without redevelopment is very tempting and can potentially reduce the total cost of health information systems (HIS) significantly. We have selected digestive endoscopy as the clinical domain which is a niche area with excellent standardisation of domain content. The Minimal Standard Terminology for Digestive Endoscopy (MST) contains a "minimal" list of terms and structure which is used to record the results of an endoscopic examination [4]. It provides a simple and uniform hierarchy for data entry which allows for consistent and intuitive generation of graphical user interfaces (GUI) automatically.

2. Methods openEHR formalism effectively separates domain knowledge from software code using domain specific models called Archetypes. This is commonly known as Two-Level Modelling. Archetypes (top level) represent clinically meaningful concepts such as blood pressure measurement. They use common technical building blocks expressed in Reference Model (RM) (lower level). In the runtime software are driven by these models for dynamic GUI creation, data binding, validation and querying [1]. Thus altering software after deployment mainly involves remodelling by domain experts without the need for another redevelopment cycle. RM consists of a small set of technical models which depicts the generic characteristics of health records (e.g. data structures and types) and context information to meet ethical, medico-legal and provenance requirements. In GastrOS RM entities usually correspond to individual GUI widgets. Archetypes provide the semantics and structure of domain concepts. They constrain RM building-blocks and form a computer processable model. Practically they specify particular record entry names, data structures, data types, value sets and default values. It is also possible to link each data item to biomedical terminologies. openEHR Templates bring together relevant Archetypes to define higher level models such as a discharge summary. Tighter constraints can be put on Archetypes (e.g. exclude some data items and values or renaming them). During implementation Templates are serialised into operational templates which contain all the structure and data items in included archetypes. 2.1. Modelling A number of openEHR Archetypes have been created using the free and open source (FOSS) openEHR Archetype Editor. The following sections from MST are included: • Examination Information: consists of reasons for endoscopy, examination characteristics and complications. • Endoscopic Findings: for each organ using MST hierarchy, terms, attributes, attribute values and anatomical sites. • Interventions: diagnostic and therapeutic procedures performed. • Diagnoses: list of diagnoses for each organ. These sections are then filled with appropriate entry archetypes which further chain a myriad of structural archetypes carrying bulk of the MST content. Finally openEHR templates have been created using the Ocean Template Designer for each of the three examination types: upper and lower gastrointestinal endoscopy and ERCP.


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2.2. Implementation GastrOS has been developed using the .Net platform and C# programming language. We have used MS Visual Studio 2008 IDE. The C# openEHR library2 (openEHR.Net) has been included in the project which implements the 1.0.1 release of the openEHR RM and Archetype Object Model (AOM) specifications. It is used to build applications by composing RM objects, validate against AOM and serialise to/from XML [5]. GastrOS architecturally consists of a simple wrapper application which is used for patient management, and the model-driven structured data entry (SDE) component. SDE takes in an operational template and dynamically creates appropriate GUI forms. This component has the additional capability of validating and persisting data. SDE follows the model-view-controller (MVC) paradigm; such that the user interaction and presentation logic is completely independent of the logic for handling and persistence. SDE first parses the input operational template into a tree-like data structure which consists of archetype objects conforming to AOM. Each archetype object acts as a blueprint for a specific part of the data to be entered and stored, as well as sets its GUI widget. SDE defines a set of mapping rules to determine what kind of GUI widget to create for what kinds of data elements. For example it would create a text box for a textual entry (e.g. name of a drug), a drop-down list for a restricted range of values (e.g. organ types), or a panel for a composite value that further contains sub-values (e.g. a list of diagnoses). These rules, which are fairly generic so as to accommodate as wide a range of clinical domains as possible, are combined with the novel GUI directives to finely adjust the aesthetics and visual behaviour of the GUI. Currently there is a hot debate about these directives as the openEHR formalism does not provide any means to handle presentation of information. It is generally accepted that this should be modelled as a different layer along with the archetypes and templates. So far studies in this area have been very scarce in the literature [6,7]. We have taken a more practical approach and exploited the annotations property in openEHR Templates, which can be defined for any data item at a distinct path in any included archetype. A skeleton data instance, which we call as value instance to hold the user entered data, is created initially by the GUI generator at once which comprises only the top level hierarchy and mandatory items depicted by the RM. Then, the GUI generator recursively creates the associated widgets on the form. Each widget, representing a leaf node data item, instantiates its own value instance and then binds to the skeleton. By this way, an exact representation of the AOM is formed. During data entry, if the user wants to create additional instances of certain data elements where multiplicity is allowed, additional data instances are appended to the skeleton. When the user decides to save, parts of the value instance which correspond to the empty widgets are first pruned and then serialised into XML and persisted saved in a relational database (both MS Access and SQLite). When a value is cleared (set to null) after data have been committed, that part of the value instance is removed from the value instance.

3. Results Table 1 shows some of the pertinent GUI Directives defined by our group which appear in Figure 2. For the full list please refer to the project website [8]. 2

openEHR.Net has initially been developed by Ocean Informatics Pty. Ltd. and then extended by our team.

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Table 1. Pertinent GUI Directives defined and used in GastrOS.

isOrganiser: when this is set item will be displayed as a group (e.g.within a frame, form etc.) which will contain all its children. The Heading items in MST, such as NORMAL, LUMEN, STENOSIS etc., have this directive set causing them to be displayed in groups within a frame. Any container item will simply be ignored when isOrganiser is not set and will be grouped under the first isOrganiser parent (if any). This simplifies working with highly nested clinical models. isCoreConcept: We assume that Core Concepts are real-world entities which we can talk about their absence. For example a clinical finding (tumour, bleeding etc.) can be reported as present but also as absent or unknown. However it doesn’t make sense to report absence of tumour grade or physical examination. This directive depicts that an item with all its children (if any) will be handled and repeated as a whole on the GUI and saved data. When data are saved it wouldn’t make sense to repeat attributes of a clinical finding defining its nature. For example in Figure 2 when Stenosis term is selected as a finding it should not have more than one Appearance attribute because the values might be mutually exclusive or potentially contraindicate with other selected attributes. Rather, the Core Concept as a group should repeat with a different set of attributes and values. An exception is the anatomical sites; in most cases more than one site will be involved. When data are saved, for each core concept only one attribute can be expected and one or more anatomical sites. The example below illustrates a case with a repeating attribute where values are mutually exclusive and should not be permitted (second): <Stenosis; Appearance=Extrinsic, Traversed=Yes, Sites=Cardia,Fundus,Incisura> <Stenosis; Appearance=Extrinsic, Traversed=Yes, Traversed=No, Sites=Cardia,Fundus,Incisura> showAs (form|splash, modal|modeless|smart): this determines the behaviour when an item’s values or children are displayed. The item's label will be shown as a reference (e.g. link, button or similar) and the contents will be shown on another page, a separate form (form) or a pop-up screen (splash). (smart) parameter causes to create a modeless form which closes when loses focus which saves one click during data entry.

Figure 1. Sample GUI form with associated GUI directives.


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4. Discussion and Conclusion The preliminary results of our larger study indicated that the openEHR based application, on the average, took nine times less time and were seven times less complex to implement; thereby making it significantly more maintainable [9]. Considering the paramount contribution of maintenance phase to total software cost (approximately 70-80%) this may translate into significant cost savings [3]. Since endoscopy domain is a narrow domain it can be argued that generalisability of our results will be limited. However as we experiment with other domains, such as anatomical pathology, our initial impression is that the GUI Directives may be applicable beyond endoscopy. However current work to extend GastrOS model to include generic archetypes such as Blood Pressure and Adverse Reactions revealed that further additions to the GUI Directives presented in this study are required; therefore more work is needed. With regard to software usability, since the appearance and behaviour is depicted rather mechanically, good usability principles can be embedded into program logic and may result in more consistent GUI. GastrOS source code, models and documentation have been published on Codeplex (http://gastros.codeplex.com) as FOSS software to enable wider dissemination of research results and also to foster collaboration [8]. In conclusion, we believe this study will help materialise how the model driven methodology, brought about by openEHR, works and bridges the gap between modelling and software development. Another important premise is the potential for enabling a high level of semantic interoperability among different HIS which is particularly important in developed jurisdictions, such as Europe, where this is not only desirable but essential. Acknowledgments: This work was supported by a research grant from the University of Auckland (Project No: 3624469/9843).

References [1]

[2] [3] [4]

[5] [6] [7]

[8] [9]

Beale T. Archetypes: Constraint-based domain models for future-proof information systems. In: Eleventh OOPSLA Workshop on Behavioral Semantics: Serving the Customer. Seattle, Washington, USA: Northeastern University; 2002. p. 16-32. ISO TR 20514 - Electronic Health Record Definition, Scope and Context. ISO; 2005. Sommerville I. Software Engineering. 6th ed. Addison Wesley; 2000. Delvaux M, Korman L, Armengol-Miro J, Crespi M, Cass O, Hagenmüller F, Zwiebel F. The minimal standard terminology for digestive endoscopy: introduction to structured reporting. International Journal of Medical Informatics 1998 Feb;48(1-3):217-225. openEHR.Net Programming Library. Available from http://openehr.codeplex.com Schuler T, Garde S, Heard S, Beale T. Towards automatically generating graphical user interfaces from openEHR archetypes. Stud Health Technol Inform 2006;124:221-6. van der Linden H, Austin T, Talmon J. Generic screen representations for future-proof systems, is it possible? There is more to a GUI than meets the eye. Comput Methods Programs Biomed 2009 Sep;95 (3):213-226. GastrOS Endoscopy Application Project. Available from: http://gastros.codeplex.com Atalag K, Yang HY, Warren J. On the maintainability of openEHR based health information systems – an evaluation study in endoscopy. In: Proceedings of the 18th Annual Health Informatics Conference, HIC 2010. Melbourne, Australia: HISA; 2010 p. 1-5.


Translational Research



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A Metadata-Based Patient Register for Cooperative Clinical Research: A Case Study in Acute Myeloid Leukemia a

Anja S. FISCHERa,1, Ulrich MANSMANN a Institute for Medical Informatics, Biometry and Epidemiology (IBE), LudwigMaximilians-University Munich, Germany

Abstract. In many medical indications clinical research is organized within study groups which provide and maintain the clinical infrastructure for their randomized clinical trials. Each group also manages a data center where high quality databases store the study specific individual patient data. Sharing this data between study groups is not straightforward. Therefore, a concept is needed which allows to represent a detailed overview on the information available across the cooperating groups. We propose a metadata based patient register and describe a first prototype. It provides information about available patient data sets to interested research partners while the typical register approach only collects a predefined limited core data set. This register implementation enables cooperative groups to allocate clinical data for future research projects in distributed data sources beyond the restrictions of core data sets. Additionally, it supports the research network in communication and data standardization and complies with a governance structure which is compatible with ethical aspects, privacy protection, and patient rights. Keywords. metadata, patient register, CDISC ODM, data integration, networked clinical research

1. Introduction Academic clinical research is organized by study groups which provide and maintain the infrastructure to run large randomized clinical trials. Typically, there a several national or international study groups working on the same medical indication. Each study group also manages a data center which performs the data management for ongoing studies but also manages large databases from completed clinical trials. Warehouse techniques can be used within those data center to explore relevant clinical information across different studies of the study group. Relevant issues which need well documented patient data are for example: meta analyses, prognostic factor research, biomarker research, subgroup analyses, simulation of future trials, health economic research, or determination of surrogate endpoints. Since those activities are mostly from exploratory character, one also needs extensive data sets to validate findings of interest. Often, data repositories of single study groups are not large enough to manage exploration and validation of specific clinical aspects. This is an incentive to 1

Corresponding author: Anja S. Fischer, Institute for Medical Informatics, Biometry and Epidemiology (IBE), Marchioninistr. 15, 81377 Muenchen, Germany; E-mail: [email protected]. This work was supported by the German José Carreras Leukaemia Foundation (DJCLS H06/04V).

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A.S. Fischer and U. Mansmann / A Metadata-Based Patient Register

establish an infrastructure for cooperation between academic study groups with clinical research in a specific indication. Since the research questions in cooperative clinical research are quite broad, it is not helpful to establish a classical patient registry between the cooperating study groups which contains a uniform core data set restricting the question of interest. Whereas many different definitions of patient registers exist [1-6] and various implementations of this concept are found [7, 8], the uniform standardized data set of every patient is common to all of them. Its size can vary; a survey on 14 German disease registers [7] found an average number of about 200 collected items per patient. Furthermore, it may be problematic to share patient information (even in a pseudomized or anonymized form) between the clinical study groups. Partners may be ready to share project specific data, but may be reluctant to provide extensive patient profiles for a central registry. Partners may be less reluctant to share information on patient information available in their repositories. This can be done by sharing study specific data dictionaries which define the data items of the study and the ways they are measured. Even, it may be easy to disclose which item is measured with good or bad quality for which patient. Consequently it is required to collect syntactic and semantic meta information about a data item in a specific study. This comprises metadata about the data item representation and stored values as well as contextual metadata concerning the data capture process. Representation and contextual metadata can be elevated from certain study documents (i.e.: Data dictionary as central information on the structure of a specific study database, data validation plan as the document which defines data quality, and a study protocol to explain the logic which sets the variables of a study in their specific logical context. Content metadata (i.e. patient wise availability and quality of item values collected in the study) has to be compiled directly from the study database and must be a updated regularly as the study data collection progresses. The metadata provides a reliable planning basis for cooperative research projects. It simplifies communication between collaborating partners and supports and accelerates the development process of a feasible common research protocol. We will present an IT infrastructure based on modern technical components and internationally accepted data standards for extraction, transformation and loading of metadata into a metadata-based patient register. The developed technical infrastructure has to be implemented into a governance infrastructure which assures data safety, privacy rights, and a transparent cooperative work. We also show that the implementation of the concept allows improving standardization of data management in clinical studies between the cooperating study groups. With our concept we follow the general principles of caBIGTM [9] of opening and implementing the cross-communication between distributed and federated data sources in oncology. Our approach is a deviation from the fully federated model of caBIGTM by establishing the metadata-based patient register as a central component. It offers a central link to available clinical research data of a patient in the research community. As a case study we consider a metadata-based patient register for four German study groups on Acute Myeloid Leukemia (AML) which is a rare disease characterized by a high mortality rate [10]. In Germany, investigator-driven multicenter treatment optimization trials are the main instrument in clinical leukemia research [11]. In the course of the trial a broad range of clinical data is collected providing the basis for evidence-based evaluation of the trial objectives. All trials together offer a rich


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information basis to perform meta-analyses, sub-group analyses, discovery and validation studies for biomarkers and surrogate endpoints, and diagnostic as well as prognostic rules. The heterogeneity in clinical documentation in AML studies (i.e. therapies and therapy outcome, concurrent diseases, etc.) is a recurring challenge in cooperative research projects. Therefore this is an interesting and significant field for evaluation of the concept of a metadata-based patient register.

2. Methods The problem of collating AML clinical data from multiple centers for meta-analysis: The classical patient data registry was discarded because of the severe restrictions implied by a uniform data set. The warehouse concept can not be applied because the partners did not agree on a permanent sharing of full patient data. The metadata based approach offers sufficient flexibility for the design of research projects by maximal protection of the individual patient data. The design of the processes for collation and for the management of metadata, the approach taken for requirements elicitation: For requirements compilation as well as documentation of available sources of clinical data semi-structured interviews with selected staff of study groups were conducted. The project stakeholders discussed and assessed the approach on a regular basis. Details of the system design: Discussions, requirement engineering and decisionmaking were supported by modeling of core data processes with the Business Process Modeling Notation (BPMN), i.e. (1) process of metadata extraction from data source (2) the load process of metadata into the register (3) the extraction and forwarding process of clinical data. Tools and techniques used for building the system: Various metadata standards (ISO/IEC 11179 [12], CDISC ODM [13], Resource Description Framework [14]) have been assessed regarding their ability to transmit extracted meta information from clinical data sources to the meta data oriented patient register. An important demand put on an appropriate metadata format is its power to convert data from legacy study databases with various technological back-ends (e.g. MS Access, MS SQL Server) to an international accepted format. The assessment resulted in the choice of CDISC ODM to act as model for implementation of metadata standardisation, extraction, transmission and storage. Software interfaces and tools were modeled with UML 2.0 and implemented with Java, JAXB, XML, Hibernate, Lauch4J, Ant, Maven. A PostgreSQL database acts as back-end for central metadata storage.

3. Results An evaluation of possible meta information about a clinical data source to be extracted and loaded into the metadata register was conducted and resulted in the following definition on which meta information will be collected about a clinical data source: (1) Attributes of the research project (e.g. project type, research plan synopsis, etc.), (2) status of data management processes (e.g. data capture, data validation, database closure, etc.), (3) Description, structure and content of (electronic) case report forms

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(e.g. scheme of study visits and forms), (4) Description of data items (e.g. item description, data type and precision, location of item in case report form, etc.) , (5) Data validation plan, (6) Pseudonyms of included patients, and (7) “Captured/MissingFlag” (i.e. a True/False flag, indicating on the data item level, if clinical information about a single patient was captured (True) or is missing (False)) Since the CDISC ODM format isn’t able to document the Captured/Missing flag an extension of the ODM standard was required. The ODM extension was documented in an amended XML schema. Software for fully automatic metadata and clinical data extraction from distributed data sources under different ownership was implemented. It allows the data owning study group to control the transferred data. On one hand it can be configured to extract patient pseudonyms and Captured/Missing information. This conversion of clinical information to the metadata format is conducted on basis of mapping information. The mapping instructions are documented in XML format defined by an XML schema. The so called ‘DB2ODMMapping’ allows the specification of mapping constraints between a relational database and ODM data items as well as constraints on interpreting the Captured/Missing-Status of data values. Second the software is able to extract clinical data from a relational database on request of a cooperative research project. The clinical data to be extracted can be configured in the ‘DB2ODMMapping’ file. Further software tools for processing of collected meta information have been implemented, i.e. for loading metadata into the central database and for creation of meta data documentation in PDF format. All project related software has been implemented in Java 6. A modular concept of three Java APIs (core, dataaccess, odm) support software maintenance and enable software re-usability. At present meta information about three clinical trials from two AML study groups has been integrated into the central meta-data based patient register. Together these three data sources contain clinical information about 4115 leukemia patients. Automatic extraction of clinical data from the study databases on basis of available meta information has been tested. Clinical evidence concerning the status and classification of AML (i.e. French-American-British classification, WHO classification) from 4102 patient data sets has been extracted and provided for statistical analysis. This process disclosed classification inconsistencies between the trials and allowed to start a process to standardize between both study groups. The prototype allows straightforward extension to the full set of available clinical trial in several study groups.

4. Discussion The challenges of clinical research ask for a cooperative efficient use of high-quality data. Such data is in general available in databases of clinical trials, especially of randomized controlled studies. Sharing the data of such studies has to be done with care and within a transparent and regulated setting to protect patient rights as well as integrity of the clinical data. The concept and prototype for a general cooperative infrastructure in clinical research is presented which complies with legal, ethical and technical requirements. It supports cooperative initiatives in consolidation of available clinical evidence for evaluation of open research questions. Potential cooperative


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projects are: (1) Discovery and validation studies for prognostic and predictive models, biomarkers and surrogate endpoints, (2) planning data capture for future trials, and meta-analyses using individual patient data (surrogate endpoints, treatment effects, subgroup analyses). Processes for metadata extraction and loading into the central register facility have been implemented and are highly supported by comfortable software tools. In addition, the metadata-based patient register acts as a platform for network communication and data standardization activities. Besides the ongoing integration of metadata from clinical study databases future work will concentrate on modeling and implementation of a web-based register platform and of data transformation processes for harmonizing clinical data from different sources.

References [1] [2] [3] [4] [5]

[6] [7] [8] [9] [10] [11] [12]

[13] [14]

Dreyer NA, Garner S. Registries for robust evidence. JAMA. 2009 Aug 19;302(7):790-1. Gliklich RE, Dreyer NA, editors. Registries for Evaluating Patient Outcomes: A User’s Guide. 2nd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2010 Sep. Drolet BC, Johnson KB. Categorizing the world of registries. J Biomed Inform. 2008 Dec;41(6):100920. Epub 2008 Feb 5. Brooke EM. The current and future use of registers in health information systems. WHO Offset Publ No. 8 1974 pp. ii + 43 pp. Arts DG, De Keizer NF, Scheffer GJ. Defining and improving data quality in medical registries: a literature review, case study, and generic framework. J Am Med Inform Assoc. 2002 Nov-Dec;9(6):60011. Gladman D, Menter A. Introduction/overview on clinical registries. Ann Rheum Dis. 2005 Mar;64 Suppl 2:ii101-2. Review. Stausberg J, Altmann U, Antony G, Drepper J, Sax U, Schuett A. Registers for networked medical research in Germany: Situation and prospects. Appl Clin Inf, 2010. 1: p. 408-418. Newton J, Garner S. Disease Registers in England. Institute of Health Sciences, University of Oxford, 2002. ISBN 1 8407 50286. National Institutes of Health, National Center for Research Resources. CaBIGTM overview. 2006. [cited at 2011 Apr 29]. Available from http://www.ncrr.nih.gov/publications/informatics/caBIG.pdf. European Medicines Agency, Committee for Orphan Medicinal Products. Public summary of opinion on orphan designation, EMA/COMP/804144/2009. London, 2010. Hehlmann R, Berger U, Aul C, Büchner T, Döhner H, Ehninger G, et al. The German competence network 'Acute and chronic leukemias'. Leukemia. 2004 Apr;18(4):665-9. ISO/IEC 11179-3+COR1 (2003) Information Technology - Metadata Registries (MDR) Part 3: Registry Metamodel and Basic Attributes. Second edition 2003-02-15 Incorporating COR1. Available from http://jtc1sc32.org/doc/N1151-1200/32N1168-ISO-IEC11179-3-2003COR1.zip. http://www.cdisc.org/models/odm/v1.3/index.html. [cited 2011 Mar 06]. http://www.w3.org/standards/techs/rdf#w3c_all. [cited 2011 Mar 06]

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De-identifying an EHR Database Anonymity, Correctness and Readability of the Medical Record a

Kostas PANTAZOSa1, Soren LAUESENa, Soren LIPPERTa Software Development Group, IT-University of Copenhagen, Denmark

Abstract. Electronic health records (EHR) contain a large amount of structured data and free text. Exploring and sharing clinical data can improve healthcare and facilitate the development of medical software. However, revealing confidential information is against ethical principles and laws. We de-identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database. Keywords. Electronic Health Record, de-identification, database, confidentiality

1. Introduction Vast amounts of data are generated from medical systems in structured and free text formats. Although the data exist, clinicians cannot access them due to confidentiality. The goal of this project is to irreversibly convert patient records from a specific EHR database to unidentifiable records with low distortion of medical correctness and readability. This de-identified database can support research in the healthcare area, improve development of medical software and train new users of the system. In the medical informatics area, several de-identification algorithms have been developed [1, 4, 5, 6, 7, 8]. Meystre et. al. [3] present a review of recent research on deidentifying electronic health records. Their results showed that most de-identification systems focus on structured data and less on free text. The ones that de-identify free text use mainly predefined medical records (e.g. pathological reports). To our knowledge, previous research focus on de-identifying datasets extracted from tables in an EHR database, and none has presented a de-identification algorithm for a full EHR database, ensuring acceptable levels of anonymity, medical correctness and readability. Furthermore, the literature review [3] showed that previous studies focus more on

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Corresponding author: Kostas Pantazos, E-mail: [email protected]; IT-University of Copenhagen, Rued Langgaards Vej 7, DK-2300, Copenhagen,

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anonymity and medical correctness and less on readability of the de-identified records. Finally, this is the first study on de-identifying Danish healthcare records.

2. Challenges Anonymity can be ensured by finding all the identifiers and altering them. Medical correctness means preserving the medical information as well as ensuring consistency. We defined two types of consistency in an EHR database: internal and external consistency. Internal consistency means that identical identifiers (e.g. civil registration numbers) in the original version are also identical in the new version for each patient. External consistency means that identical identifiers (e.g. last name) in the original version are also identical in the new version across patients. This will for instance preserve family relationships. Readability can be ensured by replacing the identifiers with appropriate real values. An electronic health record database contains tables with only structured data (e.g. civil registration number and diagnosis name) and tables with free text, often with embedded structured data (e.g. medical notes with a diagnosis name). Preserving anonymity of the patient and medical correctness in structured tables is easy because the context is pre-defined and all identifiers are replaced according to the rules of the format. In contrast, de-identifying free text tables is a challenging task due to the undefined context, language ambiguities and medical eponyms (e.g. Aaron can be a first name or part of the medical term “Aaron Sign”). Another challenge is to preserve internal and external consistency without affecting medical correctness and anonymity.

3. Solution We investigated a full 12 gigabyte database with 437,164 patient records containing diagnoses, notes, laboratory data, etc. Figure 1 outlines our process. 3.1. Database Investigation We examined the database (65 tables) to find tables that might reveal patient identity. We found 9 tables with only structured data and 13 tables with free text. We

Figure 1. Overview of the de-identification process for an entire Danish EHR database

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investigated the fields and created a list of identifiers, e.g. CPR-number (the Danish civil registration number). We also found quasi identifiers (e.g. street name) [2]. In total we found 9 identifiers (CPR-number, first name, middle name, last name, address, telephone number, e-mail, web URL, picture) and 13 quasi identifiers (zip-code, city, country, date of birth, date of death, age, hospital name, clinic name, clinician’s first name, clinician’s last name, clinician’s alias, first name and last name of relatives). We investigated identifiers and quasi identifiers in the database and found several challenging issues: number ambiguity (a phone number can also be interpreted as a CPR number), language ambiguity (Hans is a Danish pronoun, but can also be a male first name), medical eponymous names (Aaron), city names and clinic names that can also be person names, and corrupted data (invalid CPR numbers in structured data). Our algorithm extracts lists of all the identifiers from the database. The lists are used by the algorithm to identify ambiguous names and numbers in free text. 3.2. External Identifiers In addition to the identifiers from the structured parts of the database, we used public lists of place names, hospital names, clinic names and medical eponymous names. These names allowed the algorithm to find more ambiguous names in free text, and to de-identify person names that occurred only in free text. 3.3. Algorithm Structured data: The algorithm replaces all identifiers in structured data. Each family name is consistently replaced by another family name with roughly the same frequency in the database. As an example, the name Nielsen might be replaced by Hansen wherever Nielsen occurs. First male names and first female names are handled in a similar way. CPR-numbers are consistently replaced by another CPR-number. The CPR format is: DDMMYY-CSSG where DDMMYY is the birth date. The day (DD) and month (MM) are changed to a random, consistent day and month. C stands for century and denotes 1900 or 2000. This is not changed. SS (serial number) is randomized. G shows gender, and is not altered (e.g. number 280210-1546 is replaced with 2006101656).Some identifiers, e.g. telephone numbers, are replaced by a random number. Free text: The algorithm looks at each word in the free text and determines whether it is a family name, a male first name, a female first name, a place name, an eponymous medical name, etc. If it is only one of these, it is replaced according to the rule for this kind of name. If it is more than one kind, the word is ambiguous and a special rule is used. Here is an example of a special rule: If a person name is also an eponymous medical name (Aaron), it should not be replaced. This would destroy medical correctness in case it actually is a medical term. However, if it actually is a person name, keeping the name might harm anonymity. Our special rule is to keep the name if it is a frequent name (occurs more than 200 times). This will have little impact on anonymity. If it is a rare name, we delete the patient entirely from the database. The algorithm looks at each number and determines by its format and value whether it is a phone number, a CPR-number, etc. If it is only one of these, the corresponding rule is applied. Otherwise the number is ambiguous and the algorithm uses simple language analysis to determine the type.


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Figure 2. A de-identification example

Consistency: Family doctors often make notes that refer to other family members by name or CPR-number. Since the algorithm consistently replaces person names and CPR-numbers, these references remain consistent. City names, hospital names and clinic names are replaced consistently within a single free text, but not across all free texts. A consistent replacement might expose the identifier since there are rather few replacements for cities, hospitals and clinics. Readability: Since the algorithm replaces names and numbers with other real names and numbers of the same kind, the new data will look "real". However, if names were consistently replaced by a completely random name, the data pattern might look strange. As an example, the common name Nielsen might be consistently replaced by the rare name Pantazos. As a result we would suddenly have 10,000 Pantazos in the database. For this reason the algorithm replaces a name with a new name of roughly the same frequency. Figure 2 shows an example of how the algorithm de-identifies data.

4. Results We evaluated our system manually with a sample of 369 randomly chosen medical free text records extracted from MedicalRecordLine table (7.2 gigabyte). Figure 3 presents the evaluation results. The algorithm did not alter frequent Danish names (>200) that were also medical names. We were aware of this from the beginning but would not

Figure 3. Evaluation results

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distort the medical correctness. Since the names are frequent, there is little impact on anonymity. A previous version of the algorithm did not de-identify patient names in genitive form. We adjusted our algorithm to deal with the genitive form. Precision was affected because of the many ambiguous names and abbreviations that were replaced in places where they should not. This had a negative impact on readability and medical correctness. However, the result is very readable because only 109 words out of 71,721 words were wrongly replaced. Anonymity was not affected. The program took 60 hours using a computer with 4 gigabyte of memory to create the new database (12 days using a computer with 1 gigabyte memory). Of these 60 hours, 5 hours were spent on analyzing and replacing the text and 55 hours on updating the records in the database. During the de-identification process the system deleted ¼ of the data, 114,315 patient records (Danish ambiguous names: 1,282, Medical eponymous names: 43,119, corrupted data and age > 90 years: 69,914). In case we had not used the frequency rule, we would have lost another 55,000 patients from ambiguous and eponymous names. The result of our de-identification process is an EHR database containing 323,122 patient records.

5. Conclusion It is feasible to de-identify an EHR database and achieve an acceptable level of anonymity, correctness and readability of the medical record. This database is adequate for supporting research, development and training where users are aware of the confidentiality. If you know name, address and CPR-number of a specific person, you will not be able to find his/her health record. However, it is not adequate for general publication of the database where someone maliciously might look for weakness. The principle of the algorithm can be used for other EHRs, but modifications caused by database structure and language should be considered.


[4]

[5] [6]

[7] [8]

Berman J. Concept-Match Medical Data Scrubbing. How Pathology Text Can Be Used In Research, Archives of Pathology & Laboratory Medicine 2003, 680-6. Emam KE, Jabbouri S, Sams S, Drouet Y, Power M. Evaluating Common De-Identification Heuristics for Personal Health Information. Journal of Medical Internet Research 2006, 8(4):e28. Meystre S, Friedlin FJ, South BR, Shen S, Samore MH. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Medical Research Methodology 2010, 10:70. Gupta D, Saul M, Gilbertson J. Evaluation of a Deidentification (De-Id) Software Engine to Share Pathology Reports and Clinical Documents for Research, American Journal of Clinical Pathology 2004, 176-86. Sweeney L. Replacing Personally-Identifying Information in Medical Records, the Scrub System. In: Cimino JJ, ed. Proceedings, Journal of the American Medical Informatics Assoc 1996, 333-337 Szarvas G, Farkas R, Busa-Fekete R. State-of-the-Art Anonymization of Medical Records Using an Iterative Machine Learning Framework. Journal of the American Medical Informatics Association 2007, 574–8 Uzuner O, Luo Y, Szolovits P. Evaluating the state-of-the-art in automatic de-identification. Journal of the American Medical Informatics Association 2007, 550-563. Velupillai S, Dalianis H, Hassel M, Nilsson GH. Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial, International Journal of Medical Informatics 2009, 78-90.


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Service Oriented Data Integration for a Biomedical Research Network Matthias GANZINGERa,1, Tino NOACKa, Sven DIEDERICHSb,c, Thomas LONGERICHc, Petra KNAUPa a Department of Medical Informatics, University of Heidelberg. b Helmholtz-University-Group “Molecular RNA Biology & Cancer”, German Cancer Research Center (DKFZ). c Institute of Pathology, University of Heidelberg. Heidelberg, Germany.

Abstract. In biomedical research, a variety of data like clinical, genetic, expression of coding or non-coding ribonucleic acid (RNA) transcripts, or proteomic data are processed to gain new insights into diseases and therapies. In transregional research networks, geographically distributed projects work on comparable research questions with data from different resources and in different formats. Providing an information platform that integrates the data of the projects can enable cross-project analysis and provides an overview of available data and resources (tissue, blood, etc.). For a German liver cancer research network consisting of 22 individual projects, we develop the integrated information platform pelican – platform enhancing liver cancer networked research. In our generic approach, data are made available to the research network by standardized data services based on technologies provided by the cancer Biomedical Informatics Grid (caBIG). It has shown that publishing service metadata in a corresponding repository is a major prerequisite for automated discovery, integration, and conversion of data records and data services. We identified data confidentiality and intellectual property considerations as major challenges while establishing such an integrated information platform. As a first result we implemented a working prototype to validate our approach. Keywords. biomedical research, service oriented architecture, data integration

1. Introduction Biomedical informatics research can provide resources that represent, visualize and analyze large-scale genetic data efficiently and flexibly [1]. Nevertheless, a lack of interoperability among data resources from independent institutions is described as a severe problem for biomedical research in current literature [2]. The variety of representations and semantics usually leads to data sets that are stored in heterogeneous formats, described with different terminologies and analyzed with dedicated applications. This heterogeneity may hamper the development of new strategies targeting cancer [3] and their translation from bench to bedside. Several approaches have been started to address this problem. Data warehouses are introduced, so that data 1 Corresponding author: Matthias Ganzinger, Dpt. of Medical Informatics, University of Heidelberg, Im Neuenheimer Feld 305, 69120 Heidelberg, Germany; E-mail: [email protected]

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from biological databases can be integrated, locally stored, and analyzed [4, 5]. It has been recognized that collecting information from different areas of research offers important advantages: Relevant independent results are tied together and specialists are pointed into new directions [6]. In Germany, a transregional research network (TRN) on hepatocellular carcinoma (HCC) has been established. Within the TRN, 22 biomedical research projects cover the whole range of research from molecular pathogenesis to the development of new targeted therapies. The task of our group is to develop, validate, and apply an information platform that is tailored to the scale and multidisciplinary nature of the TRN. The integration of tissue, molecular, genetic, and clinical data into a common platform shall enable data sustainability and comprehensive analyses. The aim of this paper is to introduce the special requirements that arise from a biomedical research network for the information platform and to discuss the resulting architecture blueprint. We want to share our experiences in using tools from the cancer Biomedical Informatics Grid (caBIG) initiative to build the system.

2. Methods The 22 TRN projects are located at four major independent research institutions. Each project organizes its own research data. There is a considerable amount of data, distributed over the various institutions in different terminologies and in different formats. Standards, specifications, tools and standard operating procedures are necessary to ease the integration of biomedical research data on HCC. Our aim is to provide an efficient and secure environment to perform queries and analyses on integrated scientific information while respecting the distributed nature of the TRN. 2.1. The Pelican Architecture The information platform pelican (platform enhancing liver cancer networked research) is built in an iterative process. We started with a case study in two projects by analyzing the currently implemented way of data storage. In this study, we analyzed data structures, identified overlapping data and ambiguous data structures. Further, we analyzed the applicability of open-source applications and specifications to our TRN. We found two major concepts of data storage for an integration platform: first, a central data warehouse into which data from all sources are loaded. I2b2 [7] is an example for a data warehouse used in biomedical context. The other concept is to federate data. That means, all data are kept separately but are made available for integrated analyses by using standardized interfaces. For example, caBIG [8] – the National Cancer Institute’s (NCI) cancer Biomedical Informatics Grid – provides tools to build a federated system. We decided to implement pelican as a service oriented architecture (SOA). As our base framework we chose components provided by the caBIG initiative. caBIG was established to improve cancer research by sharing, discovering, integrating, and processing disparate clinical and research data resources to improve cancer research. This includes the development of applications for data management and analysis, guidelines, and informatics standards. These tools are based on a grid architecture (caGrid) to link applications and resources in the caBIG environment [2].


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Figure 1. Semantics of pelican data services are described using a standardized vocabulary. Both technical metadata and semantic descriptions of the services are published to a directory service.

3. Results In pelican, all data contributed by individual projects are transformed into data services using the Cancer Common Ontologic Representation Environment (caCORE) Software Development Kit (SDK). As shown in Figure 1, metadata are generated for all data services and published to a directory. The vocabulary used for the description has to be standardized throughout the TRN. In addition to data services, analytical services are developed and made available within pelican. In the final version of pelican, researchers will be able to find data services hosting data necessary to answer their research question in the service directory. These data services are, together with analytical services, chained into a workflow that analyses the data of various sources and presents the results to the researcher. Figure 2 illustrates this process chaining concept. Further TRN projects can be easily added. Standard operating procedures and supporting tools will be developed to provide a smooth way of converting raw data as generated in the projects into data services conforming standards for pelican. In this process, it is especially important to apply the corresponding metadata correctly. Otherwise, it will be impossible to find the data sources in the directory and apply automated correlation algorithms.

Figure 2. Individual services providing data or analytical services can be combined into a service chain. To do this, a researcher identifies the services of interest by using the service directory. The chain is executed by pelican and the results are returned.

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3.1. TRN Specific Requirements As a first step to assess the requirements for the pelican system, we conducted a survey among TRN project managers. For this purpose we developed a questionnaire consisting of 13 questions. For about one half of the questions standardized answers were provided with check boxes, the other half was free text. The survey covered various aspects of data usage, data confidentiality and intended use of the new system. The evaluation of the survey made obvious, that there is a strong concern among researcher about the confidentiality of data contributed to the system. These concerns were mostly about two aspects: 1. Researchers want to control who can access the data they contributed to the TRN. They want to keep the data confidential among specific project members or the whole TRN until they are published. 2. Getting access to the data of another project may lead to a significant advantage of somebody’s own research. If this leads to a publication, rules have to be established and enforced how the contributors of the data are to be acknowledged e.g. by means of co-authorship. 3.2. Data Confidentiality and Intellectual Property To address the TRN requirements, pelican architecture includes several confidentiality measures. Data services and as such data itself can be left under the control of the contributing project. Projects can define access control lists for their services if necessary. For the use of data generated by other projects, all TRN projects agreed on a set of rules. To support the enforcement of these rules, access to data is recorded by a comprehensive audit logging concept. Audit logs are monitored by the central project office on a regular basis. Our survey showed that 55% of the projects are only willing to share their data after data confidentiality concepts such as those proposed by us have been implemented in pelican. 3.3. Integration Platform To test the architectural design of pelican, a prototype was built. It uses caCORE SDK to implement data services. However, it does not yet allow for dynamic process generation. Instead, a process for a specific research question is prepared statically. To answer this research question, it is necessary to correlate genomic microarray data of three data services provided by two projects. Data types used are array comparative genomic hybridization data (aCGH), methylation data and expression of coding and non-coding ribonucleic acid (RNA). In parallel, the service directory has been implemented and work on the standardized vocabulary has been started.

4. Discussion When pondering whether the data warehouse or the federated approach would suit the needs of the TRN better, we chose to build a federated system. With this concept, it is possible for the individual projects to keep control over their data since data from different projects are encapsulated in distinct data services. Thereby, access control


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mechanisms are easy to comprehend and to manage. In contrast, a data warehouse would usually combine all data in one database making it much harder to apply access permissions on an individual basis and communicate these settings to the projects. Using the pelican prototype, we were able to demonstrate, that it is possible to integrate data of different TRN research projects using a SOA based on caBIG components. We were able to statically correlate several genetic data sources and thus support the researchers of two projects. Further, we started to implement the metadata directory and the implementation of caGRID [2]. The security concept designed for pelican is accepted throughout the TRN, as our survey substantiates. However, further work needs to be done regarding the user interface to ensure user acceptance: pelican should be as easy to use as the tools currently used by the researchers. To enable the dynamic composition of process chains, a workflow engine has to be added to pelican. Candidates for this are Business Process Execution Language (BPEL) based engines or the Taverna workflow management system [9]. Finally, we need to examine other caGRID enabled tools provided by the caBIG initiative to find out, if those can complement pelican to further improve the TRN research. Acknowledgements: The authors would like to thank the German Research Foundation (DFG) for funding SFB/TRR 77 – “Liver Cancer. From molecular pathogenesis to targeted therapies.”


[4] [5] [6] [7] [8] [9]

Knaup P, Ammenwerth E, Brandner R, et al. Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools. Methods Inf Med 2004; 43(3):302–7. Oster S, Langella S, Hastings S, et al. caGrid 1.0: an enterprise Grid infrastructure for biomedical research. J Am Med Inform Assoc; 15(2):138–49. Madhavan S, Zenklusen J, Kotliarov Y, Sahni H, Fine HA, Buetow K. Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol. Cancer Res 2009; 7(2):157–67. Lee TJ, Pouliot Y, Wagner V, et al. BioWarehouse: a bioinformatics database warehouse toolkit. BMC Bioinformatics 2006; 7:170. Hart RK, Mukhyala K. Unison: an integrated platform for computational biology discovery. Pac Symp Biocomput 2009:403–14. Schork NJ. Genetics of complex disease: approaches, problems, and solutions. Am. J. Respir. Crit. Care Med 1997; 156(4 Pt 2):S103-9. i2b2: Informatics for Integrating Biology & the Bedside [cited 2011 Apr 19]. Available from: URL:https://www.i2b2.org/. Welcome to the caBIG® Community Website — [cited 2011 Apr 19]. Available from: URL:https://cabig.nci.nih.gov/. Tan W, Missier P, Foster I, Madduri R, Goble C. A Comparison of Using Taverna and BPEL in Building Scientific Workflows: the case of caGrid. Concurr Comput 2010; 22(9):1098–117.

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Single Source Information Systems can Improve Data Completeness in Clinical Studies: an Example from Nuclear Medicine a

Susanne HERZBERGa, b,1, Martin DUGASa, b Institute of Medical Informatics, University of Münster, Germany b IT Department, University Hospital of Münster, Germany

Abstract. Data for clinical documentation and medical research are usually managed in separate systems. A documentation system for myocardial scintigraphy (SPECT/CT-data) was developed, implemented and assessed in order to integrate clinical and research documentation. This paper presents concept, implementation and results regarding data completeness of this single source information system. Completeness of documentation increased highly significantly (p < 0.0001) after implementation of this system. Keywords. Single source information system, EHR re-use, hospital information system, follow-up, data completeness, reminder system

1. Introduction Usually, there are separate systems for clinical documentation despite existing overlap between data items: hospital information systems (HIS) for routine medical documentation in electronic health records (EHRs) and electronic data capture (EDC) systems for clinical studies. These systems are managed in a dual source concept [1]. In contrast, a single source information system reuses routine healthcare data for clinical research. A separate documentation system possesses quite a few disadvantages, for instance, “Inefficiencies in clinical trial data collection cause delays, increase costs, and may reduce clinician participation in medical research” [2]. Moreover, “Routine data are potentially cheaper to extract and analyze than designed data…” and “... have the potential to identify patient outcomes captured in remote systems that may be missed in designed data collection” [3]. Furthermore, transcription errors are eliminated and patient recruitment for clinical trials is facilitated [4]. In the following, we present a single source information system which was designed for a study on cardiovascular risk stratification by combination of risk factor analysis, in-vitro diagnostics and single photon emission computer tomography/computer tomography (SPECT/CT) in order to be able to predict individualized risk concerning coronary events [5]. Clinical studies typically consist of several visits. After an initial assessment, several follow-up visits need to be organized and documented. Therefore, follow-up 1

Corresponding Author: Susanne Herzberg.

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data needs to be collected according to each study protocol at certain time points. According to Chan et al. “data completeness varied substantially across studies” [6] which may be caused by the huge documentation workload of physicians in routine care [7]. Forster et al. report that the median rate of loss to follow-up in a 15-country study was 8.5% [8]. Consequently, data completeness in studies is a critical and widely unsolved problem. Organizational issues, for instance regarding scheduling, can cause loss to follow-up. We implemented two workflows in the HIS and compared data completeness before and after this intervention: A HIS-based follow-up system to support study documentation by automatic creation of follow-up forms according to study protocols [9] and a generic reminder system to monitor completeness [10] in documentation.

2. Methods 2.1. Design of Single Source Information System for SPECT/CT Study Electronic case report forms (CRFs) regarding medical history, stress and rest injection protocols were designed using tools of the local HIS (ORBIS® from AGFA Healthcare [11]). These forms were identified in a process analysis of the SPECT/CT study, for details see [5]. Checkboxes, lists and number fields with only few narrative text fields are used in order to provide structured data for statistical analysis. A work list contains all uncompleted forms for physician’s review. Conditional items with related plausibility checks are applied to improve data quality. Error messages occur if data items are invalid or missing. To minimize data entry efforts, item values are calculated automatically wherever possible. The report generator of ORBIS® is used to extract HIS data for quality control and research purposes. Authorized study physicians can perform these queries. The report tool generates csv-files suitable for import in statistic software packages. HIS reports are pseudonymized to protect patient data privacy. A data management team performs monitoring to verify data validity in the research database. 2.2. Concept of HIS-Based Reminder System Regarding form Completeness A HIS-based reminder system identifies incomplete CRFs within the HIS and sends notifications to the responsible person after a certain grace period, for details see [10]. This reminder system needs a flexible configuration component, because a large number of clinical studies are performed simultaneously and each study consists of several CRFs with individual responsibilities regarding documentation. An escalation mechanism to notify different groups of people about incomplete documentation is provided. For instance, when the study physician is not completing a CRF within a certain time frame, the principal investigator will be notified. From a technical perspective, a definition table stores for each CRF type a query to identify incomplete forms. These queries are executed periodically. A schedule table manages due records. After expiration of each grace period, notifications are prepared. To avoid over-alerting, summary e-mails per study and escalation level are generated. Reminder messages are sent via a communication server.

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2.3. Concept of HIS-Based Follow-Up System A HIS-based follow-up system automatically generates follow-up CRFs in time according to each study protocol - and enqueues these forms into the work list of the responsible study personnel. This system needs a configuration component, because for each follow-up CRF in each clinical study, a different follow-up schedule needs to be applied. Similar to the HIS-based reminder system, study-specific periodic database queries identify due follow-up forms. Triggered by a due follow-up form, a database procedure creates a follow-up event which is translated by a communication server into an health level seven (HL7) message and transferred to the import interface of the clinical information system. Within this system, clinicians can access their departmental work lists with patient-specific follow-up forms. 2.4. Analysis of Data Completeness Data completeness before and after implementation of HIS-based reminders and HISbased follow-up was analyzed using HIS reports and statistic software (PASW from SPSS [12]). An exact chi-square test was applied to test for significant changes regarding completeness by CRF type before and after introduction of each system. Two-sided P values < 0.05 were interpreted as statistically significant.

3. Results A single source information system can combine clinical and scientific documentation and thus avoids multiple data entry. Regarding the SPECT/CT study, within 22 months 1308 patients were documented by 8 physicians and 8 radiographers (1358 medical history protocols, 1372 stress and 1275 rest injection protocols). Documentation consisted of 301 attributes, three quarters were conditional items. The HIS-based reminder system was started in September 2009. The documentation periods from May 2009 until July 2009 (before implementation) and from October 2009 until December 2009 (after implementation) were compared. Reminders were configured for medical history forms, stress and rest injection protocols. Two grace periods were used: the first was set to one day and the recipient was the responsible study physician, the second escalation level was set to one week and the recipient of these e-mails was the principal investigator. Completeness increased highly significantly (p < 0.0001) for each form type after implementation of the reminder system: medical history form 93% (145 of 156 forms) versus 100% (206 forms), stress injection protocol 90% (142 of 157 forms) versus 100% (201 forms) and rest injection protocol 31% (45 of 147 forms) versus 100% (208 forms). 46 reminder e-mails to the responsible study physician and 53 reminder e-mails to the principal investigator were sent to complete 2 medical history forms, 8 stress and 20 rest injection protocols. The 2 medical history forms were completed after 1 and 56 days. A HIS-based follow-up system to automatically generate follow-up forms as described in the methods section was implemented for the SPECT/CT study. 196 follow-up forms were automatically generated within 13 weeks of operation. Overall, data quality improved substantially compared to previous paper-based documentation. For comparison, we assessed the completeness of the previous paper-


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based documentation. We took a random sample of 19 forms from February and March 2009 (before implementation of electronic documentation for this study). No patient (0 out of 19) was completely documented in the paper-based documentation.

4. Discussion The design and implementation of this system in nuclear medicine demonstrate that a single source information system is technically feasible and accepted in the clinical setting. It can be integrated into the existing clinical workflow without disruption. Paper-based entries are error-prone, for instance due to legibility problems. In contrast to that, electronic forms have a significantly reduced error rate [13]. A reminder system on top of a single source information system can clearly improve data completeness. In particular, it is feasible in a commercial HIS setting with all its technical and license constraints (e.g. access to internal HIS data model is restricted, available interfaces are limited). On the other hand, because most hospitals are using commerical HIS, our approach should be scalable and transferable to other sites, at least to hospitals with the same HIS product. It would be very interesting to analyze whether our approach is also feasible with products from other HIS vendors and how much resources are required for technical implementation. Due to the fact that a physician spends nearly a quarter of his working time on clinical routine documentation [7], additional documentation efforts for research purposes need to be minimized. A first proof-of-concept study concerning a cardiology trial was published in 2007 [2]. It was integrated into the clinical environment, but there was no integration into an existing inter-departmental CIS in contrast to our approach. Furthermore, this proof-of-concept study was tested in only two live patient encounters. In our approach, all cardiological patients of the department were documented in the single source system. Timely and complete follow-up documentation is a significant issue in clinical research. This task is supported by the HIS-based follow-up system. Recruitment of suitable patients and complete documentation are key issues in clinical trials: In 2006, a meta-analysis of more than 100 trials showed that “less than a third (31 %) of the trials achieved their original recruitment target and half (53 %) were awarded an extension” [15]. Recruitment rates were increased significantly by the use of a HIS-based clinical trial alert system [16]. However, standard EDC systems are not integrated into the routine clinical workflow of the HIS. EDC systems can support follow-up documentation. Welker states that “The central storage of data and ubiquitous user access allows the inclusion of intelligence that can remind individual users to perform required tasks; i.e. remind the investigator site when an enrolled patient is due for a follow-up visit…” [17]. Because all clinicians work with the HIS, HIS work lists are attractive locations for follow-up reminders. Interventions for quality improvement should be embedded within HIS [10]. The HIS-based reminder system monitors continuously completeness of documentation and notifies responsible physicians about incomplete documentation depending on escalation level. There is evidence regarding the efficiency of HIS-based reminders in the literature, for instance Staes et al. report that computerized alerts improve outpatient laboratory monitoring of transplant patients [18]. In our experience, a second escalation level (notification of a principal investigator) is valuable, because it helps to identify and resolve organizational

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problems in the documentation process at an early stage. To avoid over-alerting, grace periods and number of e-mails need to be configured carefully in cooperation with the study team.

5. Conclusion A single source information system whose components are a follow-up system and a computer-based reminder system to identify incomplete documentation forms can improve completeness of finalized forms significantly.


[4] [5]

[6] [7] [8]

[9] [10]

[11] [12] [13] [14] [15] [16] [17] [18]

Dugas M, Breil B, Thiemann V, Lechtenbörger J, Vossen G. Single Source Information System to connect patient care and clinical research, Stud Health Technol Inform 150 (2009), 61-65. Kush R, Alschuler L, Ruggeri R, et al. Implementing Single Source: the STARBRITE proof-of-concept study, J Am Med Inform Assoc 14 (2007), 662-673. Williams JG, Cheung WY, Cohen DR, et al. Can randomised trials rely on existing electronic data? A feasibility study to explore the value of routine data in health technology assessment, Health Technology Assessment 7 (2003), 1-117. Dugas M, Lange M, Berdel BE, Müller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems – a case study, Trials 9 (2008), 2. Herzberg S, Rahbar K, Stegger L, Schäfers M, Dugas M. Concept and implementation of a single source information system in nuclear medicine for myocardial scintigraphy (SPECT-CT data), Appl Clin Inf 1 (2010), 50-67. Chan KS, Fowles J, Weiner JP. Electronic health records and reliability and validity of quality measures: A review of the literature, Medical Care Research and Review 67 (2010) 503-527. Ammenwerth E, Spötl HP. The time needed for clinical documentation versus direct patient care, Methods Inf Med 48 (2009) 84-91. Forster M, Bailey C, Brinkhof MW, et al. Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral therapy programmes in resource-limited settings, Bulletin of the Word Health Organization 86 (2008), 939-947. Herzberg S, Fritz F, Rahbar K, Stegger L, Schäfers M, Dugas M. HIS-based support of follow-up documentation – concept and implementation for clinical studies, Appl Clin Inf 2 (2011), 1-17. Herzberg S, Rahbar K, Stegger L, Schäfers M, Dugas M. Concept and implementation of a computerbased reminder system to increase completeness in clinical documentation, Int J Med Inform 80 (2011), 351-358. AGFA.com [Internet]. Agfa Healthcare; c2011 [updated 2010 Mar 25; cited 2011 Jan 16]. Available from: http://healthcare.agfa.com/. SPSS.com [Internet]. Illinois: SPSS, Inc.; c2011 [cited 2011 Jan 16]. Available from: http://www.spss.com/. Hogan WR, Wagner MM. Accuracy of data in computer-based patient records, J Am Med Inform Assoc. 5 (1997) 342-355. CDISC.org [Internet]. Clinical Data Interchange Standards Consortium; c2011 [cited 2011 Jan 16]. Available from: http://www.cdisc.org/. McDonald AM, Knight RC, Campbell MK, et al. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies, Trials 7 (2006) 9. Embi PJ, Jain A, Clark J, Bizjack S, et al. Effect of a clinical trial alert system on physician participation in trial recruitment, Arch Intern Med 165 (2005) 2272-2280. Welker JA. Implementation of electronic data capture systems: Barriers and solutions, Contemporary Clinical Trials 28 (2007) 229-236. Staes CJ, Evans RS, Rocha BH, et al. Computerized alerts improve outpatient laboratory monitoring of transplant patients, J Am Med Inform Assoc 15 (2008) 324-332.


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Reporting Qualitative Research in Health Informatics: REQ–HI Recommendations Zahra NIAZKHANIa,b, Habibollah PIRNEJADa,b,1, Jos AARTSb, Samantha ADAMSb, Roland BALb a Department of Medical Informatics, Urmia University of Medical Science, Iran, b Health Care Governance, Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

Abstract. To develop a set of recommendations for authors of qualitative studies in the field of health informatics, we conducted an extensive literature search and also manually checked major journals in the field of biomedical informatics and qualitative research looking for papers, checklists, and guidelines pertaining to assessing and reporting of qualitative studies. We synthesized the found criteria to develop an initial set of reporting recommendations that are particularly relevant to qualitative studies of health information technology systems. This paper presents a preliminary version of these recommendations. We are planning to refine and revise this version using comments and suggestions of experts in evaluation of health informatics applications and publish a detailed set of recommendations. Keywords. Qualitative research, guidelines, health informatics, HIT systems

1. Introduction Qualitative research methods are increasingly valued in evaluation of health information technology (HIT) impacts [1]. This line of research can be described as ‘inductive’, ‘subjective’ and ‘contextual’ helping to understand social phenomena such as user perceptions, the context of system implementation or development, and the processes by which changes occur or outcomes are generated [2, 3]. Qualitative research is also characterized by using methods that are flexible to adjust to circumstances and sensitive to the social context of the study. On the one hand, these methods enable studying a small number of cases in detail, capturing data that is rich and complex, developing explanations at the level of meaning or micro-social processes rather than context-free rules, and answering ‘how’, and ‘why’ questions. On the other hand, possessing these features by itself challenges comparing the results of different qualitative studies, if the researchers do not follow more or less the same rules in conducting research and reporting results. From this perspective, applying criteria for qualitative studies both at the level of conducting research and reporting their results is considered advantageous [4]. Following concerns raised in the HIS–EVAL workshop about the quality of evaluation studies and their reports in health informatics [5], Talmon et al. took a fundamental step in developing the STARE–HI guidelines in order to improve the 1

Corresponding Author: H. Pirnejad. E-mail: [email protected]

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quality of evaluation reports [6]. This guideline was endorsed by major medical/health informatics organizations worldwide, and is now contributing to the vision of evidencebased health informatics. However, largely inspired by guidelines for reporting of quantitative biomedical studies (e.g., CONSORT and QUROM), the STARE–HI unintentionally falls short in taking several critical criteria pertinent to reporting qualitative HIT studies into account. To address this shortcoming of the STARE–HI, this paper aims to provide an initial set of recommendations for authors of qualitative HIT studies on how to present their research clearly and comprehensively.

2. Methods Pertinent papers, guidelines, and checklists specific for assessing or reporting of qualitative studies were searched in PubMed, Medline, google, and googlescholar from 1990 to September 2009. We also manually checked: the journals of ‘Qualitative Health Research’, ‘Journal of Evaluation in Clinical Practice’, ‘International Journal of Qualitative Methods’, and ‘Qualitative Research Journal’, the reference list of identified articles, the website of Qualitative Research in IS [7] and writing up a qualitative study [8], the instructions for authors and reviewers of qualitative research such as [9-15]. To develop a preliminary version of recommendations that are relevant for HIT research reports, the first and second authors selected and reviewed 48 most relevant publications found in our search. This preliminary version was shared with the other authors of this paper. As experienced qualitative HIT researchers, and editorial board members and reviewers of biomedical informatics journals, all the authors of this paper discussed the most important criteria and developed the following recommendations for Reporting Qualitative research in Health Informatics (REQ–HI). This short paper presents only the reporting recommendations that are most applicable for qualitative research reports and that have not been very well developed in the STARE–HI. A detailed description of recommendations for structuring good qualitative HIT reports will be published later.

3. REQ-HI Recommendations 3.1. Abstract and Keywords The abstract of qualitative HIT reports should be structured, yet short, with the same basic structure of quantitative research except the “Outcome measures”. The label “Results” is also replaced by “Findings” [10]. After a brief general subject matter, the objective or study question must be stated clearly and concisely. In addition to the type of HIT system and the study setting, the Methods section must note the data collection methods (e.g., focus groups), types of data (e.g., pictorial data), number of participants and the type of sampling method to recruit them, and the type of qualitative analysis. Only main findings and main conclusions directly derived from the findings particularly those of high relevance to the health informatics community should be stated here. To enhance retrieving these studies in search, terms denoting the approach such as ‘qualitative research’ (MeSH heading), ‘field research’, ‘qualitative evaluation’, ‘interviews’, ‘observations’, ‘focus groups’ (MeSH heading), ‘qualitative document analysis’, and ‘ethnography’ should be noted among the study key words.


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3.2. Introduction The main goals of the ‘Introduction’ in a qualitative HIT study are: 1) to present the rationale of the proposed study. The ‘Introduction’ should identify a problematic issue in recent HIT research or a gap that a qualitative study is able to address. 2) To present the rationale behind the study method. That is to inform the reader that addressing the study objective requires a qualitative approach. The strengths of qualitative research methods lie in explorative, hypothesis generating, and conceptual analysis. It should be clear from the ‘Introduction’ that the research methods build on these strengths. 3) To present the research question. Contrary to quantitative studies, qualitative studies are most likely not testing a prediction rather they have an exploratory or conceptual nature. Therefore, instead of developing a hypothesis, in the last paragraph, the authors should re-iterate the rationale for their proposed study and clarify their research question, the one that the study aims to explore, understand, or explain. Meanwhile, carefully reviewing the HIT literature will provide a context to justify the choice of qualitative study and to set the stage for the study question. Alternatively, a theory can be used to guide the research providing that the authors clarify why this is relevant, or what this theoretical perspective adds to our understanding of the problem at hand. 3.3. Methods In qualitative research, methodology greatly influences the findings. Therefore, it should contain sufficient information for the reader to asses the rigor of data collection process and the data analysis and interpretation. This section then must include: 3.3.1. The Type of Qualitative Approach The type of qualitative approach must be described in detail and explicitly to enable the reader to judge whether it fits with the study question. If necessary, the choice of methodology should be explained in relation to alternative methodology or in the case of using several methods, it should be indicated how they complement each other and why this combination is necessary. For example, if a research aimed to gain a deeper understanding of cognitive tasks that physicians undertake to write admission orders, a phenomenological approach with think-aloud observations would likely be more appropriate than a grounded theory approach using focus groups. 3.3.2. The Type of Data It is important to explain what the data set is composed of and why it is the most useful set to answer the study question. Any textual, audiovisual, and pictorial documents that are collected and used such as meeting scripts, implementation documents, screen shots, computer-printouts, patient records, computer-generated activity reports, pictures of work stations, etc. should be described in detail. Also the number of data collection events and their duration should be specified (e.g., how many hours of observations). A thorough description of the processes of handling the data set is also relevant in some circumstance such as using an interview guide, note-taking and transcribing, ensuring anonymity and confidentiality, etc. It is recommended to keep a timeline with the methodology used e.g., to mention which data was collected when or which documents belong to what phase of the study or system use (e.g., pre- or post-HIT implementation).

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3.3.3. Participants When sampling, the qualitative researchers do not aim to establish a random or representative sample of a population, rather to identify informant people who have information or experiences about the study subject. It should be argued why the selected recruitment strategy (e.g., purposive or convenience sampling) were the most appropriate to provide access to the type of knowledge sought by the study. Enough information should be provided to help the reader to understand what the sample represents and who initially was excluded and why. It is also relevant to document how participants were approached (e.g. face-to-face or telephone). The sample size (and whether or not the saturation of data was reached and in what way), important variations within participants (e.g., their prior experience of a HIT system), and even non-participation (in case there are relevant reasons behind this) should be reported. 3.3.4. Research Team and Reflexivity Researchers of a qualitative study are considered as one of the main study equipments themselves and are seen to have far greater influence on the Findings than quantitative researchers. Their characteristics, experience or training, assumptions, interests in the research topic, potential biases, influence on the data collection (e.g., choice of location), and their dual roles (e.g., user and researcher) should therefore be reported. 3.3.5. Analysis of Data Qualitative analysis is less standardized than statistical analysis. To enable readers to accept or challenge the reasoning of the researchers, or to assess how adequate or rigorous are the ‘Findings’, the authors must clearly describe the logic and any techniques used to analyze the entire data set. It should be clear who analyzed data with what inter-rater agreement (e.g., inter-observer or inter-analyst comparisons); how the codes, themes, or interpretations were developed; and whether any triangulation, audit trial, and member checking of the findings with the research participants were done. 3.4. Findings The main findings in relation to the original research question should be presented clearly. Not only the major themes but also diverse cases (e.g., negative ones) and minor themes should be described. The presentation of findings should be in a way to allow the readers to distinguish the data, the analytic framework used, and the interpretation. The authors should give an account of the data (e.g., what the user perception is) and also an interpretation of that (i.e., what this perception mean) [8]. Presenting direct participant quotations or field notes will help authors to communicate the themes or findings effectively and to back up their argument with evidence. A table or figure (e.g., of emerging themes) can be very helpful in clarifying the ‘Findings’. 3.5. Discussion Section The first paragraph of ‘Discussion’ is the best place to answer the research question clearly. The authors then should relate their findings to other studies and discuss the contribution that their study makes to existing knowledge or understanding of an issue but be very cautious in generalizing them to a wider world. They must discuss whether


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or not their findings are transferable to other settings. It is also worth that the authors evaluate and discuss their findings or interpretations in terms of reflexivity (e.g., reflecting upon the researcher’s own influence on the construction of meanings or study process) and credibility (e.g., conducting triangulation or respondent validation). It is also better to comment on whether or not the study has had any impact on for example future updates, trainings, and management of HIT systems.

4. Conclusion This initial set of recommendations was developed to promote a clear and comprehensive reporting of qualitative HIT research. Given the diversity of methods for conducting qualitative HIT studies, however, this version of REQ–HI recommendations by no means provides detailed recommendations on all relevant aspects. We kindly invite editors, reviewers, and readers of biomedical informatics journals to comment on this version in order to improve its quality and applicability.

References [1]

[2] [3] [4] [5]

[6] [7] [8] [9]

[10] [11] [12] [13] [14] [15]

Niazkhani Z, Pirnejad H, Berg M, Aarts J. The Impact of Computerized Provider Order Entry (CPOE) Systems on Inpatient Clinical Workflow: A Literature Review. J Am Med Inform Assoc. 2009;16(4):539-49. Kaplan B, Shaw NT. Future directions in evaluation research: people, organizational, and social issues. Methods Inf Med. 2004;43(3):215-31. Ash JS, Guappone KP. Qualitative evaluation of health information exchange efforts. J Biomed Inform. 2007;40(6 Suppl):S33-9. Cohen DJ, Crabtree BF. Evaluative criteria for qualitative research in health care: controversies and recommendations. Ann Fam Med. 2008;6(4):331-9. Ammenwerth E, Brender J, Nykanen P, Prokosch HU, Rigby M, Talmon J. Visions and strategies to improve evaluation of health information systems. Reflections and lessons based on the HIS-EVAL workshop in Innsbruck. Int J Med Inform. 2004 30;73(6):479-91. Talmon J, Ammenwerth E, Brender J, de Keizer N, Nykanen P, Rigby M. STARE-HI--Statement on reporting of evaluation studies in Health Informatics. Int J Med Inform. 2009;78(1):1-9. Website of the Qualitative Research in IS. Int J Qual Health Care [cited October 6, 2010]; Available from: http://www.qual.auckland.ac.nz/ Advice on writing up a qualitative study. [cited 2010 6th of October ]; Available from: http://www.psy.dmu.ac.uk/michael/qual_writing.htm CASP. Qualitative research: appraisal tool. 10 questions to help you make sense of qualitative research. 2006 [cited November 02, 2010]; Available from: http://www.sph.nhs.uk/sphfiles/Qualitative%20Appraisal%20Tool.pdf/?searchterm=qualitative%20research Rowan M, Huston P. Qualitative research articles: information for authors and peer reviewers. CMAJ. 1997;157(10):1442-6. Kuper A, Lingard L, Levinson W. Critically appraising qualitative research. BMJ. 2008;337:a1035. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349-57. Malterud K. Qualitative research: standards, challenges, and guidelines. Lancet. 2001;358(9280):483-8. Cote L, Turgeon J. Appraising qualitative research articles in medicine and medical education. Med Teach. 2005;27(1):71-5. Qualitative research review guidelines – RATS. [cited October 13, 2010]; Available from: http://www.biomedcentral.com/info/ifora/rats

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Cell seeding of Tissue Engineering Scaffolds studied by Monte Carlo simulations Andreea ROBUa,1, Adrian NEAGU b, Lacramioara STOICU-TIVADARa a University “Politehnica” Timişoara, Romania b Center for Modeling Biological Systems and Data Analysis, Victor Babeş University of Medicine and Pharmacy, Timişoara, Romania

Abstract. Tissue engineering (TE) aims at building multicellular structures in the laboratory in order to regenerate, to repair or replace damaged tissues. In a wellestablished approach to TE, cells are cultured on a biocompatible porous structure, called scaffold. Cell seeding of scaffolds is an important first step. Here we study conditions that assure a uniform and rapid distribution of cells within the scaffold. The movement of cells has been simulated using the Metropolis Monte Carlo method, based on the principle that cellular system tends to achieve the minimum energy state. For different values of the model parameters, evolution of the cells’ centre of mass is followed, which reflects the distribution of cells in the system. For comparison with experimental data, the concentration of the cells in the suspension adjacent to the scaffold is also monitored. Simulations of cell seeding are useful for testing different experimental conditions, which in practice would be very expensive and hard to perform. The computational methods presented here may be extended to model cell proliferation, cell death and scaffold degradation. Keywords. differential adhesion, dynamic cell seeding, scaffold, tissue construct

1. Introduction Tissue engineering (TE) is a relatively new field of biomedical research. Closely related to regenerative medicine, TE develops new therapies for patients who suffered tissue damage [2, 7]. A widely used approach to TE consists in culturing cells on a porous scaffold made of a biocompatible and biodegradable material. Cells are harvested from the patient, expanded in Petri dishes, and seeded onto scaffolds. The optimization of cell seeding is essential for the development of functional tissue constructs in vitro [1, 2]. It has been shown that if the cell seeding is uniform, the development of tissue constructs is more rapid and their mechanical properties are closer to the ones of native tissues. The mechanical properties of tissue constructs are largely due to the synthesis of extracellular matrix (ECM) – a web of proteins produced by cells. ECM production depends on the quality of cell seeding. If cell seeding is uniform, the culture medium equally reaches all the cells in the scaffold, providing gas and nutrient transfer to them. 1

Corresponding Author. Andreea Robu, Faculty of Automation and Computers, Blvd. Vasile Parvan, No. 2, 300223, Timisoara, Romania; E-mail: [email protected]

A. Robu et al. / Cell Seeding of Tissue Engineering Scaffolds Studied by Monte Carlo Simulations

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Thus, a proper cell development and cell proliferation is ensured. Currently, the mechanical resistance of tissue constructs grown in the laboratory is about one order of magnitude below the corresponding native tissues [7, 8]. The objective of this study is to find the optimal conditions that lead to a uniform and rapid distribution of cells in the scaffold. The basic principle that underlies this study is the differential adhesion hypothesis (DAH) proposed by Steinberg, which states that constituent cells of a tissue tend to reach configuration of lowest energy of adhesion; that is, cells tend to establish largest possible number of strong bonds with their environment [4, 5]. Cells interact with each other due to cohesion forces, and adhere to scaffold via adhesion forces. Thus, the selfassembly of cells into multicellular constructs is governed by the interaction energy between cells and by the interaction energy between cells and the scaffold [4, 5].

2. Methods The studied model system consists of a cell suspension located near a porous scaffold, bathed in culture medium. The model is built on a cubic lattice (of 50×50×150 nodes). The Oz axis is the longitudinal axis of the system. The length unit, equal to one cell diameter, is the distance between two adjacent nodes. The cell suspension occupies the (with ), where each node of the network is occupied either by a region each node is occupied either by an cell or by a medium particle. In the region immobile (scaffold) particle or by a medium particle; this region models the scaffold, with pores filled with culture medium, and, eventually, by cells [3, 5]. The total adhesion energy of a system composed of t types of cells in the vicinity of a substrate can be brought to the form [5]: (1) is the number of links between two particles (of type and ), is the where number of links between the cells of type and the substrate; is the cell-cell interfacial tension, whereas is the cell-substrate interfacial tension [5]. To simulate the evolution of the cellular system in the vicinity of the scaffold, we used the Metropolis Monte Carlo algorithm. Running Monte Carlo Steps (MCS) consists of exchanging a cell position with another cell or a culture medium particle from its vicinity [3, 5]. The current study is based on Monte Carlo simulations performed for different values of the following model parameters: (i) the cohesion energy between cells, (ii) the adhesion energy between cells and scaffold, (iii) the radius of pores and (iv) the radius of the orifices that connect the pores. As output parameters we monitored (i) the centre of mass of all cells, (ii) centre of mass of seeded cells and (iii) the concentration of the cells remained in suspension. The centre of mass of seeded cells is an indicator of cell distribution within the scaffold; its dependence on elapsed MCS is a measure of the rate of cell seeding. Since experiments on dynamic cell seeding of scaffolds monitor the concentration of the cell suspension adjacent to the scaffold [2], we also plotted this parameter versus the elapsed MCS.

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3. Results and Discussions Table 1 presents the values of the input parameters that we used in the simulations, and the values obtained for the output parameters; it also points to the relevant figures. The input parameters values were selected for the current study on empirical basis, after many previous tests that shown which are the optimal energy values and the relevance of the scaffold’ porosity for an uniform cell seeding. Table1. Values of input and output parameters in representative simulations. Cell-cell Cell-scaffold Radius Radius of MCS interaction interaction of pores circular orifices energy energy 0

Plateau of for seeded cells

0.6

5

2

80 000

0.6

5

2

80 000 110,110,100

0

0.6

8

1

0.25

5

2;3; 4;5 2

0;0.4;0.8

80 000 80 000

110

110;110; 100;100 75

Set of Plateau of Plateau of for all fraction of simulations, Figure cells in cells suspension 90 0.2 I, Fig. 1a,1b,1c 90,90,60 0.2;0.2;0.5 II, Fig. 2a,2b,2c 90;90; 0.2;0.2; III, Fig. 80;60 0.3;0.5 3a,3b,3c 35 0.9 IV, Fig. 4a,4b,4c

The volume percent concentration of the cells in the initial suspension was 1%. As shown on Fig. 1a, in about 7×104 MCS a stationary state is reached, in which the centre of mass of seeded cells is very close to the centre of mass of the scaffold, (Fig. 1a, upper curve). This indicates that the distribution of the cells in the scaffold is uniform (see also the snapshot in Fig. 1c). The centre of mass of all cells reaches a plateau at because a part of the cells remain in suspension. In Fig. 1b we observe that already at 2×104 MCS about 75 % of the cells penetrated the scaffold, and soon a plateau is reached with 20% of the cells remaining in suspension. is reached later because cells rearrange inside the scaffold. However, the plateau of In experiments, the cell suspension is permanently homogenized (by magnetic stirring); therefore, the vast majority of the cells penetrate the scaffold. In our simulations, however, the mobility of the cells is described by the same algorithm, both in suspension and in the scaffold, so part of the cells will remain in suspension (Fig. 1c). Further refinements of the model should include the possibility to ascribe a larger motility for cells (and aggregates of cells) in suspension. In the second set of simulations, with parameters given in the second row of Table 1, we varied the cohesion between cells. For a cell-cell interaction energy of 0.8 cell aggregates emerge (Fig. 2c), and the penetration of cells into the scaffold is slower. ), and Note, however, that the cell-substrate interfacial tension is still negative, (

Fig.1a The centre of mass of all cells The centre of mass of seeded cells

Fig.1b Cell concentration in suspension (Table 1, row 1)

Fig.1c Final configuration represented using VMD[9]


Fig. 2a The centre of mass of all cells The centre of mass of seeded cells

Fig. 2b Cell concentration in suspension (Table 1, row 2)

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Fig.2c Final configuration represented using VMD[9]

cells enter the scaffold, albeit slowly, while also preserving cell-cell contacts. Figure 2b shows that after 8×104 MCS more than half of cells are still in suspension. In the third set of simulations (parameters in Table 1, row 3), we varied the radius of the orifices between pores. Surprisingly, an increase of the radius of orifices from 2 to 3 cell diameters did not influence the seeding rate (Fig 3a, crosses and dots) and the final extent of seeding (the plateau of the plots shown as + sings and dots on Fig. 3b). Moreover, as the radii of the orifices increased, the fraction of seeded cells decreased; circles (squares) on Fig. 3b refer to orifice radius of 4 (5) cell diameters.

Fig. 3a The centre of mass of all cells The centre of mass of seeded cells (Table 1, row 3)


Fig.3c Final configuration represented using VMD [9]

In the fourth simulation (parameters in Table 1, row 4) the attraction between cells is higher than twice the cell-scaffold attraction, making the cell-scaffold interfacial energy positive. Our simulations show clearly that the emergent configuration is a result of a tug-of-war between cell-cell and cell-substrate interaction. This has been suggested earlier on the basis of a careful experimental study [6]; our approach brings quantitative arguments for the correctness of this observation.

Fig. 4a The centre of mass of all cells The centre of mass of seeded cells (Table 1, row 4)


Fig. 4c Final configuration represented using VMD[9]

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4. Conclusions This work presents a lattice model and a computational algorithm able to evaluate energetic and geometric factors that may be tuned to assure optimal cell seeding. Scaffold pore sizes and the diameter of the orifices between pores influence cell seeding only in extreme conditions: if the orifices are small (comparable to the cell diameter), or if they are large (exceeding half of the pore diameter), such that the scaffold is not contiguous and does not offer enough biomaterial to be attached to. If cells do not adhere to each other, but they adhere to the scaffold, the seeding is rapid and cell distribution is uniform. If the cell-cell interaction energy is nonzero, but small enough to ensure a negative cell-scaffold interfacial tension, uniform distribution is reached, but the process is slower. Seeding is severely hampered if the cell-cell interaction energy is larger than twice the cell-substrate interaction energy, rendering the cell-scaffold interfacial tension positive. Moreover, if the cell-cell interaction energy is high, regardless of the interaction between the cells and the scaffold, cells tend to aggregate and their penetration into the scaffold is slowed down drastically. Although it accounts for the competition between cell-cell and cell-substrate interaction energies, our study of the impact of cell aggregation on the rate of cell seeding is not accurate, since the present algorithm is unable to describe the fast movement of cell aggregates in the stirred suspension. Future developments of the computational framework proposed here need to incorporate a hybrid algorithm that differentiates between individual cell motility and the movement of cells and aggregates of cells with the flow of cell culture medium. Such a development is especially appealing, since it would enable one to simulate also perfusion cell seeding [1]. Also, future models might account for cell proliferation, cell death and scaffold degradation.

References [1]

[2]

[3]

[4] [5] [6]

[7] [8] [9]

Francioli, S.E. Candrian, C. Martin, K. M. Heberer, Martin, I. Barbero. A. Effect of three-dimensional expansion and cell seeding density on the cartilage-forming capacity of human articular chondrocytes in type II collagen sponges. Journal of Biomedical Materials Research. Part A. (2010) 95(3):924-931. Vunjak-Novakovic Gordana, B. Obradovic, I. Martin, P.M. Bursac, R.Langer and L.E.Freed DynamicCell Seeding of Polymer Scaffolds for Cartilage Tissue Engineering, Biotechnology Progress Volume 14, Issue 2, 1998, 193–202 Robu, A. P. Neagu, A. Stoicu-Tivadar, L. A computer simulation study of cell seeding of a porous biomaterial, Computational Cybernetics and Technical Informatics (ICCC-CONTI), 2010 International Joint Conference, ISBN: 978-1-4244-7432-5, 225-229 Foty Ramsey, A. Malcolm S. Steinberg, The differential adhesion hypothesis: a direct evaluation, Developmental Biology,Volume 278, Issue 1, 1 February 2005, 255-263 Neagu, A. Kosztin, I. Jakab, K. Barz, B. Neagu, M. Jamison, R. Forgacs, G. Computational Modeling of Tissue Self-Assembly, Modern Physics Letters B, Volume 20, Issue 20, (2006), 1217-1231 Ryan, P.E. Foty, R.A. Kohn, J. and Steinberg, M.S. Tissue spreading on implantable substrates is a competitive outcome of cell– cell vs. cell–substratum adhesivity, Proc. Natl. Acad. Sci. U.S.A. (2001) 98(8):4323-4327. Griffith L.G. and Naughton, G. Tissue engineering - current challenges and expanding opportunities, Science 295 (5557) (2002), 1009 Semple, J.L. Woolridge N.and Lumsden, C.J. In vitro, in vivo, in silico: Computational systems in tissue engineering and regenerative medicine, Tissue Engineering 11 (3-4) (2005), 341-356. Humphrey, W. Dalke, A. Schulten, K. VMD – Visual Molecular Dynamics, J. Mol. Graphics 14, (1996) (33-38) (http://www.ks.uiuc.edu/Research/vmd/).


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The ONCO-I2b2 Project: Integrating Biobank Information and Clinical Data to Support Translational Research in Oncology Daniele SEGAGNIa, Valentina TIBOLLOa, Arianna DAGLIATIc, Leonardo PERINATIa, Alberto ZAMBELLIa, Silvia PRIORIa, Riccardo BELLAZZIb,a a IRCCS Fondazione S. Maugeri, Pavia, Italy b Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy c Institute for Advanced Studies, Pavia, Italy

Abstract. The University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM), has recently started an IT initiative to support clinical research in oncology, called ONCO-i2b2. ONCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) NIH project. Using i2b2 and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried. The core of the integration process stands in retrieving and merging data from the biobank management software and from the FSM hospital information system. The integration process is based on a ontology of the problem domain and on open-source software integration modules. A Natural Language Processing module has been implemented, too. This module automatically extracts clinical information of oncology patients from unstructured medical records. The system currently manages more than two thousands patients and will be further implemented and improved in the next two years. Keywords. I2B2, oncology research, biobanks, natural language processing, translational research, hospital information system integration

1. Introduction ONCO-i2b2 is a project funded by the Lombardia region, in Italy, which aims at supporting translational research in oncology. The project exploits the software solutions implemented by the Informatics for Integrating Biology and the Bedside (i2b2) research center, an initiative funded by the NIH Roadmap National Centers for Biomedical Computing and headed by Partners HealthCare Center in Boston [1]. The i2b2 project developed a data warehouse and a set of software solutions that are based on an architecture called “hive”. The “hive” has different software cells devoted to data extraction, data manipulation or data analysis tasks [2]. Within ONCO-i2b2, the University of Pavia and the hospital IRCCS Fondazione S. Maugeri (FSM) have integrated the i2b2 infrastructure with the FSM hospital information system (HIS) and with a cancer biobank that manages both plasma and cancer tissues. The integration with the HIS provides the access to all the electronic

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D. Segagni et al. / The ONCO-I2b2 Project

medical records of cancer patients. The majority of the data collected in the FSM HIS is represented by textual reports. It was therefore necessary to develop and integrate inside the ICT architecture a Natural Language Processing (NLP) module in order to extract important information and clinical tests results, such as patients’ histological reports [3]. The oncology biobank provides bio-specimens prepared from a collection of blood and tissue samples, taken with the informed consent of healthy individuals and oncologic patients. The aim of this paper is to describe the basic steps of the integration process and to present the current status of the ONCO-i2b2 project.

2. Method The ONCO-i2b2 software implemented at the FSM hospital is designed to integrate data from many different sources and collected for different purposes, in order to allow researchers querying and analyzing the vast amount of information coming from the clinical practice. The main data sources that we have integrated into the i2b2 data warehouse are the hospital pathology unit, the biobank and the HIS. In the following we will describe the detail of the integration process. 2.1. FSM Pathology Operative Unit and Biobank Data associated to the biospecimens stored inside the biobank are almost automatically uploaded from the hospital pathology unit. A semi-automatic procedure has been implemented to populate the biobank database in order to decrease the time of insertion and reduce the possibility of human error. One of the major efforts made during this implementation was to anonymize each cancer biospecimen, by creating a twodimensional DataMatrix barcode that does not include any direct reference to the donor patient. Cancer tissues or plasma samples are selected by researchers and placed in new tubes labeled with the new barcode. Granted users may retrieve the information related to the donors through a specialized software application that also show the patient’s informed consent. The biobank database is periodically synchronized (several times during the day) in order to keep biobank samples data constantly updated. The information on the biological samples contained in a biobank are loaded into the i2b2 data warehouse through a complex series of Extract, Transform, Load (ETL) operations that involve data extraction, processing and mapping in the data warehouse [4]. The ETL activity was performed relying on the KETTLE [5] developed within the Pentaho project [6]. Table 1 shows the number of patients and biological samples currently available in the biobank divided by hospital medical unit of origin and type. Figure 1 shows the different steps of the integration process. Step 1 is the semi automated data extraction from the pathology unit, step 2 describes the anonymization process involving biosamples before they are stored in biobank. Step 3, instead, represents the i2b2 data warehouse where data from different sources are collected through ETL transformations. Step 4 shows how the information coming from the HIS is integrated, too.

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Table 1. Biobank biospecimens count divided by hospital operative unit of origin and type. Table data are related to the period 1-12-2009 - 15-01-2010. FSM Unit Senology Surgery

Patient 237 75

Tissue 729 567

Plasma 729 243

312

1296

972

TOTAL

1. PATHOLOGY MEDICAL UNIT Asynchronous biobank database update

Anatomical Pathology Database 2. BIOBANK

Anonymized barcode

Biobank Database

E T L

3. I2B2 Reseracher Client

I2B2 Data Warehouse

E T L

I2B2 Web Server

4. FSM – INFORMATION SYSTEM

FSM Electronic Medical Record

Hospital Information System Database

Figure 1. ICT architecture designed to integrate information from the FSM medical units and the hospital information system.

2.2. FSM Hospital Information System The information collected in the FSM HIS is made available to the I2B2 service through an ETL process that transforms the medical information of interest in concepts that will be queried in the research phase. Some of these oncological concepts refer to various key facts collected in the pathological anatomy electronic report that are only available in textual format. A NLP software module has thus been developed to extract this information from the FSM HIS for each cancer patient that have at least one biological sample stored in the biobank. To address the problem of extracting structured information from pathology reports for research purposes, we developed an NLP module based on the

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GATE system [7] to automatically identify and map anatomic and diagnostic noun phrases found in full-text pathology reports to SNOMED concept descriptors. The pathology unit uses unstructured or semi-structured text documents to represent this information. Therefore, we identified a set of regular expressions that matched clinical phrases commonly found in pathology reports; such expressions are then properly processed by the NLP parser. In particular, the retrieved data relate to a set of oncological SNOMED codes, to the values derived from clinical tests, like scoring breast carcinomas stained with HercepTest or to the scores of the expression of Ki-67, a nuclear antigen protein used to determine the growth fraction of tumors [8]. The system has been internally validated by a manual verification by the medical experts involved in the study on a subset of 100 cases with 100% accuracy. This module is now a part of the overall data warehouse management strategy. 2.3. The Integrated Architecture: i2b2 The i2b2 data warehouse, called Clinical Research Chart (CRC), is designed to manage data from clinical trials, medical record systems and laboratory systems, along with many other types of clinical data from heterogeneous sources [9]. The CRC stores this data in three tables, the patient, the visit and the observation tables. The three data tables, along with two of the lookup tables (concept and provider), are the main components of the so-called star schema of the data warehouse. The most important aspect of the construction of a star schema is identifying what is a “fact”. In healthcare, a logical fact is an observation on a patient. The dimension tables contain further descriptive and analytical information about attributes in the fact table. The i2b2 infrastructure installed at FSM provides a web-based access to any type of data described in the previous paragraphs. Data information are stored in the i2b2 data warehouse through complex ETL transformations following a cancer-specific ontology that combines atomic information to create a well defined medical observation. The extracted information can be analyzed through the i2b2 web client with appropriate plug-ins specially configured [10, 11].

3. Results Since December 2010 the entire software system has been installed and is currently running at FSM. The aim of the implementation of the architecture was to allow the FSM researchers to exploit i2b2 query capabilities relying on the user-friendly web interface available. To achieve this goal we focused on the development of data integration processes, on the design of NLP modules and on the management and anonymity of the biological samples contained in biobank. Integration of these data from heterogeneous sources required several key steps: i) creation of a specific software to upload the information available in the pathology unit; ii) generation of new barcodes when the biosamples are archived in biobank; iii) design and configuration of an NLP software module to extract information from unstructured text documents relevant to the clinical characterization of patients in cancer research; iv) creation of ETL transformations to populate the i2b2 data warehouse with concepts related to cancer research. Currently, the i2b2 instance installed in FSM consists in 2214 patients (312 of them have at least one biological sample in the cancer biobank), 25826 visits, 163


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concepts (divided into demographic data, diagnoses, clinical measurements, histological reports, therapies and biobank samples) and 93680 observations.

4. Discussion The novel IT architecture created at FSM is a concrete example of how integration between different information from heterogeneous sources can be correctly implemented and make available for scientific research. In order to continuously improve i2b2 easiness of use for hospital researchers, we added at the i2b2 web client application novel plug-ins for data export and for phenotype exploration [12]. One of the major efforts made during the implementation of the i2b2 extensions was to be fully compliant with i2b2 development guidelines, so that our software modules and architecture can be reused by the other researchers of the i2b2 community. Exploiting the potential of this IT architecture, the next steps of the project will involve the extension of the data set imported by the HIS as well as the management of data from laboratory tests. We also plan to continue extending the capabilities of the FSM i2b2 architecture by implementing new plug-in devoted to data analysis; in particular, we are working on an extension of the i2b2 query engine by adding temporal query capabilities Finally, another important point for the future development of the project will be the integration of patient’s genotype data, which will require careful evaluation both in terms of the data representation and storage and of data security and privacy. Acknowledgements. This paper describes the ONCO-i2b2 project, funded by the Lombardia Region, in Italy. We gratefully acknowledge Prof. Carlo Bernasconi and the Collegio Ghislieri in Pavia for their active support.

References [1]

Murphy SN, Mendis M , Hackett K, et al. Architecture of the open-source clinical research chart from Informatics for Integrating Biology and the Bedside, AMIA Annu Symp Proc. (2007), 548-52. [2] Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc. (2010), 124-30. [3] Jurafsky D, Martin JH. Speech and Language Processing, An Introduction to Natural Language Processing,Computational Linguistics, and Speech Recognition Second Edition, Prentice Hall, 2008. [4] Kimball R, Ross M, Thornthwaite W, Mundy J, Becker B. The Data Warehouse ETL Toolkit (2nd edition), 2008 [5] Pentaho Corporation, Pentaho Data Integration Kettle Documentation (http://kettle.pentaho.com), 2011 [6] Bouman R, J. van Dongen. Pentaho Solutions , Wiley, 2009 [7] The University of Sheffield, GATE software (http://gate.ac.uk/sale/tao/split.html), 2011 [8] Broyde A , Boycov O, Strenov Y, Okon E, Shpilberg O , Bairey O. Role and prognostic significance of the Ki-67 index in non-Hodgkin's lymphoma, Am J Hematol. 2009 Jun;84(6):338-43. [9] Partners HealthCare Systems, I2B2 software (v.1.5) documentation, 2008. [10] Mendis M, Wattanasin N, Kuttan R, et al. Integration of Hive and cell software in the i2b2 architecture, AMIA Annu Symp Proc. (2007), 1048. [11] Murphy SN, Churchill S, Bry L, et al. Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res. (2009), 1675-81 [12] Bellazzi R, Segagni D et al. R Engine Cell: integrating R into the i2b2 software infrastructure, J Am Med Inform Assoc (2011)

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IT Infrastructure Components to Support Clinical Care and Translational Research Projects in a Comprehensive Cancer Center Hans-Ulrich PROKOSCHa,b1, Markus RIESb, Alexander BEYERb, Martin SCHWENKb, Christof SEGGEWIESb, Felix KÖPCKEa, Sebastian MATEa, Marcus MARTINb, Barbara BÄRTHLEINa, Matthias W. BECKMANNc,d, Michael STÜRZLe, Roland CRONERf, Bernd WULLICHg, Thomas GANSLANDTb, Thomas BÜRKLEa a Chair of Medical Informatics, University Erlangen-Nuremberg, Germany b Medical Informatics & Comm. Center, c University Cancer Center Erlangen, d Dept. of Obstetrics and Gynecology, e Division of Molecular and Exp. Surgery, f Dept. of Surgery, g Dept. of Urology, University Hospital Erlangen, Germany

Abstract. This paper presents the concept of an integrated IT infrastructure framework established at the comprehensive cancer center at the University Hospital Erlangen. The framework is based on the single source concept where data from the electronic medical record are reused for clinical and translational research projects. The applicability of the approach is illustrated by two case studies from colon cancer and prostate cancer research projects. Keywords. Comprehensive cancer, center, cancer documentation, single source concept, translational research, IT infrastructure framework

1. Introduction Oncology care is provided in complex transsectoral and interdisciplinary networks of service providers. Within cancer research in recent years we have seen a massive growth in data, especially when molecular, genomic and clinical data shall be linked [1]. In Germany comprehensive cancer centers have been established in order to provide centers of excellence for cancer care, medical education as well as clinical and translational cancer research. Traditionally however, many data collections and IT components in hospitals and research institutions have been developed and implemented independently from each other and typically without any crosslinks. In this context, Beckmann and colleagues have complained about the enormous multiplied documentation requirements for physicians [2]; Shortliffe and Sondick have emphasized, “if the submission of data for research and monitoring purposes requires 1

Corresponding author: E-mail: [email protected]

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an extra step, . . . the process will likely fail” [3]. This has lead to the design and implementation of integrated informatics research platforms on one side [4] and single source solutions [5] on the other side. In the implementation phase of the Erlangen University Cancer Center (UCCE) in 2007/2008 it has been realized, that a comprehensive and integrated information technology framework with a high level of data reuse will be a major pillar of a successful comprehensive cancer center. In this publication we describe the architecture of such a framework supporting both: cancer care and cancer research. We further present two small case studies illustrating the value gained already from this implementation.

2. Methods At Erlangen University Hospital a comprehensive workflow-based electronic medical record system (EMR Soarian® from Siemens; compare [6]) has been stepwise introduced within the last decade. Furthermore until 2008 clinical cancer registration was still performed as separate data entry based on paper chart review or pathology reports. For this purpose at Erlangen University Hospital an Oracle-based proprietary cancer documentation system (TUREK-2) has been established. Before the UCCE was started clinical trial documentation was based on paper or using individual software solutions. Biobanking was decentralized with specimen tracking and annotation data often stored in excel sheets. When in 2007 the UCCE IT infrastructure concept was defined, it was decided that a single source approach with the Soarian® EMR closely connected with the clinical cancer registry database as core components should be pursued. Further, those core components should be complemented by commercially available standard products wherever possible. Thus the requirement specification consisted of an integrated framework comprising 1) the Soarian® electronic medical record, 2) the clinical cancer registry database, 3) a centralized biobanking management software, 4) a central clinical trials database, 5) a flexible clinical data warehouse and 6) standard services to assure compliance with data protection requirements in this environment. Keeping the single source documentation approach in mind, it implied that data should be captured only once at its origin and be afterwards available for multiple reuse. Ideally, digital structured data acquisition should be an integrated part of the clinical treatment process. Thus, an analysis of the clinical workflows related with the various steps in cancer care was performed. Based on this the Soarian® electronic medical record system has been extended with numerous workflow-supported assessment forms for the documentation of cancer anamneses, diagnostic data, therapy documentation and follow-up data. Additionally all data entry forms were based on the German wide standardized definition of a minimal basic cancer dataset. The therapeutic decision process for cancer patients, pursued within interdisciplinary cancer conferences has been supported with conference planning and documentation forms. The proprietary homegrown clinical cancer registry database has been substituted by GTDS® (a cancer registration system once developed with funding support of the German Ministry of Health and the German Cancer Society and today used in more than 60 clinical cancer registries throughout Germany) [7]. Biobank management support is provided by the commercially available Starlims® system. The GCP-certified commercially available clinical trials management system SecuTrial® has been established as a campus-wide platform for clinical trials. In addition to the existing

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commercial Cognos® data warehouse, the I2B2 toolbox has been evaluated and established as a user-friendly and flexible clinical data warehouse [8, 9]. Finally, secure data flow between the framework´s components and compliance to the German data protection law is supported by standardized modules provided by the German Technology and Method Platform for Networked Medical Research (TMF) [10].

3. Results The value of the IT infrastructure framework established at the UCCE shall be illustrated with two early case studies, where the above components have been applied. 3.1. The Polyprobe Project Polyprobe is a multicentric research project, aiming at validating the major predicitive/prognostic genes for colorectal cancer in a prospective diagnostic study by applying novel automatized nucleic acid extraction procedures from formalin-fixed paraffine-embedded tissues and quantitative RT-PCR procedures for high-throughput gene expression analyses of 61 marker genes. Within a period of 3 years 650 patients shall be included in the study. The IT concept within this project completely follows the single source idea. Nine assessment forms have been implemented within the EMR to capture diagnostic, therapeutic and study-specific data integrated into the colon cancer treatment process. Within the EMR those data are identified by a hospital-wide patient identification as well as a pseudonym generated in advance for all study participants. Patient consent is documented within the EMR as well. After a final quality validation by a research physician, those data are flagged to allow the export of pseudonymized records into a CSV format, which directly matches the import format of the SecuTrial® clinical trials management system. Thus, regular data import of quality assured data into the research database is supported. Biospecimens extracted within surgery or endoscopy are transferred to the pathology department for diagnostic purposes as well as storage within the UCCE biobank for further research analysis. Those specimens stored for research purposes are identified with special probe identification numbers, which are documented as linking information within the patient´s pathology report in the EMR and also imported into probe related records in the SecuTrial® system. Besides its batch import functionality SecuTrial® provides secure web-based data entry forms which support direct eCRF-based data entry for the second study center (Frankfurt University Hospital) which has not yet been able to also implement a single source approach. Until today 141 patients have been enrolled into the study at Erlangen and were documented within the EMR. From those, data of 20 patients have currently been imported into SecuTrial® and released for monitoring purposes. The external project monitors use the SecuTrial® monitoring workflows for their study specific quality management process. 3.2. The German Prostate Cancer Consortium Database The German Prostate Cancer Consortium comprises a group of more than 70 urologists, pathologists and basic researchers throughout Germany. Founded in 2003 their aim is to improve prostate cancer research with interdisciplinary and crossinstitutional cooperation. For this purposes between 2007 and 2009 a web-based joint


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research database has been established including data on prostate cancer diagnosis, therapy and follow-up as well as the characterization of biospecimens collected at the participating centers. Data capture for this database was provided through web-based data entry screens. Despite the high research interest of all partners this solution was finally not accepted because it required time-consuming manual data entry of parameters which usually in similar form have already been documented in the local medical record system. Thus, it was decided to move towards a single source/data warehouse approach, reusing data already documented in local electronic medical records. Urology clinics at Erlangen and Münster University hospital were chosen as pilot centers, since both of them had already established a comprehensive prostate cancer documentation within their EMR system. For data protection reasons a two level architecture was established using three I2B2 installations specifically extended and adapted for this scenario. Every participating partner (currently Münster and Erlangen) has a local I2B2 installation. Datasets are regularly exported from the EMR systems, pseudonymized and imported into the local I2B2 data warehouse. Thus, those local I2B2 instances do already provide query and analysis features for the respective Urology Clinics on their “own” data. Regularly researchers can initiate transfer of further anonymized data from the local instances into one common I2B2-DPKKResearch Database. This central I2B2-instance provides a password-protected webbased secure query interface for all DPKK members.

4. Discussion Due to the complex structure of oncology documentation, which originates over long treatment periods in different clinical disciplines, implementation of an IT-based documentation process is a complex mission. Oncology data are generated by different clinical specialties, clinical care documentation and research databases are traditionally separated, biological and molecular research based on high-throughput systems is often not linked with clinical research. Translational research project in future will need integrated efficient data management platforms which can easily be accessed by various data analysis and data mining tools. The caBIG initiative in the U.S. has aimed at mastering this challenge supported by large funding efforts and a variety of gridbased tools have been developed and applied in various scenarios [11]. Reusing the electronic medical record for clinical research has been identified as one large challenge for medical informatics, therefore tools as the caBIG modules need to be closely integrated with EMR databases [12]. McConnell and colleagues for example have presented a pilot deployment of caTRIP at Duke Comprehensive Cancer Center [4]. Ochs and Casagrande have described their view on “information systems for cancer research” and provided an overview of the systems and interactions needed to handle clinical trials and high-throughput data in cancer research. Their vision was that such systems should ideally interact gracefully with institutional systems for clinical care and would utilize institutional IT infrastructure and expertise. Large parts of their vision have been implemented at Erlangen University Hospital within the last three years. Workflow-supported EMR-documentation linked with the above described single source concept has enhanced such documentation efforts, making the data available for clinical care (including billing, quality assurance programs and discharge letter creation), clinical cancer registries and research purposes at the same time. Pseudonymization tools developed to meet national data protection

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requirements could be integrated seamlessly into the transfer processes between the EMR and research databases. In the above described case studies the CSV files exported from the EMR database are currently only imported into the SecuTrial® or the I2B2 databases respectively. In a next step those data will also be used as upload-/import-files for the Erlangen Cancer Registry. Additionally, in the future UCC defined core data records of all cancer patients can be exported from the EMR and imported into a joint I2B2-based UCC research platform. Having linked those data also with the identifiers of the biospecimens within the Starlims® biobank management system, those data can also be used as clinical annotations for the biobank. This illustrates the opportunities arising based on the integrated IT infrastructure framework implemented at Erlangen Comprehensive Cancer Center, making EMR data available for multiple secondary use purposes. Nevertheless, even though this paper illustrates the successful implementation of a single source approach, we shall not neglect that on a semantic and process level, implementing those data reuse concepts has been quite complex. Major challenges which needed to be mastered were related to the definition of common cancer specific minimal data sets and the alignment of process steps for clinical care documentation, register documentation and trial documentation with each other. Describing all those aspects however, would go beyond the limits of this paper and shall be focus of a separate publication. Acknowledgement: parts of the described projects have been funded by the German Federal Ministry of Education and Research and by the German Cancer Aid.

References [1]

Ochs, M.F. Casagrande. J.T. Information systems for cancer research, Cancer Invest 26 (2008), 10601067. [2] Beckmann, K. Jud, S. Heusinger, K. Schwenk, M. Bayer, C. Häberle, L. et al. Dokumentation in der gynäkologischen Onkologie, Der Gynäkologe 43 (2010), 400–410. [3] Shortliffe, E.H. Sondick. E.J. The public health informatics infrastructure: anticipating its role in cancer. Cancer Causes Contr 17,7 (2006), 861. [4] McConnell, P. Dash, R.C. Chilukuri, R. Pietrobon, R. Johnson, K. Annechiarico, R. Cuticchia, A.J. The cancer translational research informatics platform, BMC Med Inform Decis Mak 8, 60 (2008), doi:10.1186/1472-6947-8-60. [5] Dugas, M. Breil, B. Thiemann, V. Lechtenbörger, J. Vossen, G. Single source information systems to connect patient care and clinical research, Stud Health Technol Inform 150 (2009), 61-65. [6] Haux, R. Seggewies, C. Baldauf-Sobez, W. Kullmann, P. Reichert, H. Luedecke, L et al. Soarian workflow management applied for health care, Methods Inf Med 42 (2003), 25-36. [7] Altmann, U. Katz, F. Dudeck, J. Das Gießener Tumordokumentationssystem GTDS: Software für klinische Krebsregister, Spiegel der Forschung (2002), 4–10. [8] Murphy, S.N. Weber, G. Mendis, M. Gainer, V. Chueh, H.C. Churchill, S. et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med InformAssoc 17,2 (2010), 124-130. [9] Ganslandt, T. Mate, S. Helbing K, Sax, U. Prokosch, H.U.. Unlocking Data for Clinical Research - The German i2b2 Experience. Appl Clin Inf 2 (2011), 116–127. [10] Helbing, K. Demiroglu, S.YRakebrandt, .F. Pommerening, K. Rienhoff, O. Sax. U. A Data Protection Scheme for Medical Research Networks. Review after Five Years of Operation. Methods Inf Med 49, 6 (2010) 601-607. [11] Fenstermacher, D. Street, C. McSherry, T. Nayak, V. Overby, C. Feldman, M. The Cancer Biomedical Informatics Grid (caBIG). Conf Proc IEEE Eng Med Biol Soc 1 (2005), 743-746. [12] Prokosch, H.U. Ganslandt, T. Perspectives for medical informatics - Reusing the electronic medical record for clinical research, Methods Inf Med 48, 1 (2009), 38–44.


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Using a Robotic Arm to Assess the Variability of Motion Sensors Lukas GORZELNIAKa,b,1, André DIASc, Hubert SOYERd, Alois KNOLLe Alexander HORSCHa,c a Institut für Medizinische Statistik und Epidemiologie, Technische Universität München, Munich, Germany b Institut für Epidemiologie, Helmholtz Zentrum München, Munich, Germany c Depts. of Computer Science & Clinical Medicine, University of Tromsø, Tromsø, Norway d Fakultät für Informatik, Technische Universität München, Munich, Germany e Institut für Informatik VI, Technische Universität München, Munich, Germany

Abstract. For the assessment of physical activity, motion sensors have become increasingly important. To assure a high accuracy of the generated sensor data, the measurement error of these devices needs to be determined. Sensor variability has been assessed with various types of mechanical shakers. We conducted a small feasibility study to explore if a programmable robotic arm can be a suitable tool for the assessment of variability between different accelerometers (inter-device variability). We compared the output of the accelerometers GT1M and GT3X (both ActiGraph) and RT3 (Stayhealthy) for two different movement sequences. Keywords. Accelerometer, validation, robot, inter-device-variability

1. Introduction Motion sensors ease the assessment of physical activity (PA) and provide objective recording of the PA components: intensity, frequency and duration. A common type of motion sensors is accelerometers. They vary in size, sampling rate, proprietary movement detection algorithms, calibrations, access to raw sampling data and output variables, i.e. S.I. units, or proprietary counts or vector magnitude units (VMUs). Previously published studies on reliability of accelerometers have analyzed data generated by human motion in scenarios with standardized conditions for each subject [1, 2]. In these studies the possible variability among the sensors (of the same type) is measured under controlled conditions. To assess the measurement errors of the accelerometers, devices have been mounted on vibration machines such as jigs or shakers, in order to generate acceleration data under controlled conditions [3]. However, to the knowledge of the authors, there is no study comparing the variability of the accelerometers GT1M, GT3X (ActiGraph) and the RT3 (StayHealthy) (details see section 2.1) by using an industrial robot for carrying out clearly defined and reproducible movements. Robots 1

Lukas Gorzelniak, IMSE Klinikum rechts der Isar der TU München (Bau 523), Ismaninger Str.22, D-81675 Munich, Germany.E.mail: [email protected].

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work at a very high precision. They can be programmed for simple to complex motion sequences along different spatial axes, and thus have the potential of a better simulation of human and artificial movements than shaker devices. The aim of this paper is to examine the variability of the GT1M, GT3X and the RT3 accelerometers by using an industry robot for defined and repeatable movements.

2. Material and Methods 2.1. Accelerometers For this exploratory study, 11 piezoelectric triaxial RT3 (Stayhealthy, Monrovia, CA, USA), 5 biaxial GT1M and 5 triaxial GT3X accelerometers (ActiGraph LLC, Pensacola, FL, USA) were used. The RT3 records activity in 3 orthogonal directions at a sampling rate of 1Hz. The measured accelerations are converted to a digital representation, then processed as activity counts, and finally stored as VMUs. The GT1M is a micro-electromechanical system which measures acceleration in the vertical and horizontal plane at a sampling rate of 30 Hz. PA is filtered and expressed as activity counts, which is a quantification of the amplitude and frequency of the detected accelerations summed over a user-specified time interval. The GT3X is the successor of the GT1M and can assess activity in 3 orthogonal directions. Both ActiGraph accelerometers support the PA representation in terms of VMUs. All accelerometers in our study have been customized at one second post-filtered recording as it is the highest frequency in common to all sensors with VMUs output. 2.2. Industrial Robot For defined and repeated movements the industrial robot TX90 (Stäubli Robotics, Pfäffikon, Switzerland) was used. The robot has an articulated arm and can execute movements in 6 degrees of freedom with a repeatability precision of ± 0.03 mm. The high degree of freedom can approximate (mimic) human movements by the robot. 2.3. Accelerometer Attachment For a rigid attachment of the sensors, a single RT3 holder was screwed on the robotic arm. GT1M and GT3X accelerometers were attached to the same holder by using double-sided Velcro tape. This provided a stable attachment of the sensors (Figure 1).

Figure 1. Robotic arm with a single RT3 device attached.


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The robot was mounted on a laboratory table and programmable through a cable connected interface. 2.4. Protocol Single accelerometer units were consecutively mounted on the robotic arm at exactly the same position before the programmed motion was executed. Acceleration data for two types of movement were recorded for each device during a motion sequence at two randomly selected speeds of the robot: The first sequence consisted of simple movements along each axis, beginning at the resting position of the robot. The second sequence was “random” with components along all axes. The sequences were chosen to assess each axis individually and combined. We did not try to mimic human movement in this study. The two sequences were repeated three times after short breaks of no movement at both speed levels. The robot program had to be started manually (for different speeds) thus, the data among the accelerometers were not exactly synchronized, causing a varying period of inactivity (activity gap). This gap was used to assess the signal-tonoise ratio and discarded for the comparison of the VMUs output. The gap location was known because all sequences had exact durations. The first non-zero value in the data defined the beginning of the time series. In order to evaluate the variability of the three accelerometers, descriptive statistics and illustrative figures were used.

3. Results As the output scale differs between devices of different manufacturers, a comparison was conducted based on relative rather than absolute values. All accelerometers recorded movements in VMUs, but differed in their co-domain due to different manufacturers or different numbers of measurement axes. The GT3X recorded the highest accelerations during the specified motion sequence (10.77 ± 0.29 VMU mean), reaching the highest peaks (71.80 ± 4.55 VMU max) compared to the GT1M (7.13 ± 0.21 VMU mean; 66.52 ± 10.67 VMU max) and the RT3 (5.31 ± 0.86 VMU mean; 34.91± 6.28 VMU max), all values ± standard deviation, respectively (see Table 1). The variability of the mean VMUs recorded during both types of movement was about 40 ± 24% in the RT3, 8 ± 15% in the GT1M, and 6 ± 11% in the GT3X. This is illustrated in Figures 2-4, in which data from each sensor type is plotted in a separate graph. Ideally, only a single line should be visible in each plot. Taking the displacement in the synchronization into account, the GT1M and the GT3X accelerometers overlapped fairly well in the graphs. Peaks and breaks before each repetition can be identified by small discrepancies. For the RT3 accelerometers, no clear line of measurement is observable, and small amounts of motion were continuously recorded during breaks. These are assumed to be noise. The Signal-to-Noise ratio calculated from data within the interval of no movement between the motion sequences can be found in Table 1. Both ActiGraph accelerometers identified the rigid period more precisely than the RT3. Except for one device, all RT3 accelerometers showed non-zero values for the noise standard deviation (see Table 1).

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Table 1. Accelerometer output for the entire repeated movement sequence with the robotic arm Sensor Name I RT3 II RT3 III RT3 IV RT3 V RT3 VI RT3 VII RT3 VIII RT3 IX RT3 X RT3 XI RT3 I GT1M II GT1M III GT1M IV GT1M V GT1M I GT3X II GT3X III GT3X IV GT3X V GT3X

Number of Values 124 140 138 118 136 137 129 138 119 139 130 135 134 133 133 136 134 134 134 134 134

Mean VMUs 6,35 6,81 5,36 4,46 5,93 4,73 4,12 5,65 4,44 4,87 5,65 7,14 7,13 6,85 7,08 7,43 11,01 10,34 10,60 10,91 10,97

Standard Deviation (SD) 6,95 7,09 6,59 6,78 7,83 5,93 6,96 7,61 6,39 6,57 6,56 14,56 16,88 14,35 14,71 14,95 18,76 17,47 18,86 19,68 18,55

Maximum

Noise SD

27,39 32,50 31,40 37,00 46,17 31,13 43,46 29,77 29,00 41,23 35,00 58,00 82,00 62,00 73,00 57,58 75,00 67,00 78,00 69,00 70,00

2,20 4,61 2,00 0,00 3,20 1,34 4,80 2,98 2,32 3,71 3,48 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Signal-toNoise Ratio 2,88 1,48 2,68 1,85 3,53 0,86 1,90 1,91 1,31 1,62 - -

Figure 2. Plotted VMU data assessed during the movement sequence for each RT3.

Figure 3. Plotted VMU data assessed during the movement sequence for each GT1M.

Figure 4. Plotted VMU data assessed during the movement sequence for each GT3X.


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4. Discussion Our results indicate that the data acquired by the RT3 accelerometers are less reliable than data provided by the GT1M or the GT3X. The RT3 units produced a higher noise ratio during our experiments and, in agreement with previous reports [3], we found a greater inter-unit-variability compared to the ActiGraph accelerometers. The robot provides an objective comparison method and can be programmed to mimic human movements. As this was our first attempt to use a robot for exploratory purposes, the protocol contains several drawbacks: The robot was mounted on a steel table and during the faster motion sequence, movements of the robot are likely to have caused vibrations on the table. This probable noise may have decreased the accuracy of the accelerometer output. Therefore, we advise to use a rigid, grounded positioning, e.g. by mounting it on a block of concrete. Alternatively, the vibrations of the placement ground, as well as the robot itself, should be measured by mounting additional accelerometers. Unfortunately, in this initial experiment we did not record the movement from the robot data interface, which could have served as gold standard. Regarding the movement sequence, artificial breaks should be avoided to eliminate the synchronization burden. Last but not least, the signal-noise-ratio was computed from no-motion intervals with varying length. In future studies this will be done in a more standardized way.

5. Conclusion Using an industrial robot to perform repeating movements at a very high accuracy for testing different accelerometers is a promising method and generated reliable results. Although we did not assess intra-unit-variability of different motion sensors in this study, we were able to compare inter-unit-variability for two similar movement types in three different accelerometers, despite the limitations in the study protocol. Continuation of these studies is work in progress. Acknowledgements: This research was funded/supported by the Graduate School of Information Science in Health (GSISH) and the TUM Graduate School. The authors thank Martin Eder.

References [1] [2]

[3]

Guy C. le Masurier, Sarah M. Lee, and Catrine Tudor-Locke, Motion Sensor Accuracy under Controlled and Free-Living Conditions, Med Sci Sports Exerc. 2004 May;36(5):905-10. Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA,Validity of four motion sensors in measuring moderate intensity physical activity, Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S471-80. Powell SM, Jones DI, Rowlands AV, Technical Variability of the RT3 Accelerometer, Med Sci Sports Exerc. 2003 Oct;35(10):1773-8.

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The Single Source Architecture x4T to Connect Medical Documentation and Clinical Research Philipp DZIUBALLEa,1, Christian FORSTERb, Bernhard BREILa, Volker THIEMANNa, Fleur FRITZa, Jens LECHTENBÖRGERb, Gottfried VOSSENb, Martin DUGASa a Department of Medical Informatics, University of Münster, Germany b Department of Information Systems, University of Münster, Germany

Abstract. Clinical trials often require large and redundant documentation efforts, because information systems in patient care and research are separated. In two clinical trials we have assessed the number of study items available in the clinical information system for re-use in clinical research. We have analysed common standards such as HL7, IHE RFD and CDISC ODM, regulatory constraints and the documentation process. Based on this analysis we have designed and implemented an architecture for an integrated clinical trial documentation workflow. Key aspects are the re-use of existing medical routine data and the integration into current documentation workflows. Keywords. Clinical information systems, EHR re-use, single source, system architecture, clinical data management system

1. Introduction Clinical trials require extensive documentation efforts as they often include hundreds to thousands of attributes per patient. These data are commonly captured twice in two independent information systems. Daily routine documentation is entered into a clinical information system (CIS). Study documentation occurs on Case Report Forms (CRFs) and is stored in dedicated research databases (Clinical Data Management System, CDMS). Double data entry entails occupation of time and impacts negatively on the documentation behaviour of physicians and nurses. Recent studies show that clinicians spend about a quarter to one third of their daily working time for routine documentation [1, 2] and study documentation is added on top. Many patients have at least a basic electronic medical record [3] and routine data is available in CIS, eligible for re-use [4, 5] in clinical research. Although re-use applies to 11% - 69% of data items [6, 7], trial documentation processes connecting patient care with clinical research rarely exist. To address this issue, the eSoure Data Interchange (eSDI) Initiative of the Clinical Data Interchange Standards Consortium (CDISC) has promoted the eSDI Document [8] to analyse the use of electronic technology in the context of eSource data 1

Corresponding author: Philipp Dziuballe, Institute of Medical Informatics, University of Münster; E-Mail: [email protected]

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regulations in clinical trials. In this document the “single source” scenario is envisioned among others, as a promising concept that implies to capture medical data at one single point reducing double data entry and promoting secondary use [5]. A first prototype of this concept was successfully implemented by Kush et al. [9]. Our own research projects show the feasibility of this approach [10, 11]. Another scenario of the eSDI paper is the “Extraction and Investigator Verification” solution, where documentation occurs within the CIS. With regard to this concept, the Retrieve Form for Data Capture (RFD) [12] profile was jointly developed by CDISC and the Integrating the Healthcare Enterprise (IHE) Initiative, to enable data capture for clinical research and other purposes within a CIS session. It defines four actors who participate in specific transactions based on web services. This profile is extended in the REUSE project [6], through the integration of forms towards a profile called “Retrieve & Integrate Forms for data capture” to enable direct study documentation within the CIS reusing present medical routine data. Furthermore, the procedure of clinical trials is strictly regulated by law and supervised by authorities like European Medicines Agency and U.S. Food and Drug Administration. These international regulations have to be respected while connecting routine and research documentation. In this paper, we pursue the following objectives: We intend to identify the amount of available routine data in CIS eligible for study documentation. After that, we design and implement an architecture to facilitate the re-use of available medical routine data for clinical trial documentation with due regard to regulatory constraints and the usage of established international standards.

2. Materials and Methods We have analysed CRF items of two currently conducted multicentre trials at the University Hospital Münster (UKM) concerning the availability and manifestation in CIS ORBIS from Agfa Healthcare [13]. To assess the secondary use potential in our approach, we have identified the amount of CIS data suitable for re-use in two studies conducted at the UKM; their CRFs contain 278 and 318 items, respectively. This occurs through a manually review of all implemented CIS forms available in the respective clinical department by a medical informatics professional. We have also analysed clinical trial documentation workflows through interviews with employees of the Centre of Clinical Trials in Münster and with research physicians at the UKM. After that, we have conducted a literature search and identified communication standards, established in the healthcare and clinical research domain. We have selected the Operational Data Model (ODM) published by the CDISC because of its ability to archive trials [14] and exchange metadata definitions and data. We have also reviewed the IHE RFD profile to exchange documentation forms between CDMS and investigators’ CIS. With respect to the CIS features, we have identified the existing information system architecture at the UKM. Regarding interfaces we have analysed the Health Level 7 (HL7) communication standards. Based on our analyses, we have designed and implemented a system architecture. The clinical trial metadata was processed in ODM format.

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3. Results 3.1. Analysis To identify the amount of re-usable CIS data and to examine the desirability of a single source approach, we have manually mapped 596 study items (278 items from the first trial, 318 from the second) to the CIS documentation. We assigned three categories: “"identified in CIS,” “require modification” and “not found in CIS”. Figure 1 shows that 47% of the CRF items were found within the available routine patient care documentation. For about 11%, the item values cannot be used directly because they are free text or contain only similar information. In this case, a modification of the item value is necessary. About 42% of the CRF items are not found in CIS. This result shows that the re-use of CIS data values is a rewarding step for study documentation, as almost half of the required items (47%) for study documentation are available. 47%

Identified in CIS Require modification

11% 42%

Not found in CIS 0%

20%

40%

Amount of items 60%

Figure 1. Amount of identified CRF items in CIS based on an analysis of two clinical trials.

Concerning the RFD profile, the analysis of the present CIS architecture shows that a direct implementation within the CIS is limited due to technical restrictions (proprietary system structure) as well as license issues (restrictions regarding new interfaces). Specifically, direct import of CRFs is not available in the current CIS version. ORBIS neither supports Web services nor XForms and pre-filling of CRF items as described in RFD. 3.2. Architecture To overcome these drawbacks and make use of existing CIS data, we developed an architecture to create an interface between CIS and CDMS, compliant with regulatory constraints and recommendations (GCP, eSDI, Title 21 CFR Part 11) and applying established standards. RFD was extended and refined, which broadly resulted in a combination of RFD and the single source concept of the eSDI document. We developed an integrated documentation process based on identified regulatory principles. Certainly, t is not feasible to directly implement this process in current CIS solutions and due to their limitations we designed a middleware component (Figure 2) to connect it to clinical research systems. This mediator – called x4T (exchange for Trials) – is hosted in the hospital environment to establish the integrated documentation process.

CIS

x4T

CDMS

Figure 2. Single source architecture with the middleware component x4T between the CIS and the CDMS.

x4T enables the exchange of forms and medical routine data and also enables to send notifications to study physicians. Single source eCRFs will also be prepared for


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presentation and data input. The interfaces of x4T and the CDMS are able to exchange eCRF definitions and completed ones. x4T consists of the following modules: • Interface management Due to heterogeneity in CIS landscape, the CIS-x4T connection needs to be adapted to the communication interface provided by the CIS. Standards for healthcare communication such as HL7 messages and Clinical Document Architecture are preferred. For systems that do not serve those interfaces XML and a specific wrapper are used. The CDMS-x4T communication relies on ODM wherever supported by the CDMS; otherwise it is adaptable to the specific protocol. • User management Configuration of sites including users and their associated roles is done within the user management. The completed CRFs need to be signed by the user. • Form mediator and database It is currently not possible to directly store and display eCRFs inside the CIS. Therefore a separate form database is needed to temporarily store eCRFs. The mediator transforms these eCRFs into displayable XForms and enables prefilling of items. With regard to regulatory requirements pre-filled items need to be verified by the user and after documentation a copy of the CRF will be archived in CIS. If there are several item occurrences available, for instance blood pressure values at many points in time, the correct value needs to be selected and confirmed by responsible study physicians. • Ontology matching This module handles the eCRF pre-filling with routine patient care data. Eligible data items have to be verified and mapped with controlled vocabularies. A semantic layer is currently missing in ORBIS so that data annotation occurs externally in x4T. CRF items also need to be matched with this vocabulary. Pre-filling is possible in case of overlapping semantic concepts. A conversion engine translates measurement units or calculates item values in case of different data types. For instance, patient age for a CRF is calculated from date of birth and date of visit provided by the CIS. • Notification management To support clinical workflow, the CIS user has to be notified about new eCRFs to be filled.

4. Discussion and Future Work In this paper, we have proposed a system architecture to support integrated clinical trial documentation workflows. The central component of this architecture is the middleware server x4T to establish the connection between patient care and research systems. Due to the current gap of standards, a direct link from hospital to study systems would require an adapter for every CIS and CDMS in a full mesh topology for multicentre trials. With the mediator approach, only one interface per system is required. According to our architecture, study forms are documented in a single system with the advantage that documentation forms have not to be built in any CIS. In case of CRF updates, implemented CIS forms as realised in [10, 11] would cause huge maintenance costs. x4T enables re-use of medical data and pre-filling of study forms.

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Avoiding discrepancies in the validation of eligible CIS data, clinicians or other experts need to be consulted to reach consensus. The whole amount of re-usable CIS data may differ from study to study and can finally be calculated in a real trial setting. Pre-filling is only possible if CIS data are well-structured. In order to utilize our implementation, the x4T interfaces have to be adjusted. A proof-of-concept of x4T is planned for a clinical study in dermatology.

5. Conclusion Pre-population of eCRFs with CIS data is a promising approach to avoid redundant data entry because of a quite large overlap between CDMS and CIS items. Due to limitations of current CIS and regulatory constraints, the exchange of data between CIS and CDMS should be enabled by a mediator.


[4]

[5] [6] [7] [8] [9]

[10]

[11] [12] [13] [14]

Ammenwerth, E. Spötl, H.P. The time needed for clinical documentation versus direct patient care. A work-sampling analysis of physicians' activities, Methods Inf Med. 2009;48(1):84-91. Tipping, M.D. Forth, V.E. O'Leary, K.J. Malkenson, D.M.. Magill, D.B Englert, K. Williams, M.V. Where did the day go?-a time-motion study of hospitalists, J Hosp Med. 2010 Jul-Aug;5(6):323-8. Jha, A.K. DesRoches, C.M. Campbell, E.G. Donelan, K. Rao, S.R. Ferris, T.G. Shields, A. Rosenbaum, S. Blumenthal, D. Use of electronic health records in U.S. hospitals, N Engl J Med. 2009 Apr 16;360(16):1628-38. Williams, J.G. Cheung, W.Y. Cohen, D.R. Hutchings, H.A. Longo, I M.F.. Russell, T. Can randomised trials rely on existing electronic data? A feasibility study to explore the value of routine data in health technology assessment. Health Technol Assess. 2003;7(26):iii, v-x, 1-117. Prokosch, H.U. Ganslandt, T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med. 2009;48(1):38-44. El Fadly, A. Lucas, N. Rance, B. Verplancke, P. Lastic, P.Y. Daniel, C. The REUSE project: EHR as single datasource for biomedical research, Stud Health Technol Inform. 2010;160(Pt 2):1324-8 Zahlmann, G. Harzendorf, N. Shwarz-Boegner, U. Paepke, S. Schmidt, M. Harbeck, N. Kiechle, M. EHR and EDC Integration in Reality. Applied Clinical Trials 2009. eSDI Initiative. [http://www.cdisc.org/esdi-document] Kush, R. Alschuler, L. Ruggeri, R. Cassells, S. Gupta, N. Bain, L. Claise, K. Shah, M. Nahm, M. Implementing Single Source: the STARBRITE proof-of-concept study, J Am Med Inform Assoc. 2007 Sep-Oct;14(5):662-73. Breil, B. Semjonow, A. Dugas, M. HIS-based electronic documentation can significantly reduce the time from biopsy to final report for prostate tumours and supports quality management as well as clinical research. BMC Med Inform Decis Mak. 2009 Jan 20;9:5. Fritz, F. Ständer, S. Breil, B. Dugas, M. Steps towards single source--collecting data about quality of life within clinical information systems. Stud Health Technol Inform. 2010;160(Pt 1):188-92. IHE International: IHE ITI Technical Framework Supplement: Retrieve Form for Data Capture (RFD). [http://www.ihe.net/Technical_Framework/upload/IHE_ITI_Suppl_RFD_Rev2-1_TI_2010-08-10.pdf] Agfa Healthcare. [http://www.agfahealthcare.com/] Kuchinke, W. Aerts, J. Semler, S.C. Ohmann, C. CDISC standard-based electronic archiving of clinical trials, Methods Inf Med. 2009;48(5):408-13.


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Information Technology Solutions to Support Translational Research on Inherited Cardiomyopathies Riccardo BELLAZZIa,1, Cristiana LARIZZA a,, Matteo GABETTA a,, Giuseppe MILANI a,, Mauro BUCALO a,, Francesca MULAS a,, Angelo NUZZO a,, Valentina FAVALLI b, Eloisa ARBUSTINI b a Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy b IRCCS Fondazione Policlinico S. Matteo, Pavia, Italy

Abstract. The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the condition within families that suffer from heart conditions that are caused by DCMs. The project is supported by a number of advanced biomedical informatics tools, including data warehousing, automated literature search and decision support. The paper describes the design of these tools and the current status of implementation. Keywords. Translational research, Enhancing Biomedical Research, Dilated cardiomyopathy

1. Introduction Dilated cardiomyopathy (DCM) occurs when diseased heart muscle fibres become weakened and cannot effectively pump blood to the body. The weak heart muscles also allow one or more chambers of the heart to expand. With time, the enlarged heart gradually deteriorates, causing congestive heart failure. DCM is one of the leading causes of Heart Failure due to systolic dysfunction, and at least 30% of DCM are of familial/genetic origin [1]. The INHERITANCE project (Integrated Heart Research In Translational Genetics of Cardiomyopathies in Europe), funded by the European Commission, seeks to study the genetic or inherited DCM and to understand the impact and management of the condition within families that suffer from DCMs. The INHERITANCE project is structured into 6 research areas that study different facets of the DCM condition, including clinical cardiogenetics, -omics, i.e. genetic testing, transcriptomics, proteomics and metabolomics, animal studies, structural studies, treatments, and biomedical informatics, which aims to implement information technology solutions to support the project team in managing the huge quantity of scientific, clinical and patient data generated by the project. This paper focuses on the biomedical informatics methods and tools that have been made available to the INHERITANCE researchers.

1

Corresponding author, [email protected].

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2. IT Solutions to Support Clinical Research

Figure 1. The knowledge management and data analysis architecture of the INHERITANCE project.

INHERITANCE, on top of a database application, implements a layer of software instruments to support translation of the results of the project into guidelines and clinical practice as well as to support the scientific discovery process. This layer includes data warehousing, intelligent querying of the phenotype data, integrated search on biological data and knowledge repositories, text mining of the relevant literature, and case-based reasoning. We refer to these components as the knowledgemanagement system of INHERITANCE. The overall design of the knowledge management architecture is described in Figure 1. The data-warehouse (i2b2) is populated through a set of automated queries that extract patients’ data from the INHERITANCE database. The data are then made available to the researchers through a data mining and exploration tool. Literature is searched through a text mining strategy based on Natural Language Processing (NLP). Finally a decision support tool, exploiting the patient data-base, the text mining tool and software solutions to automatically access biomedical data bases, provides support in refining patient’s diagnosis. In the following we will briefly describe each of the components included in the final architecture and the state-of-art of the project.

3. The Architecture of the Data Warehouse for Patients’ Data Exploration The INHERITANCE project collect patients’ data in a specialized database called Cardioregister [https://cardioregister.com/Pages/Main.aspx], a web-based system designed to collect, exploit and download anonymised data of patients and families with DCM, offering the ability to produce customized reports with data. The data collected in Cardioregister are automatically uploaded in a data warehouse for data exploration and dynamic querying. The data warehouse exploited in the INHERITANCE project is based on the i2b2 software system [2] (http://www.i2b2.org/software). The goal of the i2b2 project (Informatics for Integrating Biology and the Bedside) is to provide clinical investigators with a software infrastructure able to integrate clinical records and research data in the genomics age.


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The i2b2 core software tool is a data warehouse, which can be accessed via a query generation tool. The i2b2 data model is based on the “star schema” [3] and the entire i2b2 software architecture is built on web services, called cells. New i2b2 cells can be developed and added relying on the web service architecture. The i2b2 web client query interface allows queries to be dynamically created and executed by researchers and returns the patients’ set that satisfy the queries. The terms used to create the queries are specified through an ontology, which needs to be customized on the specific biomedical application. In order to empower i2b2 with fast multidimensional inspection of phenotypic data, we have included in the tool, as a plug-in, the Phenotype Miner system [4]. Phenotype miner has two main components: i) the Phenotype Editor, for the automated definition of phenotype queries, ii) a customized version of the Mondrian OLAP engine (http://mondrian.pentaho.org) for dynamic data inspection. Within the INHERITANCE project, i2b2 will be also integrated with two different software environments, including automated literature analysis and decision support.

4. Tools for Automated Literature Analysis Automated literature analysis is becoming an essential need in current biomedical research. Text Mining (TM) and Natural Language Processing (NLP) provide algorithms and techniques for automated elaboration of textual content. This task is particularly important in the early stage of any study, in which resuming the available knowledge is crucial to formulate initial hypotheses and plan next tasks. The challenge is to broaden the search of potentially useful information to generate new hypothesis [5]. For instance, an added value could be to suggest that a candidate gene is often related to another gene, which has not been previously considered. Our goal is to provide tools to INHERITANCE able to provide such kind of utilities. Therefore, we focused on genetic studies, in which a set of initial hypotheses of gene-disease association is made on some candidate genes, so that the first step is to explore the recent literature to confirm their possible role in the disease mechanism. We developed a tool able to extract the concepts of interest (genes and medical terms, like pathologies) using a structured knowledge base like Unified Medical Language System (UMLS) [6], by which we can derive genes/disease annotation. Moreover, we also implemented similarity metrics, based on a relevance measure of the terms for each gene, to identify which terms each gene shares between each other. In this way we can represent a graph in which the nodes connection reflect how tightly related those terms are according to the available literature. The analysis method we propose aims to derive a literature-based gene annotation by extracting UMLS terms related to diseases from the abstracts of the publications referencing each gene. The overall analysis consists of 3 main steps: • querying PubMed via Web Services to retrieve the most recent literature about specific genes/diseases • automatically extracting concepts (genes/disease) from PubMed abstracts based on NLP techniques • constructing annotation/co-citation networks to interpret available knowledge and suggest new hypotheses that can be tested. The details of the literature analysis system and the medical concepts extraction are described in [7].

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5. The Reasoning and Decision Support Tool A crucial aspect of the knowledge management system of INHERITANCE is the definition of a tool that can guide the clinicians in properly ranking the DCM causative genes, so that their screening can be effectively performed in the clinic. The goal is thus to prioritize around 30 genes for screening on the basis of the patients’ symptoms. The large variability of the patients’ data and the limited amount of formalized knowledge available requires the design of a decision support tool able to provide instruments for analogical reasoning to clinicians, including case similarity, information retrieval and text mining. Each clinical case is usually described by hundreds of features, including anamnesis and family information, life-style, lab tests and exams, ECGs, echo-cardiography data. Among the collected data, some of them are considered as “red flags”, i.e. biomarkers that may be related to some gene mutation, as their cause-effect relationships have not yet been fully established. To cope with this problem, we have implemented the following strategy: • all Pubmed abstracts are retrieved and included into an abstracts database; • all Pubmed abstracts are analysed in order to extract the concepts of interest (genes and medical terms); every concept is searched with all its synonyms coming from UMLS meta-thesaurus and Gene database. The results of this analysis (genes and medical terms cited in the Pubmed abstracts) are stored in the abstracts database so that the association between each article and the extracted concepts is made available for the next step. • for each candidate gene, Pubmed is queried in order to obtain the reference to the articles that are directly related. From these articles, exploiting the results of the previous step, the system generates a list of the associated UMLS concepts in order to find new gene-red flag links. A further step is to find out non-direct relationships, i.e. two concepts that are directly associated in a loose way, but strongly associated with a common concept [8]. While the first two steps are done only once to create the corpus and calculate the interesting gene/medical concepts occurrences, the third one depends on the specific analysis and can be repeated for every further investigation. Let us note that, since UMLS concepts are hierarchically interrelated, the relationships between a gene and a red flag may also occur at different levels of the hierarchy, including their descendants (more specific concepts) or their ancestors (more general ones). The final matching process give rise to an augmented weighted list of red flags related to a gene, where the weight can be calculated on the basis of the frequency of the gene/red flag relationships. Once a single patient case is available, it is possible to compute a matching function with the current patients data and the weighted list of red flag to derive a prioritized list of genes. Moreover, it is also possible to retrieve similar cases with known mutations and therapy and highlight right or wrong previous diagnostic decisions. Rather interestingly, the process evolves over time, and the list of red flags may be varied accordingly to the change in the available knowledge reported in Pubmed and in knowledge repositories. We have currently retrieved a corpus of more than 7000 documents by querying Pubmed for DCM over the period 2005-2010. From this corpus we extracted 455 genes and 867 UMLS concepts. As a preliminary task we verified that such lists contain the 27 genes and 20 red flags provided by the physicians as related to DCM. In this way it was possible to confirm the available background knowledge. To test the potentials of the proposed approach, we extracted a list of possible new genes or red flags that could


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be related to DCM. Finally, for each red flag, we produced a list of associated genes ordered by their relevance. The relevance depends on the number of articles that support the red flag-gene association. This information provides a gene prioritization list that can be useful in clinical routine for diagnostic purposes [9].

6. Conclusions The main task of the INHERITANCE project is to investigate the molecular basis of inherited DCM. To this end we have developed and implemented a set of software tools to support data management and decision support, including: 1. A datawarehouse for fast phenotype data exploration based on the i2b2 system. 2. A tool for automatic literature analysis and literature-based discovery 3. A system for supporting reasoning and decision on a single case, with the aim to prioritize gene screening and to discover new gene-concept associations. The project, after its first year, has already collected 168 patients coming from four medical centers. The data warehouse and the text mining tools have been implemented and tested, while the decision support tool is currently still under development. Acknowledgments. This work was supported by the INHERITANCE project, funded by the European Commission. We thank Lorenzo Monserrat, HealthEncode and the Cardioregister team for their effective collaboration.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Ahamad F, Seidman JG, Seidman CE. The genetic basis for cardiac remodeling. Ann Rev Genomics. Hum Genet 6 (2005) 185-216. Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 17(2) (2010)124-30. Kimball R, Ross M. The data warehouse toolkit. second edition, Wiley and Sons, 2002 Nuzzo A, Segagni D, Milani G, Rognoni C, Bellazzi R. A dynamic query system for supporting phenotype mining in genetic studies. Stud Health Technol Inform, 129(Pt 2) (2007) 1275-1279. Roos M, Marshall MS, Gibson AP, et al. Structuring and extracting knowledge for the support of hypothesis generation in molecular biology. BMC Bioinformatics. Suppl 10 (2009) S9. Lindberg DA, Humphreys BL, McCray AT. The Unified Medical Language System. Methods of information in medicine 32 (1993) 281–291. Nuzzo A, Mulas F, Gabetta M, et al. Text Mining approaches for automated literature knowledge extraction and representation. Stud Health Technol Inform. 160(Pt 2) (2010) 954-8. Ganiz M, Pottenger WM, Janneck CD. Recent advances in literature based discovery. Lehigh University, CSE Department, Technical Report, LU-CSE-05-027 (2005). Bellazzi R, Larizza C, Gabetta M, et al. Translational Bioinformatics: Challenges and Opportunities for Case-Based Reasoning and Decision Support, Case-Based Reasoning: 18th International Conference, ICCBR 2010, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, (2010), 1-11.


Usability, HCI, Cognitive Issues



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Emerging Approaches to Usability Evaluation of Health Information Systems: Towards In-Situ Analysis of Complex Healthcare Systems and Environments Andre W. KUSHNIRUKa1, Elizabeth M. BORYCKIa, Shigeki KUWATAb, Joseph KANNRY c a School of Health Information Science, University of Victoria, Victoria, British Columbia, Canada b Tottori University Hospital, Tottori, Japan c Mount Sinai Medical Center, New York, New York

Abstract. The effective evaluation of health information technology (HIT) is currently a major challenge. It is essential that applications we develop are usable, meet user information needs and are shown to be safe. Furthermore, to provide appropriate feedback to designers of systems new methods for both formative and summative evaluation are needed as applications become more complex and distributed. To ensure system usability a variety of methods have emerged from the area of usability engineering that have been adapted to healthcare. The authors have applied methods of usability engineering, working with hospitals and other healthcare organizations designing and evaluating a range of HIT applications. We describe how our approach to doing portable low-cost usability testing has evolved to the use of clinical simulations conducted in-situ, within real hospital and clinical units to rapidly evaluate the usability and safety of healthcare information systems both before and after system release. We discuss how this approach was extended to development of methods for conducting in-situ clinical simulations in a range of clinical settings. Keywords: human computer interaction, usability, usability testing, in-situ

1. Introduction A wide variety of health information technology (HIT) has appeared ranging from wireless hand-held applications to Web-based patient record systems. Although innovations in HIT have the potential to dramatically improve and streamline health care, there are a number of critical problems and issues related to their successful implementation and acceptance by end users and consumers. One of the main areas of concern revolves around the following question: how can we ensure the applications that we develop are usable, meet user information and workflow needs and are safe? The design of HIT applications that are intuitive to use and that support human information processing is essential. This has become increasingly recognized as being 1

Corresponding author: Andre W. Kushniruk: E-mail: [email protected]

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critical as more and more complex software and hardware applications appear in healthcare. Usability is a measure of how effective, efficient and enjoyable a system is to use [1]. Closely related to issues of usability are issues of software safety and workflow, with the need to ensure that new devices and software increase patient safety and that workflow can be carried out in an effective and efficient manner. Methods from usability engineering have been applied to improve the usability of systems. This includes usability inspection methods, involving analysis of a user interface by an expert to identify usability problems, and usability testing, which involves observing representative users of a system carrying out representative tasks. The importance of usability testing in healthcare has been increasingly recognized. However, the issue of how to best test and evaluate systems so that the results are both ecologically valid and generalizable to real complex clinical settings has remained to be resolved. This paper describes our work in the evolution of approaches to the evaluation of the use and usability of HIT applications, given the widespread increase in both usage and complexity of environments in which they are deployed. This paper begins with a discussion of the development of a low-cost portable usability approach that has been taken into the field to conduct studies of end users of applications in real naturalistic settings. The approach has been used to evaluate a variety of applications and devices ranging from electronic medical records (EMRs) to Web-based information resources designed for both health care professionals and lay persons [2]. We then follow this with a discussion of our most recent work in extending the concept of usability testing to conducting more realistic and ecologically valid studies involving clinical simulations conducted “in-situ” - i.e. in real clinical settings where information technology is or will be deployed. In the early stages of our work and early experimentation with usability engineering in healthcare, we employed a number of different approaches to conducting usability testing including setting up a “fixed” usability laboratory setting. However, our experience has indicated that since this approach did not allow for collection of data at the site where the software under study is actually installed, conclusions made about a system’s usability and the generalizability of findings and predictions varied in their accuracy. In addition, for many of our studies it is essential that we conduct them in the actual environment in which they are being used, in order to determine how aspects of a particular environment may be affected by interacting technologies (e.g. imaging or bar-coding technologies) and how users interact with a system in a real setting, which is not realistically possible without employing a portable in-situ approach. With the advent of inexpensive screen recording software and high quality portable digital video cameras, the costs have decreased for conducting such studies along with an increase in the portability of the equipment such that it can be taken into any hospital or clinical environment, thereby simplifying the process. Figure 1 illustrates a continuum of approaches we have developed to guide design of usability studies. Our initial projects were mainly located on the far left side of the continuum in that they involved laboratory usability testing of systems taken out of their “natural” environment. This progressed to the development of more elaborate and realistic usability testing environments and study designs, which have previously been termed “clinical simulations” [3], however they were typically still conducted within a laboratory environment. In recent years we have moved many of our studies out of the laboratory and located both simulation studies and naturalistic studies within real-world environments (e.g. clinical settings). As indicated in Figure 1, in-situ studies may

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consist of simulations taking place in a real setting (e.g. a hospital room or operating room off hours) or they may involve naturalistic recording of real healthcare activities.

Figure1. A continuum of usability/simulation studies and settings.

2. An In-Situ Approach for Evaluating HIT Applications In this section of the paper we will describe the set-up of in-situ usability testing that can be taken into any type of setting, ranging from the clinical (e.g. hospital rooms) to the home setting (e.g. to study use of e-health applications by patients and providers). This set-up has so far been used for a number of projects, ranging from the study of nurse’s information needs to its use in the evaluation of a new medication order entry system (using bar-coding technology) prior to its deployment in a hospital in Japan [3] as well as the study of an introduction of an EMR at major American medical center, involving in-situ testing both before and after system go-live. Our typical studies carried out in naturalistic clinical settings involve asking subjects (e.g. nurses or physicians) to interact with systems to carry out real tasks (in some studies subjects may also be asked to “think aloud” while carrying out the task, which is audio recorded). The subject’s overt physical activities are recorded using one or more low-cost digital cameras (and ceiling mounted cameras where required). In addition to recording physical activities and audio of think aloud, the actual computer screens are also recorded as a digital movie file, with the audio portion of the movie corresponding to subject’s verbalizations. In order to do this we are currently using a freely available software product called Hypercam©. This type of inexpensive (or free) screen recording software allows one to record all the computer screens as a user interacts with the system under study, and stores the resultant digital movie for later playback and in-depth analysis of the interaction. The equipment we have used for many of our usability studies of HIT applications is both low-cost and portable. This typically includes: (1) one or more computers to run the software under study on, (2) screen recording software which allows the computer screens to be recorded as movie files (with audio input of subject’s “thinking aloud” captured using a standard microphone plugged into the computer), (3) one or more external digital cameras to video record user’s physical interactions. In studies being conducted remotely, the equipment may also include a Webcam attached to the computer that the user is interacting with. The studies we have conducted using this equipment have been carried out in a range of settings. The total cost of the equipment is minimal (i.e. under $1,500 US). It should be noted that data collected using this combination of recording methods (i.e. screen plus

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video recordings of users’ physical interactions) can provide for very high fidelity recordings of user interactions, both in terms of the realism of the setting (as studies can be conducted in actual clinical settings where the application is being used in real life, leading to higher fidelity testing than is possible in a laboratory study) and higher quality recordings (with advances in low-cost digital recording).

3. Analysis of Data Collected The analysis of the data collected (e.g. screens of user interactions, video recordings of users’ problems) varies from informal analysis, which consists of simply playing back the movies of user interactions to identifying particular usability problems (e.g. where a user is unable to carry out a requested task) in the presence of designers, hospital staff, managers etc. The analysis can also involve video annotation of the movie file using software such as Transana© (a freeware video annotation program that allows analysts to “mark up” and time stamp movies of user interactions with a system) as described in Kushniruk and Patel [2]. The typical result of carrying out a usability test includes identification of specific usability problems (often in a meeting setting with system developers, customers, and hospital or management staff present). The intent of our work is typically to provide rapid feedback about system usability to provide useful information to improve system design, deployment, or customization in an efficient and rapid manner. Our most recent projects have involved applying usability engineering methods (including our low-cost portable approach) to identifying potential errors that may be caused by a system (e.g. inappropriate medication defaults in an order entry system), or “induced” by poor user interface design [4].

4. Experiences to Date We have carried out a number of studies at varied locations (e.g. Mt. Sinai Medical Center, New York and Tottori University Hospital, Japan). Some of our earliest work involved usability testing of a patient record system at a major US medical center where the methods described in this paper resulted in a ten-fold decrease in the number of problems encountered by users of an electronic patient record system. The data analysis was conducted in a cost-effective (under $3,000 US) and efficient manner with specific recommendations for system improvement being incorporated in an improved system within several hours to weeks from the time of data collection [5]. Usability problems related to issues such as lack of interface consistency, problems in representing time sequences and issues in matching user specified terms to computer terms were identified. We have also employed a similar approach to detecting and correcting potential user problems and preventing medical error in a range of systems [4]. More recently, we have employed the method to determine how medical workflow may be inadvertently affected by the introduction of a medication order entry system [3]. In one study, which was conducted in the actual clinical setting where a new medication order entry system was deployed, subjects (nurses and doctors) were videorecorded while they interacted with both the computer system under study and patients in order to administer and record medications given to the patient. This study was conducted as a clinical simulation in-situ (i.e. in a real hospital room) just prior to system deployment. The results from such study have been used to identify not only

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problems with user interfaces but also to assess how the new electronic application affected workflow and patient care. In this study, for example, it was found from an analysis of the video recordings that the introduction of the computer system would negatively affect the workflow by making it rigid and sequential (through the prescribed order of steps imposed by the medication order entry system) as compared to the typical workflow implemented prior to the introduction of a system. In some of the simulation cases (e.g. under emergency conditions) this very prescriptive workflow posed a safety challenge (e.g. particularly when users have to deal with patient emergencies) and hence recommendations were made for providing an override capability under such conditions prior to widespread system rollout. In a current extension of this approach we are applying the method to examine the impact of clinical best practice guidelines on physician workflow using an electronic medical record system at a major American hospital center. This is involving both in-situ testing of users interacting with the guidelines both (prior to widespread release) as well as naturalistic testing of the system after deployment for use with real patients (using the same unobtrusive recording technology and set up in both cases).

5. Discussion In-situ approaches can be used to not only conduct simulations pre-implementation but also allow for post system release recording of real naturalistic interactions with systems in “live” use. Hence predictions made from in-situ studies can be tested as the system goes live (by keeping the recording equipment already in place going). Other advantages include its low cost in terms of equipment. Furthermore, by locating the studies within the actual organization where a system is going to be used, we are able to obtain direct access to a range of representative subjects and gain an improved understanding of the impact of local organizational issues and factors upon usability and safety. The impact of interfacing technologies in the real setting can also be identified. Challenges include obtaining permission to conduct studies in a real environment and issues regarding obtaining rooms and locations after hours for simulation testing. However, it is argued that if we are to ensure that the results of usability testing apply to real-world settings these types of studies are necessary.

References [1] [2] [3] [4] [5]

Preece, J., Rogers, Y., and Sharp, H., Interaction design: Beyond human-computer interaction. New York: John Wiley & Sons, 2002. Kushniruk, A.W. and Patel, V.L. Cognitive and usability engineering methods for the evaluation of clinical information systems, J Biomedl Inform, 37, 2004, 56-76. Borycki, E., Kushniruk, A., Kuwata, S., Kannry, J. Use of simulation in the study of clinician workflow. AMIA Annual Symposium Proceedings, 2006, 61-65. Kushniruk, A.W. Triola, M. Borycki, E. Stein, B. Kannry, and J. Technology induced error and usability. Int J Med Inform, 2005, 74, 519-526. Kushniruk, A.W. Patel, V.L. Cimino, J.J. and Barrows, R. Cognitive evaluation of the user interface and vocabulary of an outpatient information system. Proceedings of the 1996 Annual AMIA Conference, 1996, 22-26.

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Contextualization of Automatic Alerts During Electronic Prescription: Researchers’ and Users’ Opinions on Useful Context Factors Elske AMMENWERTHa1, Werner O HACKLa, Daniel RIEDMANNa, Martin JUNGa a Institute for Health Information Systems, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria

Abstract. Computerized Physician Order Entry (CPOE) Systems can reduce the number of medication errors and Adverse Drug Events (ADEs). However, studies have shown that users often override alerts, as they feel these are too unspecific for the given patient context. It is unclear, however, how alerts could be contextualized, that is adapted to the clinical context. Based on a literature search, we developed a list of 20 possible context factors. We asked 69 international CPOE researchers and 120 physicians from four hospitals in two countries to judge the usefulness of each factor. Researchers judged the following factors as most important: 1.) Severity of the effect, 2.) Clinical status of the patient, 3.) Probability of occurrence, 4.) Risk factors of the patient, 5.) Strength of evidence. Physicians judged the following factors as most important: Severity of the effect, clinical status of the patients, complexity of the case, and class of drug. These topranked context factors could be used to re-design the way alerts are presented in CPOE systems, to increase sensitivity of alerts, to reduce overriding rates, and to improve medication safety. Keywords. CPOE, electronic prescribing, e-medication, Delphi, user survey, context factor, evaluation

1. Introduction Medication errors and resulting preventable Adverse Drug Events (ADEs) are an important issue of global healthcare [1]. It is estimated that in the U.S., over 770,000 people are annually injured or die in hospitals due to ADEs [2]. The use of computerized physician order entry (CPOE) systems can reduce both, medication errors as well as ADEs [3-4]. Depending on the level of decision support provided, CPOE systems may provide alerts on drug-drug interaction or other drug-related problems or provide drug-related guidance. Recent research showed that, however, users often override drug safety alerts in CPOE systems [5]. Some proposals have been made to reduce alert overriding, such as to tailor (filter, prioritize) alerts depending on age or allergies of the patients [5-6], or on experience of the user [5, 7]. At the moment,

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Corresponding Author: Elske Ammenwerth, Institute for Health Information Systems, Eduard Wallnöfer Zentrum 1, 6060 Hall in Tyrol, Austria, E-Mail: [email protected]

E. Ammenwerth et al. / Contextualization of Automatic Alerts During Electronic Prescription

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however, systematic investigations on possible context factors that can be used to tailor alerts, and on the usefulness of each factor, seems to be missing. The objective of this work is to present a list of possible context factors that can be used to tailor alerts in CPOE systems, and to assess the usefulness of each context factors from the point of view of international researchers and clinical users.

2. Methods To establish a list of possible context factors, we conducted a literature search, comprising a hand search of major health informatics journals and a PubMed search. We searched for papers on electronic prescribing and CPOE systems and we analyzed which possible context factors were mentioned in these papers. Overall, 67 were analysed in detail, and found context factors were then summarized and organized into distinct categories. We stopped the search after we felt that saturation was reached and no new factors could be detected. 2.1. International Delphi survey Through a search of recent CPOE-related publications in PubMed, we identified 214 international researchers that had broad experience in electronic medication, and invited them to participate in a Delphi survey. During this web-based survey (based on LimeSurvey), the researchers got the list of the found 20 context factors, together with a short explanation. They were then asked to mark those factors they found most useful to prioritize and filter alerts, to add factors they found missing, and then to identify the five most important context factors. The survey was conducted in two rounds. During the second round, the results of the first round were fed back to the researchers who were then able to modify their judgment. 2.2. User survey of physicians Besides the researchers’ point of view, we were also interested in the point of view of clinical users. We therefore invited 60 physicians from a community hospital in Denain (France) and 207 physicians from three hospitals in Copenhagen (Denmark) to answer the same questions as in the Delphi survey. The survey was translated in the local language and organized as a paper-based survey. Both hospitals already had implemented electronic prescribing with some basic level of decision support such as drug-drug-interaction, with automatic alerts in Region H and optional alerts in Denain.

3. Results Overall, we identified 20 context factors that were discussed in the literature as a possible way to contextualize alerts, and to reduce alert overriding rates and alert fatigue (Table 1). A more detailed description is available in a separate publication [8].

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Table 1. Context factors that have been discussed as a way to contextualize (prioritize, filter) CPOE alerts according to the clinical situation. Context factor Factor related to organizational unit (department, hospital) or the user: • Characteristics of the patient population of the unit • ADE rate of the unit • Specialty of the unit • Workload within the unit • Professional experience of the user • Current task of the user • Personal preferences of the user • Repetition of alerts to a user • Override-rate of alerts within the unit Factors related to the patient or drug: • Demographic data of the patient • Risk factors of the patient • Tolerance with regard to the drug • Complexity of the patient case • Clinical status of the patient Factors related to the alert: • Class of drug the alert refers to • Severity of the effect • Probability of occurrence • Strength of evidence • Topicality of the alert • Type of alert

3.1. International Delphi survey From the 214 invited international researchers, 69 (32.2%) completed both rounds. From these 69 researchers, 45 (65.2%) self-assessed their CPOE expertise as “advanced”. Over half of the participants held a university perspective and approximately one-third a health care provider perspective The chosen top-five useful context factors for prioritizing alerts in descending order of usefulness are: 1. Severity of the potential effect of an ADE; 2. clinical status of the patient; 3. probability of occurrence of the ADE; 4. risk factors of the patient; and 5. strength of evidence. In the free-text comments, the researchers named the following factors as potentially missing: Drug history (including stopping of a drug); given application forms of a drug (route of administration); whether a prescription is based on a clinical protocol; whether there is advice that may reduce the ADE risk; and already planned clinical actions (such as the lab monitoring). 3.2. User survey of physicians Overall, we got 26 responses from the Hospital of Denain (return rate: 43.3%) and 94 responses from the three hospitals in Copenhagen (return rate: 45.4%). The chosen most useful context factors for prioritizing alerts are: Severity of effect, clinical status of the patients, complexity of the case, and class of drug. The issued free-text comments did not bring new ideas for missing context factors.


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4. Discussion We identified 20 possible context factors from the literature and asked researchers and clinical users to judge their usefulness: Both groups emphasized the usefulness of severity of effect and clinical status of the patient (see Figure 1). The researchers additionally favored more evidence- and research-based factors, while the users additionally favored more clinically oriented factors.

Figure 1. Top-ranked context factors by CPOE researcher and clinical users.

We included researchers that were identified by their number of publications on CPOE systems, with a large majority coming from universities and health care providers. Their self-assessment showed that we in fact were able to gather more experienced CPOE experts. The point of view of industrial experts is mostly not covered. In the user survey, we included physicians from overall four hospitals in two countries. All of them had experiences with using CPOE systems, and with alerting. The return rate was sufficiently high in all cases. The results can, however, not easily be generalized to other hospitals with other CPOE systems. The found factor “severity of the potential effect of an ADE” has been controversially discussed in the literature, with some authors supporting it [9, p. 37], others being more critical [10, p. 446]. The factor “clinical status of the patient” has also been supported by others [5, p. 144]. It seems quite clear that inclusion of more clinical parameters such as lab values can help to better tailor alerts. Another highranked factor, strength of evidence, is mentioned by others [9, p. 37]. Not surprisingly, only the participating researchers, having a strong research background, rate this as an important factor. “Probability of occurrence” and “risk factors” of the patients are voted highly by the researchers, but are only seldom mentioned in the literature. An interesting result was the poor ranking achieved by the context factor “personal preferences of the user”. This factor is mentioned quite frequently in the literature [5], and is quite highly ranked by the users (but not in the overall top-5-factors), but worstranked by the experts. To our knowledge, this study is the first attempt to systematize the notion of “contextualization of alerts”, and to ask experts and users on the most useful factors. The top-ranked-factors in both groups could now be exploited in CPOE systems. For example, CPOE vendors could try to systematically integrate information on the clinical status of the patient, information of the probability of the effect indicated in the alert, or strength of evidence for the alert, to prioritize alerts and/or to filter them. Based on human-computer interaction paradigms and usability research, CPOE user interfaces could then be further optimized, with alerts of different priority shown in different ways (interruptive or not, different size and color, different location). This

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all could help to improve the sensitivity of alerts and reduce alert overriding rates and alert fatigue. Whether this is possible, it has, however, to be shown in further quantitative or qualitative trials. In our study, we looked at the clinical setting. It would be interesting to investigate whether the context factors are different when looking at the patient-oriented systems, used at home to document drug intake or self-prescriptions.

5. Conclusion Our results show various context factors that could be used to better tailor alerts to the clinical situation. The top-ranked context factors could be used to re-design the way alerts are presented in CPOE systems, to increase sensitivity of alerts, to reduce overriding rates, and to improve medication safety. Acknowledgments. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n°216130.

References [1]

Schnurrer J, Frölich J. Zur Häufigkeit und Vermeidbarkeit von tödlichen unerwünschten Arzneimittelwirkungen. Internist. 2003;44:889-95. [2] Shojania KG, Duncan BW, McDonald KM, Wachter RM, editor. Making Health Care Safer: A Critical Analysis of Patient Safety Practices, Evidence Report/Technology Assessment No. 43, AHRQ Publication No. 01-E058. Rockville, MD:: Agency for Healthcare Research and Quality; 2001. [3] Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review J Am Med Inform Assoc. 2008;15(5):585-600. [4] Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon C, et al. Adverse drug event rates in six community hospitals and the potential impact of computerized physician order entry for prevention. J Gen Intern Med. 2010 Jan;25(1):31-8. [5] van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006 Mar-Apr;13(2):138-47. [6] Khajouei R, Jaspers MW. The impact of CPOE medication systems' design aspects on usability, workflow and medication orders: a systematic review. Methods Inf Med. 2010;49(1):3-19. [7] Grizzle AJ, Mahmood HM, Ko Y, Murphy JE, Armstrong EP, Skrepnek GH, et al. Reasons provided by prescribers when overriding drug-drug interaction alerts. Am J Manag Care. 2007 Oct;13(10):573-8. [8] Riedmann D, Jung M, Hackl W, Ammenwerth E. Reducing alert overload by contextualization of CPOE alerts: Development and validation of a context factor model. BMC Med Inform Decis Mak, submitted. 2011. [9] Kuperman G, Bobb A, Payne T, Avery A, Gandhi T, Burns G, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007;14(1):29-40. [10] van der Sijs H, Aarts J, van Gelder T, Berg M, Vulto A. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc. 2008 Jul-Aug;15(4):439-48.


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Reducing Clinicians’ Cognitive Workload by System Redesign; a Pre-Post Think Aloud Usability Study L.W.P. PEUTEa, N.F. DE KEIZERa, E.P.A. VAN DER ZWANa, M.W.M. JASPERSa a Department of Medical Informatics, Academic Medical Center – University of Amsterdam, The Netherlands

Abstract: Interactive Health Information systems are often considered cognitively complex by their users, leading to high cognitive burden and increased workload. This paper explores if Think Aloud usability testing provides valuable input to effectively redesign a web-based Data Query Tool in Intensive Care and to reduce physicians’ cognitive workload during system interaction. Pre and post redesign usability testing demonstrated a major reduction in the cognitive task workload after redesign of the tool. Classification of revealed usability problems by means of the User Action Framework pointed out that usability problems related to the cognitively planning of actions by system users foremost affected cognitive task workload. This result may support Health Information system (re)design efforts on how to tackle the system’s cognitive complexity and in so doing improve on its usability. Keywords: Assessment-Evaluation, User Workstation, Design aspects, Usability

Interfaces,

Health

Professional

1. Introduction Usability evaluation is an essential but complex part of Health Information (HI) system development. Its purpose is to identify usability problems to improve the system interface design so that it can be used efficiently, effectively, satisfactory and foremost safely by clinicians [1]. In contrast to conventional usability evaluation methods, usability methods that emerged from the field of cognitive psychology are ever more viewed upon as essential in HI system (re)design. They are considered to transcend the level of revealing interface design flaws and advance to the stage of providing insight into clinicians’ cognitive processing in achieving system tasks [2]. In doing so, these methods eventually aim to contribute to designing intuitive HI systems that support and facilitate clinical care by keeping the cognitive task workload of its users to a minimum [3]. The classic Thinking Aloud (TA) method is one of the usability evaluation methods that stem from cognitive science and is generally considered the “gold standard” in usability testing [4]. Scientific research on the validity of the method has shown that subjects’ verbalized information in TA usability testing accurately reflect users thought processes when interacting with a system [5]. Inverting these insights however to successful system (re)designs and thereby minimizing the cognitive task workload of system usage is still challenging.

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This paper investigates the effect of TA usability testing’s input in a system redesign project by comparing users’ cognitive task workload in terms of improved efficacy (correctly performed tasks) and efficiency (task completion time) pre and post system redesign. Usability problems revealed in the pre and post TA test were classified and compared by use of the User Action Framework (UAF) [6]. UAF classification is based on Norman's theory of action and categorizes usability problems in the sequence of a user’s cognitive and physical actions in performing a task in a system. We hypothesize that the earlier a user is obstructed in this sequence, due to usability problems in a system, the higher the cognitive task workload of a user is. The potential of TA usability testing in supporting HI system (re)design with the aim to reduce users’ cognitive task workload in a system and the beneficial effect of applying the UAF classification in this perspective are furthermore discussed in this paper.

2. Methods 2.1. System Background: NICE Online The evaluated and redesigned system in this study is a web-based Data Query Tool of the Dutch National Intensive Care Evaluation (NICE). The NICE registry collects demographic, physiological and clinical data on patients admitted to Dutch ICUs to detect differences and trends in quality of ICU delivered care. To provide participating ICUs with the possibility to query their own data and compare their performance with their peers or with national averages a web-based Data Query Tool was developed in a standard software development cycle. In the NICE Query Tool users define a ‘query’ themselves to compose a graph or a table depicting the selected information. An example of such a query in NICE Online is: ‘compare an ICU’s standardized mortality ratio (SMR) of medical patients to the national mean SMR of medical patients in the year 2009. Figure 1 provides screen shots of both the first and redesigned NICE Query Tool. Additional information on the development of the Tool and its functionality is published in [7]. 2.2. Pre-Post Study Design In October 2008, a pre-TA usability evaluation was performed (pre-test) to assess the overall usability and cognitive complexity of developing queries in the, at that moment available, Query Tool. Eight end-users were contacted to participate in a Think Aloud (TA) study, with an equal representation of new and more skilled users. A portable usability laptop with Morae software was used to document subjects’ TA verbalizations and video and record their (mouse) actions in the system. Sessions took place in the clinical workspace of the subjects and six predefined germane tasks, consisting of several subtasks and varying in complexity, were all given to subjects during the TA test in random order. Revealed usability problems were input to redesign the Query Tool interface. In the beginning of 2010 post redesign TA testing was performed on a beta-test version of the redesigned tool to measure the effectiveness of the redesign efforts. Again, eight end-users were contacted of which four new test users participated who were comparable to the pre TA user test group in terms of computers skills and previous experience with the Query Tool, and four users who had also participated in the pre TA study. Bias of pre-defined task learnability for these four users was


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negligible, since the time between the pre and post study was around one year. In the post TA testing, similar circumstances were upheld as in the pre TA test, including the tasks to be performed in the system. To compare overall task efficacy between the pre-and post TA sessions, the shortest routes to correctly perform the tasks and the corresponding end-results in both the old and redesigned system were determined by highly experienced data managers of the NICE registry. The correct task end-results were then applied as the ‘golden standard’ to measure the percentage of tasks correctly completed by subjects in the TA sessions. To compare overall task efficiency, time on task measurements had to be adjusted for optimization of system response for the display of query results (e.g. users had to wait over one minute for display of the query result in the old system in contrast to 3 seconds in the new system). Pre-post overall task efficiency measurements were therefore compared in terms of the additional time it took users to complete tasks in the system both pre and post as opposed to the time it takes to complete the tasks by the shortest route in both system designs.

Figure 1. NICE Online; Screenshots of the Physician Data Query Tool pre (left) and post (right) redesign.

2.3. Usability Problem Classification; The User Action Framework UAF classification places detected system usability problems in the context of four subsequent phases of the user interaction cycle; Planning- high level, Planningtranslation, Physical actions and Assessment [6]. Usability problems relating to the planning phase concern users’ cognitive actions for planning how to perform a task; e.g. the inability to track where you are in a system. The translation phase is about cognitive actions to determine how to carry out the intentions; related usability problems are incorrect button labeling or vague symbols. Physical action pertains to executing the actions by manipulating user interface objects; usability problems are e.g. button proximity or small size. The assessment phase is about perceiving, interpreting and evaluating the resulting system state to decide whether the action was indeed accurately performed; related usability problems concern users’ misunderstandings of system feedback. UAF classification starts with four user interaction phases on the first level; accurate classification can go up to six levels. This paper limits its results to the first level to provide a general insight into the relation between performance measures on the cognitive task workload and the UAF classified usability problems found.

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3. Results Overall, 12 subjects were included in the pre and post TA study (4 subjects were included in both pre and post TA). In total, 36 usability problems were revealed by two usability analysts in the pre TA test and 35 usability problems were revealed in the post TA test. UAF categorization of usability problems was performed by both usability analysts separately (κ=0.91). Of the usability problems detected in the pre-test 34 (94%) were resolved by redesigning the Query Tool, 5 (14%) were considered overlapping with usability problems found in the post-test and thirty new usability problems were revealed in the post TA Test. Table 1. Pre and post redesign measurements of task efficiency and efficacy. Subjects Overall Task Efficiency (min) - Optimal route (min) - Adjusted Overall Task Efficiency (min)* Overall Task Efficacy

Pre TA Test (8) 50.16 (sd 7.62) 20.05 + 30.11 24 (50%)

Post TA Test (8) 19.40 (sd 6,01) 12.51 + 6.49 46 (96%)

Efficacy: Total number of tasks (completed) (8x6=48), * deviation in min from optimal route

Overall task efficiency in the pre TA test was extremely low; users took on average 30 minutes longer to perform the tasks in the system compared to the time it would have taken them when they would have known what to do and how to act in the system (shortest route) (Table 1). This extra time was reduced to less than 7 minutes in the post test. The fact that subjects were able to complete only 50% of the tasks during the pre TA test confirmed that usage of the Tool for developing queries was cognitively complex. This percentage increased to 96% after redesign. Table 2 shows the usability problems categorized by ‘first level’ UAF classification in both the pre and post TA tests. The majority of usability problems in the pre-test concerned the ‘planning’ phase (64%). It appears that these problems were accountable for the high cognitive task workload associated with the low values measured for task efficacy and efficiency in the pre test. After redesign the majority of the 30 new usability problems detected in the post TA test concerned the ‘assessment’ phase (63%), showing an evident shift in the phase of interaction in which the new revealed usability problems occurred compared to the pre-test. However, these post TA usability problems did not or only minimally seem to affect users’ cognitive task workload. Apparently, usability problems related to the assessment phase did not have a great impact on task efficacy or efficiency. Analysis of the verbal protocols and video recording of users’ actions showed that usability problems in the Assessment phase were mostly related to users’ preferences in interface layout, such as graph colour and display of system feedback related to information on the screen. Table 2. Usability problems in the Pre and Post TA test classified by first level UAF UAF Phase 1. Planning 2. Planning (translation) 3. Physical Action 4. Assessment

Pre TA Test (36) 8 (22%) 15 (42%) 13 (36%)

Post TA Test (35) 10 (29%) 3 ( 8%) 22 (63%)


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4. Discussion and Conclusion In this study the input of TA usability testing in redesigning a web-based Data Query Tool of a National ICU Quality Registry led to a clear reduction in its complexity and hence the cognitive task workload of its users. Optimization of the task accuracy was obtained from 50% of tasks completed in the pre-test to 96% of similar tasks completed in the post test. However, redesign of the Tool also caused thirty new usability problems to occur. This is not surprising as it is well known that usability evaluation is an iterative process; subsequent changes to a user interface design might reveal other problems that again need user testing [8]. The fact that users’ efficiency was highly improved after redesign of the Tool indicates that the new usability problems detected in the post TA test minimally affected their cognitive task workload in use of the redesigned system. Applying UAF classification in our study was particularly useful to compare the nature of the pre and post detected usability problems and their effect on users’ cognitive task workload. UAF classification revealed a potential cause-effect relation between the occurrence of usability problems in the planning phase of the users’ – system interactions and their apparent negative effect on their cognitive task workload in terms of task efficiency and efficacy. Indeed, input from the pre TA test to the Query Tool redesign efforts offered insight on how to tackle the usability problems in the planning phase and in so doing furthered the development of the Query Tool to better support users’ cognitive processes in Data Querying. The new usability problems detected in the post test were mostly related to the Assessment phase, indicating that these problems were more or less of a cosmetic nature. As such, they did not provoke additional cognitive burden. Those usability problems that placed a high cognitive burden on system use were thus successfully reduced in just one redesign iteration of the Query Tool. Future studies that apply TA testing in a redesign cycle should focus redesign efforts on those aspects of the system that affect the planning of tasks by end-users, especially when high cognitive task workload of complex HI system tasks is seen as a major barrier for system use.

References [1] STANDARDIZATION SIOO. ISO 9241-11 Ergonomic requirements for office work with visual display terminals (VDTs) – part 11: guidance on usability. 1998. [2] Kushniruk, A.W. Patel, V.L. Cognitive and usability engineering methods for the evaluation of clinical information systems, J Biomed Inform 37 (1) (2004), 56-76. [3] Horsky, J. Zhang, J. Patel, V.L. To err is not entirely human: complex technology and user cognition, J. Biomed. Inform. 38 (4) (2005), pp. 264–266. [4] Jaspers, M.W. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence, Int J Med Inform 78 (5) (2009), 340-53. [5] Hertzum, M. Hansen, K.D. Andersen, H.H.K. Scrutinizing usability evaluation: Does thinking aloud effect behaviour and mental workload?, Behaviour & information Technology 28 (2) (2009), 165-181. [6] Andre, T.S. Hartson, H.R. Belz, M.S. McCreary, A.F. The user action framework: A reliable foundation for usability engineering support tools, Int J Human-Computer Studies 54 (2001),107-36. [7] Peute, L.W. de Keizer, N.F. Jaspers, M.W. Cognitive evaluation of a physician data query tool for a national ICU registry; results of two think aloud variants and their application in redesign, Stud Health Technol Inform 1 (2010), 309-13. [8] Kaplan, B. Harris-Salamone, K.D. White paper: Health IT project success and failure: recommendations from literature and an AMIA workshop, AM Med Infom Assoc 16 (2009), 291-9.

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Impact of Alert Specifications on Clinicians’ Adherence a

M. M. LANGEMEIJER a, L. W. PEUTE a, M. W. M. JASPERS a Department of Medical Informatics, Academic Medical Center – University of Amsterdam, The Netherlands

Abstract. Computerized alerts provided by health care information systems have been shown to enhance clinical practice. However, clinicians still override more than half of the alerts. This indicates that certain aspects of alerts need improvement to fulfill their purpose of supporting clinicians in decision making. This paper reports on a systematic review on studies evaluating alert specifications and their impact on clinicians’ alert adherence. The review revealed that use of colors and icons to distinguish different alert severity levels and presenting high severity alerts in an interruptive fashion increases clinicians adherence to alert recommendations. Alert message contents that lack clinical importance or provide incorrect texts increase alert non-adherence. Few studies have yet focused on the impact of alert specifications on clinicians’ adherence. A research agenda is needed on alert specifications and their impact on clinicians’ adherence in order to develop alerts that truly support clinician decision making. Keywords. Hospital Information Systems, Alert, Reminder Systems, Clinicians Adherence, Design aspects, Clinical Decision Support

1. Introduction Clinical decision support systems (CDSS) can have beneficial effects on clinicians’ performance in daily practice (1). Certain types of CDSS provide decision support through computerized alerting of clinicians on (critical) situations that require their attention or special action. Alerts provided by Computerized Physician Order Entry (CPOE) systems have been proven to reduce duplicate orders, overdoses, allergic reactions, and drug interactions (2). Also, higher clinicians’ compliance to clinical guidelines has been reported as a beneficial effect of alert implementation (3). However, one of the barriers to attaining these beneficial effects is that 49% to 96% of the alerts are still overridden (4), undermining their purpose. Often heard reasons for overriding an alert is “alert-fatigue” as a result of low specificity (4, 5). Alerts of low specificity are often ‘clicked away’ without being read even when overriding them could cause adverse events. Next to alert specificity, the graphical alert design influences alert overriding; a minor change in the design of an alert shown on a computer screen may have a major impact on a clinician’s action (6). However, in what way alert specifications of different severity and specificity may affect clinician adherence is still unclear. In this paper we present the findings of a systematic review of studies that evaluated effects of interventions concerning different alert specifications on clinicians’ adherence.

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2. Methods In this systematic review we define an ‘alert’ as ‘a message that becomes visible to inform the user of a certain situation that requires attention’. An alert is generated by a rule base that is incorporated in a health care information system. In this review we refer to health care information system as defined by (7): “all computer-based components which are used to enter, store, process, communicate, and present health related or patient related information and which are used by health care professionals or the patient themselves in the context of inpatient or outpatient patient care”. Alert characteristics which are defined in this review are ‘type’, ‘design’ and ‘message content’. Type is defined by two characteristics; intrusive/non-intrusive and interruptive/non-interruptive. Intrusive messaging is considered if it overlays the computer ordering screen. Alert messaging is defined as interruptive if they require a user action before a clinician can proceed with the next step of ordering (e.g. providing a reason for alert overriding). Design of an alert is defined by two elements: graphical (e.g. the use of colors), and screen (e.g. the size of an alert or its components, the alignment of alert components, and the use of icons). Message content of an alert is defined as informative content of the alert that is shown to the user (e.g. alert severity, options for alternative treatments etc.). Clinician’s alert adherence is considered in terms of a clinician following the recommendation of the alerts message. MEDLINE and EMBASE were systematically searched from January 1, 1990 until January, 1 2009 using a combination of Medical Subject Headings (MeSH terms) and keywords. These terms were grouped as (A) interactive computer systems, (B) alert, warning, reminder, or feedback, (C) alert specifications (e.g. design). Within each group, the terms were combined by the operator “OR”. The three groups were combined by the operator “AND”. The search was narrowed down to articles written in English. All titles and abstracts of these articles were reviewed by the first author. The two other authors each reviewed half of the total set. Studies were rated as relevant if in the abstract the following items were mentioned: 1) the system under study is an interactive health information system, 2) the study is about clinician alert adherence, and 3) the study objective is the evaluation of at least one of the following alert specifications (type, design, or message content). Selected articles were discussed in a meeting and if all three reviewers agreed upon inclusion, full texts were reviewed. A standard data collection form was applied to review the included articles.

3. Results The literature search generated a total of 1711 articles (MEDLINE 1055, EMBASE 656) of which 386 were duplicates. From the remaining 1325 articles, 16 were selected for full text review based on their titles and abstracts. After full text review, only seven articles were found eligible for inclusion. One was excluded because it was about a system that had no interactive user interface, four were excluded because the full text did not provide detailed information on the alert specifications, and four were excluded because they did not accurately describe the study designs. Table 1 gives an overview of the included articles with the year of publication, study design, setting, system type, the results in terms of alert specifications, and the described effect on clinicians’ adherence. Full references of the included studies are provided in a technical report, which can be found at (8).

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Table 1. Overview of studies evaluating impact of alert specifications on clinicians’ adherence Investigator, Year of Pub.

Study design

Settting

System

Alert specification

Effect

Shah NR et al., 2006

Descriptive

Outpatient

CPOE

Type: Tiered based on severity level; 1) interruptive requiring elimination of interaction 2) interruptive requiring reason 3) not interruptive

Positive

Paterno MD et al., 2009

Cohort study

Inpatient

CPOE

Type: Tiered based on severity level; 1) interruptive requiring discontinuing one of the orders 2) interruptive requiring discontinuing one of the orders or providing a reason 3) not interruptive

Positive

van Wyk JT et al., 2008

RCT

Outpatient

EHR

Type: Automated alerting vs. on-demand alerting

Positive

Alexander GL 2007

Descriptive

Inpatient

EHR


No effect

Eliasson M et al., 2006

Crosssectional

Outpatient

CPOE

Design: Colors to indicate severity: red = high, yellow = medium, white = low

Unclear

Design: Different icons for domain of notification (Pregnancy, Breast-feeding, Medication). Taylor L et al., 2004

Descriptive

Outpatient

CPOE

Content: clinical importance of alert, and correctness of drug/disease information

Negative

Tamblyn R et al., 2008

RCT

Outpatient

CPOE


No effect on type, Negative effect on content

Content: clinical importance of alert, and correctness of drug/disease information

Five of the studies provided specific information about the different types of alerts. Shah et al. tiered the presentation of a selective set of alerts based on their severity levels into 3 categories. Categories one and two were considered severe and were designed to interrupt the clinician requiring a direct action; either eliminating the contraindication for level 1 or providing an override reason for level 2, while, the less severe ones, level 3, were presented in a non-interruptive fashion, requiring no action by clinicians. This study reported an adherence rate of 67% with interruptive alerts requiring action. Paterno et al. studied whether the rate of clinician compliance with drug-drug interaction alerts improved when a tiered presentation of alerts was implemented. Alert log data were analyzed at two academic medical centers using the same alerts but one displayed alerts by severity level (tiered presentation) while the other did not. This study showed that the overall compliance rate for tiered alerts was almost three times higher than for non-tiered alerts (29% vs. 10%). A randomized control trial (RCT) by Van Wyk et al. studied automated alerts (the recommendation is automatically shown to the user) and on-demand alerts (a user has to actively initiate the overview screen to access the recommendation) versus no


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intervention. The RCT showed that the alerting version significantly improved the performance of clinicians for screening and treatment of dyslipidemia as compared to the on-demand version. Another RCT study by Tamblyn et al. compared the effect of customizable automated alerts and customizable on-demand alerts on drug prescribing problems and alert overrides. A greater absolute number of automated alerts were seen and revised by clinicians, but both groups underused the alerts. As a result, there was no significant difference in the overall prevalence of prescribing problems by the end of the follow-up period. Therefore clinician adherence was not affected. Likewise, the study of Alexander investigated the impact of automated alerts compared to on-demand alerts on clinical responses of health care providers and reported no significant difference in clinicians’ adherence. Only one of the studies, Eliasson et al., provided specific information about the visual design aspects of alerts. This study investigated a system where icons (differing in type for pregnancy, breast-feeding, and medication) appeared in patient situations that required attention. The background color of the alert changed for the various severity levels; Red for high, Yellow for medium, White for low. This study showed that these types of alerts were quickly adopted in daily clinical routine. The adoption can be due to adherence to alerts, though the study did not directly mention the actual effect of the alert design specifications on clinicians' adherence. Two of the studies, Taylor et al. and Tamblyn et al, reported on content specificities of alerts. Taylor assessed the feasibility and performance of automated alerts within an electronic decision support tool of a prescribing system. Among other reasons, lack of clinical importance of alerts and incorrectness of drug/disease information respectively counted for 34% and 4% of clinicians’ non-adherence to automated alerts. Tamblyn et al. likewise showed that from the total number of alerts seen by clinicians 16% were ignored because of incorrectness of drug/disease information and 29% because of lack of clinical importance.

4. Discussion The findings of this systematic review suggest that specific types of alert presentation can influence clinicians’ adherence to the recommendations provided. First, clinicians’ acceptance of alerts and likelihood of compliance with the alert recommendations could increase when they would only be interrupted by alerts of highest severity, which is with the highest clinical importance. A reduction in the number of interrupting alerts, particularly those with low severity, could prevent alert fatigue and alert overriding by clinicians. Automated alerts rather than on-demand ones do not seem to be associated with better performance of clinicians though in the RCT by Van Wyk et al. automated alerts improved adherence in comparison to on-demand alerts. The results of this RCT are consistent with the findings of a major review (8). This review showed that clinicians’ performance is improved in conditions wherein they are automatically prompted by clinical decision support systems compared to situations which required them to activate the system themselves. These conflicting results may be explained by the fact that other factors besides alert specifications such as alert specificity and severity which likewise influence clinicians’ adherence were neglected. Certain alert design specifications have a positive influence on clinicians’ adoption of alerts. One of the studies in this review showed that the use of different colors for differentiating alert severity levels and the use of icons for indicating the domain of

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notification may enhance clinicians’ awareness of situations requiring their attention and improve quick adoption of alerts in clinical practice. The effect of these alert designs on clinicians’ alert adherence yet remained unclear. The message content specification of an alert might also impact clinician adherence. Two studies showed that that alerts with incorrect information and unclear clinical consequences were among contributing factors of clinician non-adherence, which findings were acknowledged by Van der Sijs (4). This systematic review has several limitations. Because the term “alert” is not a MeSh term, the term “alert” was combined with other but similar terms like “warning” and MeSh terms like “feedback” and “reminder” to find relevant articles. Further work is to broaden the search strategy to find more studies that might shed light on other alert specifications and their impact on clinicians’ adherence to the alerts. Furthermore, only two of the seven studies concerned RCTs which produced conflicting results, so the results are poor and inconclusive. Besides the limited number of studies and RCTs found by this review, most of the included publications focused on the effect of one single alert specification on clinicians’ adherence. Therefore, the reported adherence might be influenced by other alert specification aspects not of focus in the study, biasing the study results. Most important, adherence is influenced by alert specificity and severity as well. A research agenda is needed to investigate the impact of variations in alert specifications in relation to alert specificity and sensitivity on clinicians’ adherence. The ultimate aim is to develop alert designs that truly support clinician decision making and improve clinical outcomes. We will start this research by experiments evaluating the effect of different types, designs and message contents of alerts in relation to alert specificity and sensitivity level on clinicians’ adherence in two Dutch academic hospital settings.

References [1]

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Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293-10 (2005), 1223-38. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 23-163/12 (2003), 1409-16. Rosenberg SN, Shnaiden TL, Wegh AA, Juster IA. Supporting the patient's role in guideline compliance: a controlled study. Am J Manag Care 14-11 (2008), 737-44. van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 13-2 (2006), 138-47. Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 13-1 (2006), 5-11. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 10-6 (2003), 523-30. Ammenwerth E, de Keizer N. An inventory of evaluation studies of information technology in health care trends in evaluation research 1982-2002. Methods Inf Med 44-1 (2005), 44-56. Langemeijer MM, Peute LW, Jaspers MWM. Impact of Alert Specifications on Clinicians’ Adherence, Technical Report 2011-01, Department of Medical Informatics, University of Amsterdam. Available at http://kik.amc.uva.nl/KIK/reports/TR2011-01.pdf


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Medication Decision-Making on Hospital Ward-Rounds Melissa BAYSARIa,1, Johanna WESTBROOKb, Richard DAY c,d a Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, Australia b Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, Australia c Department of Clinical Pharmacology and Toxicology, St Vincent’s Hospital, Sydney, Australia d Faculty of Medicine, University of New South Wales, Sydney, Australia

Abstract. This research explored the decision-making process of selecting medicines for prescription on hospital ward-rounds. We aimed to determine when and with whom medications were discussed, and in particular, whether shared decision making (SDM) occurred on ward-rounds. As a low level of computerized decision support was in place in the hospital at the time, we also examined whether the decision support aided in any medication discussions. Fourteen specialty teams (46 doctors) were shadowed by the investigator while on ward-rounds and all verbal communication about medications was noted. Most medication discussions took place away from the patient bedside and the majority took place between two or more doctors. While a great deal of doctor-patient communication regarding medications took place on ward-rounds, very little of this comprised SDM. More frequently, doctors informed patients of the medications they would be or were currently taking. The computerized decision support had little impact on treatment decision-making. While the value of SDM is often acknowledged in the literature, it appears to be rarely practiced on hospital ward-rounds. Keywords. Shared decision making, prescribing, ward-rounds

1. Introduction It has been suggested that the greatest challenge to information technology development in healthcare is expanding our understanding of decision-making in the complex healthcare environment [1]. Research has shown that doctors appear to select medications based primarily on the probability that a drug will be effective in controlling the disease and on the potential side effects which may result from using a drug [2], but it has also been proposed that patient demands (i.e. patient expectations and preferences) influence clinical decision-making [3]. Shared decision-making (SDM) is the process whereby a doctor and patient exchange information and treatment preferences and reach an agreement about an appropriate treatment [4]. It follows that SDM is the ideal model for treatment decision-making from the recognition that uncertainty surrounds treatment decisions 1

Corresponding author: Melissa Baysari

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M. Baysari et al. / Medication Decision-Making on Hospital Ward-Rounds

for many conditions and that patients vary in their preferences for health states, tolerances for pain and long-term outlooks [5,6]. Active participation in treatment decision-making by patients has been associated with greater patient satisfaction and better health outcomes, possibly via increased adherence to treatment recommendations and increased perceived control over one’s illness [7,8]. Despite these potential benefits, SDM is not often practiced [9,10], although the bulk of research in this area has been done in primary care. We set out to investigate SDM in a hospital setting. Hospital ward-rounds have been identified as one of the most valuable times for sharing information, problem solving and planning a patient’s treatment [11]. We aimed to determine when and with whom medications were discussed on ward-rounds, and to determine whether patients played an active role in medication decision-making. As a low level of computerized decision support was in place in the hospital at the time, we also examined whether the decision support aided in any medication discussions.

2. Method 2.1. Details of the Computerized Provider Order Entry (CPOE) system This study was conducted at a 320 bed teaching hospital in Sydney, Australia. At the time of the study, June-November 2010, all wards were using the CPOE system MedChart (www.isofthealth.com) except for the emergency department and the intensive care unit. MedChart is an electronic medication management system that links prescribing, pharmacy review, and drug administration. The CPOE included some basic decision support comprising pre-written orders and order sets, computerised alerts (allergy, pregnancy, therapeutic duplication, and over 100 locally developed rulebased messages e.g. drug therapeutics committee decisions, administration instructions) and a Reference Viewer look up tool that allowed prescribers to access reference information (e.g. Therapeutic Guidelines) by clicking on a tab at the top of the prescribing screen. 2.2. Participants Fourteen medical teams were recruited to participate in the study via direct approach, phone or email. The teams included cardiology, clinical pharmacology, lung transplantation, colorectal surgery, two gastroenterology teams, two gerontology teams, haematology, infectious diseases, nephrology, neurology, and two palliative care teams. Medical teams typically included one senior doctor (consultant), one (or more) registrar, one (or more) resident and occasionally interns (first year post graduation) and medical students. Some ward rounds (5/37) were observed to take place without a senior doctor present. In total, 46 doctors were observed. 2.3. Procedure Medical teams were shadowed by one of the investigators (MB) while on their wardrounds. The investigator followed each team as they discussed patient cases and interacted with patients. On occasions where the computer (fixed to a lightweight trolley) was not taken to the patient’s bedside, the investigator remained in the hallway

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with the computer and only accompanied the team to the bedside if invited to do so by a participating doctor. All verbal communication about medications was noted and information was classified into the following categories: Where medication discussions took place (at the patient’s bed, in the hallway), whether the discussion took place among team members or between a team member and a nurse or pharmacist (doctor and doctor, doctor and nurse, or doctor and pharmacist) or between a team member and patient, the nature of the conversation between a team member and patient (see Table 1), whether the content of an alert was discussed, and whether the Reference Viewer was used during a medication discussion. Each medical team was observed on two or three ward rounds (except for one team that was observed only once because they reported never using a computer on ward rounds), resulting in 58.5 hours of observation in total. Ethics approval was obtained from the human research ethics committee of the hospital and the University of NSW.

3. Results One hundred and seventy-six verbal behaviours about medications were exchanged between two or more healthcare providers. Most of these conversations took place away from the patient, with only 41 (23%) verbal behaviours taking place at a patient’s bedside. The majority of medication discussions among providers were between two or more doctors (91%), with only a small number taking place between a doctor and nurse (7%) or doctor and pharmacist (2%). One hundred and twenty six verbal behaviours took place between a team member (i.e. junior or senior doctor) and a patient. The nature of these behaviours and some examples are presented in Table 1. Doctors frequently told patients what medications they should be taking but rarely involved patients in the decision to order medication. Table 1. Nature of discussions about medications between doctors and patients Type of verbal behaviour

Example

Doctor told patient what medication they are currently taking or will take (Paternalistic decision making) Doctor asked patient if/what medication they would like to take (SDM) Doctor asked patient about medications they are currently taking Patient asked doctor about medications

“You have a nasty infection so I’ve put you on antibiotics”

Doctor answered patient’s question about medications

“Would you like some medication for your constipation?” “Do you take this medication everyday?” “All this talk about Calcium and heart attacks, what does it all mean?” “One study is not gospel. We don’t want to take you off the Calcium tablets”

Number observed (%) 65 (51.5)

2 (1.5) 32 (25.5) 17 (13.5) 10 (8)

No doctor was seen discussing the content of a computerized alert with another team member or patient, but the Reference Viewer tool was used on five occasions during discussions about medications. On one occasion, a doctor was observed using the tool to look up the trade names of a number of medications and then relayed these names to a patient. On the other occasions, doctors used the tool to review medication information during a discussion about medications with other team members.

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4. Discussion In this setting, ward-round treatment decision-making typically consisted of discussions between two or more doctors away from the patient bedside. Doctors rarely involved nurses or pharmacists in the decision-making process, a finding consistent with previous research [12]. While a great deal of doctor-patient communication regarding medications did take place, very little of this comprised SDM. Several factors may have contributed to this failure to engage in SDM in this setting. Some medical problems (e.g. preventative screening) have clear decision points and so may be more suited to SDM than many hospital medical problems (e.g. acute situations). It has been suggested that SDM requires a longstanding relationship between doctor and patient so that each party is able to understand the values and biases of the other [13]. A relationship of this kind is not always possible in the hospital setting, where hospital stays are relatively short and interactions between patient and doctor often brief. Time is viewed as a limited resource on ward-rounds and time pressure has been identified as the most common barrier to SDM adoption [14]. Studies have also shown that a patient’s desire to participate in treatment decision-making is dependent on a range of factors, including patient age, sex, and the severity of their disease [5,15]. One might expect hospital patients (many of whom are elderly and experiencing serious illnesses) to be unwilling to participate. Regardless, it is still recommended that doctors offer all patients the opportunity to actively engage in the process of making treatment decisions [13,16]. In this setting, doctors employed a paternalistic approach whereby they informed patients of the treatment that was or would be initiated. It is now widely recognized that this treatment approach is only appropriate during emergency situations [16]. Little research has examined the impact of computerized decision support on medication decision-making. As the content of computerized alerts was never featured in medication discussions, it can be deduced that the alerts played a very minor role, if any, in drug choices made on ward-rounds. The Reference Viewer, on the other hand, was utilized on several occasions to obtain medication information. Decision support of this kind allows prescribers to access relevant information only when they believe it is needed and so provides a non-interruptive alternative to computerized alerts. This study was limited by the fact that observations were conducted at only one hospital so findings may not be generalizable to other settings. Patient-doctor relationships were not observed over long periods of time (only 1-3 times) so some medication conversations may have been incomplete.

5. Conclusion While a great deal of doctor-patient communication regarding medications took place on ward-rounds, very little of this comprised SDM. More frequently, doctors informed patients of the medications they would be, or were currently taking. Medication discussions typically took place between doctors on a team, not nurses or pharmacists, and usually occurred away from the patient’s bedside. While the value of SDM is often acknowledged in the literature, it appears to be rarely practiced on hospital ward rounds. A potential therefore exists for interventions, such as decision aids, to facilitate SDM in the hospital setting.


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Acknowledgements: This research is supported by NH&MRC Program Grant 568612.


[4] [5] [6] [7]

[8] [9]

[10]

[11] [12] [13] [14]

[15] [16]

Kushnirick AW, Evaluation in the design of health information systems: Application of approaches emerging from usability engineering, Computers in Biology and Medicine 32 (2002), 141-149 Bradley CP, Decision making and prescribing patterns: A literature review, Family Practice 8 (1991), 2762-2787. Geneau R, Lehoux P, Pineault R, Lamarche P. Understanding the work of general practitioners: A social science perspective on the context of medical decision making in primary care, BMC Family Practice 9 (2008), 12. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: What does it mean? (Or it takes at least two to tango), Social Science & Medicine 5 (1997), 681-692 Kaplan RM, Frosch DL. Decision making in medicine and health care, Annual Review of Clinical Psychology 1 (2005), 525-556. Frosch DL, Kaplan RM. Shared decision making in clinical medicine: Past research and future directions, American Journal of Preventative Medicine 17 (1999), 285-294 Brody DS, Miller SM, Lerman CE, Smith DG, Caputo GC. Patient perception of involvement in medical care: Relationship to illness attitudes and outcomes, Journal of General Internal Medicine 4 (1989), 506-511. Kaplan SH, Sheldon G, Ware JE. Assessing the effects of physician-patient interactions on the outcomes of chronic disease, Medical Care 27 (1989), S110-S127 Braddock CH, Edwards KA, Hasenberg NM, Laidley TL, Levinson W. Informed decision making in outpatient practice: Time to get back to basics, Journal of the American Medical Association 282 (1999), 2313-2320. Makoul G, Arntson P, Schofield T. Health promotion in primary care: Physician-patient communication and decision making about prescription medications, Social Science and Medicine 41 (1995), 12411254. Busby A, Gilchrist B. The role of the nurse in the medical ward round, Journal of Advanced Nursing 17 (1992), 339-692. Manias E, Street A. Nurse-doctor interactions during critical care ward rounds, Journal of Clinical Nursing 10 (2001), 442-450. Kon, AA. The shared decision-making continuum, Journal of the American Medical Association 304 (2010), 903-904. Lagare F, Ratte S, Gravel K, Graham ID. Barriers and facilitators to implementing shared decisionmaking in clinical practice: Update of a systematic review if health professionals’ perceptions, Patient Education and Counseling 73 (2008), 526-535. Levinson W, Kao A, Kuby A, Thisted RA. Not all patients want to participate in decision making: A national study of public preferences, Journal of General Internal Medicine 20 (2005), 531-535. Emanuel EJ, Emanuel LL. Four models of the physician-patient relationship, Journal of the American Medical Association 267 (1992), 2221-2226.

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A Qualitative Analysis of Prescription Activity and Alert Usage in a Computerized Physician Order Entry System Rolf WIPFLIa,1, Mireille BETRANCOURTb, Alberto GUARDIAa, Christian LOVIS a a Division of Medical Information Sciences, University of Geneva and University Hospitals of Geneva b TECFA – University of Geneva Geneva, Switzerland

Abstract. Medical alerts in CPOE are overridden in most cases. The need for alerting systems that are better adapted to physicians’ needs and work processes is recognized. Our study aims to shed some light on how medical alerts are used and how they are integrated in the work process. Work analysis and interviews resulted in a hierarchical task analysis of prescription during ward rounds at the University Hospitals of Geneva. The results indicate that non-modal medical alerts are appreciated as an “insurance” for drugs that are out of the routine set. In the case of drugs that are often prescribed, alerts are ignored as physicians feel comfortable prescribing them. Non-interrupting alerts do not cognitively overcharge physicians, but the question is how to display the numerous alerts so that they are easily accessible when needed. Further, inexperienced physicians lack a mental representation of what evaluations the system is doing with the prescriptions and when alerts are triggered. This may lead to lack of trust or overconfidence, both of them potentially harmful. Keywords. CPOE, medical alert, task analysis, usability

1. Introduction The aim of the present paper is to analyze the prescription behavior of physicians and their use of medical alerts with a homegrown computer physician order entry (CPOE) system with an integrated decision support system (DSS) at the University Hospitals of Geneva, a teaching hospital with 2000 beds and 15.000 electronic prescriptions a day. The scope of the study is limited to the use during ward rounds. Research in other hospitals has shown that medical alerts have a low compliance rate [1] but nevertheless improve prescription behavior and patient safety [2]. It is generally agreed that alert systems have to be better adapted to the needs and work processes of prescribing physicians. If alerts would be better timed, more specific and

1

Corresponding Author: Rolf Wipfli, University Hospitals of Geneva, Division of Medical Information Sciences, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 14, Switzerland.

R. Wipfli et al. / A Qualitative Analysis of Prescription Activity and Alert Usage

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displayed in a user-friendly way, they would act as an even more powerful decision support system than today. The prescription activity with CPOE can be described in a top-down manner accessing job descriptions, hospital guidelines, medical guidelines and their implementation in the resulting CPOE. Conversely, in a human-centered approach, the activity can be constructed on physicians’ representation of the information in the CPOE and how they handle the medical information in a real work context. As for medical alerts, there seems to be a discrepancy as the low compliance rate shows. In order to study prescription activity, ethnographic work observations and interviews [3], work simulations [4] and focus groups [5] have been applied. A method to model the prescription process is cognitive task analysis. The result is a hierarchical representation of main tasks and the depending sub tasks. Researchers have used this technique to represent the drug administration process [6]. A similar method is MAD (Method of analytic task description) [7] which is used in the present study. The goal is to represent the physician’s activity in order to make alerts better adapted to it.

2. Method In a first step, 5 deputy heads of different divisions at University Hospitals of Geneva were questioned in semi-directive interviews. The aim was to get a wide range of requirements and a broad perspective on the alerting systems in their divisions. The scope was not limited to CPOE, but aimed to cover general use of alerts in the medical field. Two divisions have been selected for conducting further analysis: the division of cardiology in the department of internal medicine and the division of pediatric surgery in the department of adolescents and children. In each division, a ward round in the morning was accompanied to see how medical personnel act and communicate during prescription activity. Work procedures were observed and notes taken. The work itself was not interrupted as far as possible. When the moment seemed right emerging questions were asked according to the methodology of contextual inquiries. Each deputy head of division selected a physician for further semi-directive interviews. In the case of cardiology it was an attending physician with 10 years of experience with CPOE and in the case of pediatric surgery an advanced resident with 2 years of experience with the CPOE. The interviews were always opened with the request “to recount a recent clinical case where an alert has been displayed”. When narrations stopped or when something was unclear, further questions were asked to complete the view on the prescription process. In each of the services we interviewed 2 more residents, each with 8-14 month of experience with CPOE. The interviews took 20-40 minutes, were audio recorded and transcribed. The transcriptions were analyzed in order to identify the different activities in the prescription process and their temporal and causal relations. This data completed the findings provided by work analysis.

3. Results 3.1. Interviews with deputy heads of division Alerts in CPOE are in general regarded as a good means to provide decision support, as the deputy head of division support projects which go further in this direction.

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However, some brought up issues make the CPOE less utile. First, alerts once entered in the system can be outdated. The processes how to keep them up-to-date is not yet implemented (i.e., for patients who were carrier of methicillin-resistant staphylococcus aureus (MRSA) and who are now readmitted to the hospital). Another example is reminder alerts that should be given the last day of hospitalization (i.e., bacteriological tests), a day the system cannot forecast. This leads to an alert every day and therefore to a low compliance rate and alert fatigue. Some physicians criticize the authentication warnings when accessing patient records out of their responsibility. They are regarded as interruptive, intimidating and as a lack of trust in them. None of them complained about the amount of alerts and they agree that it is usually difficult to make alerts more specific given that the user range is very broad (medical specialties, experience and expertise). Concerning usability issues, some deputy head of division are concerned with the quality of medical work by inexperienced physicians. They fear that novice physicians might use electronic prescribing as a poor substitute for thorough clinical analysis. According to them, residents depend too much on decision support systems. Another usability issue has been identified in the display of information. Some alerts are out of the visual focus region when using the system and thus leading to low response levels to the alerts. No one had the impression that there are superfluous alerts. However, some concerns were expressed that the number of alerts will soon overcharge the screen. Form usability was also mentioned as some interaction elements like pull down menus can lead easily to errors when choosing a wrong unit in drug prescription. 3.2. Work analysis There are two situations where drugs are prescribed. In the first case, a physician is on a night or weekend shift and does the prescription alone. In most cases however, the physician is on a ward round together with other residents, nurses and in some cases with a deputy head of division and/or attending physicians who lead and supervise the prescription process. The decision making process in these cases is collaborative. Prescriptions and medical forms are entered by one designated resident after the visit of a patient or even at the end of the ward. The question arises what impact alerts have on the prescription process when they appear some time after having made the decision. 3.3. Interviews with attending physicians and residents Only one of the interviewed physicians could recall a recent medical case where he was alerted during the prescription. Apparently, alerts like drug interaction alerts and dosage alerts do hardly lead to critical incidents which would be remembered. The alerts are rather seen as contextual information (coming from the drug compendium) for a drug or drug combination, which may also be ignored in favor of the division’s own rules. The alerts were considered by nobody to be interruptive. This may be due to non-modal alerts (not interrupting the work process) and to the fact that drug prescription is never inhibited. While the two more experienced physicians had a more detailed mental representation of what tests are conducted by the system and what alerts are triggered by these tests, the less experienced residents had only a fuzzy representation of what the system is testing. Indeed, when asked if they would expect an alert for a given use case, a typical answer was for example: “I don’t know. You have to ask the


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programmers of the system.” This issue was never stated as a problem in the interviews. Still, if this is the case, physicians will find it difficult to trust a system completely; if they do, they risk missing potential dangerous situations where there is no alert. Statements by residents, the deputy head of divisions, and research [8] indicate that they will not look for any, if the system is not warning them. Also, some express doubts on whether the system has up-to-date information (for instance for weight-based drug dosage alerts in pediatrics or drug interactions in cardiology where they often introduce new drugs). Physicians were aware that they don’t pay attention anymore to alerts. Both visited divisions had a specialized drug set they prescribed very often. Drug alerts for their most common prescriptions were routine to them and the respective alerts were ignored. When asked whether they find them useful they responded that they were confident that they know the risks for the drugs in their medical domain, but they appreciate such an alert system for drugs they don’t prescribe often as for instance psychiatric or neurological drugs. None of them could report such a situation, but it does comfort them that the system would intervene. Both divisions used a limited set of about 5 drugs per patient, but they already find it difficult to understand the visualization of drug-drug interaction alerts where one drug has interactions with several others. An important alerting mechanism stays the feedback of the nurses who are used to prescribe a set of common dosages, routes, and frequencies of prescription. In contrast to the CPOE, they are also aware for what diagnose the drug is prescribed for. 3.4. Task analysis The method of analytic task description (MAD) resulted in the hierarchical tree as shown in Figure 1. The sticky-man symbol represents a physician-initiated task, a computer represents a computer-initiated task; the label “opt” describes an optional task. The relations between a task and its subtasks are “alternative”, “parallel”, “sequential”, or “no order”. This task representation may be used to create use cases and scenarios for prototype development and usability testing.

4. Discussion The present study gives some insights in the prescription process with a CPOE and how alerts are handled. The main finding is that physicians appreciate alerts as insurance for situations they are not familiar with. Also, non-modal alerts are not overcharging the physicians, however attention should be paid on how to best visualize the ever growing number of alerts. Finally, as it is not visible to the physicians, they have in general no mental representation of what prescriptions the decision support system is checking. These issues have to be addressed in future research.

5. Conclusion The present qualitative study offers a means to understand what causes lay beneath the low compliance towards alert systems and how to improve them. We will use the findings to develop a prototype for alert systems which will be further studied in

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usability tests. The presented method may be easily adapted to other work contexts and research questions in the medical field.

Figure 1. Analytic Method of Task description for prescription process during a ward round


[4] [5]

[6] [7] [8]

Van der Sijs H, Aarts J, Vulto A, Berg A. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138-47. Schedlbauer A, Prasad V, Mulvaney C, et al.What evidence supports the use of computerized alerts and prompts to improve clinicians’ prescribing behavior? J Am Med Inform Assoc. 2009;16(4):531-8. Beuscart-Zéphir M-C, Pelayo S, Bernonville S. Example of a Human Factors Engineering approach to a medication administration work system: potential impact on patient safety. Int J Med Inform. 2010;79(4):43-57. Van der Sijs H, Van Gelder T, Vulto A, Berg M, Aarts J. Understanding handling of drug safety alerts: a simulation study. Int J Med Inform. 2010;79(5),361-9. Weingart SN, Massagli M, Cyrulik A, Isaac T, Morway L, Sands DZ, Weissman JS. Assessing the value of electronic prescribing in ambulatory care: a focus group study. Int J Med Inform. 2009;78(9):571-8. Lane R, Stanton NA, Harrison D. Applying hierarchical task analysis to medication administration errors. Appl Ergon. 2006;37(5):669-79. Scapin DL, Pierret-Golbreich C. Towards a method for task description: MAD. Work with display units. 1990;89:371-9. Campbell EM, Sittig DF, Guappone KP, Dykstra RH, Ash JS. Overdependence on technology: an unintended adverse consequence of computerized provider order entry. In AMIA Annu Symp Proc. 2007 Nov 10-14; Chicago, IL. P. 94-8.


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Combining Usability Testing with EyeTracking Technology: Evaluation of a Visualization Support for Antibiotic Use in Intensive Care Aboozar EGHDAMa,1, Johanna FORSMANa, Magnus FALKENHAVb,c, Mats LINDd, Sabine KOCHa a Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Sweden b Department of Anesthesiology and Intensive Care, Karolinska University Hospital Solna, Stockholm, Sweden c Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden d Department of Informatics and Media, Uppsala University, Sweden

Abstract. This research work is an explorative study to measure efficiency, effectiveness and user satisfaction of a prototype called Infobiotika aiming to support antibiotic use in intensive care. The evaluation was performed by combining traditional usability testing with eye-tracking technology. The test was conducted with eight intensive care physicians whereof four specialists and four residents. During three test phases participants were asked to perform three types of tasks, namely navigational, clinical and tasks to measure the learning effect after 3-5 minutes free exploring time. A post-test questionnaire was used to explore user satisfaction. Based on the results and overall observations, Infobiotika seems to be effective and efficient in terms of supporting navigation and also a learnable product for intensive care physicians fulfilling their need to get an accurate overview of a patient status quickly. Applying eye-tracking technology during usability testing has shown to be a valuable complement to traditional methods that revealed many unexpected issues in terms of navigation and contributed a supplementary understanding about design problems and user performance. Keywords. Usability evaluation, eye-tracking, information visualization, decision support, intensive care

1. Introduction Intensive care is a complex and time critical work environment. Patients in intensive care units receive a large amount of medication, their condition can change rapidly and intensive care physicians are forced to make fast decisions. One area of decision making great importance is antibiotic use. It is known that antibiotics are over-used in 1

Corresponding author: Aboozar Eghdam, Health Informatics Centre, LIME, SE 17177 Stockholm, Sweden; E-mail: [email protected]

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A. Eghdam et al. / Combining Usability Testing with Eye-Tracking Technology

intensive care units [1] and patients are often unnecessarily treated with broadspectrum antibiotics [2]. Antibiotic use in intensive care requires time critical decisionmaking based on complex information that is usually spread amongst different information systems with different logins, functionalities and user interfaces [3]. Health information systems (HIS) need therefore to be adapted to the context of use and to support, not to hamper, clinical work processes as well as clinicians’ cognitive processes. This means, both the graphical user interfaces (GUI) and the interaction with a specific health information system or e-service should be designed according to clinicians work practice [4]. A number of methodical and empirical methods from the area of human-computer-interaction (HCI), human factor and usability engineering have been applied to evaluate health information systems in order to verify and optimize HIS usability [5] [6]. Usability testing usually combines quantitative measures, such as for example time measurements, and qualitative measures such as user perception. Subjective measures to gather cognitive data derived by for example “thinking aloud” are often applied. Eye-tracking technology is an objective way to measure and analyze eye movement, point of gaze, patterns of visual attention and eye fixation. The analysis of provided data from the eye-tracking equipment is based on an assumption about the relationship between eye fixation and people’s thoughts [7]. The eye-tracking technique increases the understanding of what users are looking at, for how long, and on their visual navigation path [8]. Eye tracking supplements traditional usability testing approaches by providing further information which researchers cannot observe and provides distinctive intuitiveness about search, attention and reading patterns which the test participant cannot report during the evaluation. Exploring eye movements is a contribution to HCI by understanding users’ desires on interfaces and adjusting them consequently in real time [7]. We think that a combination of usability testing as a practical assessment of system effectiveness, efficiency and user satisfaction, and eye-tracking technology can provide additional understanding of design problems and user performance. Improving the human-computer interface and summarizing patient-level information are seen as some of the core challenges for the design of clinical decision support systems (CDS) [9]. To enable targeted, patient-specific antibiotic use, we have therefore developed a visualization support for decision making during antibiotic use, called Infobiotika, and we will present its evaluation in this paper. We did an exploratory usability investigation and an initial performance testing of Infobiotika. The purpose of this research was to investigate if Infobiotika supports efficient and effective navigation and observe the users’ navigation paths, visual scan patterns and distribution of visual attention. Furthermore, the purpose was to explore if users find the information needed to support antibiotic treatment in intensive care and the learnability of Infobiotika. In addition to quantitative results, qualitative comments were captured during the test to obtain the participants’ thoughts and feelings about Infobiotika and its functionality.

2. System Description Infobiotika is a visualization support to provide a patient overview during antibiotic treatment in intensive care. It provides an overview of a patient’s clinical condition by gathering clinical data from different systems, that is; an electronic patient record


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system, a patient data management system (PDMS), a bacteriological laboratory system and a radiology information system. Infobiotika is expected to complement the current system environment during rounds, in resting-rooms, at surgical wards, and in time-critical situations when the intensive care physician is without the support of an infection disease consultant. The context of use is therefore treatment with antibiotics in intensive care. Infobiotika gives an overview in presenting the data by tables, trees and graphs.

3. Method We applied usability testing, measuring system effectiveness, efficiency and user satisfaction according to ISO 9241-11 [10], combined with eye-tracking technology. 3.1. Participant Characteristics Two main user groups were identified as test subjects: Specialists and residents, depending on their full time experience at the intensive care unit. Both groups were intensive care physicians with medical responsibility for intensive care demanding patients and with anesthesiology as a medical specialty covering both anesthesia and intensive care. None of the participants had worked with Infobiotika before and neither had participated in a former research project on visualization nor in a usability test. All worked at hospitals different from the one where the prototype was developed. To be able to validate the outcome of the tasks in the study, four so-called super-users were requested to participate in a pilot-test and in the preparations of the tasks. (Table 1) Table 1. Number and type of participants in Infobiotika usability evaluation study

Specialists Residents Usability experts

Study Participants (Danderyd Hospital) 3 2 0

Study Participants (S:t Görans Hospital) 1 2 0

Pilot-test Participants (Karolinska Hospital) 2 0 2

3.2. Study Design and Procedure A mixed method design was used for this test with between-subjects design for type of users with two levels of experience (specialists and residents) and within-subjects design for tasks. The test consisted of pre-test arrangements, introduction to the study and prototype, performance of the tasks and a debriefing session. One at the time, participants were greeted by the test moderator and guided to the test room which was a non-clinical area at the intensive care unit. The moderator started with an introduction and provided guidance. A short video was shown in order to give exactly the same introduction about the prototype’s functionality and features to each participant. Further instructions during the test were provided by slides shown on the screen during the test. All participants used the same computer, prepared with installed eye-tracking equipment, and performed pre-designed tasks. Participants were supposed to perform three types of pre-defined tasks in three phases of the test, i.e. 15 navigational tasks, 8 clinical tasks and 6 tasks to measure the learnability effect after 35 minutes free exploring time. At last, a post-test debriefing session was arranged with

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the moderator and a post-test questionnaire (SUS2) with 10 questions was answered by the test participants. The test materials such as consent forms, a background questionnaire, introduction to the study, pre-recorded video, interviews, the SUS questionnaire and the prototype were in Swedish language. The regional ethical review board in Stockholm approved the study no. 2010/1202-31/1.

4. Results The participants started the evaluation by completing 15 navigational tasks which were designed to give information on performance time, navigation paths and the accuracy of responses. Results of navigational tasks of both target groups showed that the physicians most often succeeded to solve the tasks. In average, they finished 79.4% of the tasks. The participants’ time spent on performing tasks was productive and paths taken were close to expected paths which had been prepared by a senior ICU physician prior to the test. A number of participants started to use tables but changed to the use of graphs further along the test session. In general, residents were more interested in graphs and specialists in tables. In addition, the results showed that specialists performed the tasks slightly faster than residents. Specialists were faster in 7 and equal to residents in 2 out of 15 tasks. In the second phase of the test, participants were asked to complete 8 clinical tasks, structured on the basis of an example dialogue with an infection disease consultant. These results showed that 91% of specialists and 100 % of residents completed all tasks. According to the participants’ comments during the test and in the post-test interview, Infobiotika fulfilled most of their expectations to support antibiotic use at intensive care. Although they had some suggestions for improvements, their overall impression about Infobiotika was positive. After 3-5 minutes of free exploring time in the third phase, the participants performed 6 tasks selected out of the 15 tasks from the first phase but slightly modified to avoid the possibility of memorizing previous answers. The performance in this phase of the test measured the effect of learning. The results showed that participants who used the same path to solve comparable navigation tasks were faster in 5 out of the 6 tasks, indicating a positive learning effect. Based on recorded eye-tracking data of all the participants, specialists stayed more focused on specific screen elements while residents were exploring the user interface more in its entirety. The eye-tracking data showed further an increasing use of charts and graphs during the test session. This could indicate that graphs will give a patient overview more efficiently and effectively with more practice. Results of the SUS questionnaire showed that the average overall satisfaction rate of the 8 participants based on the 10-item SUS assessment scale is 79.5% and the participating physicians perceived Infobiotika to be a quick and acceptable way to provide an overview of a patient status.

5. Discussion The goal of this usability testing was to observe end users interacting and performing tasks with Infobiotika. Participants proposed a few improvements in the current design but considered Infobiotika to be a potentially valuable aid in supporting faster decision2

System Usability Scale


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making concerning antibiotic treatment options. The eye-tracking equipment applied in this test was extremely useful for analyzing and understanding users’ actions and helped the analyzer to discover additional issues about the difference between specialists and residents’ performances. However, there were some limitations in conducting the test and analyzing data obtained from the usability testing. The test environment was not the real clinical setting and the number of participants was limited to 8 but they came from different hospitals and had not been involved in developing Infobiotika. Because of participants’ time constraints, we also limited the test sessions to 30 minutes each and could therefore not explore all features.

6. Conclusion Applying eye-tracking in the mobile laboratory setting has shown to be a valuable complement to traditional usability methods and revealed many additional issues in terms of navigation and user behavior, especially when comparing specialists and residents. However, results would need to be confirmed through evaluation with a larger number of participants in real clinical settings. Acknowledgements: This study was financed by the Health Informatics Centre, Karolinska Institutet. We also would like to thank the participating physicians for performing the test.

References [1]

Cars, O., Högberg, L. D., Murray, M., Nordberg, O., Lundborg, C. S., So, A. D., et al. (2008). Meeting the challenge of antibiotic resistance. BMJ, 337(3), 726-728. [2] Harbarth, S., & Samore, M. H. (2005). Antimicrobial Resistance Determinants and Future Control. Emerg Infect Dis., 11(6), 794-801. [3] Sintchenko, V., Coiera, E., & Gilbert, G. L. (2008). Decision support systems for antibiotic prescribing. Current Opinion in Infectious Diseases, 21(6), 573-579. [4] Ash, J. S., Coiera, E., & M., B. (2004). Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors. Journal of the American Medical Informatics Association, 11(2), 104-112. [5] Tang, P., & Patel, V. (1994). Major issues in user interface design for health professional workstations: summary and recommendations. International Journal of Bio-Medical Computing, 34(1-4), 139-148. [6] Kushniruk, A., Triola, M., Borycki, E., Stein, B., & Kannry, J. (2005). Technology Induced Error and Usability: The Relationship between Usability Problems and Prescription Errors When Using a Handheld Application. International Journal of Medical Informatics, 74(7-8), 519-526. [7] Tobii. (2010, November 15). What is eye-tracking? Retrieved November 15, 2010, from Tobii Technology: http://www.tobii.com/corporate/eye_tracking/what_is_eye_tracking.aspx [8] Pool, A., & Ball, L. J. (2006). Eye tracking in HCI and usability research. In C. Ghaoui, Encyclopedia Of Human Computer Interaction (pp. 211-219). Hershey PA: Idea Group Reference. [9] Sittig, D. F., Wright, A., Osheroff, J., Middleton, B., Teich, J. M., Ash, J. S., et al. (n.d.). Grand challenges in clinical decision support. Journal of Biomedical Informatics, 41(2), 387-392. [10] ISO 9241-11 (1998). Ergonomic requirements for office work with visual display terminals, Part 11: Guidance on usability. Geneva: International Organisation for Standardization;

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Design of a Mobile, Safety-Critical in-Patient Glucose Management System Bernhard HÖLLa,1, Stephan SPATa, Johannes PLANKb, Lukas SCHAUPPb, Katharina NEUBAUERb, Peter BECKa, Franco CHIARUGIc, Vasilis KONTOGIANNISc, Thomas R. Pieberb, Andreas HOLZINGERd a JOANNEUM RESEARCH Forschungsges.m.b.H., Institute for Biomedicine and Health Sciences, Graz, Austria b Medical University of Graz, Department of Internal Medicine, Division of Endocrinology and Nuclear Medicine, Graz, Austria c Foundation for Research and Technology - Hellas, Institute of Computer Science, Computational Medicine Laboratory, Heraklion, Crete, Greece d Medical University of Graz, Institute of Medical Informatics, Research Unit HumanComputer Interaction, Graz, Austria

Abstract. Diabetes mellitus is one of the most widespread diseases in the world. People with diabetes usually have long stays in hospitals and need specific treatment. In order to support in-patient care, we designed a prototypical mobile in-patient glucose management system with decision support for insulin dosing. In this paper we discuss the engineering process and the lessons learned from the iterative design and development phases of the prototype. We followed a usercentered development process, including real-life usability testing from the outset. Paper mock-ups in particular proved to be very valuable in gaining insight into the workflows and processes, with the result that user interfaces could be designed exactly to the specific needs of the hospital personnel in their daily routine. Keywords. Diabetes Mellitus, User-Computer Interface, Mobile Computing, Computer-Assisted Drug Therapy, Workflow

1. Introduction Diabetes mellitus is one of the most widespread diseases in the world. People with diabetes are more likely to be hospitalized and to have longer durations of hospital stay than those without diabetes. It is estimated that 22% of all in-patient days were accounted by people with diabetes and that in-patient care accounted for half of the total US medical expenditures associated with this disease [1]. These findings are due, in part, to the continued worldwide expansion of type 2 diabetes. The in-patient glycemic control of acute diseased patients with diabetes is often considered secondary in importance. However, studies demonstrate that in-patient hyperglycaemia has been found to be an important marker of poor clinical outcome and mortality among diabetic patients and that aggressive treatment of diabetes and 1

Corresponding author: Bernhard Höll, JOANNEUM RESEARCH Forschungsges.m.b.H., Institute for Biomedicine and Health Sciences, Elisabethstraße 11a, 8010 Graz, Austria, E-mail: [email protected]

B. Höll et al. / Design of a Mobile, Safety-Critical In-Patient Glucose Management System

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hyperglycaemia results in reduced mortality and morbidity [2]. Therefore, patients suffering from diabetes require continuous glycemic control during in-patient stays including close monitoring of blood glucose and determination of suitable treatment strategies. In this paper we discuss the requirement engineering process and the lessons learned from the iterative design and development phases of a mobile in-patient glucose management system with decision support for insulin dosing.

2. Methods The development of mobile applications in a medical context provides engineers with a complex task. In addition to the aim of supporting workflow requirements, usability and clinical safety are important issues to consider when designing the user interfaces and system functionalities. Therefore, the consistent pursuit of an user-centered design is a crucial condition and must include an understanding of the users, their environment and the context in which the application is used [3,4,5,6,7]. A team consisting of physicians and nurses of the Division of Endocrinology and Metabolism at the Medical University of Graz, as well as engineers from JOANNEUM RESEARCH and the Medical University of Graz, was established to develop the user interface design and the functionalities of the in-patient glucose management system, tailored to the needs of the end-users. Project partners from the Foundation for Research and Technology Hellas performed a first design of the conceptual data model starting from the first user interface mock-up and organised external reviews of the obtained results. We discussed each design decision relating to the user interface, system functionality and the underlying protocol for decision support for insulin dosage within this team. We integrated the results into an intuitive software system based on essential, but user-tailored functionalities. Figure 1 shows the iterative development process of the in-patient glucose management system. In the first step we interviewed physicians and nurses about current treatment workflows for type 2 diabetic patients at the Division of Endocrinology, in order to understand and determine workflow patterns for medical decision-making and problems and risks associated with glucose management. We generated a status report describing current workflows, based on various patient scenarios, as a starting point for the target analysis. We then identified and discussed relevant publications related to the ideal in-patient management of hyperglycaemia including validated glucose control protocols with diabetes specialists [8,9,10,11]. The protocol based on a basal/bolus regimen as provided by the RABBIT 2 Trials 2 proved to be most promising for the clinical diabetes experts due to its straightforward advice for insulin dosing, which was shown to be associated with improved outcomes. In the final step, we extracted the most important user requirements from the status report and the findings of the protocol reviews. These were then implemented in a software prototype.

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RABBIT 2 - Randomized Study of Basal-Bolus Insulin Therapy in the In-patient Management of Patients With Type 2 Diabetes

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Figure 1. Process chart of prototype design

The last step of the first iteration of the development process, involved performing real-life usability trials with three diabetes specialists as participants. We used the Thinking Aloud testing method [12] followed by a semi-structured interview. We documented all tests on video, interpreted the results and integrated suggested improvements into the revised requirement specification document. The second design iteration consisted of integrating test results into the requirement set and developing a detailed user-interface for the application using paper-mockups. The development process is accompanied by continuous interdisciplinary meetings regarding risk identification, evaluation and the setting of appropriate measures to avoid these risks. Emphasis is placed on both technical and medical risks.

3. Results This section reports the results of the prototype development and the usability tests. In the first development iteration, we demonstrated the identified basic functionality using Microsoft Excel with VBA Scripts. Microsoft Excel was chosen, due to the extensive display options of charts, the quick and easy visualization of glucose and insulin profiles, as well as optical alarm borders. The user evaluation of the first glucose management prototype resulted in an extensive requirement specification document with the following main conclusions, which formed the starting point for the second design iteration: • Execution of the application via a mobile device to allow activities to be performed directly at the patient’s bed. • No data storage on the mobile device. Wireless communication via web services to an external server, on which the data should be placed. • Documentation and visualization of the most important parameters relating to diabetes care on the mobile device. • Automated decision support for insulin dosage. • Reminder for open tasks through an active task management. • Avoidance of manual (and multiple) inputs. A connection to the hospital and laboratory information system is necessary in order to transfer administrative data automatically. We designed the revised user requirements using Visio stencils for Android in a paper mockup screenplay consisting of all functionalities of the glucose management system and again discussed the results in the team. Based on the design and functionality, which was identified through the mockups, we are currently implementing an Android-based mobile client application, which communicates via web services (Apache CXF) with a java-based web server running on Apache Tomcat.


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The server application has been implemented using Hibernate and the Spring Framework based on a model driven design and development approach and transfers data securely from and to the HIS of the hospital via HL7 v2.4 interface. Figure 2 shows the already implemented main screen with the visualization of the most important measurement and insulin administration parameters of the mobile inpatient glucose management application. In addition, the figure shows the main functionalities of the application. ‘Patient List’ presents all patients administered at the ward including a filter function to show only patients enrolled for the glucose management. ‘Open Tasks’ reminds users of the system to perform all recommended tasks like ‘Blood Glucose Measurement’, ‘Insulin Administration’ or ‘Therapy Adjustment’ in time. ‘Blood Glucose Measurement’ enables users to retrieve the blood glucose value directly from the laboratory information system and documents the measured values in the glucose management system. Physicians approve the current therapy for the patient (e.g. insulin medication, current insulin dosage, hypoglycemia borders) using the function ‘Therapy Adjustment’. Finally, the decision support protocol for insulin dosing suggests the needed insulin dosage of the patient based on the measured blood glucose values and administered food using the function ‘Insulin Administration’.

Figure 2. Screenshot of the Android-based Prototype

4. Conclusions and Future Research In this paper, we presented the user-centered design process of a safety-critical inpatient glucose management system. Medical end-users have been involved in every step of the design phase. In other words, clinicians have conceptualized the design of the system. Engineers now have to implement the design in an optimal software solution. Our experiences through the first and second iteration steps show that clinicians and engineers have very different points of view concerning software. While engineers often focus on gathering as much functionality as possible, clinicians prefer software which offers only the required base functionality but a well sophisticated user interface, tailored to current workflow patterns. A problem we encountered during the requirement analysis is that end-users without a trigger, often do not know what

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specific functions should be provided by a software solution. Therefore, as a result of the first iteration step, a Microsoft Excel prototype was used as a trigger to give clinicians a preliminary idea as to how an in-patient glucose management system, including a computerized decision support, could look. After the presentation of the prototype, the participants were able to give a clearer idea of their requirements for a glucose management system, which were then used as inputs for the second iteration step. We used paper mockups of the second iteration step, which simulate the full system functionality on a mobile device, as the next trigger. At the moment we are implementing the server application and an Android-based mobile prototype, which already contains full functionality. We will test the resulting prototype of the second iteration in a clinical study. Acknowledgements. This work was partly funded by the E. C. under the 7th Framework Program in the area of Personal Health Systems under Grant Agreement no. 248590. [13]

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[7] [8] [9]

[10]

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[12]

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Moghissi, E.S. Korytkowski, M.T. Dinardo, M. Einhorn, D. Hellman, R. Hirsch, I.B. et.al., American Association of Clinical Endocrinologists and American Diabetes Association Consensus Statement on Inpatient Glycemic Control, Endocrine Practice 15 (2009), 1-17. Clement, S. Braithwaite, S.S. Magee, M.F. Ahmann, A. Smith, E.P. Schafer R.G. and Hirsch, I.B. Management of Diabetes and Hyperglycemia in Hospitals, Diabetes Care 27 (2004), 553-591. Hameed, K. The application of mobile computing and technology to health care services, Telematics and Informatics 76 (2007), 66-77. Wu, J. Wang S. and Lin, L. Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics 76 (2007), 66-77. Holzinger A. and Errath, M. Mobile computer Web-application design in medicine: some research based guidelines, Universal Access in the Information Society 6 (2007), 31-41. Holzinger, A. Hoeller, M. Bloice M. and Urlesberger, B. Typical Problems with developing mobile applications for health care: Some lessons learned from developing user-centered mobile applications in a hospital environment, International Conference on E-Business (ICE-B 2008), Porto (PT), IEEE (2008), 235-240. Svanaes, D. Alsos O.A. and Dahl, Y. Usability testing of mobile ICT for clinical settings: Methodological and practical challenges, International Journal of Medical Informatics 79 (2010), 24-34. Inzucchi, S.E. Management of Hyperglycemia in the Hospital Setting, New England Journal of Medicine 355 (2006), 1903-1911. Umpierrez, G.E. Smiley, D. Zismann, A. Prieto, L.M. Palacio, A. Ceron, M. Puig A. and Mejia, R. Randomized Study of Basal-Bolus Insulin Therapy in the Inpatient Management of Patients with Type 2 Diabetes (RABBIT 2 Trial), Diabetes Care 30 (2007), 2181-2186. Umpierrez, G.E. Hor, T. Smiley, D. Temponi, A. Umpierrez, D. Ceron, et.al., Comparison of Inpatient Insulin Regimens with Detemir plus Aspart Versus Neutral Protamine Hagedorn plus Regular in Medical Patients with Type 2 Diabetes, Journal of Clinical Endocrinology Metabolism 94 (2009), 564569. Korytkowski, M.T. Salata, R.J. Koerbel, G.L. Selzer, F. Karslioglu, E. Idriss, A.M. Lee, K. Moser A.J. and Toledo, F.G.S. Insulin therapy and glycemic control in hospitalized patients with diabetes during enteral nutrition therapy: a randomized controlled clinical trial, Diabetes Care 32 (2009), 594-596. Holzinger A. and Leitner, H. Lessons from Real-Life Usability Engineering in Hospital: From Software Usability to Total Workplace Usability, Holzinger, A. & Weidmann, K.-H. (Eds.) Empowering Software Quality: How can Usability Engineering reach these goals? Vienna, Austrian Computer Society (2005), 153-160 http://www.reactionproject.eu/news.php, last visit: 2011-04-30.


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Facilitating the Iterative Design of Informatics Tools to Advance the Science of Autism David R. KAUFMANa , Patrick CRONIN a , Leon ROZENBLIT b , David VOCCOLA b , Amanda HORTON b , Alisabeth SHINEa , and Stephen B. JOHNSON c a Department of Biomedical Informatics, Columbia University, New York, NY, USA b Prometheus Research, LLC, New Haven, CT, USA c Simons Foundation, New York, NY, USA

Abstract. This paper describes a usability evaluation study of an innovative first generation system (Data Dig) designed to retrieve phenotypic data from the large SFARI data set of 2700 families each of which has one child affected with autism spectrum disorder. The usability methods included a cognitive walkthrough and usability testing. Although the subjects were able to learn to use the system, more than 50 usability problems of varying severity were noted. The problems with the greatest frequency resulted from users being unable to understand meanings of variables, filter categories correctly, use the Boolean filter, and correctly interpret the feedback provided by the system. Subjects had difficulty forming a mental model of the organizational system underlying the database. This precluded them from making informed navigation choices while formulating queries. Clinical research informatics is a new and immensely promising discipline. However in its nascent stage, it lacks a stable interaction paradigm to support a range of users on pertinent tasks. This presents great opportunity for researchers to further this science by harnessing the powers of user-centered iterative design. Keywords. Usability evaluation, clinical research informatics, iterative design.

1. Introduction Recent advances in basic and applied clinical science are increasingly being translated into clinical practice and affording greater opportunities for improved patient care across a broad spectrum of medical conditions. The rapid pace and scope of this research has necessitated the development of new information technologies to support data integration, management and workflow. Clinical research informatics (CRI) is a burgeoning discipline whose efforts are focused at the intersection of clinical research and biomedical informatics [1]. CRI affords new opportunities to make tangible progress on longstanding, seemingly intractable clinical problems by leveraging new technologies for exploring very large data sets for prediction, visualization, and hypothesis generation [2]. Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by a multitude of behavioral, social and communication problems. The scope and complexity of ASD requires the development of large and comprehensive collections of individuals and their families to facilitate genotypephenotype studies[3]. The Simons Foundation Autism Research Initiative (SFARI) has

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established a permanent repository of phenotypic and genetic data set from 2,700 families, each of which has exactly one child affected with ASD. SFARI Base, a web-based platform developed in collaboration with Prometheus Research LLC, provides access to scientific data and associated information management and analytic tools to advance the science of autism[4]. The primary function of SFARI Base is to gather scientific data and biospecimens from studies conducted at clinical sites, and to pool the results of analyses carried out on these materials. It not only affords researchers the possibility for accessing data to test hypotheses or explore relationships in a data set, but also may offer new methods for discovery and hypothesis generation. However, even the best designed systems present difficulties for users. It is increasingly recognized that the usability and learnability of a system are critical determinants of both the acceptance of a technology and its efficacy as a productive tool [5]. The objective of the work reported in this paper is to extend a usability and iterative design framework to clinical research informatics tools. CRI is a new area of research and presently lacks an established paradigm for supporting user interaction. At present, there is a paucity of usability research in this area.

2. Evaluation of SFARI Base 2.1. Usability Framework The research is grounded in a cognitive engineering framework, which is an interdisciplinary approach to the development of principles, methods and tools to assess and guide the design of computerized systems to support human performance. The approach is centrally concerned with the analysis of cognitive tasks. The objective is not only to characterize deficiencies, but to identify the ways in which resources (e.g., through redesign or training) can help structure task performance and guide accelerated learning (e.g., via the use of better cues that signal the next step). The framework focuses on the sorts of competencies and knowledge required by users to accomplish tasks in knowledge-rich domains. The approach incorporates both usability inspection methods and usability testing [6-8]. Usability inspection methods are performed by trained analysts and usability testing involves the use of representative subjects. Through a process of triangulation, these methods are likely to reveal a wider range of problems that impede productive use of a system than any single method alone[5]. 2.2. Evaluation and Iterative Design of SFARI Base SFARI Base provides a suite of database tools that serves a broad spectrum of users including: scientific investigators conducting autism research, research coordinators, data managers, and autism data curation experts. Each of these users plays a different role in the research enterprise, has different needs and possesses different skills. The long term objective is to discover and characterize the ways in which the suite of SFARI tools can be used productively to advance the science of autism. The methods employed in our research program include: a) cognitive walkthrough, b) heuristic evaluation, c) usability testing, d) web-based survey of scientists, and e) participant design study. In this paper, we present data from the first in a series of usability evaluations of SFARI Base tools.


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2.3. Usability Evaluation of Data Dig Data Dig is a database query tool designed to retrieve phenotypic data from the large SFARI data set of 2700 families across more than 6000 variables. Investigators can search for a particular subset of the data by selecting variables of interest. For example, a researcher may only be interested in male probands (subjects with autism) under the age of 10 with a particular range of scores on one of the autism diagnostic tests or behavior checklists. Variables can be discovered by browsing through expert-created variable-groups (instantiated via a tagging model) or by searching for substrings within variable names, titles, and descriptions. A cognitive walkthrough (CW) was performed on the Data Dig Tool to identify issues with the interface. A CW is a task-analytic method that represents the goals and subgoals for each task, each step or action to be taken, the necessary knowledge, and the feedback presented to the user (i.e., what’s visible on the display) after an action has been completed. At each step, we can identify potential problems in the interface or in the cognitive demands of the task. Task complexity can be revealed in variables such as: 1) number of actions needed, 2) number of screen transitions, 3) time needed to complete a task and 4) required chunks of knowledge. Two experienced investigators conducted the CW and 30 unique usability issues and the problems were revealed. They were subsequently coded according to a modified version of Nielsen usability heuristics. In addition, a panel of three researchers independently ranked the issues according to the severity of the problem on a 5 point scale. Usability testing was performed on three individuals including a psychiatrist who studies autism and two informaticists who had extensive experience working with clinical and scientific databases. Each user had different levels of domain and system knowledge which became apparent during the testing. Recent autism related literature was surveyed to create sample questions that could be answered by querying the SFARI database using Data Dig. The questions were divided into three levels of difficulty based on the query complexity. Each subject was given a ten minute instructional period by the experimenter. Then the users formulated queries to answer the questions using Data Dig and performed the think-aloud protocol while the interaction was recorded using Morae video-analytic usability software. The subjects were instructed that their goal was to identify the data in the SFARI database that would allow them to answer specific questions. The following are samples of these questions, reflecting different levels of complexity: 1. How many probands (autistic children) are in the database? 2. Can you get the Autism Diagnostic Interview – Revised (ADI-R) total score? 3. Is there data on the proband’s birth? Specifically I want to know the proband’s head circumference and weight at birth. Also, can you tell if the proband was born vaginally or by a C-section? The users were able to answer most of the queries with some help from the moderator. However, the users experienced numerous difficulties learning how to master the different elements of the system. We documented more than 50 usability problems ranging from relatively minor to more serious ones that impeded effective and efficient use of the tool to answer queries. The problems with the greatest frequency resulted from users being unable to understand meanings of variables, filter categories correctly, use the Boolean filter, and correctly interpret the feedback provided by the system. Subjects had difficulty forming a mental model of the

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organizational system underlying the database. This precluded them from making informed navigation choices while formulating queries. The usability issues identified through the cognitive walkthrough were matched with instances recorded through the usability testing. The categories were discussed by the team, and issues relevant to improving the functionality of Data Dig were identified. The modifications identified were designed to improve user’s mental model of the system, query construction proficiency, ability to correctly interpret system feedback, and limit user frustration. Our objective was to identify tractable changes that could be implemented for the next iteration. We proposed recommendations for application improvements from four major categories: navigation, feedback, enhancing functionality, and consistency. One of the problems is illustrated in Figure 1.

Figure 1: Lack of visual cues to mark selected variables.

The first step in using the tool is to select a set of variables. The problem, as illustrated in Figure 1, is that it is difficult for an individual to determine which variables they had selected because there is no visual feedback. In the picture above, the user had selected 4 of the 5 available variables that are displayed in Step 2 (specifying filter criteria on scores of a measure). We observed three instances of users failing to select variables because they had thought that it had been previously selected. We proposed 3 possible solutions: 1. a checkbox could be added next to the variable in step 1 to indicate if it had been selected; 2. Once a variable had been selected it could disappear in step 1 after it was displayed in step 2, 3. The link with the variable in step 1 could change to a different shade once it has been selected. Any of these options could reduce the barrier to completing the variable selection process. The Boolean filters also posed significant challenges for the users. The problem is that there were 10 different methods of Boolean filtering, and the methods were always available even when they did not apply. For example, if a user set the filter to less than male, then all of the females will be displayed. There was no method


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of setting multiple Boolean filters in a single variable. For example, a user cannot identify all individuals between the ages of 4 and 12. There was no ability to have a disjunctive (OR) criteria between different variables; all filters added automatically assume conjunctive (AND) conditions on the data. Thus an individual could not filter on all individuals with an ADI-R diagnosis of autism OR an ADOS diagnosis of autism (the two most commonly used diagnostic measures).

3. Discussion Clinical research informatics is a discipline concerned with providing new tools to advance clinical science and practice. Although this work is immensely promising, we presently lack a stable interaction paradigm for enabling scientific researchers and other users to access and analyze large data sets. This presents significant challenges as well as opportunities for human-computer interaction researchers to contribute to the advancement of effective and enabling tools. The study presented in this paper was a usability evaluation of an innovative CRI database tool. The study documented a range of significant usability problems and presented potential solutions. Researchers at Columbia University and the Simons Foundation are collaborating closely with developers at Prometheus Research in the iterative design process. Data Dig represented a first generation application and subsequent applications proved to be more robust and easier to use as determined by usability evaluations. Future work includes participatory design studies involving scientists in the process of fashioning prototypes. The findings from this work could result in more effective tools and also contribute to the development of a stable interaction platform and thus serve to advance this important new discipline.

References [1]

Embi PJ, Payne PR. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc. 2009;16: 316-27. [2] Lehmann CU, Kim GR, Johnson KB, Lehmann HP, Law PA, Tien AY. Pediatric Research and Informatics. In: Pediatric Informatics: Springer New York; 2009, p. 439-454. [3] Johnson SB, Whitney G, McAuliffe M, et al. Using global unique identifiers to link autism collections. J Am Med Inform Assoc. 2010;17: 689-95. [4] Simons Foundation Research Initiative (SFARI). Available at http://sfari.org [5] Jaspers MW. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int J Med Inform. 2009;78: 340-53. [6] Kaufman DR, Mehryar M, Chase H, et al. Modeling knowledge resource selection in expert librarian search. Stud Health Technol Inform. 2009;143: 36-41. [7] Kaufman DR, Patel VL, Hilliman C, et al. Usability in the real world: assessing medical information technologies in patients' homes. J Biomed Inform. 2003;36: 45-60. [8] Yu H, Lee M, Kaufman D, et al. Development, implementation, and a cognitive evaluation of a definitional question answering system for physicians. J Biomed Inform . 2007;40: 236-51. [9] Nielsen J. Usability Engineering. Boston: Academic Press; 1993. [10] Polson PG, Lewis C, Rieman J, Wharton C. Cognitive Walkthroughs - a Method for Theory-Based Evaluation of User Interfaces. International Journal of Man-Machine Studies. 1992;36: 741-773.

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Evaluation of Computer Usage in Healthcare Among Private Practitioners of NCT Delhi a

GANESHKUMAR P a 1, ARUN KUMAR SHARMAb and RAJOURA OP b Deptartment of Community Medicine, SRM Medical College Hospital & Research Centre, Kattankulathur, Tamilnadu, India. b Department of Community Medicine, UCMS & GTB Hospital, Delhi

Abstract. Objectives: 1. To evaluate the usage and the knowledge of computers and Information and Communication Technology in health care delivery by private practitioners. 2. To understand the determinants of computer usage by them. Methods: A cross sectional study was conducted among the private practitioners practising in three districts of NCT of Delhi between November 2007 and December 2008 by stratified random sampling method, where knowledge and usage of computers in health care and determinants of usage of computer was evaluated in them by a pre-coded semi open ended questionnaire. Results: About 77% of the practitioners reported to have a computer and had the accessibility to internet. Computer availability and internet accessibility was highest among super speciality practitioners. Practitioners who attended a computer course were 13.8 times [OR: 13.8 (7.3 - 25.8)] more likely to have installed an EHR in the clinic. Technical related issues were the major perceived barrier in installing a computer in the clinic. Conclusion: Practice speciality, previous attendance of a computer course, age of started using a computer influenced the knowledge about computers. Speciality of the practice, presence of a computer professional and gender were the determinants of usage of computer. Keywords: Medical informatics applications, Attitude to computers, Computer utilization, Health Personnel, Cross-sectional studies, India

1. Introduction Indian health system is straining to deal with increasing cost and demand pressures and a shortage of skilled health care workers till the root of our community. Given this reality, we can achieve maximum impact on health outcomes and where scarce financial and human resources are deployed as effectively as possible. The strategy by which this can be achieved is through the implementation of world class E-Health capability. Further, information and communication technology (ICT) has been proposed as an important strategy to combat medical errors and quality-of-care deficits.[1] In India, 70% of the health care services are being provided by the private sector [2], which is not integrated with the government system. Hence application of 1

Corresponding Author: Dr.P.Ganeshkumar, Assistant professor, Department of Community Medicine, SRM Medical College Hospital & Research Centre , SRM University, Kattankulathur - 603203, Tamilnadu, India. Telephone number: +91-44-9840640483, +91-44-45030120 Fax number : +91-44-2745 5106. E-mail: [email protected]

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ICT in this sector remains incompetent and the large amount of information about their health services has not been shared or reported to any government body which is a major lag in the regulation of healthcare in our country. We did not find an Indian study pertaining to computerization of health services in private sector. Hence it became an important question to find out what is the status of computerization and uses of e-health by the private practitioners. Hence the study was designed to evaluate the usage and knowledge of computer and Information & Communication Technology (ICT) among private practitioners and to evaluate the determinants of the computer usage.

2. Materials and Methods This cross-sectional study was conducted in a randomly selected three districts out of 10 administrative districts of New Delhi, the Capital city of India, among clinic based private medical practitioners from November 2007 to December 2008. Private medical practitioners are those who are self-employed and not attached to any hospital. Only modern medicine practitioners and doctors who practiced at least for one year in the same location were included in the study. Ministry of health & family welfare, Govt. of India mentions modern medical practitioners are those who practice allopathic medicine in contrast to traditional Indian medicine. Due to the lack of previous studies in India, we decided to go by convenience sampling technique; hence data was collected from 600 practitioners. In order to make the sample representative of the private practitioners registered with Indian Medical Association (IMA), stratified random sampling method was used where as a first step we randomly selected 3 out of 10 administrative districts. Each eligible practitioner was assigned a digital code and from each district 200 practitioners were randomly selected using random number table. After obtaining an informed written consent, the participants were contacted for a prior permission. They were interviewed by a structured, pre-tested, pre-coded investigator administered interview schedule which collected the information about their usage, knowledge, potential barriers in using a computer and determinants of owning a computer in the clinic. The information thus collected was entered in the MS Excel spreadsheet and analysis was done using SPSS software where descriptive tables were generated and logistic regression analysis was done to demonstrate the findings.

3. Results 3.1. Demographic and Professional Details In the study population, 85.5% were males and their age ranged from between 29 and 62 years where the mean age of the study population was 45.46±5.52 years. In the study population only 1.8% was super specialty practitioners whereas MBBS graduates were in large number (58%). Nearly one tenth of the practitioners has a computer professional in the family and nearly half of the practitioners were practicing more than 10 years and also consulting more than 4 hours per day.

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3.2. Usage of Computers in the Clinic About 77% of the practitioners reported to have a computer but only 63(10.5%) practitioners installed it in their clinic and about three-fourth of the practitioners have the accessibility to internet but only 10(1.5%) had it in their clinic. Though 22% of all the respondents had known about EHR, only 8.8% of them were using it in their clinic and almost all of them appointed a separate staff for data entry in the EHR. Reported usage of EHR was very limited which were mostly for the purpose of registration and maintaining a list of patients they consulted with limited information of them. General surgeons and general practitioners were the least common users of EHR and more of super specialty practitioners were the most common users of EHR (see Table 1). Table 1. Distribution of practice speciality with presence of EHR & Knowledge about Computers PRACTICE SPECIALITY

PRESENCE OF EHR IN THE CLINIC (n=53)

COMPUTER KNOWLEDGE MEAN SCORE

Number

(MEAN±SD)

General practice

20(5.7)

2.26±1.05

General surgery

1(3.6)

2.48±1.04

Internal medicine

11(17.2)

2.42±1.07

Super speciality

16(24.6)

3.1±0.98

Others(Paeds,O&G)

5(5.3)

2.43±1.03

Statistical test

X2: 32.22

df:4

p: value: 0.000

ANOVA SSB:40.02 df:3 p:value :0.000

Factors such as gender (p=0.056), number of years of practice (p=0.211), age of first usage of computers (p=0.834) and income (p=0.233) didn’t influence the EHR usage in the clinic whereas those practitioners who attended a computer course were 13.8 times [OR: 13.8 (7.3 - 25.8)] more likely to have installed an EHR in the clinic. 3.3. Knowledge of Computers A composite score of the knowledge on computers was calculated by giving weightage of 60%, 10% and 30% to software, hardware and internet respectively. Hence a final score was obtained by summing up the weighted scores of the knowledge questions. The mean scores of knowledge about hardware, software and internet were 2.19±1.32, 2.22±1.46 and 2.95±1.35 respectively, which showed that, knowledge regarding internet was high among practitioners compared to that about hardware and software. It was seen that mean score of male respondents was significantly higher than that of the female respondents and one-fourth of the practitioners who scored more than 3.3 were less than 42 years old .Also it was seen super speciality practitioners were significantly more knowledgeable than others (see Table 1). Availability of computer (p=0.000),

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previous attendance of computer course (p=0.000) influenced positively the knowledge on computers. 3.4. Potential Barriers in Using a Computer Fifteen questions were used with a Likert scale for assessing the perceived barriers in using a computer. It was seen that technical issues were considered as the major perceived barrier and logistic related issues were the least perceived barrier. Most of the practitioners (86.3%) thought that lack of time was the major barrier in installing a computer in their clinic and early half of the practitioners disagreed to that high maintenance cost of the computer and data entry being a cumbersome process could be reasons for not installing a computer in their clinic. 3.5. Predictors of Owning a Computer Logistic regression analysis was used to find the predictors of owning a computer among the private practitioners. It was found that, super speciality practitioners were 8 times [OR: 8.18(2.57-9.99)] more likely to own a computer, compared to general practitioners and also presence of a computer professional in the family increases the likelihood by 4 times and females were 50% less likely to own a computer than males(see Table 2). Table 2. Predictors of owning a computer INDICATOR

ODDS RATIO

ADJUSTED

P VALUE

Speciality practice

1.9(1.15-3.12)

0.011

Super speciality practice

8.18(2.57-5.99)

0.000

Presence of computer

3.93(1.67-9.26)

0.002

0.493(0.27-0.87)

0.016

professional in the family Female practitioners

4. Discussion In our study, out of 600 practitioners, there was an over representation of male practitioners which may be due to the presence of more male practitioners in the study area. There was a considerably high work load among the practitioners in the study area and only 10.8% of the doctors had a computer professional in their family which shows that the practitioners had a lesser chance of getting influenced by the professionals other than healthcare. It was observed that 77% of the practitioners reported to have a computer but only 63(10.5%) practitioners installed it in their clinic and only 10(1.5%) had an internet connection in their clinic. Computer usage in private practice was very less, which may be due to reasons such as technologies being less

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understood and not given due priority. Absence of even a single research paper published regarding this issue in India itself explains the poor adoption and less understanding of these technologies in the healthcare. Out of 600 practitioners only 53(8.8%) were having an EHR in the clinic. Though gender, number of years of practice, age of first usage of computer and income haven’t influenced the EHR usage, those practitioners who attended a computer course were 13.8 times more likely to have an EHR in their clinic. It was found also that awareness about maintaining patient records in electronic form (EHR) existed among less than 10% of the respondents which shows that there was a poor understanding about usage of computer in the clinic. This shows that existing knowledge by training influences more positively in practicing a new technology in their clinic. Nearly half of the practitioners were having a satisfactory knowledge on computers and overall knowledge regarding internet was high. It was seen that super speciality practitioners scored more in knowledge about computers than other category of practitioners. Not only had the availability of a computer and internet favoured an increase in knowledge of computers, it is also the age of first usage of computer and previous attendance of any computer course that influenced positively the knowledge about computers. Being a cross sectional study, it was difficult to ascertain whether having knowledge about computers increased its usage or vice versa. Most of the practitioners (83.3%) thought that lack of time was the major barrier in implementing a computer in their clinic. When the barriers were categorized by giving scores, it was seen that technical related issues were the major perceived barrier among the practitioners. Logistic regression analysis was carried out to identify predictors of owning a computer by private practitioners in our study. It was observed that practice speciality, income, presence of a computer professional in the family and gender were significant determinants of owning the computer and usage of it. It was also found that the super speciality practitioners were 8 times and presence of a computer professional in the family were 4 times more likely to have the probability of owning a computer. At present the computer usage in healthcare among private practitioners is extremely limited and the purpose they use EHR is only maintaining a list of patients they consult.

5. References [1] [2]

Institute of Medicine. Crossing the quality chasm. Washington DC: National Academy Press, 2001. Ministry of statistics and programme implementation, Government of India. Morbidity, Health Care and the Condition of the Aged: Jan –June, 2004.NSS 60th round. New Delhi. March 2006. Report No. 507.


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Contextual Inquiry Method for UserCentred Clinical IT System Design a

Johanna VIITANENa Strategic Usability Research Group, Aalto University, Finland

Abstract. Little can be found in the literature about the applicability of field study methods, particularly contextual inquiry, in the health informatics field. This paper aims to inform and promote the use of contextual inquiry for user-oriented design of clinical information technology (IT) systems. The paper describes how the method was applied in two empirical studies to gather data about end-users’ needs, as well as the use and usability of dictation solutions and electronic nursing documentation systems from the viewpoint of their end-users’ in real working surroundings. Experience indicates that, compared to typical usability evaluation methods, contextual inquiry may provide valuable support for user-centred design activities: the method is suitable for increasing researchers’ understanding of clinical practices, contexts of work, and end-users’ interaction with numerous IT systems. However, in clinical settings there are special challenges related to recording and privacy issues, a wide variety of clinical practices and contexts of technology usage, as well as the hectic nature of clinical work. Keywords. Contextual inquiry, user-centred design, clinical IT system

1. Introduction Field study methods have not been widely adopted in the health informatics field, although the need for a participatory and user-centred design approach in technology development has been strongly acknowledged. Research literature on user involvement in healthcare technology development typically deals with a usability evaluation approach and studies that are conducted in the later phases of system development. Recently, researchers have suggested that, compared to the evaluation approach, field studies of clinical work are more suited for informing conceptual problems and developing an understanding of the wider context in which the clinical information and communication media are used [1,2]. Experiences from field studies have indicated that ethnographic methods (such as interviews, observations, and artefact analysis) have helped to efficiently explain relevant work practices (e.g. [3,4]). Furthermore, methods used to derive the requirements for healthcare systems are criticised as being inadequate (e.g. [5,6]). Among others, Malhotra et al. [5] and Croll and Croll [6], have stated that the biggest risk faced in developing IT systems for a healthcare setting is to understand the complex environments in which these systems are used. Little can be found in the literature about the applicability of field study methods, particularly contextual inquiry, in healthcare technology development. A few researchers have reported contextual inquiry studies. Gennari and Reddy [7] applied the participatory design approach and used contextual inquiry to design and build a protocol screening tool of clinical trial protocol management. Gil-Rodríguez et al. [8]

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applied the method to collect information about cognitive, symbolic, and practical characteristics of information technology (IT) systems use on daily tasks in clinical settings, with the aim of supporting the design of graphical user interfaces for telecardiology applications. Furthermore, some researchers have aimed at encouraging user-oriented methods for assessing clinicians’ needs, and user requirements, for system design purposes. Already in the year 1995 Colbe et al. [9] argued that the contextual inquiry method has several advantages in obtaining a more comprehensive analysis of the true needs of users. In their review-based articles, Chan [10] and Martin et al. [11] introduced the contextual inquiry method with reference to its developers Holtzblatt and Beyer [12], and explained the principles of the method. This paper aims to promote the adoption of the contextual inquiry method among practitioners and researchers in the health informatics field and provide information about the specific characteristics of healthcare contexts that are essential to be considered when applying the method. The described experiences and lessons learned are based on two empirical studies: a dictation study and an evaluation of nursing documentation systems.

2. What is the Contextual Inquiry Method? Contextual inquiry is a field data gathering technique that forms the core of contextual design. The method enables researchers to create an understanding of who the users really are and how they work on a day-to-day basis. This understanding becomes the basis for developing a system model that will support users’ work. From the user’s viewpoint, the method helps people crystallise and articulate their work experience. Throughout the design process, contextual inquiry can be used to challenge the developers’ current understanding and system design for users [13]. Contextual inquiry does not provide a set of steps to follow for collecting and interpreting user information; rather it describes concepts that guide the design and implementation of information collection and analysis sessions [12]. Inquiry studies typically involve four to eight users. In practice, the procedure of the inquiry is simple: while observing the user at work, the researcher asks about the user’s actions in order to understand their motivation and strategy. The four principles of the method are [12]: − Context: Inquiry takes place in the actual work environment, with emphasis on gathering concrete data and ongoing experience. − Partnership: The overall aim is to create a partnership which fosters the creation of a shared understanding and discovery of work and practices. In the inquiry, the user is the expert on the work, whereas the researcher is an apprentice who is willing to learn about and understand the user’s work. − Interpretation: Interpretation means determining what the user’s words and actions mean together. It is a chain of reasoning that turns a fact into an action relevant to the designer’s intent. Design is built upon interpretation of facts. Researchers share these interpretations during inquiries with users. − Focus: Focus defines the point of view a researcher takes while studying work. The focus steers the conversation and gives the interviewer a way to keep the discussion on topics that are useful without taking control back from the user.


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3. Overview of the Dictation and Nursing Documentation System Studies The contextual inquiry method was applied in two empirical studies to gather data about end-user needs as well as the use and usability of a range of technology applications in clinical settings. In both studies, the overall aim was to create a comprehensive understanding of the use situations, and thereby gather data to support the further development and redesign of the currently used clinical IT systems. The first study, Dictation Study with Physicians, had its focus on investigating the procedures of dictation utilising a variety of techniques. The study was carried out in spring 2008 in a large hospital in Finland and involved seven physicians from three hospital units. Of these physicians, two used cassette dictation as their primary method for dictation, whereas three used digital and two voice-recognition techniques [14]. The second study, Evaluation of Nursing Documentation Systems, focused on documentation tasks in nursing work and incorporated four system implementations in electronic health record (EHR) systems [15], which were all based on the Finnish national nursing model [16]. The study was conducted in spring 2010 with 18 Finnish nurses who were representatives of seven healthcare organisations. All of the contextual inquires with physicians and nurses were conducted in real working environments and followed the principles of the contextual inquiry method [12]. In general, the inquiries followed the same structure. The structure and themes for inquiries are presented in Table 1. Each inquiry lasted about one hour and was guided by an experienced usability practitioner. A recorder and a digital camera were used to record interviews for later analysis. Table 1. The predetermined structure and themes for inquires in two empirical studies. Phases of inquiry. Phase 1: Background Discussion about users’ backgrounds and their previous experiences with the clinical IT systems and tool. Phase 2: Practical exercise The user is asked to conduct a dictation or documentation entry as they would normally do and, while working, explain and give reasoning for their action. Phase 3: Summary and futuristic views

Themes in dictation study - Education and current job description - Information technology skills and enthusiasm - Dictation methods and experiences

Description of the situation and surroundings in which documentation is typically conducted. A dictation walkthrough in practice from the beginning till the end using a real patient case: - the beginning of the dictation - dictating, the use of the dictation solution and related IT systems and applications - end of the dictation - approval of transcribed dictation (cassette and digital dictation) - discussion of performed activities Evaluating and discussing mobile phone dictation concepts (prepared concepts illustrated using storyboards)

Themes in nursing documentation system evaluation - Education and current job description - Information technology skills and enthusiasm - Working history and experiences with nursing documentation techniques - Descriptions of daily work and situations in which documentation is conducted, as well as patient information retrieved A documentation entry exercise using prewritten patient case scenarios, which included the following: - background of the patient (e.g., age, the reason for coming to the hospital) - what the patient has told the nurse about her condition - description of the conducted nursing activities, including medication given and interaction with related parties - how has the situation evolved during the shift/outcome of the appointment. After the exercise, discussion on performed activities Discussions on collaborative use of documented data, fluency of documentation, availability and accessibility of the information, and ideas for improvements

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4. Experiences with the Contextual Inquiry Method: Advantages and Challenges Experiences from the described empirical studies showed that the contextual inquiry method has several advantages and challenges when employed in clinical contexts. The experiences and lessons learned are summarised in the following table (Table 2). Table 2. Summary of methodology findings: Advantages and challenges of applying contextual inquiry. Advantages Enables the researchers to make insightful observations, enquire about the clinicians’ actions, and identify general and context-specific needs when the studies include numerous healthcare units and organisations. Addresses the issues of clinical IT system usage from the task and end-user oriented perspectives (versus system-centred evaluation of the usability characteristics of a single clinical IT system). Thereby, the study can include a number of techniques (e.g., for dictation) and diverse systems (e.g., different implementations of nursing documentation systems), which are used to perform similar tasks in various clinical environments. Makes it possible to analyse clinicians’ actions with interactive systems in environments in which numerous systems are used simultaneously and some of those are integrated together. Provides the researchers with an opportunity to increase their understanding of healthcare technology, as well as medical terminology and working practices. Can reveal needs and problems in system usage that the clinicians cannot articulate. Enables the gathering of a large amount of qualitative data. The gathered data can be used for several purposes, e.g., to analyse the success of interaction and user interface design; to describe the contextual issues around healthcare ICT use; to addressed issues of usability from a wide perspective; to determine users’ needs and wishes concerning improvements; to support the design of new applications. Provides concrete data about IT systems’ usage in clinical settings: interaction between the user and the systems, effectiveness of use, and communication and information sharing aspects. Challenges Requires an access to real healthcare settings and permission to record audio or voice data. Might be time-consuming to conduct due to its highly qualitative nature. Requires clinicians’ participation. While working in hectic and critical environments, clinicians tend to be busy with customary clinical tasks and unexpected emergencies. Issues of recording of medical and patient data, as well as patient privacy and health data security aspects, are essential to be considered. All of the pictures and other recorded data need to be carefully anonymised, at the latest, in the analysis phase. It is easy to question the representativeness of the data, since the interview studies typically involve a rather small number of users per user group. When the total number of involved users is rather small, how are we to take into account the wide variety of clinical practices and contexts of technology usage?

5. Conclusion The relatively small number of usability studies conducted in the health informatics domain may derive from the identified challenges in applying user-oriented methods in the health informatics domain. Different from typically applied evaluation methods, contextual inquiry approaches the study issues from the perspective of performing clinical tasks in real environments. Thereby, contextual inquiry may provide valuable support for user-centred design activities. The method enables researchers to approach usability from a broader perspective and reveals results that go beyond what can be


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found by a traditional stationary user-interface evaluation. Contextual inquiry is suitable for increasing researchers’ understanding of clinical practices, the characteristics and various contexts of clinical work, as well as end-users’ interaction with numerous IT systems. Additionally, inquiries conducted in real clinical contexts provide rich qualitative data for the purposes of developing new concepts and visions of future ICT systems. What is more, findings from inquiries indicate direct clinical response and have high descriptive value. Nevertheless, special challenges in clinical settings are related to recording and privacy issues, the wide variety of clinical practices and contexts of technology usage, diversity of clinical applications, heterogeneity of studied user groups, as well as the hectic nature of clinical work.

References [1] [2]

[3]

[4]

[5] [6] [7] [8]

[9] [10] [11] [12] [13]

[14] [15]

[16]

[17]

Alsos OA, Dahl Y. Towards a Best Practice for Laboratory-Based Usability Evaluations of Mobile ICT for Hospitals, Proc. NordiHCI 2008, ACM Press, Lund, Sweden, 3-12, 2008. Horsky J, McColgan K, Pang JE, et al. Complementary Methods of System Usability Evaluation: Surveys and Observations During Software Design and Development Cycles, Journal of Biomedical Informatics 43 (2010), 782-90. Weng C, McDonald DW, Sparks D, McCoy J, Gennari JH. Participatory Design of a Collaborative Clinical Trial Protocol Writing System, International Journal of Medical Informatics 76S (2007), 245251. Reuss E, Naef P, Keller R, Norrie M. Physicians’ and Nurses’ Documenting Practices and Implications for Electronic Patient Record Design, Proc. USAB2007, Springer-Verlag, Berlin, Heidelberg, 113-118, 2007. Malhotra S, Laxmisan A, Keselman A, Zhang J, Pavel VL. Designing the Design Phase of Critical Care Devices: A Cognitive Approach, Journal of Biomedical Informatics 38 (2005), 56-76. Croll PR, Croll J. Investigating Risk Exposure in e-Health Systems, International Journal of Medical Informatics 76 (2005), 460-465. Gennari JH, Reddy M. Participatory Design and an Eligibility Screening Tool, Proc. AMIA2000, Philadelphia, Hanley & Belfus, 290-294, 2000. Gil-Rodriguez EP, Ruiz IM, Iglesias AA, Moros JG, Rubiò FS. Organizational, Contextual and UserCentered Design in e-Health: Application in the Area of Telecardiology, Proc. USAB2007, SpringerVerlag, Berlin, Heidelberg, 68-82, 2007. Colbe JM, Maffitt JS, Orland MJ, Kahn MG. Contextual Inquiry: Discovering Physicians’ True Needs. In Gardner RM, ed.: Proc. AMIA Fall Symposium, Philadelphia, Hanley & Belfus, 469-473, 1995. Chan W. Increasing the Success of Physician Order Entry Through Human Factors Engineering, Journal of Healthcare Information Management 16 (2002), 71-79. Martin JL, Murphy E, Crowe JA, Norris BJ. Capturing User Requirements in Medical Device Development: The Role of Ergonomics, Physiological Measurements 27 (2006), R49-R62. Beyer H, Holtzblatt K. Contextual Design: Defining Customer-Centered Systems, Academic Press, San Diego, USA, 1998. Holtzblatt K, Jones S. Contextual Inquiry: A Participatory Technique for System Design. In Schuler D, Namioka A, eds.: Participatory Design Principles and Practices, Lawrence Erlbaum Associates, Inc., New Jersey, USA, 1993. Viitanen J. Redesigning Digital Dictation for Physicians: A User-Centred Approach, Health Informatics Journal 15 (2009), 179-190. Viitanen J, Kuusisto A, Nykänen P. Usability of Electronic Nursing Record Systems: Definition and Results from an Evaluation Study in Finland. In Borycki EM, Bartle-Clar JA, Househ MS, Kuziemsky CE, Schraa EG, eds.: International Perspectives in Health Informatics. Studies in Health Technology and Informatics 164 (2011), IOS Press, Amsterdam, 333-338, 2011. Nykänen P, Viitanen J, Kuusisto A. Hoitotyön kansallisen kirjaamismallin ja hoitokertomusten käytettävyys, (Project report in Finnish), University of Tampere, Report D-2010-7. [Internet] 2010. [cited 2011 April 20] Available from: http://www.cs.uta.fi/reports/dsarja/D-2010-7.pdf. Tanttu K. National Nursing Documentation Project in Finland 5/2005-5/2008: Nationally Standardized Electronic Nursing Documentation, presentation. [Internet] 2008. [cited 2010 June 10] Available from: http://www.vsshp.fi/fi/dokumentit/15158/National-Nursing-Project-2005-2007.pdf.

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A Method to Measure the Reduction of CO2 Emissions in E-Health Applications a

Paola DI GIACOMOa,1 , Peter HÅKANSSON b University of Udine, Faculty of Medicine – Ericsson Telecommunications Italy b Ericsson Telecommunications LTD, Sweden

Abstract. Climate change is perhaps the topmost challenge of our time. To prevent climate change from severely impacting almost every facet of life on the planet, scientific consensus points to a need to reduce the emissions of greenhouse gases (GHG), measured in terms of CO2 equivalents (CO2e), by as much as 80 percent by 2050. So far the focus has centered on incremental reductions of CO2 e emissions in areas in which they are highest, without negatively impacting the economy. But there is also a large untapped opportunity to drive economic growth by applying transformative solutions. In this paper, a method to evaluate CO2e reduction in the e-health applications is presented. Keywords. CO2 reduction, e-health, sustainable broadband-enabled services.

1. Introduction The Information Communication Technology (ICT) industry sector is today responsible for about 2% of global CO2 emissions [1]. ICT services and applications like virtual meetings, flexi-work, e-commerce and e-health have a potential to significantly reduce CO2 emissions. Life cycle assessments (LCA) [2] constitute a well-established methodology and tool for measuring CO2e emissions, and are used for comparing the emissions of different systems. This paper takes the traditional LCA approach one step further, by presenting: • A method for assessing the potential reduction of future CO2e emissions and the results of a case study in the e-health domain • Indeed, this method is especially useful for evaluating the potential ICT-based solutions to reduce CO2e emissions in other sectors not traditionally associated with ICT.

2. Current Measurement Methods At present, several methods are used to analyze the effect of introducing ICT-based solutions to replace traditional solutions and thereby reduce CO2e emissions [3]. These methods are seldom based on life cycle assessment and typically only include end-user 1

Corresponding author: Paola Di Giacomo. University of Udine at Ericsson Telecommunications Italy, Via Anagnina 203, 00118 Roma (Italy), E-mail: [email protected].

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equipment. Most standards and assessment methods tend to focus on particular aspects of the life cycle – there are, for example, a number of “energy labeling” standards for products that use electricity. One such is the EU energy label, Energy Star. Unfortunately, none of these methods include “infrastructure” as pat of their assessment of direct and indirect impacts. Consequently, these methods have only limited value. Recent scenario-building studies have demonstrated that comprehensive LCAs are, in fact, necessary to provide a holistic image of environmental impacts. 2.1. LCA Methodologies LCA methodologies, such as “Process-Sum” and “Economic Input-Output”, have two different approaches for evaluating environmental impact. There are also hybrid models that use adaptations of these methods in an effort to take advantage of key benefits while overcoming certain inadequacies. Several standardization organizations (European Telecommunications Standards Institute (ETSI) and the International Telecommunication Union (ITU) [4]) are currently developing standards that should provide guidelines for performing an LCA relating to ICT-based products, services and solutions. In summary, there is not currently an agreed methodology for measuring the potential reductions in CO2e emissions that ICT-based solutions can provide. Nevertheless, the industry should adopt the use of comprehensive LCAs to calculate the potential of ICT-based solutions to reduce CO2e. The adoption of a holistic LCA methodology would: • Enable companies/policy makers to prioritize/support solutions and balanced decisions regarding sustainability • Help put focus on the total level of energy usage and highlight the potential for CO2e reductions in business cases, thereby motivating investments in ICT.

Figure 1. Overview of a holistic LCA method for comparing ICT-based systems with conventional systems that deliver equivalent services (C-LCA = CO2e-baes LCA).

3. Methodologies for Assessing the Use of ICT to Reduce CO2e Emissions Figure 1 presents a schematic illustration of a holistic LCA method for comparing a new ICT-based service with a conventional service. The method builds on results from LCA studies of ICT-based systems, for example, PCs and network access, and LCA

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studies the conventional systems from traditional sectors, for example buildings and transport [5]. The conventional system and the ICT-based system are each assessed in the same way and compared. 3.1. System Definition The first step – system definition – entails defining the processes and boundaries of the system. In some cases, depending on the service, it might be necessary to consider both fixed and mobile broadband in order to analyze the environmental impacts of the introduction of ICT [6]. 3.2. Data Collection During the second step – data collection – data is collected from a variety of sources, such as LCA databases, field studies, and statistics. This baseline data enables the comparison against with reductions or increases can be measured or estimated. The availability of published LCA data is limited. Statistics about travel (distance, in kilometers), transportation (weight, in 1000Kg*kilometer), building area (in square meters), and so forth, must be collected during the life cycle inventory (LCI) phase. Some LCA data about mobile and fixed broadband networks (for instance, data about PCs) has already been published. 3.3. CO2e Assessment Based on the LCA Method In the third step – assessment of CO2e impacts – the CO2e emissions of the defined ICT-based system and the conventional system including the infrastructure. This assessment is based on the LCA methodology (see Table 1). For ICT-based system, the use of mobile broadband services must, based on data traffic, also consider: user profiles and behavior, including type of mobile device and the characteristics of mobile network access and the core or transmission network and specified data centers. Fixed broadband has many different user profiles with individual types of PCs, modems, or home network setups. Access sites and data traffic may be aggregated to form a total or average ICT system user profile for all users or for an entire company, hospital, general organization etc. Finally, it is necessary to determine the total use of the specific service and all related services [7]. Table 1. Mandatory elements to consider when assessing the CO2e emissions of an IT service. Mandatory Elements to Consider Type of end-user equipment (PC, mobile phone) Use time and baseline operation (standby)

Energy consumption for network access

Average data traffic of the service

Electricity mix in the “organization” studied

Comments This information provides manufacturing impact and operation characteristics This is dependent on electrical power consumption and the type of end-user equipment. The specification needs to be based on user behavior The type of access and use time is used to quantify network access. Data traffic can’t be used because most energy consumption related to network access standby This is used to quantify data transport, cable infrastructure and data centers, in order to calculate the services share of the total network infrastructure All electrical power consumption in the operation phase can be adjusted to the specific organization


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4. Comparison of Systems After the CO2e assessment is complete, it is possible to compare the two systems and evaluate the potential of the ICT-based service to reduce CO2e emissions. The total results of the analysis are twofold: potential reduction factor and relative reduction factor. The potential reduction factor is the total reduction in CO 2e divided by the total CO2e of the new ICT-based system.

5. Introduction of E-Health in Croatia 5.1. Background The term e-health refers to the application and use of ICT in all aspects of healthcare to provide better and more efficient services and to facilitate access to healthcare [8-9]. The Healthcare Networking Information System, developed by Ericsson in Croatia, is a comprehensive ICT solution for integrating healthcare processes, information management and business workflows. In this study, the system was particularly used to transfer prescriptions and referrals electronically, reducing also the need for printing prescriptions for patients and patients to travel. 5.2. Data and Assumptions Croatia has about 4.5 million inhabitants, 55 percent of whom live in Croatia. There are about 260 cars per 1000 people. In addition, there are 6600 primary healthcare terms/units in Croatia and approximately one doctor for every 450 people. The main assumptions were, as follows, like in a typical GP-centered organization of health care: • The e-referral service can reduce patient visits to hospitals or specialists (approximately 12 million per year) in average by 50 percent taking into account that the referrals do not need additional visits, if most of them are based on an examination • On average, patients travel 10km + 10 km per visit; twenty-five percent of patients travel by car and the other 75 per cent by public transport and the eprescription service can reduce paper consumption by 50 percent. 5.3. The New E-Health System The actual data center consumes 400MWh of electrical power a year. There are approximately 10,000 PCs in the network. Therefore, in all likelihood, the allocation of the total system to the two services studied will decline over time, especially as new services are introduced. The main assumptions were as follows: • About 10 per cent of the system’s PCs were installed in parallel to the system. There, 10 percent of the total system will be allocated to the e-referral service • One percent of the total system is allocated to the e-prescription service. In total, the two services in the e-health system account for about 330 metric tons of CO2e emissions per year. Of this amount, PCs and networks account for over 90 percent and the data center accounts for the rest. Given that patients reduce their travel, on average by almost three visits per year, the potential reduction in travel about 7 kg

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CO2e per patient and year. This is a result in a reduction of up to 15,000 metric tons of CO2e provided 50 percent of all travel can be avoided (see Figure 2).

Figure 2. Graphical presentation of e-health case study results.

6. Conclusions The e-health system is installed to support primary healthcare in Croatia can significantly reduce CO2e emissions, thanks to reduction in patient travel as well as to the reductions in paper consumption. Taken together, the e-referral and e-prescription services have the potential to reduce CO2e emissions by up to 15,000 metric tons per year while two services only add 330 metric tons of CO2e/year from operation and manufacturing activities. The potential reduction factor over a 20-year period is up to 50, depending on whether infrastructure is included and, if so, to what extent.

References [1] [2]

[3] [4] [5]

[6] [7] [8]

[9]

Boutin JP, Villeneuve C, Wells JP. Greenhouse Gas emissions offsets through videoconferences and teleconferences, Literature review, Global e-Sustaniability Initiative (GeSi) (Phase 1), 2006. Miyamoto S, Irie Y, Harada H. Factor analysis of environmental load reduction induced for various information technology systems, Proceedings of 11th LCA Case Studies Symposium, Joint SETAC Europe, ISIE meeting and LCA Forum, Lausanne, Switzerland, 3-4 December 2004. U. Östermark, Eriksson E. LCA of a videoconference: a comparative study of different ways of communication, Proceedings of 7th LCA Case Studies Symposium, SETAC-Europe, 1999. Berkhout F, Hertin J. Impacts of Information and communication Technologies on environmental Sustainability: speculations and evidence, Report to the OECD, 2001. Fuchs C. The implications of new information and communication technologies for sustainability, Center for Information and Communication Technologies & Society, University of Salzburg, Austria, 2006. NTT Service Integration Laboratories in Japan, The Green Vision 2020, NTT Group CSR Report 2010, 2010. Pamlin D, Szomolányi K. Saving the climate @ the speed oflight, Proceedings of European Telecommunications Network Operators´ Association (ETNO) and World Wildlife Fund (WWF), 2006. Burton D, Cavanagh J, Johnston G, Mallon K. Towards a High-Bandwidth, Low-Carbon future Telecommunications-based Opportunities to Reduce Greenhouse Gas Emissions, Climate Risk Pty Limited Telstra Report, Australia, 2007. Nakamura J, Nishi S, Kato K, Takahashi KI. Environmental Assessment of e-learning based on a Customer Survey, Proceedings of Fourth International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Eco Design 2005, Tokyo, Japan, 12-14 December, 2005.

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Medical Informatic Research Management in Academia - the Danish Setting. Stig KJÆR ANDERSENa Department of Health Science and Technology, Aalborg University 1

a

Abstract. The condition that the Danish universities have been subject to severe changes through the last decade has had huge consequences for management of research at the level of a discipline as Medical Informatics. The presentation pinpoints some of the instruments, which is on top of the management agenda in the new academic reality in Denmark. Performance contracts, organizational structure, general management, research constraints, ranking and performance issues, economy linked to production, ownership, and incitements are issues affecting the way research are done. The issue of effective research management is to navigate in this reality, ensure inspiration and influx from other environments dealing with medical informatics problems, in theory as well as in praxis - and shield the individual researcher from emerging bureaucracy, leaving room for creativity. Keywords. Management, research management, research agenda, creativity,

1. Introduction The conditions for research management at all levels at the Danish universities have been subject to radical changes since 2002, where a new university law was passed in the Danish parliament revealing a new governing structure. Furthermore, in 2009 major restructuring of the Danish universities took place, where the 12 universities were merged to 7 and, as a consequence, large organizational reallocations took place and new revised management structures were imposed on the Danish universities. The political mantra in the changes has been to manage universities in the same way as it is done in industry, implemented as a top down management structure with a powerful board and a director (rector) referring to a board who has a majority of nonuniversity external members. The agenda for the government funding research has shifted the ratio between basic research programs and political defined strategic research programs to the expense of basic research, and the offspring of new companies based on research results has been highly prioritized. “From idea to invoice” was one of the announcements from the Ministry of Science Technology and Innovation indicating the new trend. The classical Humbolt university virtues has slowly but surly been down prioritized in the Danish academic environment as a consequence of the new university management and its late revisions [1]. These significant changes in running universities have of course a significant impact on research management in the field of medical informatics. 1


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2. The national level Medical informatics academic research has also been influenced by a long gliding change of the national healthcare setting, where all have equal rights to be treated, to a system where private insurance systems and private hospitals causes unequal opportunities for treatments. In 2007 another shift took place that had consequences for the Danish health care settings: the Danish regional structure was changed from 13 counties to 5 regions as well as the financing model for the delivery of health care changed to a centralistic model, argued in a possible increase in efficiency and a less growing resource consumption. The continuous hunt for possibilities for keeping the resource consumption not growing too fast in relation to the GNP is serious affecting the conditions for implementing e-health solutions and hence affecting the context for the medical informatics research in academia. The managing of medical informatics research in a continuous changing world is a challenge in the cross field between basic research, applied technology and a demanding clinical situation. The politically imposed changes have made it an even bigger challenge. These are the conditions for the research management in Medical informatics in a academic environment where the focus on education and dissemination are equally important.

3. The governmental instruments Following key initiatives are the general prerequisite for the research management in academia in general and in Medical Informatics in particular and this cause in some way the step away from the classical university. Some of the initiatives are recognized internationally, others are tailored to the reality of the Danish governmental research policy. This lists what the management of medical informatics research has to navigate through. Performance contracts: Between the research ministry and the universities performance contracts have been signed. The key research related performance requirements are: passed PhD’s, number of publications, international recognitions, and number of start up companies. This forces the management of medical informatics research to focus on measurable recognizable quantity on expenses of quality. Organizational structure: A clear top-down single-strand level-structure has been introduced: the university board, the university management, the faculties, the departments, and the individual researches organized in research groups. The influences of traditional university collegial organs and councils have been severely reduced. The success of a strong governance influence the research agenda and priorities are severer dependent on the scientific capacity of the leaders in office. General management: managerialism has been introduced with the purpose of having more professional management by generalists in all levels of management. The consequences are increased control and a more homogenous acting organization. The pay is more resources for administration instead of for research. Research constraints. Strategic programs, national as well as international, have considerable influence on the research agenda for research groups, partly due to encouragement from university management as a aid for resource and partly due to the

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research subject defined by experts. This gives less power and freedom to chose research subjects for the individual researcher and research group. The concern is here for academia to fulfill the obligation of focus on the long-term agenda for the next 510 years. Ranking and performance measure: At the macro level, it has been a integrated part of branding universities to be as high as possible on some of the “top 100” lists of universities. This is one of the ways the competition between universities in Denmark has materialized. At the micro level, the number of publication in international journals, the impact factor, the h-factor, and other bibliometric measures are the parameters. Basically this is a way of quantifying the requirement of recognition in the scientific society. As such it is efficient, but other obligations as broader dissemination may be down prioritized. Economy linked to production: The public part of the university funding has become increasingly coupled to Key Performance Indicators (KPI), meaning that the production focus is on optimizing these KPI, whish could lead to unintentional publication strategies and less optimal dissemination of results. Ownership: A shift in ownerships to research inventions from the individual (the researcher) towards the employer (the university) and the call from the management to focus on patents and commercialization add a dilemma between closeness and open ness, which is especially delicate at a basically public funded institution.

4. The research group level The research in Medical Informatics has a long tradition, the first international conferences, the foundation of international associations and scientific societies emerged in the 60ties and 70ties and have over the years established its own research agenda. The emerging of new technologies and the global “go internet” has heavily influenced this agenda, which is well displayed in the program of the present MIE2011. The task for research management is to carry the tradition outlined in the continuously development of the Medical Informatics research agenda. The task for research management of Medical Informatics in an academic setting is to navigate in reality using the available instruments in a positive manner. Medical Informatics is cross disciplinary by nature and hence inspiration and influx from other environments dealing with medical informatics problems, in theory as well as in praxis, is very important. Handling these interfaces between environments, often a cultural gap is another important task for research management. At the bottom line, the Medical Informatics research is powered by the creativity and knowledge of individual researchers having a common research toolbox of experience, methods, theories and praxis. To shield the individual researcher from emerging bureaucracy and the unforeseen consequences is another important issue.

References [1]

Madsen, O.M: Universitetets død, Kritik af den nyliberale tendens (in Danish), Bogforlaget Frydenlund A/S, 2009, ISBN 9788778878205.

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Research Management in Healthcare Informatics — Experiences from Norway Arild FAXVAAGa,1, Pieter TOUSSAINT a,b, Trond S JOHANSENa Norwegian EHR Research Centre, Faculty of Medicine, University of Science and Technology (NTNU), Trondheim, Norway b Department of Computer and Information Science, Faculty of Information Technology, Mathematics and Electrical Engineering, NTNU, Trondheim, Norway a

Abstract. This paper reports on the experiences with establishing a multidisciplinary healthcare informatics research community at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. A multidisciplinary research group in healthcare informatics must maintain strong connections to computer science, social science, biomedicine and healthcare researchers. Those organizing the research must create a milieu that fosters true collaboration across disciplines. The researchers must have good access to healthcare institutions, to healthcare professionals as well as to patients. A healthcare informatics laboratory creates an arena for experiments as well as for validation of health-it technologies. Keywords. Healthcare informatics research, Research management

1. Background Although healthcare informatics now is recognized as a research field in its own, healthcare informatics research intersects, and is shaped by research in computer sciences, social sciences, biomedicine and healthcare. The field is also influenced by a trend towards making health-IT development and implementation programs important component of healthcare modernizing efforts. The vision of health-it systems as catalysts of change and means of empowering the patient has also improved the opportunities for funding of research within healthcare informatics. During the last 10 years, the Norwegian University of Science and Technology (NTNU) has built up a multidisciplinary healthcare informatics research community. NTNU is the only Norwegian technical university that also has a medical faculty. The technical faculties are located within walking distance from the medical faculty and the university hospital, creating a fertile ground for building research groups in biomedical science and engineering. In the 1990’s the Research Council of Norway established a program for healthcare informatics research. In 2002, NTNU established a research program for health informatics with participation from the Faculty of Medicine, the Faculty of Information Technology, Mathematics and Electrical Engineering, and Faculty of Social Science and Technology Management. The next year NTNU was 1

Corresponding Author: Arild Faxvaag, The Norwegian EHR Research Centre, Medical-Technical Research Centre, N-7489 Trondheim, Norway; E-mail: [email protected]

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awarded a grant to establish a national centre for research on electronic health records. In this short paper, we will report our experiences with establishing this research centre and what we believe contributes a multidisciplinary research community.

2. The research domain Healthcare informatics research lies at the intersection between healthcare research, computer sciences and social sciences. Health-IT systems can inform hospital leaders, healthcare professionals and patients. Health-IT systems can provide data for healthcar services research, and health technology assessments. Healthcare informatics has intersections to bioinformatics as well as to cognitive science, workflow management, knowledge representation and guideline systems. Healthcare informatics research can inform the design of commercial health-IT systems and provide methods for validation and assessment of implemented systems. The breadth of the research domain and the large number of stakeholders makes it hard to decide on which particular sub-field to engage. A multidisciplinary healthcare informatics research community must lay the ground for researchers with a broad spectrum of interests. Further, the research should be organized so that the groups learn from each other.

3. Creating an environment for multidisciplinary research In the 1990’s the Research Council of Norway established a program for healthcare informatics research. Towards the end of the decade, before the centre was established, some PhD students working with health-it complained of not having a milieu to discuss and share their experiences. They typically worked in a small group, unaware of other university groups that could be doing almost the same type of research. At the same time, the health authorities planned for building a new university hospital. Those who managed the hospital planning process envisioned modern health-IT systems in the new hospital. As a response to these challenges NTNU established a research program for healthcare informatics [1]. It began as a series of open meetings that created an arena for fostering discussions about healthcare informatics. The meetings were announced via e-mail lists and the web. Typically researchers, representatives from industry, hospital managers, healthcare professionals and students attended. In hindsight, this created an arena for networking, and for developing a language for sharing and discussing problems related to healthcare and information technology. A meeting where healthcare professional presented a problem could result in design of a student project by a researcher from social sciences. The healthcare professional would secure access to the domain and act as a co-supervisor to the student. If the student project became successful, it could be developed into a PhD project. Our experiences with establishing this networking arena has led us to conclude that such an activity is necessary for the success of a healthcare informatics research community. When NTNU was awarded the grant for establishing a National EHR research centre, the university could offer PhD students and their supervisors a shared office environment at the university hospital campus. Since the establishment the centre has become a place where PhD students and faculty can choose to do their work. However, all researchers at the centre are also affiliated to, and have office space, at another

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department at the university. Organizing the research centre this way, we provide a second office to researchers, while the researchers at the same time keep their connection to their “mother department” and research domain. This “second office policy” has been crucial for the success of the research centre. Keeping the connection the different university departments makes it easy to recruit PhD and Master students to the healthcare informatics field. At the same time unnecessary tensions between the “mother department” and the research centre is avoided since the researcher always primarily belong to the latter.

4. Securing access to healthcare institutions and personnel. A healthcare informatics research community must have good access to the domain. This means having the opportunity to do ethnographic studies, observing healthcare personnel in their interaction with information systems as well as with patients. The list of actors also includes hospital managers, and people employed at the IT-department of the institution. Access should be secured through bilateral agreements between the university and the healthcare institution. It is our experience that good access to the domain also requires an engagement of the leader of the department that is to participate. Based on our experiences, we believe that researchers should interact with these leaders, and invite and encourage them to present health-it related problems from their own perspective. Researchers should also be able to recruit healthcare personnel for participation in usability experiments, design workshops and other activities at the healthcare informatics laboratory at the centre. We consider it an advance that hospital employees can participate in the laboratory while dressed in white.

5. Create fruitful interactions with healthcare informatics industry and consulting companies A healthcare informatics research community must foster interaction between researchers and healthcare informatics industry. The industry can participate by providing the researchers with working versions of their systems for use and testing in the laboratory. Further, they can co-sponsor research projects to benefit from theory, models and prototypes that come out of the project. It is our experience that representatives from the industry should be encouraged to participate in networking events. The industry should also recruit from our students. As alumni, these could strengthen the network between the healthcare informatics researcher environment and the industry.

6. Establishing laboratory facilities Having a healthcare informatics laboratory creates novel opportunities for healthcare professionals and patients to participate in experiments where new health-IT prototypes and concepts are tested. The test results can inform further design of the prototype. A laboratory also creates an arena where the prototype, test object or situation can provide

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stimuli for the test persons to reflect on how they work with information and how information systems can support different tasks. Our laboratory has also been used during workshops and in focus group interviews. Finally, our laboratory has been used to assess health-IT technologies that already are in use.

7. Secure funding of healthcare informatics research One of the challenges faced by health informatics researchers is finding appropriate grant programs to apply for research funding. There are three types of programs to choose from: informatics oriented, medical science oriented and social science oriented. All three pose very different requirements and expectations to project proposals, which must be taken into account when developing the proposals. As a result, even though projects are multi-disciplinary, they will be formulated in a biased way in order to meet the requirements of the specific funding program they apply for. In principle this jeopardizes the ideal balance between the different research communities that are represented in the project. It would be preferable to have funding programs that are dedicated to medical informatics research and accommodate true multi-disciplinary proposals. These types of programs are, or have been, around. But very often they have a short life span and tendency to favor either the medical or the IT community.

8. What might go wrong Below we list a number of threats for multi-disciplinary research within healthcare informatics: • A project fails to balance research and development tasks evenly. So, either the project becomes mere development or consultancy, focused at solving local problems, or the project becomes a fundamental research project without sufficient relevance for the domain. • Researchers coming from different research traditions fail to understand each other and/or don't respect each other’s research approach. As a result the collaboration is poor or even absent. • Tension and conflict between different stakeholders due to poor coordination and ambiguous vision. There are many stakeholders in the multidisciplinary healthcare informatics environment, both internal (at the university) and external (i.e. health institutions and industry). A minimum of staff is necessary to coordinate, and mediate between, different stakeholders. Further, a clear vision and strategy may function as a powerful tool to create a transparent and eclectic culture were people pull in the same direction. • Industrial partners have their own agenda, not so much geared towards knowledge development but more to product development. This can make them disinterested in the research part of a project. • Health care partners mindful of their day-to-day clinical work responsibilities may limit the opportunity for experimentation and innovation in a project. So, although the intention is to involve these partners in a project as providers of a so-called 'work place' practice, this work place role may be very limited.

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Prototypes developed will never be transformed into proper, well-evaluated, products

References [1] NTNUs program for healthcare informatics: http://hi.ntnu.no


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Research Management: the case of RN4CAST a

Dimitrios ZIKOSa1 John MANTASa Laboratory of Health Informatics, National and Kapodistrian University of Athens

Abstract. Successful research management requires multifunctional, equal teamwork and efficient coordination, aiming to increase the impact of the research outcomes. Aim of this paper is to present the strategies that have been followed to successfully manage the RN4CAST study, one of the largest multi country research projects ever conducted. The paper focuses on the core research strategies rather than on the administrative management activities also required for the success of this case report. Management of a multi-country nursing survey requires the use of common data collection tools, applicable to every context, research protocols supporting the scope of the research, data models for multi-country analyses and global dissemination strategies.

Keywords. Research Management, Nursing, RN4CAST

1. Introduction The methodological approach for the efficient management of research has been discussed many decades ago in research papers [1], [2]. Recently many authors define research management as opposed to the “research administration” which is a centralized approach to conduct a medical research [3]. This new approach requires all partners’ active participation, but also of the communities, potential interest groups, policymakers and other stakeholders [4]. The link between research strategies and successful management is very important while the achievements of a research can be proved to be the key of scientific research management [5]. General management practices applicable in research management include the need for empowering partners and equally working together beyond institutional boundaries; communicating effectively with stakeholders to create new knowledge and utilize it throughout unique practices. Successful research management does not only imply project management in financial and administrative terms but also involves the research itself. Nowadays research involves international collaboration; therefore resource mobilization and use of proper methods of dissemination to different stakeholders are key success factors. The success is also based on the ability to mobilize multi-country and multi-disciplinary teams while knowledge management and use of essential informatics tools for health research are important. Finally the role of coordination is equally important for the efficient management of a large scale research [6].

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2. Scope Aim of this paper is to present the strategies followed in order to successfully manage the RN4CAST study, one of the largest ever multi-country nursing workforce research projects. This case study focuses on the RN4CAST practices that have been agreed through a common consensus and collaborative work to tackle lingual, conceptual and organizational variations between the participant countries, thus developing an effective and at the same time democratic multi-country research environment.

3. Research Management in the case of RN4CAST RN4CAST, the largest nurse workforce study in Europe will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe. RN4CAST is a consortium of 15 partners in 11 European countries. Each European partner conducted surveys from over 50,000 nurses and outcomes of tens of thousands of patients [7]. 3.1. Common Study Protocols Nursing job varies across European countries participating in the RN4CAST study. Despite common characteristics, there are differences in the organization of the healthcare system [8]. In order to agree on common principles regarding the research methodology in all countries, an international protocol was prepared to standardize data collection process and instruments for the cross-country analyses. Differences between the national study protocols were reported by each team, discussed by the consortium and approved by the coordinator. 3.2. Data Sources and definitions An opening discussion regarding data sources identified a limitation in the case of some countries, regarding the availability and/or quality of routinely collected data. This limitation was tackled using an additional instrument to primarily collect patient data not readily available in routinely collected databases and this strategy allowed the timely inclusion in the analysis. Participating hospitals were selected through a common strategy, explicitly describing the type and size of eligible hospitals, nursing units and the type of eligible nurses. ’Nurses’ have been clearly defined in all countries based on the European Union definition (directive 2005/36/EC), therefore variations in the local interpretation of what is a nurse have been overcome. The survey instruments were based on a common template that all partners agreed to use. The instruments were translated into all primary languages using the backwardforward translation method and evaluated with the CVI instrument [9] by experts in every country, while no changes to the core template were allowed. Standard definitions of all variables were agreed, based on (i) previous knowledge (ii) wellknown validated instruments and (iii) research team expertise [10]. Finally, identifiers indicating survey variables (ie International Classification of Diseases-ICD, Diagnosis Related Groups-DRGs) were decided and commonly used by most national studies.


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3.3. Data Collection, Analysis and results exploitation The strategy followed to facilitate data collection was based on the enrollment of a field manager in each hospital as key contact with national research teams. Once data was collected by all countries, there have been gathered centrally by the research coordinator to perform preliminary analyses of the raw datasets to identify out-of-range, missing values and data entry errors, producing a cleaned version. A statistical analysis model was selected to explore specific research questions within each country but also through cross-country analyses. The strategy for the dissemination of the results is comprised by (i) yearly stakeholder meetings during the project life circle (ii) agreement upon a common strategy for publications and authorship (iii) a special issue of scientific journal dedicated to RN4CAST (iv) drafting and co-authoring a synthesis document presenting and comparing the conclusions of the data analyses across countries, with possible Europe-wide conclusions (v) an observatory book bringing together a sample of country case studies and contextual contribution of nursing in the quality of care.

4. Discussion The case of RN4CAST indicates that the road to the successful management of a multi country large scale research crosses two different levels of challenges. Other than successful financing, mobilization, reporting etc, which mainly refer to the project management/administration, there are challenges directly addressing the content and methodology of the research itself. These challenges address the methods of the survey, data harmonization issues, data collection, multi-level data analysis strategies and finally dissemination of the results providing added value on the national surveys in EU level. The above mentioned challenges are key factors for the validity of the survey results and the scientific quality of large scale surveys. Acknowledgements: RN4CAST is coordinated by the Centre for Health Services & Nursing Research at the Catholic University Leuven. University of Pennsylvania, USA, contributes with its specialized research expertise derived from previous international research. Many thanks to the principal investigators of the RN4CAST consortium: Tomasz Brzostek, Reinhard Busse, Maria Teresa Casbas, Sabina De Geest, Peter Griffiths, Juha Kinnunen, Anne Matthews, AnneMarie Rafferty, Carol Tischelman, and Theo Van Achterberg and to all RN4CAST partners

References [1] [2] [3] [4] [5] [6] [7]

Russel R. Management of Research. Nature. 1947; 160(4068):547. Smith W. Research management. Science. 1970; 167(3920):957-9. Peiró S, Artells Herrero JJ.Management of research in healthcare centers. An exploration through nominal group dynamics. Gac Sanit. 2001; 15(3):245-50. De Rosa C, Rosemond ZA, Cibulas W, Gilman AP.Research management in the Great Lakes and St. Lawrence River basins: challenges and opportunities. Environ Res. 1999; 80(3):274-9. Zhang WY, Zheng J, Li YC. Practice and experience of the scientific research management. Zhonghua Nan Ke Xue. 2003; 9(8):634-8. Merry L, Gagnon AJ, Thomas J. The research program coordinator: an example of effective management. J Prof Nurs. 2010; 26(4):223-31. The RN4CAST project official website. Online. Available from: www.rn4cast.eu

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Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K: Nurse-staffing levels and the quality of care in hospitals. N Engl J Med 2002; 346: 1715-1722. [9] Polit D, Beck C, Owen SV: Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health 2007; 30: 459-467. [10] Sermeus W, Aiken L, van den Hede K, Rafferty AM, Griffiths P, Moreno-Casbas M, Busse R, Tishelman C, Scott A, Bruyneel L, Brzostek T, Kinnunen J, Schubert M, Schoonhoven L, Zikos D, RN4CAST Consortium. Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology. BMC Nursing 2011; 10:6.


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eMeasures: A standard format for Health Quality Measures Catherine CHRONAKIa1, Charles JAFFEa, Bob DOLINa On behalf of HL7 International,

Abstract. Health quality measures can be used to improve the effective use of Electronic Health Record systems (EHRs) in health care delivery. The Health Quality Measures Format (HQMF) is a standard for representing a health quality measure as an electronic document. This presentation will present the standard, review the development process of quality measures for EHR system using HL7 CDA R2, and reflect on the outlook for eMeasures implementation and adoption. Keywords. health information technology standards, quality measures

1. Introduction Health quality measures can be used to improve the effective use of Electronic Health Record Systems in health care delivery. The National Quality Forum aims to significantly improve the quality and efficiency of patient care by making possible the capture and reporting of quality measure information for physicians and other health care providers [1]. The Collaborative for Performance Measure Integration with EHR systems has the following objectives [2]: (a) To create a standardized way of communicating Performance Measures; (b) To establish standards that permit structured, encoded Performance Measure information to be incorporated into EHR applications while preserving the clinical intent of the Performance Measure; and (c) To improve the process of Performance Measure update and maintenance for EHR vendors. The Health Quality Measures Format (HQMF) is a standard for representing a health quality measure as an electronic document. Quality measures or indicators provides indications of outcome regarding the performance of an individual or an organization in relation to specific actions, processes or outcome measured based on a set of clinical criteria and evidence base [3]. The next section (Methods) describes the HQMF standard, which as of March 2010 is a HL7 Draft Standard for Trial Use (HL7 DSTU). Then, Results and Outlook cites areas that the HQMF reflecting on opportunities for global adoption.

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2. Methods Through standardization of a measure's structure, metadata, definitions, and logic, the HQMF provides for quality measure consistency and unambiguous interpretation. A health quality measure encoded in the HQMF format is referred to as an "eMeasure". Standardization of document structure (e.g. sections), metadata (e.g. author, verifier), and definitions (e.g. "numerator", "initial patient population") enables a wide range of measures, currently existing in a variety of formats, to achieve at least a minimal level of consistency and readability, even if not fully machine processable. An HQMF document is a defined and complete information object that can exist outside of a messaging context and/or can be a payload within an HL7 Version 2 or Version 3 message. Thus, the HQMF complements HL7 messaging specifications. The exact method by which an eMeasure is exchanged is outside the scope of this standard.

Figure 1: Structure of an HQMF document.

HQMF requires that a receiver of an eMeasure be able to algorithmically display the document on a standard Web browser such that a human reader would extract the same quality data as would a computer that is basing the extraction on formally encoded eMeasure entries. Material within a section to be rendered is to be placed into the section.text field. The content model of this field is the same as that used for other Structured Document specifications (see Figure 1). The HQMF Model is derived from the HL7 Reference Information Model (RIM), through the use of the HL7 XML Implementation Technology Specification (ITS). It is a "Constrained Information Model" (CIM), derived from a broader "Domain Information Model" (DIM). The QualityMeasureDocument class is the entry point into the HQMF model, and corresponds to the XML element that is the root element of an eMeasure document. An eMeasure document is logically broken up into a header and a body. The QualityMeasureDocument class inherits various attributes from the InfrastructureRoot class of RIM, including templateId and typeId. Setting the value of.templateId in an instance signifies the application of a set of templates, which may be applicable at the level of the QualityMeasureDocument or at a finer granularity i.e. section or entry.

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Key notions of HQMF are: Data Criteria, Population Criteria, and Measure Observations. Data Criteria are assertions that can be True or False frequently looking at raw EHR data and they are used primarily to define whether a patient is included in Numerator, Denumerator, etc. In HL7 terms, Data Criteria are formalized as RIM patterns coupled with vocabulary. Population criteria, just like data criteria are assertions that can be found to be true or false, thereby providing a means for HQMF to formalize a measure's population parameters based on combinations of Data Criteria. Measure observations are not criteria, but rather, are definitions of observations, used to score a measure and are tied to a specific population e.g. average systolic blood pressure.

Figure 2: eMeasure Development Process

3. Results and Outlook Health Quality Measures Format (HQMF) supports the development process of eMeasusres for quality reporting (see Figure 2). It is an HL7 standard developed to streamline the process of developing interoperable quality measures for EHR systems using HL7 CDA R2 [4]. Looking into the future of HIT, it is important that eMeasures are taken into account in the HL7 EHR-S Functional Model and its emerging profiles. Furthermore, education with eMeasures and wide world-wide awareness and adoption fostering shared understanding of concepts and interoperable implementations will help develop consistent tools for measuring health care quality, and as Lord Kelvin put it: "If you cannot measure it, you cannot improve it." Lord Kelvin (1824-1907).

References [1] [2] [3] [4]

National Quality Forum http://www.qualityforum.org Collaborative for Performance Measure Integration with EHR systems http://www.amaassn.org/ama1/pub/upload/mm/472/wkgrparecommendation.pdf Health Quality Measures Format: eMeasures http://www.hl7.org/v3ballot/html/domains/uvqm/uvqm.html HL7 CDA R2 Quality Reporting Document Architecture (QRDA) http://www.hl7.org/documentcenter/Ballots/2008sep/downloads/CDAR2_QRDA_R1_DSTU_2009AP R.zip

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Clinical information systems: cornerstone for an efficient hospital management Christian LOVISa1 Division of Medical Information Sciences University Hospitals of Geneva, Geneva, Switzerland a

Abstract. The university hospitals of Geneva are the largest consortium of public hospitals in Switzerland. This organization is born in 1995, after a political decision to merge the seven public and teaching hospitals of the Canton of Geneva. From an information technologies perspective, it took several years to reach a true unified vision of the complete organization. The clinical information system is deployed in all sites covering in- and outpatient cares. It is seen as the cornerstone of information management and flow in the organization, for direct patient care and decision support, but also for the management to drive, improve and leverage the activities, for better efficiency, quality and safety of care, but also to drive processes. As the system has become more important for the organization, it has required progressive changes in its governance. The high importance of interoperability and use of formal representation has become a major challenge in order to be able to reuse clinical information for real-time care and management activities, and for secondary usage such as billing, resource management, strategic planning and clinical research. This paper proposes a short overview of the tools allowing to leverage the management for physicians, nurses, human resources and hospital governance. Keywords. Hospital management; clinical information system

1. Introduction Implementing and deploying a clinical information system in a care organization should be one of the most disruptive changes ever [1, 2], if they understand the need for improving processes and culture, the need for improved efficiency and quality of care. Thus, implementing such a system is only possible with deep changes in care and management processes. The high human and economic cost of inefficient care and errors has been well documented and has received a lot of attention [3]. One of the most striking facts is that, while care providers are daily using technologies of the 21 century, the healthcare system is often working and managed with paperwork and processes of the 19th century [4]. Unfortunately, deploying information technologies in healthcare organization is an important challenge, and the road is a large cemetery of failures and painful experience [5]. Success factors are, however, well described. Clinical leadership; strong involvement at the highest decision levels of the 1

Corresponding author ; Christian Lovis, University Hospitals of Geneva, Division of Medical Information Sciences, 1211 Geneva 4, e-mail : [email protected]

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organization; long and sustained financial and human investments; reliable infrastructure; added-value for all actors and updated decision-support are key factors. This work at presenting some aspects of what has been developed at the University Hospitals of Geneva to leverage the return on investment of the clinical information system in the domain of decision support for the management.

2. Background The University Hospitals of Geneva (HUG) constitute the major public care providing consortium and teaching hospitals in Switzerland. It covers primary, secondary, tertiary and ambulatory care. HUG are using an in-house developed clinical information system (CIS) that integrates commercial systems and covers all clinics and care. The system is Java, service-oriented (SOA) and has a component-based architecture with a message-oriented middleware. It has a full paperless computerized provider order entry (CPOE) coverage, it supports workflows, clinical pathways and complex decisionsupport. The system builds a complete transversal support for physician and nursing orders, for planning and execution of all care activities.

3. Decision support for management Only some examples are shown to illustrate the various type of secondary usage of clinical information to support and leverage the management in a hospital. These examples are grouped according to the various professions in the hospital. 3.1 Medical management Several support for the medical management has been developed. Some of them are about standards and quality of care, such as whiteboards to see the way clinicians use clinical pathways or how fast discharge letters and reports are signed. Other are more devoted to patient flows, such as synoptic views of the activity of the emergency department (Fig 1).

Figure 1: Realtime emergency department activity

The Figure 1 illustrates one of these tools. The dashboard can be seen in all terminals and is shown on large screens. It is automatically updated in real-time with

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activities in the ER, including display of admission-discharge-transfers, diagnosis, infectious status and numerous other clinical information. It helps the management of the ER and the proactive preparation of wards that will have to admit patients later. 3.2 Nursing management One of the challenges in our hospitals is to manage in a clever and proactive manner our nursing staff. This means achieving a good adequacy of staffing and needs in each ward. Figure 2 illustrates two reports used daily to organize human resources in wards. The left image shows the consolidated load per patient in a ward, and the right images displays the daily detailed load for one patient, for each type of care

Figure 2: Predictive nursing load in a ward

. Because the complete nursing activity is planned and computerized, it is possible to know in advance the exact care planned for each patient individually, and to compute the global care requested for each ward, by care, by group of patients, and thus to allocate resources accordingly. Because all care is validated after execution, the nursing management can then measure the adequacy between what was requested and what has been really produced. 3.3 Hospital management There are numerous whiteboards and indicators used by various people in the administration of the hospital, from logistics such as the pharmacy, the billing center and the top management. The figure 3 illustrates one of the consolidated views, that displays a “radar” view of a department with each branch being one of the institutional wide indicator. These indicators include high level information, such as: Satisfaction; absenteeism; bed occupancy; patient cost weigh; outpatient clinics revenue; percentage of discharge letters signed 7 days after discharge; evolution of the number of FTE’s; number of inpatients; length of stay; etc. That is, indicators about satisfaction, revenues, costs, means and resources and efficiency. These indicators are computed using the information existing in the hospital information system and have been built in order to bring a real added-value for the management of the departments.


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Figure 3: Global view of institutional indicators for the management of departments

4. Conclusion Hospital and clinical information are cornerstones to build management decisionsupport. Daily routine information, from demographics to direct patient care, can be reused to provide decision-support at all management levels of hospitals. The real challenge is to have a tightly interoperable system, with common and shared semantics and definitions. Without these, the large data warehouses will not be able to provide high added-value knowledge with consolidated sources, such as logistics, human resources and care. Providing this kind of decision-support is a very strong incentive for sustained investment in this field and brings healthcare management to the 21st century.

References [1] Thouin MF, Hoffman JJ, Ford EW. The effect of information technology investment on firmlevel performance in the health care industry. Health Care Manage Rev. 2008 JanMar;33(1):60-8. [2] Lorenzi NM, Ash J, Einbinger J, McPhee W, Einbinger L. Transforming Health Care through Information. Lorenzi NM, Ash J, Einbinger J, McPhee W, Einbinger L, editors. New York, LLC: Springer-Verlag; 2004. [3] Kohn LT, Corrigan J, Donaldson MS, eds. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press; 2000. [4] ITAC. Report to the President, Revolutionizing Health Care through Information technology. President’s Information Technology advisory Committee. June 2004. [5] Aarts J, Berg M. Same systems, different outcomes--comparing the implementation of computerized physician order entry in two Dutch hospitals. Methods Inf Med. 2006;45(1):53-61.

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Patient Centered Integrated Clinical Resource Management a

Jacob HOFDIJKa1 Partner in Casemix, Coordinator Implementation integrated care funding at the Ministry of Health, the Netherlands

Abstract: The impact of funding systems on the IT systems of providers has been enormous and have prevented the implementation of designs to focused on the health issue of patients. The paradigm shift the Dutch Ministry of Health has taken in funding health care has a remarkable impact on the orientation of IT systems design. Since 2007 the next step is taken: the application of the funding concept on chronic diseases using clinical standards as the norm. The focus on prevention involves the patient as an active partner in the care plan. The impact of the new dimension in funding has initiated a process directed to the development of systems to support collaborative working and an active involvement of the patient and its informal carers. This national approach will be presented to assess its international potential, as all countries face the long term care crisis lacking resources to meet the health needs of the population. Keywords: Problem Oriented Medical Record, Chronic Disease Management, Integrated care, Casemix, Individual Careplan, Personal Health Record.

1. Introduction The health care reforms of the last thirty years have been driven by the impotence of stakeholders to manage the health care system, and most of all its outcome. The first steps have been to try to identify the health care process within hospitals. After the initial work of Ernest A. Codman in 1914 [1] on the definition of the product of the hospital it took until in the end of the seventies Robert Fetter and John Thompson introduced the concept of the Diagnosis Related Groups (DRG) [2]. The development of the system was focused on managing the quality of health care delivery in US hospitals by identifying outliers within a group of similar patients. The DRG or CaseMix approach has travelled across the globe in the last three decennia and has been tested adapted and applied in many countries, but mainly for funding purposes. The original purpose to measure the outcome of the health delivery system still has to be achieved. One of the main reasons for not achieving this has been the focus on the inpatient episode, which limits the view on one admission of a patient. As the admission is not seen in the perspective of the health issue of the patient, it is only a fragment of the journey of the patient in the health care system. The main reason for this limitation is the lack of data about the journey of the patient. The health care system is traditionally constructed in silos, following the way the funding systems are organised. The traditional funding of health care systems has a main focus on the 1

Corresponding author: [email protected]

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provision of care by primary, secondary and tertiary care. Porter and Teisberg introduced in their book “ Redefinition of health care competition “ the need to focus on the full treatment cycle as the way to really add value to the patient [3]. The application of this approach requires a paradigm shift from supply to demand orientation. A demand orientation is in line with the broadly adopted patient centred approach, which however still lacks international implementation. The best guidance for this approach still is the problem-oriented methodology introduced by Lawrence Weed as early as 1969 [4]. Although widely acknowledged as the core concept for medical records and medical treatment, it has not been adopted nor implemented within IT systems on a scale that would match the support for the patient centred approach. A missing link with funding health care delivery seems to be key.

2. The Dutch breakthrough The Dutch healthcare system has introduced in its reform the paradigm shift by changing the relationship between the main stakeholders: the patients, the providers and the insurance companies. One of the important actions was the introduction of a national insurance scheme, which is mandatory for all citizens and provides access to the base set of services. The new law also requires the insurance companies to contract health care services on price and quality. The new infrastructure offers the opportunity to really focus on the patient and to change the traditional relations between payers and providers. By the introduction of care products as the base for the contract resource management in hospitals have changed fundamentally. Instead of a focus on dumb parameters, like admissions, bed days, first visits, information is required at the level of patients treated for a specific disease. 2.1. Hospital funding based on contracts The Dutch decided not to implement the DRG system as it only focuses on inpatients and the objective of the Dutch approach was to take the health issue of the patient as focal point. That required a new approach to the registration of clinical data in hospitals. A step towards the problem oriented medical record as data had to be recorded at the level of the episode of care, as defined by Hornbook [5]. Since 2000 Dutch hospitals have started to register data by episode, starting with a referral to the hospital to a medical specialist. At that moment within the IT systems a care trajectum record is created for that specific health issue. It is used as reference to both clinical information, like the referral information, the care request, the diagnosis and the treatment, and process information about encounters, examinations, tests, diagnostics and surgical procedures. The information at the level of the episode is used to support the physician during the care process, but is also gathered in management databases at institutional level. With this information profiles can be created at different levels of aggregation, for individual patients, at the level of providers, at the level of diseases treated and many other ad hoc views. By the structural link of the data to the health issue of the patient both described by the care request and the diagnosis a new dimension has been created in the resource management of hospitals. As the shift in the funding of hospitals from budgeting to contracting will be completed in 2012, the hospitals need to change their information

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management strategies. In 2011 hospitals need to contract over 75 % of their products with insurance companies, so they need to have information about the profiles of their care products and the associated costs. So the sense of urgency to have actual cost price information about the procedures and ancillary services is growing by the day. Examples of the newly developed management information will be presented. 2.2. Information on the quality Since the introduction of the new funding scheme attention has been given both by the Inspection for health (IGZ) as by the providers coordinated by the Dutch Medical Association “Quality of care as front”. The focus of these projects was the development of indicators to be used in the contracting process between hospitals and insurers. The ambition was high, but it turned out to be quite difficult to achieve consensus about the indicators. The end report describes the indicators, which have been defined for 10 diseases. It was a tedious process to define the indicators and required parameters. A very positive development was that the insurers confirmed that they would use the indicators as important parameters in the contracting process. The insurer CZ has committed actively excluded from contracting hospital providing Colon cancer treatment below the national quality thresholds. The announcement of this policy has created a lot of discussion, only showing the breakthrough of the quality dimension in the hospital contracting equation. 2.3. Chronic care funding The next step in the process of the health reform dealt with chronic diseases, partially driven by the spectacular growth expectations for the coming decades. To prevent a long-term care crisis in 2025, action was needed. An important development was the introduction of the concept of the care standard, which describes good care for chronic care patients based on the guidelines and protocols. The Dutch Diabetes Federation developed the first care standard in 2003. The standard was the result of a close collaboration of over twenty different provider associations and the patient association. The care standard describes three main aspects of the prevention and care for chronic diseases: the care, the organization and the indicators of quality. One other principle of the care standard is the individual care plan, which will be coordinated for and with the patient and a multidisciplinary team of care providers. In 2007 a pilot project was run by ZonMW [6] with 10 different so called care groups to organise and contract the delivery of a disease management program for diabetes. It was a kind of extension of the health issue approach in hospitals, but now for chronic diseases. The care group was introduced as a new entity to contract in one market the different care providers involved in chronic disease management and in a second market the insurance companies. After the pilot the contracting of disease management programs for Diabetes has been nationally covered. One important element of the program is the development of software to not only exchange information between providers, but also to manage the treatment plan. Currently a number of regional datacenters are in place collecting information and sharing performance indicators among the care providers [7]. These datacenters will combine their information to a national registry, which will both provide benchmark information to providers, as it will publish information for patients to better choose providers in supporting them to manage their disease.


999

3. Conclusion The Dutch shift to patient centered care has resulted in real changes in the delivery system. It has changed the relation between the stakeholders so structural, that there is no way back. The traditional silo’s crack; while care providers and patients are looking for state of the art 2.0 solutions to develop supporting information systems linking to the personal health record of the patient to further improve the quality of life of the patients. The impact on information systems is enormous and the use of information for clinical resource management will gradually shift from a price orientation to finding the best quality for the patient in a joint process of providers, patients and insurers. So in the end the dreams of Codman and Weed will come true and will provide the next generation a sustainable health care system using the problem oriented record even across institutions and involving the patient.

References [1] [2] [3] [4] [5] [6] [7]

Codman EA., Arch Pathol Lab Med. 1990 Nov;114(11):1106-11. Case mix definition by diagnosis-related groups. Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD.Med Care. 1980 Feb;18(2 Suppl):iii, 1-53. No abstract available. Harv Bus Rev. 2004 Jun;82(6):64-76, 136. Redefining competition in health care. Porter ME, Teisberg EO. Harvard University, Harvard Business School, Boston, USA. [email protected] Medical records, medical education and patiënt care. The problem-oriented record as a basic tool. Lawrence L. weed, M.D. ISBN 0-8511-9188-X Hornbrook MC, Hurtado AV, Johnson RE, Health care episodes: definition, measurement and use, Med Care Rev, 1985;42(2):163-218.p. 171 Integrating Care through Bundled Payments — Lessons from the Netherlands Jeroen N. Struijs, Ph.D., and Caroline A. Baan, Ph.D. N Engl J Med 2011; 364:990-991March 17, 2011 Samenwerking en Samenhang in de keten, Evaluatie en resultaten Project DiabeteszorgBeter, Zwolle H. Bilo, 2009.



1001

Subject Index 3LGM2 537 abstracting and indexing 492 accelerometer 897 access by mobile phone 417 access control 601 accreditation 218 Actigraph GT3X 445 activity analysis 295, 422 activity 18 AdaBoost 574 adolescent 8 adoption 335 adverse drug event 412, 569 adverse drug events (ADE) detection 699 affordance 63 Africa 666 agents 300 alert 930 ambient assisted living 460 ambulance 349 analysis methods 460 Android 83 annotation 559 antibiotic cost 477 anticoagulant therapy 43 aortic aneurysm 359 application development framework 724 application 213 archetype 255, 774, 789, 799 archetype-conform EHR extract 799 architecture 739 architecture of participation 280 Arden syntax 165 assessment-evaluation 925 AT 275 ATC drug classification 512 attitude to computers 960 augmented medical intervention 175 autism 270 automatic IE from patient records 527

awareness 364 baseline survey 387 benchmarking 542 binary classifiers 579 biobank 644, 887 biocomputational modelling 432 biomedical ontologies 714, 739 biomedical relations 739 biomedical research 867 biomedical terminology 844 bio-ontologies 145 blended e-learning 213 booklets 63 BPMN 2.0 482 business layer 537 camera 455 cancer 8 cancer documentation 892 capacity building 666 cardiac rehabilitation 88 case-based learning 203 Casemix 996 cataloguing 492 categorial structure 844 CDA 305 CDISC ODM 857 CDSS 135 CEN EHR 13606 689 center 892 certification 654 change management 829 chemotherapy 392 children 18 chronic disease management 996 chronic disease 33 citizen empowerment 98 ClaML 744 classification 465, 749, 754 classifier performance 532 clinical application 724 clinical coding 594 clinical core processes 482

1002

clinical data management system 902 clinical decision support (CDS) 130, 140, 930 clinical decision support system (CDSS) 103, 120, 150, 195, 412 clinical finding 809 clinical guideline 130, 477 clinical information provision 155 clinical information system 335, 902, 965, 992 clinical investigation 834 clinical knowledge resource 839 clinical pathways 482 clinical process monitoring 507 clinical reasoning 666 clinical research informatics 955 clinical rules 115 clinical terminology 809 clinical text 559 clinical times 507 clinical trial 170, 325, 734 clinical work practice 374 clinical workflow 432 clinicians adherence 930 cloud computing 93, 115, 379 CME 238 CO2 reduction 970 co-construction 68 cognitive rehabilitation 779 collaboration 280, 364 collaborative health care delivery 417 communication server 170 communication standard 704 communication support 359 comprehensive cancer 892 computer assisted medical intervention 175 computer communication networks 522 computer simulation 180 computer utilization 960 computer-assisted drug therapy 950 computer-assisted image analysis 465 computerized patient simulator 666 concept representation 774 conceptual model 427 confidentiality 862 consent 621 consumer health information 38

consumer participation 13 context factor 920 contextual inquiry 965 continuous sensor data 460 controlled vocabulary 492, 502 coordination 606 COPD 28, 455 CPOE 135, 290, 320, 392, 920, 940 creativity 977 critical care 402 cross-sectional studies 960 data collection 13 data binding 724 data completeness 872 data driven 140 data integration 185, 857, 867 data privacy 661 data transfer 58 data warehouse 170 database 862 date elements 517, 714 decision making 397 decision support 125, 165, 839, 945 de-identification 606, 862 Delphi 920 dementia 120 Dengue fever 629 design 392, 925, 930 detailed clinical models 774 diabetes 23, 48, 103, 369, 594, 950 diagnosis 120 diagnosis reasoning 559 dictionary 38 diet 23 differential adhesion 882 digital pen 325 dilated cardiomyopathy 907 discrete wavelet transform 470 disease surveillance 160, 639 diversity 330 drug prescription 125, 512 drug safety 325, 794 drug toxicity 794 durability 68 dynamic cell seeding 882 dynamic Web server 270 dyslipaemia 125 economic evaluation 407 economics 208

1003

eConsent 344 edge detection 470 edge strength 470 education 213, 218, 422 e-health 68, 155, 265, 407, 970 e-health service delivery 537 EHR re-use 872, 902 elderly people 681 e-learning 238, 248 electronic collaboration 354 electronic data capture 325 electronic health record (EHR) 38, 58 243, 255, 285, 295, 305, 310, 344, 349, 354, 369, 374, 379, 437, 559, 589, 601, 694, 774, 799, 809, 849, 862 electronic medical record 502, 824 electronic patient record 83, 94, 260, 285, 339, 374, 584 electronic prescribing 320, 920 electronic symptom reporting 13 e-medication 920 emergency 349 emotions 63 empirical study 243 encounters 295 enhancing biomedical research 907 entity-relationship graph 3 epidemic intelligence 160, 639 epidemiologic surveillance 629 e-prescription 374 EPS 374 evaluation 18, 78, 120, 125, 208, 238, 339, 402, 432, 920 event-driven architecture 160 evidence 208 evidence-based guidelines 125 evidence-based medicine 769 exercise adaptation 779 expected medical benefit 175 expert system 195, 714 eye-tracking 945 Facebook 616 factuality levels 559 federation 644 flexibility 68 follow-up 872 form generation 799 formative evaluation 417 French Guiana 629

fuzzy logic 170 gait parameters 445 GAITRite 445 game based training 228 GCM 537 GELLO 130 genomic medicine 165 goal directed design 228 GP (General Practitioner) 344, 354 GPS 349 grid networks 450 GUI 849 guideline compliance computation 512 guidelines 487, 877 H1N1 564 Health 2.0 649 health care 88, 260 health informatics 208, 218, 223, 877 health information 73, 616 health information systems 295, 335, 422 health information technology (HIT) 387, 877 health information technology standards 989 health insurance 649 health interventions 754 health personnel 960 health professional workstation 925 health services research 407 health technology assessment 407 health Web 53 healthcare 379 healthcare informatics research 980 healthcare interoperability 729 healthcare modernisation 374 healthcare policy issues 285 healthcare practices 63 healthcare processes 93 healthcare professionals 28 healthcare standards 804 healthcare teams 719 healthcare terminology 759 healthcare Web 2.0 280 health-enabling technologies 18, 460 heath care quality assurance 180 heterogeneous data integration 502 HIS management 522

1004

HL7 170, 694, 709, 774, 834 HL7 Version 2.x 704 HL7/ISO CDA R2 689 HL7/ISO Clinical Genomics 689 HL7/ISO RIM 689 home monitoring 671 HONcode 654 HONcode certification 53 Hooke and Jeeves Pattern Search 554 hospital 335, 507 hospital acquired infection 145 hospital information system (HIS) 402, 542, 849, 872, 930 hospital information system integration 887 hospital information system success 427 hospital management 992 hospital network 155 human-computer interaction 280, 915 human factors 412 hypertension 634 i2b2 502, 887 IBM Medics 3 ICNP 759, 764 IHE 265 image segmentation 470 impact assessment 432 implementation 330, 392, 809 indexing 584 India 960 individual careplan 996 infectious disease 629 information and communication technology 78, 719 information modeling 774 information overload 369 information provision 8 information retrieval 477, 549, 584 information security 601 information sharing 764 information storage and retrieval 492 information system success 427 information system (IS) 270, 392, 427, 517, 634 information technology 33 information translation 155 information visualization 945 infrastructure 537, 644

innovation 78 INR 43 in-situ 915 in-situ interviews 63 insulin 103 integrated care 996 integrated medical-dental electronic health record 387 integrating the healthcare enterprise 482 intensive care 397, 402, 945 interaction design 228 inter-device-variability 897 Internet 53, 73, 270, 315, 492 Internet usage 73 interoperability 98, 165, 185, 295, 305, 694, 704, 709, 849 intervention 749 intractable disease 255 ISO 13119 839 ISO/CEN 13606 255, 799 ISO/IEC 11179 744 isolated healthcare professionals 666 IT governance 275 IT infrastructure framework 892 iterative design 955 k-nearest neighbour method 579 knowledge discovery on databases 734 knowledge management 145, 699 knowledge representation 689 knowledge-sharing 190 knowledge-utilisation 190 laboratory medicine 487 laboratory results interpretation 195 language 769 law and security 417 liver diseases 195 liver function tests abnormalities 195 logical information model 804 logistics 315 machine learning 140, 554 magnetic resonance imaging 465 management 977 mapping 709, 764 MDR 644 meaning 829 medical alert 940 medical consultation 190 medical device 834

1005

medical education 233, 248 medical imaging 611 medical informatics 223, 549, 794 medical informatics applications 960 medical intelligence 671 medical providers’ dental data need 387 medical terminologies 739 medical text archiving 190 medical text retrieval 190 medical ward – technical service communication 135 medical-dental holistic care 387 medication automation 374 MedWISE 280 messaging 804 metadata 203, 644, 839, 857 metadata registry 175, 744 methodology 719 m-health 33, 48, 83 mobile computing 950 mobile phone 23 mobile Web services 349 modeling 487, 497, 729 monitoring and clinical context 412 MRI 784 multi-class classification 579 multidimensional data 115 multi-method approach 427 multimodal mining 477 multi-modal information search 450 narrative medical records 764 national deployment 354 natural language processing (NLP) 527, 549, 589, 594, 769, 794, 887 NCI Thesaurus 714 neonatal intensive care 115 network analysis 564 network 644 networked clinical research 857 neurological diseases 671 nomenclature 460 nosocomial infection 554 nurse 320, 402 nursing 985 nursing information system 339 nutrition 23 observable entity 809

obstructive lung disease 594 occupational medicine 238 occupational therapy 676 oncology research 887 ontological reasoning (OWL2) 512 ontology 165, 185, 584, 661, 694, 699, 719, 734, 749, 754, 779, 784, 789, 844 ontology modularization 517, 714 open source 265, 445 openEHR 255, 724, 789, 849 opereffa 724 optimization 554 organisational change 260 organization 135 organs transplantation 300 OSCE 233 osteoporosis 432 otoneurology 579 OWL 714, 729, 784 P2P environment 661 PACS 397 palliative care 437 Parkinsonian syndromes 465 Parkinson’s disease 594 partial least squares 243 patient consent 58, 203 patient empowerment 681 patient management 364 patient non-adherence 634 patient records 275 patient recruitment 170 patient register 857 patient safety 601 PCT 437 PEHR 344 personal health information 606 personal health record (PHR) 63, 98, 108, 344, 996 personalization 48 pervasive developmental disorder 270 pervasive health 497 pharmacogenetics 569 pharmacovigilance 794 physician acceptance 150 physician-patient relations 13 physicians’ information needs 369 podcast 248 point of care decision making 190

1006

policy 208 politics 330 practice consultants 354 practitioner liabilities 611 prediction 574 prescribing 935 prescription appropriateness 487 preventive integrated care 28 privacy 285, 497, 606, 616, 621 problem lists 819 problem oriented medical record 996 process analysis 507 process assessment 542 professionalism 218 prognostic 140 provider and organization registry 265 public health surveillance 629 pulmonary rehabilitation 455 qualitative research 290, 392, 877 quality assessment 814 quality criteria 654 quality indicators 88, 634 quality measures 989 quality of care 374 quality of information systems 542 question answering 549 radiology 359 radiology information systems 402 real-time analysis 115 reasoning 789 regional health information networks 310 regional health networks 265 relevance 339 reminder system 872, 930 repositories 203 requirements 354, 392 research agenda 977 research management 977, 980, 985 resistance profile 477 REST architecture 108 review 13, 223, 769 RF2 829 risk adjustment 180 RN4CAST 985 robot 897 ROC curve 532 safety 374

scaffold 882 scales of infrastructure 68 schizophrenia 574 scientific medical corpora 814 screening for abdominal aortic aneurysm 228 SDLC 392 secondary use 502 security 285, 450, 621 self care 103 self-help 23 self-management 23, 33, 43 self-monitoring 43 semantic integration 185 semantic interoperability 517, 804, 824 semantic mediation 734 semantic model 754 semantic reasoning 699 semantic web tools 512 Semantic Web 729 semantic wiki 93 semantics 502, 794 sensitivity 574 serious adverse event 834 service events 295 service-oriented architecture (SOA) 98, 295, 310, 349, 867 shared decision making 935 shared record 359 signal detection 794 signal generation 639 single source 892, 902 single source information system 872 site visit 422 skill training application 228 smart objects 315 SNOMED CT 764, 809, 814, 819, 824, 829 social constructivism 374 social media 48 social networking 616 social-medical discovery 3 socio-technical approach 339, 422 software design 412 specificity 574 spontaneous reporting system 564 standard 98, 344, 709, 749, 839, 844 statistical modeling 532

1007

stroke 676 structuring and contextualization of medication events 527 support vector machines 579 surgery 359 surgical site infections 145 survey 73 sustainable broadband-enabled services 970 Swedish 559 SWOT 379 SWRL 714 system architecture 305, 522, 902 system implementation and management 285 system theory 739 systematic review 407 systems integration 310 task analysis 940 technical infrastructure 325 tele-assistance 681 telehealth 621 telemedicine 103, 611, 661, 666 tele-rehabilitation 28, 676 teletriage 407 term mapping 814 term validation 814 term variation 814 terminology 759, 769, 794 terminology life cycle model 759 terminology system 764, 824 ternary logic 170 test ordering 487 text accessibility 681 text mining 160 theoretical models 223 time consumption 320 tissue construct 882 traceability 275 transinstitutional collaboration 359 translational research 887, 892, 907 translations 819 transparency 53, 654

triangulation study 369 trust 497 trustworthiness 53, 654 ubiquitous computing 497 UML 729 UMLS 819 UML class diagram 704 usability 208, 260, 915, 925, 940 usability evaluation 228, 945, 955 usability testing 915 usefulness 339 user configurability 280 user interface 487, 925 user involvement 392 user survey 920 user training 93 user-centred design 965 user-computer interface 950 VAERS 564 validation 897 value-set 517, 714 vector-borne disease 629 virtual medical record (vMR) 130 virtual patient 203, 233 virtual reality 676 virtual university 248 vocabularies 744 VPH 432 ward round 213, 397, 935 Warfarin 569 watermarking 611 wavelet domain 470 Web 2.0 649 Web based ulcer record 417 Web services 661 wellbeing 78 Wiimote 455 wireless technology 28 workarounds 290 workflow 482, 734, 950 WWW 589 XML 709



1009

Author Index Aarts, J. Abdoune, H. Acharya, A. Adams, S. Adlassnig, K.-P. Allaert, F.-A. Altmann, J. Ammenwerth, E. Andersen, S.K. Andresen, H. Angelova, G. Anguita, A. Arbustini, E. Ardillon, V. Asim, M. Atalag, K. Auverlot, B. Avery, A. Backfried, G. Bagayoko, C.O. Baker, C.J.O. Bakken, S. Bal, R. Balka, E. Ball, R. Bánhalmi, A. Barber, N. Barch, A. Bärthlein, B. Bartholomäus, S. Barthuet, E. Bartz, C.C. Baujard, V. Baysari, M. Beck, P. Beck, T. Beckmann, M.W. Bediang, G. Bellazzi, R. Bellika, J.G. Ben Said, M. Bergh, B.

v, 290, 392, 877 819 387 877 165 611 482 208, 369, 522, 799, 920 v, 28, 977 606 527 734 907 629 621 849 611 374 160 666 145 280 392, 877 285 564 671 374 233 892 644 155 759 53, 654 935 950 265 892 666 887, 907 455 270 265, 344

Bernicot, T. 584 Bernonville, S. 412 Berntsen, G. 13 Bertaud Gounot, V. 714 Bertaud, V. 517 Betrancourt, M. 940 Beuscart-Zephir, M.-C. 208, 412 Beyer, A. 892 Bianchi, S. 689 Bird, L. 804 Birkle, M. 265 Blinn, N. 649 Blobel, B. 305, 497, 694, 704, 739 Blom, S.R. 78 Boelmans, K. 465 Boere-Boonekamp, M.M. 78 Bohec, C. 517 Bonderup, M.A. 43 Borycki, E.M. 379, 915 Botsis, T. 564 Bouaud, J. 125, 512 Bourdé, A. 517, 714 Bousquet, C. 749, 754, 844 Boyer, C. 53, 73, 654 Boytcheva, S. 527 Brattheim, B. 359 Breil, B. 502, 902 Brender, J. 208 Briggs, J. 223 Bringay, S. 629 Brochhausen, M. 734, 739 Broeren, J. 676 Brooks, C. 804 Brunet, P. 248 Brüntrup, R. 437 Bucalo, M. 907 Buckeridge, D.L. 145 Bucur, A. 734 Buffa, F. 734 Burgun, A. 784 Bürkle, T. 325, 502, 892 Cameron-Tucker, H. 33 Campillo-Gimenez, B. 584

1010

Cao, F. 699 Carmeli, B. 140 Carrasqueiro, S. 407 Carvalho, L. 629 Casey, A. 844 Catley, C. 115 Ceusters, W. 829 Cheong, Y.C. 804 Chevrier, R. 195 Chiarugi, F. 950 Chiba, T. 255 Chomutare, T. 48 Choquet, R. 185 Christiansen, E.K. 417 Chronaki, C. 989 Chyou, P.-H. 387 Cinquin, P. 175 Coatrieux, G. 611 Cohen, G. 554 Colombet, I. 135, 769 Comac, P. 218 Conti, C. 689 Cornet, R. 824 Creswick, N. 397, 402 Croner, R. 892 Cronin, P. 955 Cruchet, S. 73 Cruz-Correia, R. 275, 300 Cserti, P. 671 Cuggia, M. 248, 517, 584 Cummings, E. 33 Cunha, J.P.S. 310 Cusi, D. 689 Dagliati, A. 887 Dahamna, B. 492 Dalianis, H. 559 Darmoni, S.J. 492, 819 Daskalakis, S. 243 Daumke, P. 594 Davies, D. 203 Day, R. 935 de Bruijn, B. 532 de Clercq, P.A. 103 de Keizer, N. 88, 180, 208, 824, 925 de la Cruz, E. 305 Defude, B. 661 Denecke, K. 160, 639 Detschew, V. 507 Deuster, T. 265

Di Giacomo, P. 970 Dias, A. 445, 897 Diederichs, S. 867 Dinesen, B. 28 Dolin, B. 989 Dolog, P. 160 Donfack, V. 714 Dormann, H. 325 Döring, A. 445 Dreesman, J. 160, 639 Duclos, C. 487 Duftschmid, G. 369, 799 Dugas, M. 502, 872, 902 Dulai, T. 671 Dumontier, M. 165 Dupuch, M. 794 Durand, T. 155 Durieux, P. 135 Duvauferrier, R. 517, 584, 714, 784 Dziuballe, P. 902 Ebrahiminia, V. 125 Eccher, C. 108 Egbert, N. 335 Eghdam, A. 945 Ehrler, F. 83 Ekeland, A.G. 417 Ekinci, O. 507 Eklund, A.-M. 549 El Ghazali, A. 270 El-Masri, S. 349 Encarnação, P. 407 Eyraud, E. 155 Falcoff, H. 125 Falkenhav, M. 945 Faraggi, M. 135 Farkash, A. 689, 729 Favalli, V. 907 Favre, M. 125 Faxvaag, A. 359, 364, 601, 980 Fayn, J. 661 Fernandez Luque, L. 455 Fernández-Breis, J.T. 789 Ferraz, V. 300 Fescharek, R. 794 Finlay, D. 218 Finozzi, E. 238 Fischer, A.S. 857 Fischer, M. 265 Fitzpatrick, P. 33

1011

Flamand, C. Flatow, F. Forkert, N.D. Forsman, J. Forster, A.J. Forster, C. Fosse, E. Frey, A. Fritz, F. Gabetta, M. Gallos, P. Ganeshkumar, P. Ganslandt, T. Ganzinger, M. Garcelon, N. Garin, E. Garin-Michaud, A. Gattnar, E. Gaudinat, A. Geissbuhler, A. Georg, G. Georgiou, A. Ghedira, C. Gietzelt, M. Gilad, D. Giorgi, I. Gjære, E.A. Gobeill, J. Goldschmidt, Y. Golse, B. González, C. Goossen, W. Göransson, B. Gorzelniak, L. Grabar, N. Graf, N. Griffon, N. Grimsmo, A. Grisot, M. Guardia, A. Guirao Aguilar, J. Hackl, W.O. Hains, I.M. Håkansson, P. Handels, H. Hangaard, S.V. Hanmer, L.A. Hanna, P. Hanser, S.

629 265 465 945 145 902 38 335 902 907 243 960 502, 892 867 584 584 155 507 477, 654 53, 666 135 223 661 460 233 238 606 477 689 270 305, 694 774 260 445, 897 769, 794 734 492 601 68 940 455 920 397, 402 970 465 43 427 218 594

Happe, A. Harrison, J. Hartvigsen, G. Hartz, T. Harvey, J. Hauser, J. Haux, R. Hege, I. Heid, J. Heimly, V. Heinrich, R. Heinze, O. Hejlesen, O.K. Helm, E. Henriksen, E. Hermanides, J. Herzberg, S. Hibberd, R. Hoekstra, J.B. Hofdijk, J. Höll, B. Holleman, F. Holst, B. Holzinger, A. Horsch, A. Horton, A. Househ, M. Hoy, D. Hrdlicka, J. Hurlen, P. Hübner, U. Hübner-Bloder, G. Hyppönen, H. Iltanen, K. Imbriani, M. Isaacs, S. Ishihara, K. Issom, D. Itälä, T. Jaffe, C. Jahn, F. Jais, J.P. James, A. Jamet, A. Janols, R. Jaques, D. Jaspers, M.W.M. Jaulent, M.-C. Jessup, M.

584 634 23, 48, 445 437 374 33 18, 460 203 203 354, 601 265 344 28, 43 482 13 103 872 374 103 996 950 103 465 950 13, 445, 897 955 616 759 574 v 335 369, 799 208 579 238 427 255 83 295 989 542 270 115 794 260 195 150, 925, 930 794 33

1012

Johansen, M.A. Johansen, M.D. Johansen, T.S. Johansson, B. Johnson, S.B. Joubert, M. Joutsijoki, H. Juhola, M. Jung, B. Jung, C. Jung, M. Kajiwara, M. Kanatani, Y. Kannry, J. Kapp, C. Kashfi, H. Katharaki, M. Kaufman, D.R. Kemps, H. Kenealy, T. Kennelly, J. Kent, C. Kilsdonk, E. Kim, S. Kimura, E. Kirchner, G. Kirchner, M. Klein, G.O. Klema, J. Knaup, P. Knijnenburg, S.L. Knoll, A. Kobayashi, S. Koch, S. Koetsier, A. Kohler, M. Kokkinakis, D. Kommeri, J. Kononowicz, A.A. Kontogiannis, V. Korpela, M. Kortekangas, P. Koster, P. Kozmann, G. Köpcke, F. Kósa, I. Kraaijenhagen, R. Kreuzthaler, M. Kuehne, M.

13 43 601, 980 676 955 819 579 579 754 754 920 73 255 915 265 724 243 955 88 634 634 140 150 754 255 160 325 839 574 867 150 897 255 945 180 369, 799 814 450 203 950 422 295 621 671 502, 892 671 88 589 649

Kumar, A. Kuo, M.-H. Kushniruk, A.W. Kuwata, S. Kuziemsky, C.E. Kvist, M. Lablans, M. Laforest, F. Lamy, J.-B. Landais, P. Landau, D. Langemeijer, M.M. Lapão, L. Larizza, C. Lasbleiz, J. Lauesen, S. Laurent, J.-F. Laversin, S. Le Beux, P. Lechtenbörger, J. Lee, B.C. Lee, E. Lemkes, B.A. Leonardi, G. Lerch, M. Leroy, N. Lewalle, P. Li, B. Li, J. Liaskos, J. Liebe, J.-D. Lilholt, P.H. Lillebo, B. Lind, M. Lindgren, H. Lindsköld, L. Line, M.B. Linge, J. Lippert, S. Liu, H. Liu, S. Longerich, T. López, D.M. Loškovska, S. Lovis, C. Luciano, J. Ludwig, W. Luukkonen, I.

749, 754, 844 379 379, 915 915 719 559 644 155 125, 487 270 140 930 275 907 714, 784 862 584 654 248 902 569 23 103 779 794 412 749, 754, 844 699 699 243 335 43 364 945 120 228 606 160 862 130 130 867 305, 694 190 83, 185, 195, 320, 477, 940, 992 165 18 422

1013

Luzi, D. Maas, R. Mabotuwana, T. Madden, R. Mahnke, A. Majeed, R.W. Maknickas, R. Malamateniou, F. Maman, Y. Mansmann, U. Mantas, J. Marcilly, R. Marschollek, M. Marshall, M.S. Martin, L. Martin, M. Martinovic, D. Massari, P. Mate, S. Mathews, A. Mazzoleni, M.C. McAllister, G. McCullagh, P. McGregor, C. Mei, J. Melby, L. Mels, G. Menárguez-Tortosa, M. Merabti, T. Mesika, Y. Meyer, R. Michel-Verkerke, M.B. Milani, G. Mimori, T. Mn Ngouongo, S. Moen, A. Moreau-Gaudry, A. Morvan, F. Moskal, L. Mulas, F. Müller, F. Müller, H. Münch, U. Münchau, A. Mykkänen, J. Nageba, E. Nakić, D. Nave, R. Neagu, A.

834 325 634 749 387 170 470 93 140 857 243, 985 412 18 165 734 892 58 492 502, 892 325 238 218 218 115 130 601 185 789 819 3, 569 320, 554 339 907 255 744 v 175 661 749 907 325 450 315 465 98, 295 661 190 233 882

Neubauer, K. 950 Neuvirth, H. 689 Névéol, A. 492 Niazkhani, Z. 392, 877 Nies, J. 135 Niinimäki, M. 450 Noack, T. 867 Noussa Yao, J. 512 Nuettgens, M. 649 Nuzzo, A. 907 Nyheim, B. 417 Nykänen, P. 208, 497 Oemig, F. 704 Okhmatovskaia, A. 145 Oliveira, G. 300 Oliveira, I.C. 310 Oliveira, M. 407 Oliven, A. 233 Padbury, J. 115 Pagani, M. 238 Pan, Y. 699 Pantazos, K. 862 Panzarasa, S. 779 Papakonstantinou, D. 93 Pareto, L. 676 Park, H.-A. 764 Park, H.K. 569 Pasche, E. 185, 477 Patapovas, A. 325 Pecoraro, F. 834 Peek, N. 88, 103, 180 Pein, W. 18 Pelayo, S. 412 Perinati, L. 887 Petkovic, M. 58, 621 Peute, L.W.P. 150, 925, 930 Pfeifer, F. 482 Pieber, T.R. 950 Pintér, B. 671 Piras, E.M. 63, 108 Pirnejad, H. 392, 877 Plank, J. 950 Pletneva, N. 73 Poulymenopoulou, M. 93 Priori, S. 887 Prokosch, H.-U. 315, 325, 502, 892 Punys, V. 470 Quaglini, S. 779 Quantin, C. 611

1014

Quenel, P. Raetzo, M.-A. Rajoura, O.P. Ralevich, V. Rasmussen, A.R. Reid, D. Renly, S. Riazanov, A. Riedmann, D. Riemer, J. Ries, M. Rigby, M. Rinner, C. Rinott, R. Rizzi, F. Robel, L. Robu, A. Rodrigues, J.M. Rodrigues, P.P. Rognoni, C. Roitman, H. Roode, J.D. Rootjes, I. Rose, G.W. Rosenbeck, K. Rosenkranz, C. Rottscheit, C. Roux, C. Rozenblit, L. Röhrig, R. Rubin, Y. Ruch, P. Ruotsalainen, P. Rüping, S. Rydmark, M. Saboor, S. Saddik, B. Saint-Jalmes, H. Salvi, E. Samwald, M. Sandblad, B. Saranto, K. Savolainen, S. Schack, P. Scharnweber, C. Schaupp, L. Schmidt-Richberg, A. Schmuhl, H. Schneider, B.

629 666 960 58 809 33 729 145 920 265 892 208 369, 799 140 689 270 882 749, 754, 844 275 238 3, 569 427 290 145 809 649 387 611 955 170 140 185, 477 497 734 676 369, 522, 799 349 784 689 165 260 422 295 18 18 950 465 344 265

Schober, D. Schubert, R. Schuler, A. Schulz, S. Schwenk, M. Scott, P. Seddig, T. Sedlmayr, M. Segagni, D. Seggewies, C. Seim, A. Senathirajah, Y. Sengstag, T. Seppälä, A. Séroussi, B. Sfakianakis, S. Shaban-Nejad, A. Shabo, A. Sharma, A.K. Shillabeer, A. Shine, A. Silvent, A.-S. Simon, A.C.R. Simon, C. Simon, M. Simonet, M.-A. Skipenes, E. Skorve, E. Slaughter, L. Slonim, N. Smrz, P. So, E.-Y. Sojer, R. Sorvari, H. Soualmia, L.F. Soyer, H. Spat, S. Staemmler, M. Starren, J.B. Stausberg, J. Stegwee, R.A. Stenico, M. Stenzhorn, H. Stoicu-Tivadar, L. Stoicu-Tivadar, V. Storck, M. Strasser, M. Stroetmann, K. Stürzle, M.

185 18 482 589, 594 892 223, 709 594 315 887 892 364 280 734 497 125, 512 734 145 689 960 8 955 175 103 125 265 73, 654 417 330 38 140 160 764 325 497 492 897 950 537 387 744 78 108 165, 734 882 681 213 482 432 892

1015

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