Mobile Health Solutions for Biomedical Applications

Mobile Health Solutions for Biomedical Applications Phillip Olla Madonna University, USA Joseph Tan Wayne State Univers...

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Mobile Health Solutions for Biomedical Applications Phillip Olla Madonna University, USA Joseph Tan Wayne State University, USA

Medical Information science reference Hershey • New York

Director of Editorial Content: Senior Managing Editor: Managing Editor: Assistant Managing Editor: Typesetter: Cover Design: Printed at:

Kristin Klinger Jamie Snavely Jeff Ash Carole Coulson Larissa Vinci Lisa Tosheff Yurchak Printing Inc.

Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanbookstore.com Copyright © 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identi.cation purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Mobile health solutions for biomedical applications / Phillip Olla and Joseph Tan, editors. p. ; cm. Includes bibliographical references and index. Summary: “This book gives detailed analysis of the technology, applications and uses of mobile technologies in the healthcare sector by using case studies to highlight the successes and concerns of mobile health projects”--Provided by publisher. ISBN 978-1-60566-332-6 (hardcover : alk. paper) 1. Telecommunication in medicine. 2. Mobile communication systems. 3. Wireless communication systems. 4. Cellular telephones. 5. Medical technology. I. Olla, Phillip. II. Tan, Joseph K. H. [DNLM: 1. Telemedicine. 2. Ambulatory Monitoring. 3. Cellular Phone. 4. Computers, Handheld. 5. Medical Records Systems, Computerized. 6. User-Computer Interface. W 83.1 M6865 2009] R119.9.M58 2009 610.28--dc22 2008040451 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

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Editorial Advisory Board

George Demiris, University of Missouri, USA Nayna Patel, Brunel University, UK Thomas M. Deserno, RWTH Aachen University, Germany Jyoti Choudrie, University of Hertfordshire, UK Paul Hu, University of Utah, USA Patrice Monthrope, University of West Indies, Jamaica Richard Hull, University of Newcastle upon Tyne, United Kingdom Elena Qureshi, Madonna University, USA Francis Lau, University of Victoria, Canada Venus Olla, Nottingham University, UK Dave Parry, Auckland University of Technology, New Zealand Mathew Guah, Erasmus University, The Netherlands Jim Warren, University of Auckland, New Zealand H. Joseph Wen, Southeast Missouri State University, USA Yvette Miller, University of Toronto, Canada Yufei Yuan, McMaster University, Canada Daniel Zeng, University of Arizona, USA Kai Zheng, The University of Michigan, USA Jacqueline Brodie, Napier University, Scotland Carla Wiggins, Idaho State University, USA Bendik Bygstad, Norwegian School of IT, Norway

Table of Contents

Preface . ...............................................................................................................................................xiii

Section I Mobile Health Applications and Technologies Chapter I Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations with Low Literacy Skills ........................................................................................................................ 1 Katie A. Siek, University of Colorado at Boulder, USA Kay H. Connelly, Indiana University, USA Beenish Chaudry, Indiana University, USA Desiree Lambert, Trilogy Health Services, USA Janet L. Welch, Indiana University School of Nursing, USA Chapter II Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture .......... 24 Ana Ferreira, Center for Informatics, Faculty of Medicine in Porto, Portugal Luís Barreto, Instituto Politécnico de Viana do Castelo, Portugal Pedro Brandão, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal Ricardo Correia, Center for Informatics, Faculty of Medicine in Porto, Portugal Susana Sargento, Universidade de Aveiro, Portugal Luís Antunes, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal Chapter III Personal Health Records Systems Go Mobile: Defining Evaluation Components................................ 45 Phillip Olla, Madonna University, USA Joseph Tan, Wayne State University, USA Chapter IV Medical Information Representation Framework for Mobile Healthcare ............................................ 71 Ing Widya,University of Twente, The Netherlands HaiLiang Mei,University of Twente, The Netherlands Bert-Jan van Beijnum,University of Twente, The Netherlands Jacqueline Wijsman,University of Twente, The Netherlands Hermie J. Hermens,University of Twente, The Netherlands

Chapter V A Distributed Approach of a Clinical Decision Support System Based on Cooperation...................... 92 Daniel Ruiz-Fernández, University of Alicante, Spain Antonio Soriano-Payá, University of Alicante, Spain Chapter VI Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge Creation and Reuse.............................................................................................................................. 111 Teppo Räisänen, University of Oulu, Finland Harri Oinas-Kukkonen, University of Oulu, Finland Katja Leiviskä, University of Oulu, Finland Matti Seppänen, The Finnish Medical Society Duodecim, Finland Markku Kallio, The Finnish Medical Society Duodecim, Finland

Section II Patient Monitoring and Wearable Devices Chapter VII Patient Monitoring in Diverse Environments ..................................................................................... 129 Yousef Jasemian, Engineering College of Aarhus, Denmark Chapter VIII Monitoring Hospital Patients Using Ambient Displays....................................................................... 143 Monica Tentori, CICESE, Mexico Daniela Segura, CICESE, Mexico Jesus Favela, CICESE, Mexico Chapter IX Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks........................... 159 Javier Espina, Philips Research Europe, The Netherlands Heribert Baldus, Philips Research Europe, The Netherlands Thomas Falck, Philips Research Europe, The Netherlands Oscar Garcia, Philips Research Europe, The Netherlands Karin Klabunde, Philips Research Europe, The Netherlands Chapter X Sensing of Vital Signs and Transmission Using Wireless Networks................................................... 180 Yousef Jasemian, Engineering College of Aarhus, Denmark

Chapter XI Towards Wearable Physiological Monitoring on a Mobile Phone....................................................... 208 Nuria Oliver, Telefonica Research, Spain Fernando Flores-Mangas, University of Toronto, Canada Rodrigo de Oliveira, State University of Campinas, Brazil

Section III Context Aware Systems

Chapter XII A Framework for Capturing Patient Consent in Pervasive Healthcare Applications.......................... 245 Giovanni Russello, Imperial College London, UK Changyu Dong, Imperial College London, UK Naranker Dualy, Imperial College London, UK Chapter XIII Technology Enablers for Context-Aware Healthcare Applications..................................................... 260 Filipe Meneses, Universidade do Minho, Portugal Adriano Moreira, Universidade do Minho, Portugal Chapter XIV Modeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270 Bjorn Gottfried, University of Bremen, Germany Chapter XV Context-Aware Task Distribution for Enhanced M-health Application Performance......................... 285 Hailiang Mei, University of Twente, The Netherlands Bert-Jan van Beijnum, University of Twente, The Netherlands Ing Widya, University of Twente, The Netherlands Val Jones, University of Twente, The Netherlands Hermie Hermens, , University of Twente, The Netherlands

Compilation of References................................................................................................................ 308 About the Contributors..................................................................................................................... 332 Index.................................................................................................................................................... 341

Detailed Table of Contents

Preface . ...............................................................................................................................................xiii

Section I Mobile Health Applications and Technologies Chapter I Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations with Low Literacy Skills ........................................................................................................................ 1 Katie A. Siek, University of Colorado at Boulder, USA Kay H. Connelly, Indiana University, USA Beenish Chaudry, Indiana University, USA Desiree Lambert, Trilogy Health Services, USA Janet L. Welch, Indiana University School of Nursing, USA In this chapter, the authors discuss two case studies that compare and contrast the use of barcode scanning, voice recording, and patient self reporting as a means to monitor the nutritional intake of a chronically ill population. Chapter II Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture .......... 24 Ana Ferreira, Center for Informatics, Faculty of Medicine in Porto, Portugal Luís Barreto, Instituto Politécnico de Viana do Castelo, Portugal Pedro Brandão, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal Ricardo Correia, Center for Informatics, Faculty of Medicine in Porto, Portugal Susana Sargento, Universidade de Aveiro, Portugal Luís Antunes, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal The main objective of this chapter is to model, develop and evaluate (e.g. in terms of efficiency, complexity, impact and against network attacks) a proposal for a secure wireless architecture in order to access a VEPR. This VEPR is being used within a university hospital by more than 1,000 doctors, on a daily basis. Its users would greatly benefit if this service would be extended to a wider part of the hospital and not only to their workstation, achieving this way faster and greater mobility in the treatment of their patients.

Chapter III Personal Health Records Systems Go Mobile: Defining Evaluation Components................................ 45 Phillip Olla, Madonna University, USA Joseph Tan, Wayne State University, USA This chapter provides an overview of Mobile Personal Health Record (MPHR) systems. A Mobile personal health record is an eclectic application through which patients can access, manage, and share their health information from a mobile device in a private, confidential, and secure environment. Specifically, the chapter reviews the extant literature on critical evaluative components to be considered when assessing MPHR systems. Chapter IV Medical Information Representation Framework for Mobile Healthcare ............................................ 71 Ing Widya,University of Twente, The Netherlands HaiLiang Mei,University of Twente, The Netherlands Bert-Jan van Beijnum,University of Twente, The Netherlands Jacqueline Wijsman,University of Twente, The Netherlands Hermie J. Hermens,University of Twente, The Netherlands This chapter describes a framework which enables medical information, in particular clinical vital signs and professional annotations, be processed, exchanged, stored and managed modularly and flexibly in a mobile, distributed and heterogeneous environment despite the diversity of the formats used to represent the information. Chapter V A Distributed Approach of a Clinical Decision Support System Based on Cooperation...................... 92 Daniel Ruiz-Fernández, University of Alicante, Spain Antonio Soriano-Payá, University of Alicante, Spain This chapter presents an architecture for diagnosis support based on the collaboration among different diagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinical meetings in hospitals in which the members of a medical team share their opinions in order to analyze complicated diagnoses. Chapter VI Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge Creation and Reuse.............................................................................................................................. 111 Teppo Räisänen, University of Oulu, Finland Harri Oinas-Kukkonen, University of Oulu, Finland Katja Leiviskä, University of Oulu, Finland Matti Seppänen, The Finnish Medical Society Duodecim, Finland Markku Kallio, The Finnish Medical Society Duodecim, Finland

This chapter aims to demonstrate that mobile healthcare information system may also help physicians to communicate and collaborate as well as learn and share their experiences within their work community. Physicians’ usage of a mobile system is analyzed through a knowledge management framework known as the 7C model. The data was collected through the Internet among all of the 352 users of the mobile system. The results indicate that frequent use of the system seemed to improve individual physicians’ knowledge work as well as the collective intelligence of a work community. Overall, knowledge management seems to be a prominent approach for studying healthcare information systems and their impact on the work of physicians.

Section II Patient Monitoring and Wearable Devices Chapter VII Patient Monitoring in Diverse Environments ..................................................................................... 129 Yousef Jasemian, Engineering College of Aarhus, Denmark This chapter intends to explore the issues and limitations concerning application of mobile health system in diverse environments, trying to emphasize the advantages and drawbacks, data security and integrity suggesting approaches for enhancements. These issues will be explored in successive subsections by introducing two studies which were undertaken by the author. Chapter VIII Monitoring Hospital Patients Using Ambient Displays....................................................................... 143 Monica Tentori, CICESE, Mexico Daniela Segura, CICESE, Mexico Jesus Favela, CICESE, Mexico In this chapter the authors explore the use of ambient displays to adequately monitor patient’s health status and promptly and opportunistically notify hospital workers of those changes. To show the feasibility and applicability of ambient displays in hospitals they designed and developed two ambient displays that can be used to provide awareness patients’ health status to hospital workers. Chapter IX Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks........................... 159 Javier Espina, Philips Research Europe, The Netherlands Heribert Baldus, Philips Research Europe, The Netherlands Thomas Falck, Philips Research Europe, The Netherlands Oscar Garcia, Philips Research Europe, The Netherlands Karin Klabunde, Philips Research Europe, The Netherlands Wireless Body Sensor Networks (BSNs) are an indispensable building stone for any pervasive healthcare system. Although suitable wireless technologies are available and standardization dedicated to BSN communication has been initiated, the authors identify key challenges in the areas of easy-of-use, safety,

and security that hinder a quick adoption of BSNs. To address the identified issues we propose using Body-Coupled Communication (BCC) for the automatic formation of BSNs and for user identification. They also present a lightweight mechanism that enables a transparent security setup for BSNs used in pervasive healthcare systems. Chapter X Sensing of Vital Signs and Transmission Using Wireless Networks................................................... 180 Yousef Jasemian, Engineering College of Aarhus, Denmark This chapter deals with a comprehensive investigation of feasibility of wireless and cellular telecommunication technologies and services in a real-time M-Health system. The chapter bases its investigation, results, discussion and argumentation on an already developed remote patient monitoring system by the author. Chapter XI Towards Wearable Physiological Monitoring on a Mobile Phone....................................................... 208 Nuria Oliver, Telefonica Research, Spain Fernando Flores-Mangas, University of Toronto, Canada Rodrigo de Oliveira, State University of Campinas, Brazil In this chapter, we present our experience in using mobile phones as a platform for real-time physiological monitoring and analysis. In particular, we describe in detail the TripleBeat system, a research prototype that assists runners in achieving predefined exercise goals via musical feedback, a glanceable interface for increased personal awareness and a virtual competition. We believe that systems like TripleBeat will play an important role in assisting users towards healthier and more active lifestyles.

Section III Context Aware Systems

Chapter XII A Framework for Capturing Patient Consent in Pervasive Healthcare Applications.......................... 245 Giovanni Russello, Imperial College London, UK Changyu Dong, Imperial College London, UK Naranker Dualy, Imperial College London, UK In this chapter, the authors describe a new framework for pervasive healthcare applications where the patient’s consent has a pivotal role. In their framework, patients are able to control the disclosure of their medical data. The patient’s consent is implicitly captured by the context in which his or her medical data is being accessed. Context is expressed in terms of workflows. The execution of a task in a workflow carries information that the system uses for providing access rights accordingly to the patient’s consent. Ultimately, the patient is in charge of withdrawing consent if necessary. Moreover, the use of workflow enables the enforcement of the need-to-kwon principle. This means that a subject is authorised to access sensitive data only when required by the actual situation.

Chapter XIII Technology Enablers for Context-Aware Healthcare Applications..................................................... 260 Filipe Meneses, Universidade do Minho, Portugal Adriano Moreira, Universidade do Minho, Portugal This chapter focuses on how context and location can be used in innovative applications and how to use a set of solutions and technologies that enable the development of innovative context and locationaware solutions for healthcare area. It shows how a mobile phone can be used to compute the level of familiarity of the user with the surrounding environment and how the familiarity level can be used in a number of situations. Chapter XIV Modeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270 Bjorn Gottfried, University of Bremen, Germany This chapter introduces spatial health systems, identifies fun¬damental properties of these systems, and details for specific applications the methods to be applied in order to show how problems are solved in this field. On the one hand, this chapter gives an overview of this area, on the other hand, it is written for those who are interested in designing spatial health systems. The result is that different spatial scales and pur¬poses require different representations for describing the spatiotemporal change of objects, that is their spatiotemporal development, showing how fundamental aspects of spatial health systems are dealt with. Chapter XV Context-Aware Task Distribution for Enhanced M-health Application Performance......................... 285 Hailiang Mei, University of Twente, The Netherlands Bert-Jan van Beijnum, University of Twente, The Netherlands Ing Widya, University of Twente, The Netherlands Val Jones, University of Twente, The Netherlands Hermie Hermens, , University of Twente, The Netherlands As well as applying the traditional adaptation methods such as protocol adaptation and data prioritization, the authors investigate the possibility of adaptation based on dynamic task redistribution. In this chapter, the authors propose an adaptation middleware that consists of a task assignment decision mechanism and a task redistribution infrastructure. The decision mechanism represents task assignment as a graph mapping problem and searches for the optimal assignment given the latest context information. Once a new assignment is identified, the member tasks are distributed accordingly by the distribution infrastructure. A prototype implementation based on the OSGi framework is reported to validate the task redistribution infrastructure. Compilation of References................................................................................................................ 308 About the Contributors..................................................................................................................... 332 Index.................................................................................................................................................... 341

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Preface

Pervasive healthcare environment, focusing on the integration of mobile and ubiquitous technology to reform working and living conditions for individuals and organizations in the healthcare sector, sets the stage for an innovative emerging research discipline. Healthcare systems are experiencing a variety of challenges including the prevalence of life-style related conditions, growing consumerism in healthcare, the need to empower patients with information for better decision making, requests for better tools for self-care and management of deteriorating health conditions, the need for seamless access for healthcare services via the Internet and mobile devices, and the growing costs of providing healthcare. Mobile health (m-health) is an integral and significant part of the emerging pervasive healthcare field. M-Health contains three core components integrated into the healthcare environment. The first component is the availability of a reliable wireless architecture; the second component is the integration of medical sensor or wearable devices for monitoring; and the final component is a robust application and services infrastructure. M-Health relates to applications and systems such as telemedicine, telehealth, e-health, and biomedical sensing system. The rapid advances in information communication technology (ICT), nanotechnology, bio-monitoring, mobile networks, pervasive computing, wearable systems, and drug delivery approaches are transforming the healthcare sector and fueling the m-health phenomenon. MHealth aims to make healthcare accessible to anyone, anytime, and anywhere by elimination constraints such as time and location in addition to increasing both the coverage and quality of healthcare. Mobile and wireless concepts in healthcare are typically related to bio-monitoring and home monitoring; however, more recently the trend to incorporate mobile technology has become more prevalent across almost the entire healthcare data acquisition task domains. Bio monitoring using mobile networks includes physiological monitoring of parameters such as heart rate, electrocardiogram (ECG), electroencephalogram (EEG) monitoring, blood pressure, blood oximetry, and other physiological signals. Alternative uses include physical activity monitoring of parameters such as movement, gastrointestinal telemetry fall detection, and location tracking. Using mobile technology, patient records can be accessed by healthcare professionals from any given location by connecting the institution’s internal network. Physicians now have ubiquitous access to patient history, laboratory results, pharmaceutical data, insurance information, and medical resources. These mobile healthcare applications improve the quality of patient care. Handheld devices can also be used in home healthcare, for example, to fight diabetes through effective monitoring. A comprehensive overview of some of these mobile health applications and research has been presented in this book. This book provides an international perspective on the benefits of mobile health technology to illustrate different examples and applications implemented in the global healthcare sector. The work features 32 contributing authors representing six countries including the United States, United Kingdom, Spain, Portugal, Italy, and Denmark. Even though the healthcare policies and governance of healthcare systems

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in these countries differ, the benefits to be realized from a future of implementations of mobile health technology are not inconsistent among the countries. The book may be divided into three major sections: 1. 2. 3.

Mobile Health Applications and Technologies Patient Monitoring and Wearable Devices Context Aware Systems in Healthcare

The first section “Mobile Health Applications and Technologies” provides an analysis of the technology. Case studies highlighting the successes and challenges of mobile health projects offer real-world illustrations of applications and uses of mobile technologies in the healthcare sector. M-Health is a broad area transcending multiple disciplines and utilizing a broad range of technologies. “Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations with Low Literacy Skills,” authored by Katie A. Siek, Kay H. Connelly, Beenish Chaudry, Desiree Lambert, and Janet L. Welch, discusses two case studies that compare and contrast the use of barcode scanning, voice recording, and patient self reporting as a means to monitor the nutritional intake of a chronically ill population. Chapter II “Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture” by Ana Ferreira, Luís Barreto, Pedro Brandão, Ricardo Correia, Susana Sargento, and Luís Antunes presents the concept of a virtual electronic patient records system that enables the integration and sharing of healthcare information within heterogeneous organizations. The VEPR system aims to alleviate the constraints in terms of physical location as well as technology in order to access vital patient records. The use of wireless technology attempts to allow access to patient data and processing of clinical records closer to the point of care. The ubiquitous access to information can minimize physical as well as time constraints for healthcare, enhancing users’ mobility within the institution. The next chapter in this section entitled “Personal Health Records Systems Go Mobile: Defining Evaluation Components” is authored by Phillip Olla and Joseph Tan. It provides an overview of Mobile Personal Health Record (MPHR) systems. A Mobile personal health record is an eclectic application through which patients can access, manage and share their health information from a mobile device in a private, confidential, and secure environment. Chapter IV focusing on “Medical Information Representation Framework for Mobile Healthcare” was written by Ing Widya, HaiLiang Mei, Bert-Jan van Beijnum, Jacqueline Wijsman, and Hermie Hermens. This chapter describes a framework which enables medical information such as clinical, vital signs and professional annotations to be manipulated in a mobile, distributed and heterogeneous environment despite the diversity of the formats used to represent the information. It further proposes the use of techniques and constructs similar to the internet to deal with medical information represented in multiple formats. Chapter V is “A Distributed Approach of a Clinical Decision Support System Based on Cooperation,” authored by Daniel Ruiz-Fernández and Antonio Soriano-Payá. This chapter discusses an architecture that supports diagnosis based on the collaboration among different diagnosis-support artificial entities or agents and the physicians themselves. The proposed systems architecture, which was tested in a melanoma and urological dysfunctions diagnosis, combines availability, cooperation and harmonization of all contributions in a diagnosis process. Chapter VI, the final chapter in this section, “Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge Creation and Reuse” was authored by Teppo Räisänen, Harri Oinas-Kukkonen, Katja Leiviskä, Matti Seppänen, and Markku Kallio. This chapter focuses on mobile access to medical literature and electronic pharmacopoeias, aiming to demonstrate that using these recourses effectively may help physicians to communicate and col-

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laborate as well as learn and share their experiences within their user community. The chapter presents a case study of the users of Duodecim mobile healthcare information system. The second section presents research on Patient Monitoring and Wearable Devices. Chapter VII, the first chapter in this section, is titled “Patient Monitoring in Diverse Environments” and is authored by Yousef Jasemian. This chapter discusses the benefits of recording of physiological vital signs in patients’ real-life environment by a mobile health system. This approach is useful in the management of chronic disorders such as hypertension, diabetes, anorexia nervosa, chronic pain, or severe obesity. The author explored the issues and limitations concerning the application of mobile health system in diverse environments, emphasizing the advantages and drawbacks, data security and integrity while also suggesting approaches for enhancements. The following chapter, Chapter VIII, is titled “Monitoring Hospital Patients using Ambient Displays” authored by Monica Tentori, Daniela Segura, and Jesus Favela. This chapter explores the use of ambient displays to promptly notify hospital workers of relevant events related to their patients. To highlight the feasibility and applicability of ambient displays in hospitals, this chapter presents two ambient displays aimed at creating a wearable connection between patients and healthcare providers. The authors also discuss issues and opportunities for the deployment of ambient displays for patient monitoring. Chapter IX is titled “Towards Easy-to-uUse, Safe, and Secure Wireless Medical Body Sensor Networks” and is authored by Javier Espina, Heribert Baldus, Thomas Falck, Oscar Garcia, and Karin Klabunde. This chapter discusses the use of wireless body sensor networks (BSNs), which are an integral part of any pervasive healthcare system. It discusses suitable wireless technologies and standardization dedicated to BSN communication and highlights key challenges in the areas of easyof-use, safety, and security that hinder a quick adoption of BSNs. To address the identified challenges, the authors proposed the use of body-coupled communication (BCC) for the automatic formation of BSNs and for user identification and presented a lightweight mechanism that would enable a transparent security setup for BSNs used in pervasive healthcare systems. Chapter X is titled “Sensing of Vital Signs and Transmission Using Wireless Networks” and is authored by Yousef Jasemian. This chapter investigated the feasibility using wireless and cellular telecommunication technologies and services in a real-time m-health system. He based his investigation, results, discussion and argumentation on an existing remote patient monitoring system. His results indicated that the system functioned with a clinically acceptable performance, and transferred medical data with a reasonable quality, even though the system was tested under totally uncontrolled circumstances during the patients’ daily activities. Both the patients and the healthcare personnel who participated expressed their confidence in using the technology. The author also suggested enhancing features for more reliable, more secure, more user-friendly and higher performing M-Health system in future implementations. Chapter XI, “Towards Wearable Physiological Monitoring on a Mobile Phone” by Nuria Oliver, Fernando Flores-Mangas, and Rodrigo de Oliveira discusses the experience gained from using mobile phones as a platform for real-time physiological monitoring and analysis. The authors presented two mobile phone-based prototypes that explore the impact of real-time physiological monitoring in the daily life of users. The first prototype is called HealthGear; this is a system to monitor users while they are sleeping and automatically detect sleep apnea events; the second is TripleBeat, a prototype that assists runners in achieving predefined exercise goals via musical feedback and two persuasive techniques: a glanceable interface for increased personal awareness and a virtual competition. The third and last section focuses on research and on the theme of Context Aware Systems in the healthcare arena. Chapter XII, the first chapter in this section, is titled “A Framework for Capturing Patient Consent in Pervasive Healthcare Applications.” It is authored by Giovanni Russello, Changyu Dong, and Naranker Dualy and describes a new framework for pervasive healthcare applications where the patient’s consent plays a pivotal role. In the framework presented, patients are able to control the

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disclosure of their medical data. The patient’s consent is implicitly captured by the context in which his or her medical data is being accessed. Context is expressed in terms of workflows. The execution of a task in a workflow carries information that the system uses for providing access rights accordingly to the patient’s consent. Ultimately, the patient is in charge of withdrawing consent if necessary. Chapter XIII is titled “Technology Enablers for Context-Aware Healthcare Applications” authored by Filipe Meneses and Adriano Moreira. This chapter discusses how context and location can be used in innovative applications and how to use a set of solutions and technologies that enable the development of innovative context and location-aware solutions for healthcare area. The chapter highlights how a mobile phone can be used to compute the level of familiarity of the user with the surrounding environment and how the familiarity level can be used in a number of situations. The increasing availability of mobile devices and wireless networks, and the tendency for them to become ubiquitous in our dally lives, creates a favourable technological environment for the emergence of new, simple, and added-value applications for healthcare. Chapter XIV is titled “Modeling Spatiotemporal Developments in Spatial Health Systems” is authored by Bjorn Gottfried and discusses Spatial health systems and the support these systems can provide to disabled people and the elderly in dealing with everyday life problems. The author also addresses every kinds of health related issues that can develop in space and time. The work focuses on how spatial health systems monitor the physical activity of people in order to determine how to support the monitored individuals. Chapter XV, the final chapter in this section, titled, “ContextAware Task Distribution for Enhanced M-Health Application Performance” authored by Hailiang Mei, Bert-Jan van Beijnum, Ing Widya, Val Jones, Hermie Hermens. This chapter describes the importance of context-aware mobile healthcare systems. Due to the emergence of new medical sensor technologies, the fast adoption of advanced mobile systems to improve the quality of care required by today’s patients context aware healthcare systems is clearly needed . The authors propose an adaptation middleware that consists of a task assignment decision mechanism and a task (re-) distribution infrastructure. The decision mechanism represents task assignment as a graph mapping problem and searches for the optimal assignment given the latest context information. The research presented in this book is important due to the emergence of pervasive computing and health care systems that provide quality patient care services. By reviewing the diverse chapters presented a healthcare provider or practitioner will learn about the potential applications that will become the norm in the future.

Section I

Mobile Health Applications and Technologies

Chapter I

Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations with Low Literacy Skills Katie A. Siek University of Colorado at Boulder, USA Kay H. Connelly Indiana University, USA Beenish Chaudry Indiana University, USA Desiree Lambert Trilogy Health Services, USA Janet L. Welch Indiana University School of Nursing, USA

ABSTRACT In this chapter, the authors discuss two case studies that compare and contrast the use of barcode scanning, voice recording, and patient self reporting as a means to monitor the nutritional intake of a chronically ill population. In the first study, they found that participants preferred unstructured voice recordings rather than barcode scanning. Since unstructured voice recordings require costly transcription and analysis, they conducted a second case study where participants used barcode scanning or an integrated voice response system to record nutritional intake. The authors found that although the latter input method provided participants with a faster method to input food items, participants had difficulty using the system despite training. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Evaluation of Two Mobile Nutrition Tracking Applications

INTRODUCTION Chronic diseases, such as chronic kidney disease (CKD) and heart disease, are among the leading causes of death and disability in the world. At least half of the chronic disease related deaths could be prevented by adopting a healthy lifestyle, such as good nutrition, increased physical activity, and cessation of tobacco use. Researchers believe that the world must put a higher priority on interventions to help prevent and successfully manage chronic illness (Preventing Chronic Diseases: A Vital Investment, 2005). Current interventions to help chronically ill populations improve their nutritional health and self-manage therapeutic diets include paperbased food diaries, 24 hour recalls, and food frequency questionnaires (Dwyer, Picciano, & Raiten, 2003; Resnicow et al., 2000). Patients who use these interventions must have high literacy and memory recall skills. Unfortunately, over a quarter of the United States population do not have the necessary literacy or numeracy skills needed to successfully self-monitor themselves (Kirsch et al., 1993). If people cannot self-monitor themselves, they cannot manage their chronic conditions (HRSA Literacy) and may lead them to worse health outcomes (Schillinger et al., 2002). In addition, to administer current interventions medical professionals must spend a significant amount of time evaluating the data from paperbased forms. We are currently developing a mobile handheld application to assist CKD patients on hemodialysis monitor and maintain their nutritional intake. Initially, we thought a personal digital assistant (PDA) would be the best solution for health professionals and patients (Connelly, Faber, Rogers, Siek, & Toscos, 2006). Participants could scan barcodes on food items for their primary input or select items from an interface as a secondary input. These input mechanisms are ideal for low literacy populations because there is no reading required – participants only have to identify a barcode or

select a picture. Health professionals could easily administer the intervention and evaluate data without intermediate steps of electronic transcription. The low literacy chronically ill participants benefit from using the application because they can use the application anytime they consumed a food item, receive immediate visual feedback on their nutritional intake, and make decisions on a prospective basis. In addition, the interface and content could be customized for populations with varying literacy and computing skills. In this chapter, as part of a larger study, we will compare and contrast the use of barcode scanning, integrated voice response system (IVRS), and patient self reporting as a means to monitor their nutritional intake relative to their dietary prescription of CKD patients. In the first case study we found that participants preferred unstructured voice recordings rather than barcode scanning. Unstructured voice recordings are difficult to automatically parse and require transcription. We had to find out if patients would use a menu-based structured voice input system, such as IVRSs for automated recognition. In the second case study, we explored participant use of an IVRS and found although the system provided participants with a quicker way to input food items, participants had difficulty using the system and some could not use the system despite training. We will discuss the methodology and findings from these two case studies. We will conclude the chapter with lessons learned during the user study and provide considerations for future areas of research.

RELATED WORK PDAs with scanner input and mobile phones used for IVRS input gather information in many domains. PDAs and scanners have been used to show clinicians videos about specific unit appliances (Brandt, Björgvinsson, Hillgren, Bergqvist, & Emilson, 2002), save and search for information about food products, music, and


books (Bernheim, Combs, Smith, & Gupta, 2005), and obtain information about an environment from embedded barcodes (Fitzmaurice, Khan, Buxton, Kurtenback, & Balakrishnan, 2003). Mobile phones used for IVRSs have been used for patient counseling to enhance time spent with health professionals (Glasgow, Bull, Piette, & Steiner, 2004) and assess patient status with chronic illnesses such as depression, cancer, heart failure, and diabetes (Piette, 2000). In this section, we discuss specifically how PDAs and mobile phones have been used for interventions and nutritional monitoring.

PDA Nutrition Monitoring Interventions Currently, there are many PDA applications that can assist with the self-monitoring of nutritional intake. The United States Department of Agriculture (USDA) has a PDA nutrient database that provides people with a mechanism for looking up the nutritional information of foods. Users must correctly type the first few letters of a food item they are looking for into a search box and then click through a series of menus to find the appropriate food item based on portion size and preparation (“USDA Palm OS Search,” 2008). DietMatePro ( http://www.dietmatepro.com) and BalanceLog (http://www.healthetech.com/) use the USDA database along with other fast food nutritional information to create a PDA program that provides users with a way to save consumption information for a set of specific nutrients. CalorieKing (http://www.calorieking.com/) uses its own nutritional database and provides users the ability to save consumption information. In addition, it has a nutritional tracking application specific to diabetic populations. The applications are similar to the USDA database in that users must be able to spell the first few letters of food items. Unlike the USDA database, users must type in portion size. Food items are also broken up into three subsections - breakfast, lunch, and

dinner. The nutritional analysis is given on a separate screen. Researchers at Indiana University studied how three people with CKD used DietMatePro to monitor nutritional consumption over a three-month period. They found participants had difficulty navigating standard PDA menu navigation and preferred using a large PDA screen with touch sensitive icons (Dowell & Welch, 2006). Sevick and colleagues evaluated how five CKD participants used BalanceLog over a four-month period. They found that participants improved their dietary intake using the electronic nutrition monitoring system (Sevick et al., 2005). Both applications evaluated in these studies required significant literacy and cognitive skills. Stephen Intille et al. created a proof-of-concept PDA application that provides users with a way to scan food items and obtain nutritional information to assist users in making healthy choices (Intille, Kukla, Farzanfar, & Bakr, 2003). The application did not have an extensive UPC/nutrition database because none are freely available. Although the application does not allow users to save intake information, the application shows that integration of scanners and nutrition information is possible given enough resources. Researchers at Microsoft created a generic barcode look-up system that gave participants the opportunity to look up product information available online about specific food items. During their five-week study with twenty participants familiar with PDA technology, they found participants had mixed reactions to the system in terms of enjoyment and usefulness. Similar to a recent mobile phone study at Georgia Tech (Patel, Kientz, Hayes, Bhat, & Abowd, 2006), participants in the Microsoft study did not always bring the PDA with them despite being enthusiastic PDA owners (Bernheim et al., 2005). In addition to PDA monitoring of nutrition, there have been great strides in mobile phone nutrition monitoring applications. Those who use the commercial application myFoodPhone take pictures of foods they are consuming with


their mobile phone and post the pictures to an online food journal to receive feedback from a nutritionist (http://www.myfoodphone.com/). However, users must have access to a computer and be able to properly upload the information. Tsai and colleagues developed a mobile phone application where participants input food items via the keypad and immediately receive feedback on caloric balance on the phone screen. During the month-long feasibility study with 15 collegeeducated participants, they found participants preferred the mobile phone input system to traditional paper and pen journaling methods (Tsai et al., 2006). These applications use mobile phone input via pictures or key presses, but a more natural input interaction would be voice recognition software. In the next subsection, we discuss the use of IVRSs in health interventions.

Integrated Voice Response Systems in Interventions IVRSs in healthcare have been used for reminders, surveys, screening and assessments, and disease management (Lavigne, 1998). A review of IVRS feasibility studies in populations with chronic illnesses such as depression, cancer, heart failure, and diabetes led Piette to conclude that IVRSs are feasible for chronically ill populations, including populations that have mental health problems or low-income (Piette, Weinberger, & McPhee, 2000). According to Mundt et al. (2002), IVRSs benefit healthcare because they ensure procedural standardization, automatic data scoring, direct electronic storage, and remote accessibility from multiple locations. Long-term alcoholism and coital studies have supported the feasibility of interventions (Aharonovich et al., 2006; Helzer, Badger, Searles, Rose, & Mongeon, 2006; Mundt et al., 2002; Hays, Irsula, McMullen, & Feldblum, 2001; Schroder et al., 2007), though the populations are well educated and technically savvy. Notably Aiemagno et al. (1996) assessed substance abuse treatment

needs among 207 homeless adults, finding some evidence of greater disclosure of risky behaviors with IVRS. Long-term IVRS usage has had mixed reporting rates and health-related quality of life benefits. A 91 day coital study by Schroder et al. (2007) found a significant decrease in self-reports over time, while a two-year study with daily reports of alcohol consumption by Helzer et al. (2006) had a 91.7% reporting rate, but compensated participants per call. Daily alcoholism reports among HIV patients found a decrease in drinking over time (Aharonovich et al., 2006). In contrast, an IVRS intervention with diabetes patients found no measurable effects on anxiety or health-related quality of life (Piette et al., 2000). Disease management IVRSs that act as diaries have improved participant satisfaction over paper diaries (Hays et al., 2001). Two recent studies have challenged this result (Weiler, Christ, Woodworth, Weiler, & Weiler, 2004; Stuart, Laraia, Ornstein, & Nietert, 2003). Weiler et al. (2004) conducted a 3-week, 3-way, cross-over trial including 87 adults with allergic rhinitis recording daily through an IVRS or paper diary. A majority (85%) of the participants preferred the paper instrument, whereas only 4% preferred the IVRS. Stuart et al. (2003) conducted a year-long study with 642 patients to enhance antidepressant medication compliance. One of three different treatment strategies included a 12-week IVRS component, yet no significant differences in patient compliance were found and 50% of the 232 patients assigned to the IVRS component either never used the system or stopped before the 12 weeks were completed. IVRSs in healthcare typically limit response input to yes/no or numeric responses (Levin & Levin, 2006). Recent work exploring how IVRS vocabulary is expanded in a two week pain monitoring study by Levin et al. found that number of sessions per subject ranged from 1 to 20, accumulating 171 complete sessions and 2,437 dialogue turns. Only 2% of responses recorded


Table 1. Overview of case study 1 Study Phase #

Length of Phase

Phase 1

1 week

Break

3 weeks

Phase 2

2 weeks

Motivating Research Question(s) 1. Can participants find, identify, and successfully scan barcodes on food items? 1. Will participants remember how to use this application after a 3 week break? 2. Will participants actively participate without meeting with researchers every other day?

were out-of-vocabulary. Though volunteers in the evaluation were not trained, the results suggested that training sessions could have significant value and that IVR-based data collection is not a replacement for existing data collection, but simply another option for healthcare providers and researchers. Whereas the research discussed in this section primarily focuses on how well educated, technically savvy users interact with various technology interventions for monitoring in their everyday lives, our work deals with how non-technical users with varying literacy skills use two different types of input mechanisms. The IVRS literature especially shows how compliance is studied with this technology, but it does not research if participants could use the system and how the system can be improved. We are iteratively studying input mechanisms because our target population will depend on the application for their personal health and thus will have to find using the application efficient and enjoyable for long-term adoption. This chapter details two case studies that provided insight into finding the ideal input mechanism for nutrition monitoring.

C STUDY 1: BARCODE AND UNSTRUCTURED VOICE RING In this section we present our initial formative study that examines what, when, and how CKD

participants input food items into an electronic intake monitoring application. The study required that participants complete PDA application training exercises, meet with researchers during dialysis sessions three times per week, and use the Barcode Ed application during two study phases for a total of three weeks. Table 1 shows that there was a three week break between the two phases that allowed researchers to evaluate the data and decide on future directions for the application. All interactions with participants were done during dialysis treatment in an urban, hospital-based, outpatient dialysis unit. We documented how we conducted user studies in a dialysis ward in previous work (Siek & Connelly, 2006).

Methodology In this section, we discuss why we selected the hardware and application used for this case study.

Hardware We chose an off-the-shelf Palm OS Tungsten T3 PDA for our study. The Tungsten T3 has an expandable screen, large buttons, voice recorder, SDIO slot, 52 MB of memory, and Bluetooth. We chose an off-the-shelf PDA so the results could be useful to the consumer health informatics community for future studies. The Socket In-Hand SDIO card scanner (Socket Scanner) was chosen as the barcode scan-


ner because it was small, easy to use, and gave visual and audio feedback to users. Participants must press the predefined scanning button, line up the scanning light perpendicular to the barcode, and hold the PDA and object steady. The

PDA beeps and shows appropriate feedback when participants have successfully scanned a barcode. Previous studies have shown that CKD patients can use the Tungsten T3 and Socket Scanner (Moor, Connelly, & Rogers, 2004)

Figure 1. Screen shots from Barcode Ed. (a) Home Screen; (b-c) Voice recording and playback screens; (d-e) Barcode Scanning feedback screens


Application Design We created a simple application, Barcode Ed, because we wanted to isolate participants’ ability to scan and yet have an alternative input mechanism (e.g., voice input) to record all food items consumed. In initial interviews, half of the CKD patients said they did not eat any foods with barcodes. However, once they were prompted, we found they primarily ate frozen, canned, and prepared foods. Thus, for participants to use an easy input mechanism like scanning, they would have to learn how to identify barcodes and use the scanner. We only used scanning and voice recording in this study because we did not want to overburden novice computer users with a complex interface because they may have decreased cognitive function during treatment (Martin-Lester, 1997). Barcode Ed consists of five screens as shown in Figure 1. Since our user group had low literacy skills, we relied on icons 11mm large with some text for navigation. We found these CKD patients could view icons 10mm or larger (Moor et al., 2004). When participants turned on the PDA, they would view the Home screen. Participants could choose to voice record by pressing the Voice button or scan a barcode by pressing the Scan button. As soon as participants pressed the Voice button, the application would begin voice recording and show participants how many minutes and seconds they recorded on the Voice recording screen. When participants were finished recording, they could press the Stop button and play back their recording on the Voice recording play back screen. When participants were satisfied with their recording, they could return to the Home screen. When participants pressed the Scan button, participants could see a red laser line emitted by the scanner. Participants lined the scanner line perpendicularly across the barcode they were attempting to scan. If the food item was successfully scanned, a green check mark would appear on the Barcode scanning success screen.

If the food item was not successfully scanned, a red “X” would appear on the Barcode scanning unsuccessful page and participants could decide whether to scan again or return to the home screen and voice record the item instead. The application recorded the time the participant first pressed a Scan or Voice button, the barcode number or voice recording, and the time the recording was saved. We also recorded how many times participants played back their voice recordings. We did not record how many failed barcode scans were attempted because it was difficult to differentiate when a participant was scanning the same object or gave up and attempted to scan a new object during the same period of time. Also, participants sometimes did not use the scan button on the Barcode scanning unsuccessful page - instead they went to the Home screen and then pressed the scan button again. The times recorded assisted us in determining when participants recorded what they consumed. Recording the number of voice recording playbacks gave us insight into how participants used the application.

Participants Participants were asked to participate in the study during their dialysis session. They had to be (1) over 21 years of age, (2) able to make their own food or have the ability to go out and purchase food, (3) willing to meet with researchers during each dialysis session during the week, and (4) willing to carry the PDA and scanner with them and input food items consumed. Ten participants volunteered for the study. During the first phase, one participant could not participate anymore because of a medical emergency and another participant dropped out because he did not want to record what he was eating (n = 8). We lost two participants during phase two for similar reasons (n = 6). The average age of participants was 52 years old (s.d. = 16.28). Half of the participants were


male; all of the participants were black. One participant completed an associate degree, four participants graduated from high school, and one participant completed 10th grade. Participants had been receiving dialysis treatments on average of five years (s.d. = 3.5 years). Only four participants reported using a computer. Usage frequency ranged from every couple of months to once a week for a half hour. Participants primarily played games and surfed the Internet. Only two of the participants owned a mobile phone that they used for emergencies only. The participants were equally divided about how many food items they consumed had barcodes - some thought all and some did not think any food items had barcodes. Five patients said they did not have to monitor any nutrients or fluid. However, by the end of the first phase, the researcher had established a trusting relationship with the participants and found that all of them had to monitor fluid and nutrients such as sodium, potassium, phosphorus, and protein. None of the patients recorded their fluid or nutrient consumption prior to the study.

Design and Procedure We met with participants during dialysis sessions four times during each phase of the study for approximately 30 minutes. During the first session, we collected background information and taught participants how to turn the PDA on, insert the scanner, and use the application. Participants practiced scanning various food items and voice recording messages. Researchers met with participants during the study sessions to discuss any problems participants may have had with the PDA, retrain participants how to do certain tasks (e.g., barcode scanning), and collect recordings and barcodes from the PDAs via Bluetooth. The researchers played back the voice recordings to ensure the correct information was transcribed and informed participants if they voice recorded

a food item that could have had a barcode. Participants returned the PDAs at the end of each phase of the study, talked to researchers about their experience, and verbally completed a modified Questionnaire for User Interface Satisfaction (QUIS) (Chin, Diehl, & Norman, 1988) survey. Participants received ten dollars (U.S.) for every time they met with researchers for a total of thirty dollars during phase 1. For phase 2, participants received five dollars each time they met with the researcher for a total of fifteen dollars. Competency skills tests were administered at the end of the second and fourth meeting of the first phase and during the first and last meeting of the second phase to test basic Barcode Ed skills - turning the PDA on; inserting the scanner; scanning three to five objects with different physical qualities; voice recording with play back; and do a combined barcode scanning and voice recording sequence. The items participants had to scan ranged from a cardboard soup mix box that is easy to scan because of the material; a can of chips that is somewhat difficult to scan because of material and barcode orientation; and a bag of candy that is difficult to scan because it is amorphous and made of shiny material. Researchers measured how many times it took participants to successfully complete each task. We measured the time it took to complete each competency skill with the Barcode Ed application. Participants were instructed to scan or voice record food items when they consumed the items. Participants were instructed to scan the barcodes on food items first and voice recording items only if they could not scan the barcode or if a food item did not have a barcode. When participants mastered scanning and voice recording, researchers encouraged participants to note via voice recording how much they were consuming and the portion size. Each participant was given a phone number of a researcher to contact if they had any questions during the study. Participants were given a visual state diagram of the application to assist them with any questions regarding


use of the application that had images similar to those shown in Figure 1.

Barcode Scanning and Voice Recording Frequency

Findings

One of the motivating factors for the first phase of the Barcode Education study was to teach participants how to identify and scan barcodes. In Figure 2, we see that there was a learning curve associated with identifying and scanning barcodes during the first study phase. Participants voice recorded more individual food items during the first few days of the study because they were either unsure of where the barcode was located on the food item or were unable to scan the barcode. Gradually during the week, we noticed an increase of barcode scans up until the last day of the first study phase when participants barcode scanned more than they voice recorded. A goal of the second study phase was to see if this trend of increased barcode scans would persist and if participants would continue actively participating in the study without meeting with

The key findings of our study were: • •

•

•

Participants preferred voice recording once they mastered the application Participants with low literacy skills needed extra instruction on how to sufficiently describe food items for voice recordings Participants reported more individual food items with the Barcode Ed application than what they thought they consumed Electronic monitoring provides researchers with ways to identify participant compliance

In this section, we present the results in more detail.

Figure 2. Graph of the number of voice recordings and barcode scans participants input over the two barcode education study phases (dotted line denotes study break). Faces underneath each day denote when researchers met with participants


researchers every other day. The first two days of the second study phase were promising because participants were scanning everything they consumed and only voice recorded items without barcodes (e.g., fresh produce). However, after the second day, participants realized everything had barcodes and were overwhelmed with the amount of time it took to scan each individual food item. Thus, during the third and fourth day of the study, participants began voice recording food items they had previously scanned to save time. The lack of items input at the end of phase one shown in Figure 2 can be attributed to not seeing a study researcher to encourage them to participate at the end of the week. Indeed, three participants acknowledged that they had forgotten to input foods on more than one occasion because they had not been visited by a researcher. Participants were more likely to forget to input foods on weekends (days six, seven, thirteen, and fourteen). During the second week of the second study phase, participants rarely scanned barcodes and typically voice recorded what they consumed. The voice recordings listed multiple food items in an unstructured manner. For example, one participant recorded, “I ate a small apple, a lunch meat sandwich, and a boost for lunch. I ate … eggs, and bacon for breakfast. Tonight for dinner I am planning on eating…” When we asked participants why they scanned more on the 13th day of the study, they told us that they had remembered they would see a researcher on the following day to finish the study. Of course, the researchers called the participants to remind them to bring the PDAs to the last day of the study.

Voice Recording Food Items We thought voice recording food items was an easy alternative input method when participants could not scan. However, participants with low literacy skills were initially unable to give sufficient identifying information in their voice

10

recordings. Since the participants were unable to read the name on the food item, they were not able to say what they were eating (e.g., Lucky Charms cereal). Instead, participants said, “I had cereal for breakfast.” When we met with participants and played the recordings for transcription, we were able to suggest ways to be more descriptive (e.g., describe what is on the box) to help us identify the food items. After two to three sessions, the low literacy participants recorded more descriptive input (e.g., I ate the cereal with the leprechaun and rainbow on the box) and it was easier to identify what they were eating. However, even with descriptive input, we were unable to identify three of the items mentioned in the 195 recordings.

Barcode Ed vs. Self Reported Food Items In pre-study interviews, participants told us they had good and bad days that affected how much they consumed and discussed how many meals they typically consumed on each of these days. The participants usually had a good and bad day fairly recently and could easily describe to us the exact number of items they consumed. We asked participants if they had a good or bad day each time we met during the first study phase. We then compared how many items they electronically input to how many items they said they would consume, including the type of day they were having in the calculation. Participants ate more than they estimated for an average of three days (s.d. = 2.875) during the seven day period. When participants did consume more than they estimated, they typically consumed on average 3.5 more items than estimated – nearly doubling their normally recorded intake of 4.4 items (s.d. = 3.27)1.

Participant Compliance For this study, we loosely defined compliance as inputting at least one food item a day. Similar


Figure 3. Example of voice recordings, barcode scans, and voice recordings that should have been barcode scans (wrong record) a participant made during the first phase. The participant did back filling as shown by the green circle and increased input during the end of the study. The dotted lines denote the next day. Faces denote when researchers met with participants

to traditional monitoring methods, participants could back fill and modify their compliance record. However, unlike traditional methods, with electronic nutrition monitoring, researchers can identify this behavior more quickly. For example, a participant back filled entries in Figure 3 (green circle) by recording what he had consumed for the last two days since he had not actively participated. Another indicator of back filling is the number of times a participant recorded a food item that could be scanned during a short time interval since participants cannot scan items that have been consumed and discarded. Participants were unaware that we were recording the date and time of inputs and thus assumed if they said, “Today, on February 11, I ate…” the researcher would not know that it was recorded on February 12. When we showed participants similar graphs as shown here, participants attempted to decrease backfilling or were more truthful in disclosing lack of participation. In addition to backfilling, we see in Figure 3 an example of End-Of-Study compliance where the participant realizes the end of the study is near

and increases participation in hopes the researcher will not notice. We discussed earlier that once participants realized everything had a barcode on it, participants began to voice record more. We see this behavior in Figure 3– the participant starts to scan items, but then starts to hoard consumption information in one voice recording a day. The participant told us in a post-study interview that reporting everything he ate in one voice recording was more time efficient.

CASE STUDY 2: BARCODE AND IVR In this section we present our follow-up study that examines what, when, and how CKD participants input food items into an electronic intake monitoring application and an IVRS with a borrowed mobile phone. Similar to the first case study, participants complete PDA application and mobile phone training exercises, meet with researchers during dialysis sessions, and use either the PDA

11


barcode monitoring application or the mobile phone IVRS over a two week period. Participants were recruited and trained at the same dialysis unit from the first case study.

Methodology In this section, we discuss the hardware selected for the study and design of the applications used for capturing participant input.

Hardware We designed an application to run on a PDA with an attached barcode scanner to test participants’ ability to scan barcodes of food items. For the PDA, we chose an off-the-shelf Pocket PC from Hewlett Packard: the iPAQ hx2495b. We decided to use an iPAQ for the second case study because the Windows CE operating system provides a better rapid prototyping environment with Visual Studio .NET CF. The iPAQ hardware includes a large, color, touch screen, stylus and large buttons. We used the same SDIO In-Hand Scan Cards (SDSC Series 3E).

Figure 4. Integrated voice response system overview

12

We provided participants with a Nokia 6682 mobile phone to provide participants the ability to record food at any time. The phone has a highresolution color screen and large buttons. As with the PDAs, we provided soft leather cases with belt clips to the participants. We programmed the phone so that pressing any button would dial the number for recording their food items.

Application Design The scanning application was similar to the Barcode Ed application used in the first case study. The only difference in the application was that participants did not have the ability to record unstructured voice recordings. If the food item did not have a barcode, the participant could not record the food item. We implemented an IVRS that could be accessed with any phone to test participants’ ability to use structured voice input. As Figure 4 shows, we implemented the IVRS by transferring a call through a Session Initiation Protocol (SIP) gateway to Voxeo, an IVRS platform provider. The caller identifier was then submitted to our web


server where a CGI script selected participant grammar files (Nuance GSL Grammar Format), returning a VoiceXML form to collect items. The initial grammar included 152 food items and 2 command operators, ‘done’ and ‘wrong.’ The same grammar was available at every prompt. ‘Done’ submitted the results and terminated the call. ‘Wrong’ incremented a counter, such that if said twice without an intervening positive recognition, the participant was prompted to voice record the item for addition to the grammar. With food items, 45 were single words (e.g., bagel), 12 were compound words (e.g., fish sticks), 27 used optional phrase operators where a portion need not be uttered (e.g., French fries; French is conditional) and 50 optional phrase operators initially existed. There were 4 subset uses of the disjunction operator [] (e.g., ([green baked] beans) is valid for ‘green beans’ or ‘baked beans’). We updated the grammar throughout the study based on participant interviews and the items voice recorded through IVRS interaction. The Voxeo platform also provided detailed logs of each call, identifying the caller and the interaction sequence between the participant and VoiceXML prompts. The interaction sequence logs included timeouts, grammar recognition errors labeled No Match, prompts, and recognitions. With a completed call, two lists of items and counter variables were submitted to a MySQL Database—a list for food items misinterpreted by the IVRS when identified as wrong by the participant and a list of identified food items. When a participant recorded an item for addition to their grammar, the WAV file was submitted to our web server, written to disk, and a VoiceXML file returned to continue prompting for additional food items.

Participants We used the same criteria for selecting participants as we described in case study one. Nine people volunteered for the study, but three dropped out

before completion. Two people dropped out after the second day due to lack of interest and one person was forced to drop out at the end of the first week because she had to undergo emergency surgery and remained in the hospital during the second week of the study. This high dropout rate is consistent with our previous studies and is a result of working with this type of chronically ill population. Here, we report on the six participants who completed the study (n=6). The participants’ average age was 55 years, with a standard deviation of 10.9 years. The youngest participant was 36 and the oldest was 65. Four of the participants were female. Five participants identified themselves as Black or African American, and one as White. One participant had a ninth grade education, two had completed high school and three had some community college. One participant had undergone dialysis for 23 years. The remaining participants ranged from 2-5 years of dialysis treatment. Two participants said they did not try to keep track of their nutrient or fluid consumption. Two participants did not keep track of nutrients, but attempted to limit their fluid intake by either not drinking liquids over the weekend or “staying conscious” of how much they drank. Two participants claimed to keep track of both nutrients and fluid. One used a journal and was conscious of portion sizes; the other could not describe their method of monitoring but said they carefully monitored sodium and potassium intake. We have found in previous studies that participants in this population often tell researchers what they think they want to hear in regards to their nutrient and fluid consumption, regardless of the reality. Two participants were very familiar with computers. One took surveys on the Internet, while the other used his laptop daily, including bringing it to the dialysis sessions. One participant had some familiarity with computers. This participant had a computer at home, but did not use it very often. The final three participants said they

13


were not familiar with computers, although one had three years of typing experience and said she could use a keyboard. Three participants owned mobile phones.

Participants were paid ten dollars (US) at the end of each week of the study, for a total of twenty dollars. Payment did not depend on the number of times they recorded food items

Design and Procedure

Findings

For most participants, the study lasted a total of two weeks. However some participants had extra time with one of the applications because bad weather caused them to miss the dialysis session in which they were supposed to change technology. For these participants, we extended the total length of the study to ensure they had a minimum of one week with each technology. We primarily used the same methods described in the first study. In this section, we describe additions we made to the methods. For the phone application, we taught participants how to turn the phone on and off, how to dial the number to record their meals and how to record food items with the voice recognition application, making sure to speak one food item at a time very clearly. During each session, the researcher asked participants about any problems they were having with the application, if there were any food items they did not record, why they did not record a food item, when and how they used the application and their general opinions about its usefulness. In addition, we asked participants to list the foods they had eaten in the last 24 hours so that we could compare their recall with what they recorded with the applications. Similar to the first study, competency tests were given to participants during all but the final day of the study. For the mobile phone, participants were asked to record their last meal, which required them to turn the mobile phone on, dial the number, and follow the prompts to record the meal. We recorded the number of times participants attempted to complete each task and noted any difficulties they were having. If necessary, we retrained and retested the participant.

The key findings of our second case study were:

14

• •

•

•

Participants spent less time recording input with the IVRS Participants performed better with the scanner application on non-dialysis days and better with the IVRS on dialysis days Participants can record more items consumed with the IVRS, but the scanner application is more usable for a larger audience Input mechanism preference is not always linked with the participants’ performance with the technology

Barcode Scanning and IVRS Frequency of Use Despite participants using each technology for at least seven days, we found that in reality participants used the PDA to scan items on average only five days (s.d. = 1.4 days) and the mobile phone to input items with the IVRS on average of 4.5 days (s.d. = 2.95 days). We found that participants who used the technologies on most of the study days did so because they enjoyed using the application systems and wanted to tinker with the technology to identify breaking points. In addition, participants mentioned a desire to help advance medical research to help themselves and their peers. Participants also mentioned the compensation rewards, although the compensation was not dependent on frequency of use. Participants who did not use the technologies regularly in the study sometimes forgot the PDA in their homes and expressed a reluctance to integrate technologies


Table 2. Number and length of time (minutes:seconds) of sessions for each device. Averages are calculated per week PDA #sessions (avg.)

PDA

CP

CP length (avg.)

#session (avg.)

length (avg.)

1

18 (2.57)

72:23 (4:01)

10 (1.43)

24:10 (2:25)

2

16 (2.29)

29:07 (1:49)

25 (3.57)

28:19 (1:08)

3

4 (0.57)

5:27 (1:22)

4 (0.57)

0:04 (0:01)

4

19 (2.71)

48:48 (2:34)

22 (3.14)

15:26 (0:42)

5

6 (0.86)

9:17 (1:33)

13 (1.86)

17:41 (1:28)

6

7 (1.00)

16:14 (2:19)

8 (1.14)

0:52 (0:07)

into their daily routines. We found no correlations between personal computer and mobile phone usage outside of the study and their willingness to incorporate the technology into their lives. We examined usage patterns more closely by looking at participant input sessions. We defined an input session for the PDA scanner application as events that occurred within 10 minutes of each other because we found participants took longer to scan items in realistic situations (e.g., cooking meals). We defined an input session for the IVRS as any time a participant called into the system. When we analyzed usage of each technology on a per input session basis, we found participants overall had more input sessions with the IVRS than with the PDA (13.67 input sessions versus 11.67 input sessions), but they had similar amount of input sessions when averaged over the week (1.95 input sessions versus 1.67 input sessions). In Table 2, we show the total and average number of sessions each participant had with each device, and the total and average time spent in each session. Participants 1-3 had the PDA the first week of the study, while participants 4-6 had the mobile phone. Looking at the time participants spent on input gives us insight into how realistic it would

be to use these systems in their everyday lives. If technology is going to take too much time, then individuals will not be willing to use it. We see in Table 2 that participants spent less time on input sessions when using the IVRS in comparison to the PDA scanning application. Scanning took more time because (1) occasionally the scanner popped out of the SDIO card holder and had to be replaced multiple times and (2) participants were multitasking during scanning sessions and input food items as they were doing an activity (e.g., cooking a meal) instead of input all at once later on (e.g., right after eating). Participants’ who multi-tasked with the PDA application showed that they are willing to integrate the technology into their lives. However, it also shows that raw input times may not be the best measure of efficient usage of the PDA application.

Performance Besides the actual usage of the technologies in this study, we wanted to study the participant performance with each input mechanism. For this study, we defined performance as the ratio of unsuccessful to successful attempts at recording food items. We observed that performance was

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not consistent on all days. The ratio of unsuccessful to successful barcode scans on dialysis days was two times higher than on non-dialysis days (2.43 to 1.11). Conversely, we found participants performed better with voice recording on dialysis days – they had better performance on three out of the four non-dialysis days. Thus, on non-dialysis days participants performed better with the scanner application and on dialysis days, participants performed better using the IVRS. We also studied how participants interacted with the IVRS. Unlike the first study, participants would have to say items one at a time and use command operators to record food items. We found on average that 53% of the time participants did not use command operators correctly during IVRS sessions. Participants did not say, “Wrong,” when items were not recognized by the IVRS for 27% of the total calls. Participants did not say, “Done,” when they finished their calls 26% of the total calls. These errors effect how the IVRS interprets the input and thus could affect giving participants feedback on their food consumption in future implementations.

Electronic Input vs. Self Reported Food Items We asked participants to recall all of the food they ate in the last 24 hours each time we met with them. We then compared their 24 hour recall to the foods they electronically input into either the scanning program or IVRS with Venn diagrams shown in Figures 5 and 6 . The relative ratios between these three numbers provide us insight into how participants used the electronic application. The Venn diagrams for voice and scanning show that participants did not record everything they ate. Indeed, participants were somewhat limited with their ability to electronically record because the scanning application required all recorded items to have barcodes and the IVRS required the items be in the database to be recognized. We found that sometimes participants electronically recorded items they did not eat. One participant in particular recorded non-food items. Overall, it appears that participants can capture more items they consume with the IVRS.

Figure 5. Venn diagram of food items in 24 hour recall and items scanned

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Figure 6. Venn diagram of food items in 24 hour recall and items reported to IVRS

However, more participants with varying abilities can capture items they consume with the scanning applications as shown by only one participant not using the scanning application as opposed to two participants not using the IVRS successfully. We also see that providing alternative input mechanisms, scanning or IVRS, did not motivate participants 4, 5, and 6 to input a majority of the food items they consumed during the study.

Electronic Input Preference At the end of the study, we asked participants which device they preferred. Overall, two participants preferred scanning and four preferred voice. Once we identified their preferred device, we looked at their performance with each input mechanism as described in previous sections and pictorially compared preference with performance as shown in Figure 7. We found that performance influenced preference in only 3 participants. Participants 4 and 6 chose the IVRS input despite not being able to successfully use the system. They told us that they still preferred the mobile phone despite moderate success with the scanning

application because they were comfortable with using phones and with practice, could improve using the IVRS.

DISCON Even though barcode scanning is a quick method for inputting individual food items, our results show that it may not be usable over an extended period of time when participants do not receive immediate feedback about their nutritional intake. Participants were overwhelmed with the amount of work associated with scanning every food item they consumed. However, participants did think that this application would be helpful for CKD patients who have recently been diagnosed with the chronic illness to assist them in learning about the restrictive diet. Participants thought CKD patients in their first year of dialysis treatment would be more likely to spend extra time scanning barcodes if it meant clinicians could give them better feedback about their diet and health. Another possibility for an electronic self monitoring application would be to have people

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Figure 7. Participant preference of electronic input mechanism and overall performance with each input mechanism

use it periodically (e.g., quarterly when dietitians are conducting nutritional assessments with patients) to raise awareness and help them stabilize their diet. We did not anticipate the amount of training participants needed to create descriptive voice recordings. In retrospect, it made sense that people with low literacy skills would not be able to gather enough data from the food item to identify it. Transcribing the data was time consuming, but was easier as the study continued because the participants typically consumed the same food items. Researchers need a better understanding of their user group so they can accurately identify food items that may be culturally or economically influenced. Since our user group has a restrictive diet, not being able to identify food items is unacceptable since it can have such a drastic change in participants’ overall health. Participants’ underestimation of what they thought they would consume in comparison with what they actually consumed has been documented by other nutrition researchers (Dwyer et al., 2003; Resnicow et al., 2000). However, electronic self monitoring gives more detailed information (e.g., date, time, food item) than 24 hour recalls and food frequency questionnaires as had been

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used in the previous studies. Indeed, the standard deviation for days participants ate more than they estimated is large for our small sample. This is significant because of the participants’ restrictive diet – overconsumption of the restricted nutrients is dangerous and can result in death. Backfilling and hoarding are subject to retrospective biases and may not completely be accurate. In addition, researchers have shown that memory recall is undependable – thus participants may not be able to accurately describe what they had consumed during the past days even if they are attempting to be accurate (Stone, Shiffman, Schwartz, Broderick, & Hufford, 2003). The end of study compliance we discussed is similar to Rand’s parking lot compliance where participants attempt to be compliant by complying with the study procedure in the parking lot of the research facility. Since it is difficult to scan food items once they are consumed (or disposed of), participants increased participation before the end of each study phase with voice recording or wrong records. It is difficult to determine if patients were increasing participation before dialysis sessions where they met with researchers because participants may have been having a bad day (e.g., not feeling well due to dialysis session recovery).


We occasionally had difficulties with participants forgetting the devices, especially the PDAs, at home when we met with them. Since the participants were not use to having these devices in their lives, it is not surprising that they forgot them sometimes. In a recent study with “enthusiastic” PDA owners, three out of the eight participants forgot their PDA during a scheduled observation time (Bernheim et al., 2005). In addition, we had a number of participants who had to stop the study early because of medical concerns or a lack of motivation to complete the study. Losing participants is not localized to chronically ill populations - in the enthusiastic PDA owner study, out of 20 total participants, only 5 participants finished all the tasks in the study (Bernheim et al., 2005)! In case study two, the IVRS we used had difficulties recognizing inputs when the vocabulary became too large. Participants became increasingly frustrated when the system could not recognize even the simplest word, such as “egg” during the second week of the study. Participants voiced their frustrations by attributing human traits to the hardware (e.g., “It [IVRS] was dragging last night.”). IVRSs that use large vocabularies must be robust and able to handle slight variations between words. Despite updating the vocabulary each night and thereby increasing it by 30% by the end of the study, participants continued to voice record new food items not in the vocabulary showing that although participants typically eat the same foods, there is some variation that must be considered when designing a nutrition monitoring system. Another difficulty we encountered with the IVRS was that participants did not use the command operators correctly. It would be difficult to create an IVRS without some command operators to provide the system information about correctness and when to store the information. We attempted to use a minimal amount of command operators, but participants did not use them half of the time. We would encourage researchers to conduct more thorough training sessions with a

speaker phone so they can hear participants’ utterings and the system response. One weakness that all monitoring methods have is that we are not sure if participants are truthfully recording what they consume. Without subjecting participants to costly blood work or requiring participants to wear an invasive device that could detect what a person is eating, we can only assume participants are being truthful. As we discuss above, electronic self monitoring can help researchers identify noncompliant, untruthful trends more quickly and discuss non-compliance with participants, but this is not a fault proof method. We recognize that the case studies presented in this chapter are relatively small. Although researchers have shown that conducting usability studies with 4-6 participants will sufficiently provide enough data to determine the effectiveness and usability of a system (Nielsen, 2002; Virzi, 1992), we are currently conducting a larger scale study with a fully functioning version of the system. We recommend researchers who work with chronically ill populations conduct smaller studies to better understand their target user group better before conducting a larger study.

FUTURE WORK The research discussed in this chapter provides many avenues for future research projects. Interface designers must find a way to visually display portion sizes that low literacy populations understand. Visualizing portion sizes is fairly complicated because the type of visualization must be customized based on the type of food. For example, water would have a different portion visualization than bread or meat. In addition, the portion size visualization has to be informed by current methods dietitians use to educate CKD patients about portion sizes. We must find a way to verify consumption to ensure self monitoring assistive applications

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can provide participants with accurate measures of their dietary intake. Consumption verification could provide participants with reminders to record what they consume instead of estimate time reminders not based on actual context. Indeed, there is already work being done in this area (Amft & Troster, 2006), but we need to continue development to make less obtrusive or invasive devices for everyday use. In the second case study, one team member spent a significant amount of time updating the IVRS vocabulary each night. We could decrease the update time and distribute the work load if we better utilize all research members’ time to help listen to and decipher unrecognized phrases throughout the day. This idea builds on the human solver attack for Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). Websites, such as web mail and blogs, make users who want an account or post a comment identify the wavy characters in a picture – this challenge-response is known as a CAPTCHA. In the CAPTCHA human solver attach, a computer script would automatically fill out an online form, identify a CAPTCHA, and then pass the CAPTCHA to a high traffic website and promise the Internet surfer something in return (e.g., free porn) for identifying the characters in the CAPTCHA. The computer script would then take this response, enter it in the form, and create a malicious third party account (Doctorow, 2004). In an IVRS update setting, team members would be prompted throughout the day to identify phrases. Depending on ethics board approval, this method could be distributed among a broader Internet community for faster turn-around time.

CONCLUSION In this chapter, we highlighted results from two case studies that compared and contrasted the use of barcode scanning, IVRS, and patient self reporting as a means to monitor the nutritional

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intake relative to their dietary prescription of CKD patients. When we found that participants preferred unstructured voice recordings rather than barcode scanning in the first case study, we decided to study structured voice recording in a follow-up study. We found in the second case study that although the system provided participants with a faster method to input food items, participants had difficulty using the system despite training. We are continuing to study if patients will increase their usage of nutrition monitoring systems if they receive immediate feedback.

ACKNOWLEDGMENT Katie A. Siek was supported in part by the National Physical Science Consortium and Sandia National Laboratories/CA during case study 1. This work was supported by NSF grant EIA0202048, a grant from the Lilly Endowment, and Grant R21 EB007083 from the National Institute of Biomedical Imaging and Bioengineering to J.L. Welch.

LIST OF MAIN ACRONYMS PDA – Personal Digital Assistant CKD – Chronic Kidney Disease IVRS – Integrated Voice Recognition System CAPTCHA - Completely Automated Public Turing test to tell Computers and Humans Apart

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Dwyer, J., Picciano, M., & Raiten, D. (2003). Estimation of Usual Intakes: What We Eat in America-NHANES. J. Nutr., 133(2), 609S-623. Fitzmaurice, G., Khan, A., Buxton, W., Kurtenback, G., & Balakrishnan, R. (2003). Sentient Data. Queue, 1(8), 52-62. Glasgow, R., Bull, S., Piette, J., & Steiner, J. (2004). Interactive behavior change technology: A partial solution to the competing demands of primary care. American Journal of Preventive Medicine, 27(2, Supplement 1), 80-87. Hays, M., Irsula, B., McMullen, S., & Feldblum, P. (2001). A comparison of three daily coital diary designs and a phone-in regimen. Contraception, 63(3), 159-166. Health Resources and Services Administration. Health literacy. Available at www. hrsa. gov/healthliteracy/ Helzer, J., Badger, G., Searles, J., Rose, G., & Mongeon, J. (2006). Stress and Alcohol Consumption in Heavily Drinking Men: 2 Years of Daily Data Using Interactive Voice Response. Alcoholism Clinical and Experimental Research, 30(5), 802-811. Intille, S., Kukla, C., Farzanfar, R., & Bakr, W. (2003). Just-in-time technology to encourage incremental.

Connelly, K. H., Faber, A. M., Rogers, Y., Siek, K. A., & Toscos, T. (2006). Mobile applications that empower people to monitor their personal health. e & i Elektrotechnik und Informationstechnik, 123(4), 124-128.

Kirsh, I. S., Jungeblut, A., Jenkins, L., et al. (1993). Adult literacy in America: A first look at the results of the National Adult Literacy Survey. Washington DC: National Center for Education Statistics, United States Department of Education.

Doctorow, C. (2004). Solving and Creating CAPTCHAs with free porn. Boing Boing. http://www. boingboing.net/2004/01/27/solving-and-creating. html

Lavigne, M. (1998). Interactive Voice Response in Disease Management: Providing Patient Outreach and Improving Outcomes (pp. 1-16).

Dowell, S., & Welch, J. (2006). Piloting the Use of Electronic Self Monitoring for Food and Fluid Intake. Nephrology Nursing Journal, 33(3), 271277.

Levin, E., & Levin, A. (2006). Evaluation of spoken dialogue technology for real-time health data collection. J Med Internet Res, 8(4).

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Martin-Lester, M. (1997). Cognitive function in dialysis patients. Case study of the anemic patient. ANNA J, 24(3).

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Schillinger, D., Grumbach, K., Piette, J. et al. (2002). Association of health literacy with diabetes outcomes, JAMA, 288, 475-482.

Mundt, J., Bohn, M., King, M., & Hartley, M. (2002). Automating Standard Alcohol Use Assessment Instruments Via Interactive Voice Response Technology. Alcoholism: Clinical and Experimental Research, 26(2), 207-211. Nielsen, J. (2002). Why you only need to test with 5 users. From http://www.useit.com/alertbox/20000319.html Patel, S., Kientz, J., Hayes, G., Bhat, S., & Abowd, G. (2006). Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones. In UbiComp 2006: Ubiquitous Computing (pp. 123-140). Piette, J. D. (2000). Interactive voice response systems in the diagnosis and management of chronic disease. Am J Manag Care, 6(7), 817-827. Piette, J. D., Weinberger, M., & McPhee, S. J. (2000). The effect of automated calls with telephone nurse follow-up on patient-centered outcomes of diabetes care: a randomized, controlled trial. Med Care, 38(2), 218-230. Preventing Chronic Diseases: A Vital Investment (2005). World Health Organization. Resnicow, K., Odom, E., Wang, T., Dudley, W., Mitchell, D., Vaughan, R., et al. (2000). Validation of Three Food Frequency Questionnaires and 24-Hour Recalls with Serum Carotenoid Levels in a Sample of African-American Adults. Am. J. Epidemiol., 152(11), 1072-1080. Schroder, K., Johnson, C., & Wiebe, J. (2007). Interactive Voice Response Technology Applied

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Sevick, M. A., Piraino, B., Sereika, S., Starrett, T., Bender, C., Bernardini, J., et al. (2005). A preliminary study of PDA-based dietary selfmonitoring in hemodialysis patients. J Ren Nutr, 15(3), 304-311. Siek, K. A., & Connelly, K. H. (2006). Lessons Learned Conducting User Studies in a Dialysis Ward. Paper presented at the Extended Abstracts of CHI 2006: Workshops - Reality Testing. Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2003). Patient compliance with paper and electronic diaries. Control Clin Trials, 24(2), 182-199. Stuart, G. W., Laraia, M. T., Ornstein, S. M., & Nietert, P. J. (2003). An interactive voice response system to enhance antidepressant medication compliance. Top Health Inf Manage, 24(1), 15-20. Tsai, C., Lee, G., Raab, F., Norman, G., Sohn, T., Griswold, W., et al. (2006). Usability and Feasibility of PmEB: A Mobile Phone Application for Monitoring Real Time Caloric Balance. Paper presented at the Pervasive Health Conference and Workshops, 2006. USDA Palm OS Search: Health Tech (2008). ht t p://w w w.a r s.u sd a .gov/Se r v ice s /do cs. htm?docid=5720 Virzi, R. A. (1992). Refining the test phase of usability evaluation: how many subjects is enough? Human Factors, 34(4), 457-468. Weiler, K., Christ, A., Woodworth, G., Weiler, R., & Weiler, J. M. (2004). Quality of patient reported outcome data captured using paper and


interactive voice response diaries in an allergic rhinitis study; is electronic data capture really better? Program and Abstracts of papers presented during Scientific Sessions - AAAAI 60th Annual Meeting, 113(2, Supplement 1), S78.

Endno

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The standard deviation is large because it depends if participants were having a good or bad day in terms of consumption and physical health.

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Chapter II

Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture Ana Ferreira Center for Informatics, Faculty of Medicine in Porto, Portugal

Ricardo Correia Center for Informatics, Faculty of Medicine in Porto, Portugal

Luís Barreto Instituto Politécnico de Viana do Castelo, Portugal

Susana Sargento Universidade de Aveiro, Portugal

Pedro Brandão LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal

Luís Antune LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal

ABSTRACT Virtual electronic patient records (VEPR) enable the integration and sharing of healthcare information within large and heterogeneous organizations by aggregating known data elements about patients from different information systems in real-time. However, healthcare professionals need to access a terminal every time they treat a patient. This may not be trivial as computers are not available around every corner of big healthcare institutions. The use of wireless technology can improve and fasten healthcare treatment because it can bring information and decision to the point of care allowing also for healthcare professionals’ mobility. However, as healthcare information is of a very sensitive nature, it has to comply with important security requirements. The wireless technology makes it more difficult for these requirements to be achieved as it is harder to control disruptions and attempts to access information can be more common and less simple to detect. The main objective of this chapter is to model, develop Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Accessing an Existing Virtual Electronic Patient Record

and evaluate (e.g. in terms of efficiency, complexity, impact and against network attacks) a proposal for a secure wireless architecture in order to access a VEPR. This VEPR is being used within a university hospital by more than 1,000 doctors, on a daily basis. Its users would greatly benefit if this service would be extended to a wider part of the hospital and not only to their workstation, achieving this way faster and greater mobility in the treatment of their patients.

INTRUCTION Virtual electronic patient records (VEPR) enable the integration and sharing of healthcare information within heterogeneous organizations (Blobel, 2004). Hospitals are an example of such healthcare institutions with great turnover in terms of healthcare professionals. However, there are usually some constraints in terms of physical location as well as technology in order to access it. Healthcare professionals need to access a terminal in order to get information about the patients they are treating. This may not be easy to attain within a big and complex healthcare institution where computers are not available around every corner. The use of wireless technology tries to take this integration further. It allows access to patient data and processing of clinical records closer to the point of care. The ubiquitous access to information can minimize physical as well as time constraints for healthcare, enhancing users’ mobility within the institution. There have been some experiences with the use of wireless technology in the healthcare environment. These have shown that healthcare professionals were usually satisfied with the use of portable devices to access patient information. They save time and are bound to improve patient care (McAlearney, Schweikhart, & Medow, 2004). The most common devices include mobile wireless patients’ health monitoring systems. These equipments add more security concerns (Ramon Marti & Jaime Delgado, 2003) but are out of the purpose of this research. Among other problems, the lack of security processes is one of the main reasons for the dif-

ficult integration of VEPRs into medical processes, within large environments such as hospitals (Benson, 2002). The lack of security increases users’ reluctance for VEPRs’ acceptance. Both patient and healthcare organization trust can be seriously damaged if no proper security is provided (Denley & S. W. Smith, 1999) Furthermore, wireless technology adds a higher level of security issues. Disruptions and attempts to access information can be more common and easier to try, and less simple to detect and control; so security needs to be studied and analysed thoroughly before wireless networks are implemented in a larger scale within a hospital (Dixie B. Baker, 2003). Security requirements need to be considered and applied from the beginning to the end of a system’s development and implementation (Ana Ferreira, Ricardo Correia, & A. Costa-Pereira, 2004; Ana Ferreira et al., 2005; Ana Ferreira et al., 2004). This chapter proposes a wireless architecture in order to model access to an existing VEPR within a university hospital that can provide an extra security layer to the wired system. The next section describes the VEPR architecture along with the security requirements for the wireless version. The third section presents the wireless architecture that uses the latest wireless standards and security protocols and takes into account the security services that were implemented within the wired version of the system. Section four describes an evaluation of the proposed solutions against network attacks and its efficiency in terms of complexity and impact on the network. The last section discusses the results and shows some of the challenges where to focus future research.

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THE VIRTUAL ELECTRONIC PATIENT RECORD With the objective to face one of the major problems within large and complex health organizations - data retrieval and integration - a VEPR was built within a University Hospital with over 1350 beds, by the Biostatistics and Medical Informatics Department, at the Faculty of Medicine in Porto. This system provides a cost-effective solution for most clinical information needs (Ricardo CruzCorreia et al., 2005). Currently, more than 1000 doctors use the system on a daily basis. Other healthcare professionals (namely nurses) are expected to start using it soon.

Architecture This VEPR allows the collection, integration and availability of clinical reports providing an upto-date overview of a patient medical history at all points of care. The system uses a traditional three layered approach composed by presentation, business and data layers. The presentation layer is composed by a web application (VIZ) and a package of graphical user interface components to be used by third party applications. The web-interface was designed to include graphical components and layouts to summarise past patient data (patient chronological bars), and folders that reproduce the traditional types of patient record organisations (source, chronological and problems views). The application layer is composed by an integration engine (Multi-Agent system for Integration of Data – MAID), and a set of web-services that allow access to the data layer. The data layer includes all repositories, namely the CRep that comprises the VEPR database and clinical documents file system, the central patient system (SONHO) and the hospital statistics system (IEG) (Figure 1).

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MAID collects clinical reports from various hospital Departmental Information Systems (DIS), and stores them on the central repository (CRep) consisting of a database holding references to these clinical reports and a file system where reports are stored. After searching the database, VEPR users can access the integrated data of a particular patient through the web-based interface (VIZ). When selecting a specific report, its content is downloaded from the central repository file system to the browser. MAID (the agents’ server) communicates with the DIS using XML. MAID connects to the database server through JDBC1 and operates the files using NFS protocol2. The application in the Web Server (VIZ) communicates with the CRep database server using OCI/ PHP (Oracle Call Interface with PHP: Hypertext Preprocessor Language) functions and operates the files using NFS protocol. The Web browser client accesses the Web Server using HTTPS protocol. The Web services connect to the CRep database server, SONHO server and IEG server using JDBC, and use SOAP messages to deliver information to the GUI Components. The VEPR has been working for 4 years, regularly scanning eleven DIS and collecting a mean of 3000 new reports each day (currently holds about 3 million documents). A viewing module for the VEPR was made available in October 2004. Integrated DISs have evolved to send different documents to the VEPR without the need of any type of adaptation.

Integration and Communication The integration of hospital data in VEPR is accomplished with the use of different agents assigned to different tasks. Some collect reports’ references and others the actual reports from the DISs. When a user requests a report whose file is not in CRep, there is an explicit report request made directly to MAID, by the VIZ module. This request activates the express agent from the agent


Figure 1. VEPR generic architecture

platform in order to get the report requested by the user, from the right department (Figure 2). Several integration models had to be used to achieve the necessary integration level. The selection of appropriate integration model was often conditioned by the maturity of the IS being integrated, and by the resources available at that time. It should be noted that the development of communication interfaces was simpler for the eight applications that had Web interfaces because they were already using standard communication protocols such as HTTP. Web-services and shared graphical components were very useful in delivering an integrated view to other ISs. The process of integration of heterogeneous clinical information systems has shown the existence of organisational or technical problems and, indirectly, contributed to their solution. While some reports cannot be associated with identified hospital patients (e.g. outpatients who are not administratively considered as Hospital patients), some patients had multiple, rather than unique, identification numbers, making their correct identification difficult. A similar problem was found

with staff identification numbers, which were reused after staff members left the hospital.

Statistics VIZ was made available for testing in October 2004 but has only been known and routinely used since December 2004. The number of sessions and report views has been growing steadily since. The number of sessions increased 147% in 2006, and 70% in 2007. The number of distinct users using the VEPR has also grown in the same period, representing an annual growth of 29% users in the 4th quarter of 2006 and 41% in the 4th quarter of 2007. Currently, 4th quarter of 2007, 1.24 reports are viewed per session, 0.4 reports are viewed per patient encounter and 82.4 reports are viewed per user. Also the use of the VEPR is more widespread by the hospital computers (975 computers in 4th quarter of 2007). The number of report views per user per patient encounter has stabilized around 3.8 views per user per 10 000 encounters since the first quarter of 2006.

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Figure 2. UML sequence diagram of the VEPR

The number of direct access to the VEPR from the computer desktop hyperlink has been diminishing since the first quarter of 2006, whilst the number of accesses that originate in the Hospital Patient Record (SAM) as been growing. The number of report views from other referrals is small when compared with direct access and hospital patient record. The number of views per session for direct access is 1.81, for the DISs is 1.20, for the Hospital Patient Record is 1.18 and for the Emergency Department IS is 1.05. The introduction of wireless technology will allow the access to this VEPR system to a wider number of people and locations. The healthcare professionals will be able to access patient information whenever they need without having

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to return to their workstation. This allows overcoming most physical and logical obstacles that the hospital offers, therefore increasing VEPR availability.

Security and Monitoring VEPR present many security challenges namely the need to provide protection to patient’s sensitive information. The implementation of security mechanisms was thought from the beginning of the project’s development and implementation, allowing for its better integration and acceptability (Ana Ferreira et al., 2004). This subject was tackled according to the three main security characteristics: integrity, confidentiality and availability. One of


the main security issues relies in the information collected in the stored patient reports. Digital signatures are security mechanisms that provide the integrity of a report by enabling the detection of unauthorized modifications. If the digital signature does not match the report contents then this report is marked as not valid (Ana Ferreira et al., 2004). Confidentiality relates mainly to the access to sensitive information by authorized individuals. It is obtained by controlling access to information and by protecting it while in transit along network communications. Access control policies were defined by the hospital administration after a proposal from a specifically assigned committee defining roles and levels of access to VIZ. These policies were implemented using rolebased access control (RBAC) (Ferraiolo, Sandhu, Gavrila, Kuhn, & Chandramouli, 2001), an access control model used for large organizations (Ana Ferreira et al., 2005). In order to provide for an efficient way for user identification and authentication, development of access control tools was based on ENV 12251 European prestandard (CEN, 1999). As the network wiring and equipment is spread all over the hospital, it is necessary to protect the network infrastructure from eavesdropping. This was accomplished using TLS authentication protocol (B. Aboba & D. Simon, 1999) which provides encryption of all information whilst in transit. Availability focuses on means to provide for the continuous access to information by authorized users. Equipment and power redundancy, backups and system monitoring were all put in place to guarantee availability of the system at all times. The number of reports daily retrieved from each DIS is compared to what is expected and the number of sessions of different users is monitored. Any deviation from expected values triggers an alert message to the system administrator. Monitoring sensors have also been developed within the VEPR in order to detect problems in any of the three security characteristics, as well as for instance systems’ malfunctions, errors,

services that are not working and even improper behaviour. As an example, to detect users that share their logins and passwords the logs of sessions from October 2004 until December of 2007 were analysed. The suspicious behaviour that was searched for was users working for more than 24 hours (in some cases doctors work for 24 hours consecutively). All user sessions that started less than 10 hours from the last session were considered to be referring to the same working day. The number of suspicious cases found was 508; the calculated working days ranged from 24 to 63 hours (average = 29 hours). These working days referred to 139 of 1434 logins (rVPR=9.7%). The 10 logins that more frequently have suspicious behaviour referred to the following medical specialties: Anaesthesiology (4 logins), Emergency (2 logins), Infectious Diseases (2 login), Cardiothoracic Surgery (1 login), Gastroenterology (1 login). Although technical solutions exist to provide secure access control, they demand a clear definition of permissions for each group of actors. Healthcare organisations must comply with current legislation, ethical rules and internal processes which are very difficult to be objectively defined into access control rules. The number of shared logins found may probably just represent the tip of the iceberg. However, it is high enough to raise concern.

Scurity Requirements All the security services implemented for the wired VEPR mentioned in the previous section are obviously valid for the wireless architecture. The wireless technology stresses however the need for extra layers of security. In order for a healthcare professional to access the VEPR with a wireless device, there are 3 main security issues to address: 1.

Authentication and authorization from the wireless to the wired network;

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2. 3.

Secure communications of information in transit; Integrity & trust in the information that is requested and visualized by the users.

For (1) there is the need to create an access control infrastructure that will prevent problems of confidentiality such as masquerading and password sniffing. Also, policy rules need to be set so that access from the wireless to the wired network is properly controlled. Still, the process of access control must be transparent to the users and simple to use and manage. Point (2) requires that information in transit must travel encrypted at all times to avoid eavesdropping. It should always be available in a certified and trusted manner. For (3) there is the need for the means to guarantee that the information in transit within the wireless network is protected from unauthorized or accidental modifications. Healthcare professionals must trust the information they use to treat patients. The most accurate and correct it is the better and adequate the treatment will be.

PROPOSED WIRELESS ARCHITECTURE As previously mentioned, users of healthcare environments would greatly benefit with the availability of information anywhere through a wireless local area network (WLAN). Usually, the healthcare institution where the WLAN is going to be deployed has already a LAN in use. Setting a WLAN on top of this one is seldom trivial. Building dimensions, user locations, connectivity and the security requirements previously mentioned account for the stringent issues. The need for a good location map and distribution is essential for tackling the first two issues. The last two will be the focus of this section. Healthcare professionals must trust not only the technology they use (e.g. robust, usable) but also the information they ac-

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cess. They need quick and reliable access to carry out their job, or the system will be circumvented (Lehoux, Sicotte, & Denis, 1999). Another important concept is the requirement to access the VEPR infrastructure from outside the local network (eg. from the internet) (Yu & Jothiram, 2007). Also the security in pervasive sensor networks for healthcare monitoring (Ng, Sim, & Tan, 2006) is another relevant trend. These subjects are however outside the scope of this discussion. This section describes some possible solutions to support security in WLANs. These include a general framework to communicate authentication details (EAP) to allow or deny network access and exchange cryptographic material (802.1X). Building on these, WPA and 802.11i (WPA2) are able to control the access to the network and provide encryption of the communications. IPsec addresses authentication and encryption at the network (IP) layer whereas the previous technologies lie on the data link (medium) layer. The next sub-sections describe all these technologies in more detail.

Extensible Authentication Protocol The Extensible Authentication Protocol (EAP) (B. Aboba, L.Blunk, J. Vollbrecht, J. Carlson, & H. Levkowetz, 2004) is a general authentication protocol defined by the IETF. It was originally developed to be used with a point-to-point protocol. EAP provides an interface to several authentication mechanisms, as Kerberos, public key ciphering or one time passwords. EAP cannot be used independently as an authentication protocol. It is just a set of rules of how an authentication server and a client can exchange messages and provides a pluggable architecture for different security protocols. EAP uses the data link layer for message exchange, and so does not require IP addresses for communication. A network with EAP capabilities has three independent identities: the client (also known as


supplicant), the authenticator and the authentication server. The client has to deliver the authentication credentials (a certificate or a username and a password). The authenticator is the equipment that implements security at the port level and does also network access control. According to the EAP authentication protocol used, the authenticator re-transmits the necessary messages, between the client and the authentication server, acting as an intermediary and enforcer in the authentication request. The authentication server specifies the authentication protocol to be used and validates the credentials delivered by the client. EAP enables the support of multiple authentication protocols without the need to configure the authenticator with each specific authentication mechanisms. EAP allows also the authentication server to control which authentication protocols should be supported. These features increase flexibility to the process and allow greater interaction.

802.1X IEEE 802.1X (IEEE 802.1X, 2004) is a network security specification initially developed for wired networks, with its concepts and utilization extended afterwards to wireless networks. 802.1X defines a network access control based in ports. It was developed to deny or accept requests based on user authentication information (credentials). 802.1X uses EAP for authentication. The access control is performed at the Medium Access Control (MAC) level and is independent from the physical layer. A port in 802.1X is any type of controlled access element (i.e. switch, router, AP). In this context, the association between one client and one AP is called a virtual port and the access to the network is seen as another virtual port. The client associates first if the port is available and uses this connection to authenticate. If the authentication is successful the AP gives access to the network (thus granting access to the network virtual port). 802.1X provides keys for

each client and session. This means that keys have to be regularly changed, thus reducing repetition problems. The 802.1X three main processes are the mutual authentication between the client and the server, the cryptographic keys dynamically generated after authentication and the centralized policy control. 802.1X is not a protocol; it is an authentication and key management process. In a wireless network it defines authentication and the dynamic generation of cryptographic keys. The ciphering is accomplished using any of the wireless security protocols.

WPA – Security and Architecture WPA (“(Wi-Fi Protected Access)”) was developed with the aim of decreasing the problems associated to Wired Equivalent Protocol (WEP)3 (Walker, 2003). WPA is based on the principles of the IEEE802.11i standard (IEEE 802.11i, 2004) with some simplifications to be compatible with the equipments at the time WPA was released. WPA uses a robust cipher algorithm and introduces user authentication, one of the WEP missing characteristics. WPA is intended to be implemented in a home/office environment and is available in all Access Points (APs) and Network Interface Cards (NICs) currently available4. To improve data codification, WPA uses the Temporal Key Integrity Protocol (TKIP) (IEEE 802.11i, 2004) which, when compared to WEP, improves data level ciphering by using temporal and per packet keys. WPA also has a key mixing function for each packet, a Message Integrity Check (MIC), extended initialization vectors (IV) with sequential rules and a key renewal mechanism. WPA makes use of 802.1X for user authentication, making it possible to use one of the EAP methods. For security matters in these environments, the EAP- Transport Layer Security (TLS)

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(B. Aboba & D. Simon, 1999) method is used. This method uses digital certificates for each user authentication. A central authentication server is used to manage mutual authentication, which apart from authenticating the user, it eliminates the danger of rogue APs. The authentication server usually employed is the Remote Access Dial-In User Service (RADIUS) (C. Rigney, A. Rubens , W. Simpson , & S. Willens, 1997). The RADIUS server authenticates the WLAN user and determines the session key to be used. RADIUS is only used to communicate between the AP and the authentication server; in the WLAN, EAP is used between the user and the AP (Figure 3). Notice that other Authentication, Authorization and Accounting (AAA) protocols (Kim & Afifi, 2003) could be used such as Diameter (Ventura, 2002), COPS (Durham, Boyle, Cohen, Rajan, & Sastry, 2000) or TACACS (Finseth, 1993) server. However, RADIUS is used for WPA. A Lightweight Directory Access Protocol (LDAP) (J. Hodges & R. Morgan, 2002) server can also be used for a centralized user authentication. All RADIUS implementations can interact with an LDAP server, making it possible to use a central point of administration of all users, thus creating a strong security policy. Other centralized user authentication implementations that can use LDAP are Active Directory (Microsoft, 2004) and Novell eDirectory (Novell, 2004).

Figure 3. Authentication architecture

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For connectivity between the different networks a layer 2 or 3 switch is used. This type of switch adds a new layer of filter/protection to the system with the use of Virtual LANs (VLANs) and, if needed, allows to route data between the different networks. This solution provides an access level linked to the security standard used by the clients. The proposed architecture uses two security VLANs. These VLANs are configured in such a way that only WPA and 802.11i clients are able to access all network services. The VLANs distinguish, transparently to the user, the security protocol used by the client and trigger all the necessary and specific procedures needed for authentication and authorization. The implementation of a WPA system requires the development of an 802.1X infrastructure. All the necessary elements for building a WPA network are shown in Figure 4. It is worth noting that there is a possibility of using a password based user authentication (for either WPA or 802.11i). However, this approach is not recommended in high security infrastructures (Moskowitz, 2003).

802.11i Security and Architecture In June 2004, the Institute of Electrical and Electronics Engineers (IEEE) ratified the 802.11i standard, also called Robust Security Network (RSN)5 (IEEE 802.11i, 2004). This security stan-


Figure 4. WPA and RSN/IEEE802.11i architecture

dard includes the following functionalities: uses the Advanced Encryption Standard (AES) (NIST, 2001) block cipher to encrypt the data packets, 802.1X for user authentication and TKIP for the management of the cipher keys. The standard also recommends a set of new improvements to WEP in 802.11b NICs. Some NICs, due to design limitations, cannot support AES but are able to support TKIP with a small update. 802.11i requires that all clients announce their cipher capabilities in their AP association requests. The AP and the wireless client then establish the appropriate channel for data ciphering. This key agreement is based on their mutual cryptographic capabilities and configured in one of the security policies (eg.: “allowing only associations with AES clients”). Moreover, 802.1X authentication assures key renewal during a session. AES is currently widely recommended for confidentially. However, AES entails more demanding cryptographic functions. This means that older devices do not have processing capacity to handle AES and keep a normal network performance. To circumvent the problem 802.11i enables the use of TKIP as the cipher protocol. This method is more feasible for less capable devices. Nonetheless, there is already a wide selection of products

compliant to 802.11i and WPA2 (including some PDAs)6. 802.11i actually defines three protocols for data protection: the Counter Mode with Cipher Block Chaining Message Authentication Code Protocol (CCMP) (Whiting, Housley, & Ferguson, 2003), the Wireless Robust Authenticated Protocol (IEEE 802.11i, 2004) and TKIP. CCMP will be the ‘de facto’ IEEE802.11i cipher protocol. It is based in AES counter mode. This protocol derives from lessons learned with 802.10 (IEEE 802.10, 1998) and IPsec protocols. It uses strong cipher primitives, which makes it reliable against all (currently) known attacks. As with WPA, for implementing an 802.11i solution it is necessary to deploy an 802.1X infrastructure. Figure 4 shows the required elements to support an 802.11i architecture.

IPSec – Security and Architecture The two previous solutions are specially designed for wireless networks. However, it is also possible to protect these networks with a network layer protocol originally developed for wired networks, like IP Security (IPSec) (B. Aboba et al., 2004).

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This protocol, though intended to protect Internet communications and wired networks, has some characteristics that make it suitable to protect wireless communications. While the previously mentioned solutions protect the information at the data link layer, IPsec protects the information at the network layer. This functionality makes it a versatile protocol, which can be used to protect any kind of IP network, and is independent of the application and type of data flow. It comprises a set of protocols for the development of Virtual Private Networks (VPNs). IPsec VPNs are a very common method for protecting data that traverses public networks (or non-protected networks). IPsec adds security through a set of tunnelling and ciphering mechanisms: it implements network layer authentication and ciphering; keeping end-to-end security within the network architecture. Its main advantage is that it can protect any kind of data packet routed through the network independently of the source application7. Its main disadvantage is its complexity. IPsec has two modes of operation: tunnel and transport. In tunnel mode IPsec protects a completely normal IP packet, thus its payload is an IP packet. This mode is used when the IP packet needs to be sent unchanged to the destination. Transport mode IPsec is integrated with IP and thus transports an UDP/TCP packet from the transport layer.

Figure 5. Wireless network IPsec VPN

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The IPsec standard includes two security protocols: the Authentication Header (AH) (Kent & Atkinson, 1998a) that provides data integrity and the Encapsulating Security Payload (ESP) (Kent & Atkinson, 1998b) that adds confidentiality. All IPsec parameters are negotiated using the Internet Key Exchange (IKE) (Harkins & Carrel, 1998) protocol. IKE uses digital certificates for end points authentication. ESP makes use of cipher techniques for data confidentiality, and digital signatures for source authentication, while AH only uses digital signatures for source authentication (AH does not cipher data). Thus ESP should be used when confidentially is an issue. Figure 5 shows an IPsec VPN adapted to a wireless network and the elements required for an IPsec protected wireless network. The network has wireless terminals with VPN client software. This software should be able to start protected tunnels between the terminals and the gateway. The firewall assures the right establishment of a tunnel and also guarantees that only specified devices can establish that tunnel. Recent Windows OS have a native VPN client. The wireless terminal connects to the AP that offers, between the wireless and the wired networks, initial filters to the IP protocol. Between the AP and the wired networks there is a layer 2 switch responsible for the connectivity. Recent models of this kind of switch allow Virtual LAN Access Control Lists (VACL), which adds a new


filter/protection layer to the system (as discussed previously). As in the previous architectures, LDAP and RADIUS servers are used for centralized user authentication.

Wireless Architecture Proposal This section discusses a secure wireless architecture for accessing the VEPR taking into account the specific characteristics of a health care institution (importance of security) and the characteristics of the available solutions. This architecture uses the WPA-TLS protocol and also considers the use of the new 802.11i standard. All existing equipments can, with a small firmware upgrade, support WPA-TLS and therefore, be reused reducing implementation costs. WPA-TLS should only be considered a transition solution until all devices support 802.11i. As such, the aim is to support WPA and 802.11i into a single network. The way to accomplish this is by dividing the physical network into separate logical security networks. Most of the last generation APs support WPA and 802.11i protocols, as well as the ability to create separate service set identifiers (SSIDs)8. Therefore, in the proposed architecture, each AP is configured with two different SSIDs (SSID=802.11i-VEPR and SSID=WPA-VEPR)

and two different security protocols. The APs are enabled with both 802.11i and WPA. This configuration creates a secure logical network, allowing healthcare professionals to have a secure and controlled access to the VEPR. A RADIUS server acts as the policy enforcement point (PEP), configured with different access control policies for each SSID9. These policies define the data protection protocol, the key management protocol and the key length used with a specific SSID. The RADIUS server is coupled with the actual VEPR solution in terms of user management. The previous sections discussed the use of LDAP for the VEPR. For this case, the RADIUS authentication should use the LDAP of the VEPR. This is very important as it will enable the use of the current VEPR access control in the new wireless architecture. As expected, all terminal/client equipments should support either WPA-TLS or 802.11i. Figure 6 shows the proposed architecture, where the two logical secure access networks are presented.

EVALUATION AND INSIGHTS This section presents an evaluation of the security and performance capabilities of WPA EAP-TLS.

Figure 6. VEPR secure wireless architecture

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IPsec. 802.11i is not addressed due to the unavailability of 802.11i compliant devices at the time the experiments where undertaken. The discussion comprises the evaluation of the proposed solutions against network attacks and its efficiency in terms of performance and impact on the network.

General Testbed The testbed built to perform the experiments is depicted in Figure 7. Unless otherwise mentioned, all the experiments hereby described were built upon open source operating systems and tools. The FreeRADIUS (FreeRADIUS , 2008) implementation was used as the RADIUS server. For the public key infra-structure the OpenSSL (OpenSSL, 2007) suite was used. The IPSec infrastructure was implemented on FreeSwan (FreeS/WAN Project, 2004). The software was installed in computers running the Linux Operating System. In the IPsec tests, open source software was also used to implement Access Points: HostAP (HostAP team, 2007). This software allows building a fully functional AP. In the WPA infrastructure, the wpa_supplicant software (HostAP team, 2007) was employed. The ettercap tool (Ettercap Team, 2005) was used to perform the tests/security attacks.

Security Experiments The network reaction to network attacks was observed in order to evaluate the security of the proposed solutions. These attacks comprise manin-the-middle (MITM), impersonation, Denial of Service (DoS) and session hijacking. In the MITM attack an intruder tries to see (“sniff”) the information exchanged between the active hosts and insert itself in the middle. This allows the intruder to eavesdrop the communications and even alter the data exchanged. A basic approach for this attack, when no security is used, is a technique called arp spoofing (Whalen, 2001). In the impersonation attack an intruder tries to use the same IP address and the same hostname, as one of the valid clients of the network, to get access to network resources. It differs from the MITM attack in that the attacker’s objective is only to access the network. So the intention is not to eavesdrop or alter the data exchanged by the valid host. The session hijacking consists in an intruder trying to obtain full control of a client successful session. It is an extension of the impersonation attack, where the attacker needs to use the session credentials/identifiers from the valid host to steal

Figure 7. Wireless architecture used for testing procedures

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its current session. It may use a MITM attack to acquire such information. The Denial of Service (DoS) attack consists of disabling some (or all) of the network services (for example denying authentication) by overwhelming the targeted service. The ultimate objective is to deny network access.

IPSec Results In the IPsec solution, the DoS attack was only successful before the establishment of the IPsec tunnel; after the establishment of the tunnel the attack did not succeed. For the MITM attack, the arp spoofing option was used. We observed that, with the IPsec tunnel established, the MITM attack did not succeed (it was not possible to see or detect any kind of data flow). The impersonation attack also did not produce any result. For this attack an intruder used the same network address and hostname of a recognized client and then tried to establish an IPsec tunnel. As IPsec uses digital certificates for client authentication, the intruder is not authenticated and the tunnel is not established as was expected. Finally, the same negative results were achieved with session hijacking.

WPA/EAP-Tsults The same tests were performed to the WPA EAPTLS implementation. One advantage of the WPA solution is that it is a link layer security protocol. As ettercap is a tool that relies on the network layer, it was not possible to do MITM, impersonation and session hijack attacks. Other tools were also used to try to break the security of WPA such as Cain e Abel (Oxid IT Team, 2005) and Kismet (Kismet Team, 2004). However, none of them was able to achieve a successful result. On the other hand, DoS attacks were performed with a high percentage of success. WPA disconnects the network for 1 minute if it detects an attack against the MIC, this is done as part of a protection against brute force attacks. Although difficult,

it is possible with a WPA client to trigger this behaviour with fake network access messages. This issue makes it possible to do a DoS attack against WPA, since it is just necessary to activate a WPA client and ask an AP for network access. The AP verifies the message and, if it detects a fake message, it blocks all network access, and stops all communications, including the access of valid clients. It is important to refer that, with the new 802.11i standard, this vulnerability has not been solved (Wullems, Tham, J. Smith, & Looi, 2004).

Comments From the above experiments we can conclude that the IPsec and WPA EAP-TLS solutions are very efficient against MITM, impersonation and session hijacking attacks. Both solutions are not efficient against DoS attacks. It is possible to successfully perform DoS attacks using freely available tools. For systems where availability is essential, it is necessary to complement those solutions with mechanisms that reduce the risk of such attack. It is thus necessary to use tools like Intrusion Detection Systems (IDS) and vulnerability scanners.

Complexity Experiments The system performance was measured in order to evaluate the complexity introduced in the network elements. For this purpose the sysstat (Systat Team, 2008) and vmstat (“vmstat Man page”) tools were used. These tools allow evaluating CPU utilization, memory and interrupts. The results given by those tools are shown in Figure 8 and Figure 9. The WPA experiment impacts on the WPA client and RADIUS server; in the IPsec experiment, the impact is on the VPN components10 (see Figure 4 and Figure 5 for the architectures). The scale is the percentage of resource utilization except for the processes and interrupts that are absolute values. The pictures only show

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Figure 8. System performance – sysstat results

Figure 9. System performance – vmstat results

the RADIUS impact results for the WPA-TLS experiment, as they were negligible in the IPsec experiment. The presented results represent the average values obtained by 35 simulations, with a stochastic confidence interval of 90%. An UDP flow of 54Mbps was used to represent a fully loaded network. These results show that the IPsec system requires more: CPU utilization, memory, interrupts and processes, therefore, its impact on devices’ performance is not negligible.

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The results of WPA are similar to the ones of the plain system, introducing low impact in the network elements. From Figure 8 and Figure 9 we can observe that different absolute results are obtained by each tool. This is due to the specific requirements of each tool and its design, i.e. the number of processes, memory usage and number of interrupts is influenced by the specific characteristics of each tool.


Note that the processing of the WPA packets is done in the WPA client and the AP. Thus, the encryption/decryption occurs at these two elements. In the IPsec case the secure tunnel is between the VPN client and the VPN Gateway, thus not impacting the AP (Figure 5).

Impact on Data Flows To evaluate the impact on data flow when the security mechanisms are in place, we performed experiments using TCP and UDP traffic, and considering a network with and without security implemented. For traffic generation, IPERF (Iperf Team, 2005) and Crude (Crude team, 2002) tools were used. All traffic was generated after the negotiation of the specific security protocol (IPsec and WPA-TLS). Figure 9 shows the results of throughput and transferred bytes of a TCP flow with a duration of 120 seconds and a default window size of 85.3 Kbytes, when no security, WPA and IPsec are in place. The presented results represent the average values obtained by 48 simulations, with a stochastic confidence interval of 92%. As can be seen, IPsec is the mechanism that achieves lower

throughput; it also adds more overhead, since it conveys less information per bytes transferred (total amount of data transferred for each TCP window) than the WPA solution. The throughput and transferred bytes of WPA is larger than IPsec, but obviously lower than the plain network (without security). These results are due to the larger complexity introduced by IPsec (ESP with tunnel mode was used, which adds a new header and new authentication field). WPA does not make significant changes to a packet, just ciphers it and adds an IV field. The same experiment was done for different TCP window sizes11, which also confirmed the fact that IPsec is the solution with less throughput and bytes transferred. To evaluate the jitter12 and the number of lost packets, IPERF was used with UDP flows in networks with bandwidths of 10 Mbits/s and 54 Mbits/s. These consisted of 5 flows with duration of 60 seconds, simulating a voice communication. The obtained results represent the average results of 20 simulations with a stochastic confidence interval of 95%. Figure 11 and Figure 12 show the results for a network bandwidth of 10 Mbits/s. The results demonstrate that, due to its complexity and packet processing, IPsec has worse

Figure 10. Throughput and bytes transferred

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Figure 11. Jitter of UDP flows in a 10 Mbits/s network

Figure 12. Lost packets of UDP flows in a 10 Mbits/s network

jitter results. Regarding the number of lost packets, IPsec is the security solution that has better results. This is due to the fact that the process of packet protection happens between the VPN gateway and the client, while in the WPA solution this is done between the AP and the client. As the gateway has more capacity for processing the packets, it can keep its buffer available and the number of lost packets is reduced. The results obtained with 54 Mbits/s and with CRUDE confirm the ones of IPERF with 10 Mbits/s.

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These data flow results led to the natural conclusion that for TCP communications (e.g. with file transfers), the WPA implementation has more advantages. For UDP communications the IPsec protocol achieves lower loss rates.

Deployment Discussion The deployment of the infrastructure requires studies regarding the location of access points for the intended coverage, as mentioned in the


introduction. The costs associated with the hardware (APs, wireless cards, Ethernet switches and servers) would depend on the required coverage and the number of users enabled with this access. However, notice that current laptops and PDAs have already wireless capabilities supporting 802.11i. The software associated with the framework is already available with the hardware except for the servers (LDAP, RADIUS). Nevertheless, they are readily available in reliable free open source packages (with support available separately) and also in commercial products. In terms of user impact, the access to the network could be coupled with the existing credential system, thus easing the needed user interaction. However, as a first approach these two authentication points in the network and in the VEPR should be done separately. The final purpose is to build a single-sign-on system that would provide only one authentication control.

believed to be very similar to the WPA solution. The impact of the WPA security is negligible in terms of the performance of the system. The throughput achieved was slightly worse in WPA than in a plain system. However, the difference should not be noticeable to users. As discussed, the existing VEPR wired solution was designed and implemented with all the security requirements; adding this extra layer of security results in an easier process, as long as it respects the security goals of the VEPR. With the proposed architecture, secure access to the current system is increased due to the wireless connectivity advantages (e.g. mobility, everywhere access and access to wider range of devices). This access provides secure authentication and authorization, secure communications and also maintains the integrity of the retrieved information, thus preserving the security goals of the VEPR. This is very important and justifies the need for similar studies when implementing wireless solutions.

CUSiON

Open Challenges

Discussion

As future work, a prototype will be implemented within the real scenario so that the wireless solution can be evaluated. Several issues need to be tested and enhanced. These include performance, access control, availability issues (such as DoS), access point correct distribution and usability. Further issues are related to the presentation of the VEPR within wireless devices. This needs proper study as its usefulness and success may depend upon it.

The wireless architecture discussed above is able to provide wide as well as mobile and flexible access to the VEPR implemented within a healthcare institution. The architecture is modular and flexible in order to adapt itself to the existing features so that it can be incorporated when a LAN is already in place. In particular, the proposed architecture takes into account the fact that the existing devices can be reused with WPA/EAPTLS; it also integrates the recent 802.11i standard, making it versatile and upgradeable. To account for the security and performance of the system, several studies and tests were made with the presented technologies. The only exception is the recent 802.11i because no compliant devices were available at the time of testing. Nevertheless, its overall observed performance is

ACKknowledgmen This VEPR has already won 2 prizes for its innovation and results from Portuguese Government Institutions. As such, the first author would like to thank all the parties that collaborated in its implementation, specially the Security Commis-

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sion of Hospital S. João, LIACC and CINTESIS for their interest and support.

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IEEE 802.1X. (2004). Ieee standards for local and metropolitan area networks—port-based network access control. Retrieved from http://standards. ieee.org/getieee802/download/802.1X-2004.pdf. Iperf Team. (2005, May 3). Nlanr/dast : iperf - the tcp/udp bandwidth measurement tool. Retrieved February 12, 2008, from http://dast.nlanr. net/Projects/Iperf/. Kent, S., & Atkinson, R. (1998a). Rfc 2402 ip authentication header. IETF. Retrieved from http://tools.ietf.org/html/rfc2402. Kent, S., & Atkinson, R. (1998b). Rfc 2406 ip encapsulating security payload (esp). IETF. Retrieved from http://tools.ietf.org/html/rfc2406. Kim, H., & Afifi, H. (2003). Improving mobile authentication with new aaa protocols. Retrieved February 15, 2008, from http://citeseer.ist.psu. edu/article/kim03improving.html. Kismet Team. (2004). Kismet. Retrieved February 18, 2008, from http://www.kismetwireless.net/.

Moskowitz, R. (2003, November 4). Weakness in passphrase choice in wpa interface. Wi-Fi Net News. Retrieved from http://wifinetnews. com/archives/002452.html. Ng, S., & Tan. (2006). Security issues of wireless sensor networks in healthcare applications. BT Technology Journal, 24(2), 138-144. doi: 10.1007/ s10550-006-0051-8. NIST. (2001). Fips-197: advanced encryption standard. Natioanl Institute of Standards (NIST). Novell. (2004). Novell edirectory vs. microsoft active directory. Retrieved February 15, 2008, from http://www.novell.com/collateral/4621396/4621396.pdf. OpenSSL. (2007, October 19). Openssl: the open source toolkit for ssl/tls. Retrieved February 12, 2008, from http://openssl.org/. Oxid IT Team. (2005). Cain & abel. Retrieved February 15, 2008, from http://www.oxid.it/cain. html.

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Rigney, C., Rubens, A., Simpson, W. & Willens, S. (1997). Rfc 2138 remote authentication dial in user service (radius). IETF. Retrieved from http://tools.ietf.org/html/rfc2138. Systat Team. (2008, January 6). Sysstat. Retrieved February 12, 2008, from http://pagesperso-orange. fr/sebastien.godard/. Ventura, H. (2002). Diameter next generation’s aaa protocol. Vmstat man page. Retrieved December 17, 2007, from http://linuxcommand.org/man_pages/vmstat8.html. Walker, J. (2003). 802.11 security séries part ii: the temporal key integrity protocol. Intel Corporation. Retrieved from http://softwarecommunity. intel.com/articles/eng/1905.htm. Whalen, S. (2001, April). An introduction to arp spoofing. Retrieved February 15, 2008, from http://www.node99.org/projects/arpspoof/. Whiting, D., Housley, R., & Ferguson, N. (2003, September). Rfc 3610 - counter with cbc-mac (ccm). Retrieved March 8, 2008, from http://www. faqs.org/rfcs/rfc3610.html. Wpa . Retrieved February 8, 2008, from http:// www.wi-fi.org/knowledge_center/wpa/. Wullems, C., Tham, K., Smith, J., & Looi, M. (2004). A trivial denial of service attack on ieee 802.11 direct sequence spread spectrum wireless lans. Wireless Telecommunications Symposium, 129-136. Yu, W. D., & Jothiram, V. (2007). Security in wireless mobile technology for healthcare systems In , e-Health Networking, Application and Services, 2007 9th International Conference on (pp. 308311). doi: 10.1109/HEALTH.2007.381659.

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EndNoTES

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Java version of the Open DataBase Connectivity (ODBC) designed by Microsoft to provide a common API for accessing databases. Network File System is an IETF protocol to allow client systems to access remote storage as if it were locally available. WEP is part of the original 802.11 standard. Some older products that do not support directly WPA can (most likely) be software upgradable. The Wi-Fi Alliance certifies products compliant to 802.11i as WPA2. See http://certifications.wi-fi.org/wbcs_certified_products.php?advanced=1. Note that WPA and 802.11i also are independent of the source application. SSIDs identify the network that a device is connecting to. For technical reasons the AP needs to map SSIDs with VLANs. The AP marks all IP packets with the VLAN associated with the corresponding SSID. For interconnecting the AP and the RADIUS server, a layer 2 or 3 switch is used. It was not technically possible to evaluate the impact on the Access Point. The TCP window size controls the number of packets that can be sent without being acknowledged. Increasing its size will mean that a higher number of packets can be sent but if the receiver’s buffer can not cope with the amount it will mean that the sender will have to re-send more packets. Jitter pertains to the variation of packet delay; the delay is composed by sender delay, travelling in the network delay and receiver delay. The variability of this total delay is measured by jitter.

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Chapter III

Personal Health Records Systems Go Mobile:

DeILning Evaluation ComSonents Phillip Olla Madonna University, USA Joseph Tan Wayne State University, USA

ABSTRACT This chapter provides an overview of mobile personal health record (MPHR) systems. A Mobile personal health record is an eclectic application through which patients can access, manage, and share their health information from a mobile device in a private, confidential, and secure environment. Personal health records have evolved over the past three decades from a small card or booklet with immunizations recorded into fully functional mobile accessible portals, and it is the PHR evolution outside of the secure healthcare environment that is causing some concerns regarding privacy. Specifically, the chapter reviews the extant literature on critical evaluative components to be considered when assessing MPHR systems.

Inoduion Information technology (IT) is dramatically transforming the delivery of healthcare services. This can be seen through the increased activity in Mobile Health (M-Health) and promotion of the Electronic Health Record (EHR) systems in

the healthcare industry and the recent attention and increased activity in the adoption of Personal Health Record (PHR) systems. By distinction, PHR systems have not established a similar height of interest as the EHR (Tang, 2006), but this is changing as more government bodies such as the U.S. Secretary of Health and Human Services,

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Personal Health Records Systems Go Mobile

the National Coordinator for Health Information Technology, and the Administrator of the Centres for Medicare and Medicaid Services (CMS) have all identified PHRs as a top priority. In addition to the government organizations involvement, standards organization such as Health Level Seven (HL7) have began the standard definition process to formalize a system model for PHRs. PHR aims to allow individual health consumers the ability to monitor and manage their personal health information from multiple sources in a single repository. Research shows that maintaining a PHR encourages personal participation in healthcare and cultivates an increased emphasis on communication between the individual and the healthcare provider (Kupchunas, 2007). The use of a PHR provides the opportunity for healthcare providers to monitor and educate patients on health matters and lifestyle changes, and it also acts as a tool for enhancing health literacy. The PHR will eventually improve the decision making capabilities as the patients become more proficient at recording and monitoring vital health information (Lee, Delaney, & Moorhead, 2007). The goal of utilizing personal health records would be to enhance and optimize the healthcare practices while allowing patients to manage their own health care decision-making. For the caregivers, PHR technology can improve efficiency, costeffectiveness, timeliness, safety, and efficacy of the care processes, whereas for the individual consumers, it can help improve their quality of life. Large organizations and government bodies have recently gained an interest in the PHR phenomenon; for example, Intel, Wal-Mart and BP have formed a consortium, called Dossia, to supply PHRs for their employees; Medicare and Medicaid Services are trialing PHR with Medicare claims; and Google and Microsoft have also entered this market with new products such as Microsoft Health Vault and Google Health. In addition, Verizon Communications in combination with WebMD now offers a password-protected site for PHR (Reese, 2007).

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The PHR migration to the mobile platform offers immense benefits such as portability and convenience in the accessing and transmitting of personal health records from a single location, the empowerment of the health consumers to control, verify, and manage their own health information, and the potential enhancement of patient-caregiver relations. Unfortunately these benefits can be overshadowed by the concerns regarding security, privacy, mobile technology choice, and validity of information. This chapter will highlight the important evaluation components that need to be considered when the PHR is modified to support mobility. The discussion is structured as follows. Following the introduction, the next section describes the history and background of PHRs. Against this backdrop, an overview of the literature and progress being made on PHR research will be highlighted. This will be followed by a discussion on the four categories of PHR systems, namely “individually maintained”, “tethered” to a health plan or employer, “comprehensive” or “Health 2.0”. Once the advantages and disadvantages of the various types of PHR systems have been presented, the discussion will converge on a framework for Mobile PHR systems evaluation, which is then followed by the review of three commercial Mobile PHR systems using components from the framework. The chapter will then conclude with a summary of thoughts on future growth and development in this area.

Ba and HISTORY of PHR The Personal Heath Record (PHR) is not a completely new phenomenon; accordingly, one of the earliest references to a PHR can be found in an article by Okawa (1973) entitled, “A personal health record for young female students.” Several references to personal health records surfaced prominently as “paper records” up until the mid


1990’s, when the computerized version of the PHR appeared. With the diffusion and general use of computers, individuals became interested in maintaining an electric copy of their personal health information. Sittig (2002) conducted a search of available Internet-based PHR’s and came up with 27 identified programs or usable tools; by April of 2003, only 7 of these tools were relevant and still accessible (Sittig, 2002). The demise of these Internet-based companies occurred with the meltdown of the dotcoms, poor business plans, inefficiency, and decreased value to the individual. (Personal Health Working Group, 2005). Even though the early PHR’s were ultimately unsuccessful, they provided valuable insight into the next generation of PHR’s by supporting the fact that by “adopting EMRs, providers and health care delivery systems can facilitate the development and implementation of PHR tools and PHR’s should offer clear benefits to users and critical stakeholders.” (Personal Health Working Group). In summary, PHRs have evolved over the past three decades from a small card or booklet with immunizations recorded on it to a computerized application that stores an individual’s personal health information. A report by the Markle Foundation, Connecting for Health (2003), defines PHR’s specifically as “An electric application through which individuals can access, manage and share their health information, and that of others for whom they are authorized, in a private secure, and confidential environment.” Page 4 Imperative to the usefulness and functioning of the PHR is the establishment of a common data set. At the very least, it should contain information regarding items such as: allergies and adverse drug reactions, illness and hospitalizations, surgeries and procedures, vaccinations, lab test results, and family history. The National Committee on Vital and Health Statistics (NCVHS) reported in 2005

that it is not possible or even desirable to attempt a unitary definition of a PHR. The NCVHS did however state that it is possible and useful to characterize PHRs by their attributes. Attributes associated with PHRs are elements such as the nature of the PHR’s content, the source or sources of information, and the functions that they offer (Sprague, 2006). In retrospect, the consumer and health care provider will be able to maintain this health care information in a way that is assessable to both parties. More recently, President Bush and Secretary Leavitt brokered a plan that would allow patients and their health care providers the ability to access their personal health records through the use of technology (Gellman, 2008). Having this technological capability ensures that patient and health care provider can access healthcare information at any time when seeking medical care. As a result, in 2001, the National Committee on Vital and Health Statistics identified areas in deliverance of healthcare services that required improvements in systems in order to promote quality patient care services, continuity of care and beneficial treatment modalities. Technological systems were further explored in order to change the way personal health information was maintained by the patient and the health care provider. The Hurricane Katrina saga in the USA has caused the healthcare industry to recognize the importance of seeking ways to secure personal health records. For years, people had collected, maintained and stored their health information on paper, in memory, and other manual means. During such disaster, all of the information kept by patients and health care providers became inaccessible. But with the advances in technology, consumers are provided with tools and mechanisms to maintain their own health records through the capability of computer-based applications. The consumer then has the ultimate responsibility of keeping track and updating their personal information so that their medical care is effective and efficient. Additional benefits of a

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PHR include access to health information, data collection, improved health, disease management and tracking, improved communication with health care providers, and although not well documented, it is also believed to lower costs related to chronic disease management and wellness programs (Tang, Ash, Bates, Overhage, & Sands, 2006) According to Kupchunas, “maintaining a PHR encourages increased personal participation in healthcare and fosters a greater emphasis on communication between the individual and the caregivers” (Kupchunas, 2007: pg 185).

PHRLiterature Review PHRs aim to enhance and optimize the healthcare practices while allowing patients to manage their own health care decision-making. On the side of healthcare practices, this would improve efficiency, effectiveness, timeliness, safety; on the side of the patients, it would help them to improve their quality of life. A comprehensive review of the extant literature published on issues related to PHRs using scropus tools (www.scropus.com) is now presented. Scopus is the largest abstract and citation database of research literature and Web sources. Scropus contains over 33 million abstract and provides access to publications in 15,000 peerreviewed journals and 200 book series from more than 4,000 publishers. The system also has access to over 1200 Open Access journals, 500 conference proceedings and 600 trade publications. The literature search undertaken not only investigated the bulk of the academic literature but also reviewed data from 386 million scientific Web pages and 22 million patent records from 5 patent offices. Even though our search criteria covered a period of 20 years, we have chosen to present data that are only from the last decade. The search excluded medical health records, electronic health records, and electronic medical records. This essentially directs the review to focus

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on system functionality of PHRs along with the unique elements associated with a PHR.

PHR Funionali and Componen There have been considerable research and investment into PHRs over the past decade. This is evident in the growing number of publications and patents registered in this area. Based on a review of the extant literature, the main areas of functionality are illustrated in figure 1. As shown in figure 2, the functionality can be broken down into various aspects of consumer functionality (Denton, 2001). Two major aspects, which are considered to be among the most promising uses for PHR system, are the use of PHR for health education and for managing specific patients conditions(Arbogast & Dodrill, 1984; Bent, Bolsin, Creati, Patrick, & Colson, 2002; Bhuyan, 2004). Another aspect is linking the PHR to decision support. This is also a key research component with obvious benefits to the patients choosing and understanding a suitable treatment plan (Abidi & Goh, 2000; Ackerman, 2007). Other important aspects include consumer information (managing clinical and personal data) and the growing use of PHR systems to support and improve administrative support functions. As issues relating to the area of privacy and security (Agrawal & C. Johnson, 2007; Alban, Feldmar, Gabbay, & Lefor, 2005; Albright, 2007; Alhaqbani & Fidge, 2008; Al-Salqan, Jagannathan, Davis, Zhang, & Reddy, 1995; Anderson, 1996; Armitage et al., 2008; Blobel, Pharow, Spiegel, Engel, & Engelbrecht, 2001) have now become more important than ever given the nature of the new generations of PHR systems, we will examine them separately and more closely in a later section of this chapter. . Although there seems to be a peak in academic research around 2006 (see Figure 1), this has not


Figure 1. Personal health records publications

slowed down the commercial field. There is an increase in Web based activities and patent applications. This shows that as the research matures more commercial offerings are being launched. The industry-based publications are focusing on issues such as standards, interoperability, security and integration (Ball & Gold, 2006). Personal health records are maturing rapidly and several diverse actors’ have full-scale implementation of PHR systems - these actors include employer groups such as DOSSIA, and commercial vendors such as Google and Microsoft. The emergence of large organizations such as Google who already hold significant volumes of data about individuals is creating serious concerns. The concept map below provides a snapshot of the current work being undertaken in the field, the research covers a broad spectrum, which highlights the multidisciplinary approach to research in this field.

Business Models A study by Adler, highlighted the fact that 74.6% of patients surveyed were willing to pay a small annual fee for one or more of the following online services: viewing parts of their medical record, messaging with their physician, medication refills, appointment requests, and billing inquiries (Adler, 2006). A variety of sources, such as healthcare providers, insurers, employer or commercial suppliers offer a wide range of products that are available to help you create your own PHR. Some PHRs include products that have free tools, others offer products for purchase, Figure 3 summarizes the various existing business models. An array of complex business models exists in the PHR domain. However, from a general marketing perspective, these can be grouped into three commercial types – consumer purchase, consumer subscription, and a combination of the two (Gellman, R. 2008). For the purpose of this

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Figure 2. Concept map of personal health records research from literature

review, the business models have been classified into five categories:

3.

Consumer subscription model: The consumer is responsible for an ongoing service fee to access the data and related systems. Consumer purchase model: With this approach the consumer pays a fixed fee to purchase the software that provides the core functionality of the PHR outright. This type is typically a desktop application. The subscriber may also have the option to purchase a support contact.

4.

1.

2.

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5.

Free model: With this model the PHR is free to consumers because the service is supported by advertising. Employee support model: With this model, an employer or health plan will contribute part or all the fees to run the service. This is seen as preventative medicine as there is strong evidence that they could save money on health care costs in the long run. Combination: There is also the possibility that a combination of these various models could be in effect. For example a PHR service paid for by an employer or health plan may allow advertising.


Figure 3. Business model for personal health records

Although these models may be the obvious source of revenues, there may be other elements in play such as affiliation models with informational Web sites, niche search engines, articles, surveys, software downloads, and a variety of products that are not directly associated with the PHR system. Prices and formats vary widely, with diverse levels of technical abilities and functionality among the different product lines. From the perspective of Health Insurance Portability and Accountability Act (HIPPA), many of the PHR sites state in their guidelines that they ‘comply with’, or ‘we voluntarily operate within the guidelines’, but most mentioned nothing about HIPPA

certification. Most sites stated they protect the privacy of personal health information (PHI) and will not share your PHI. A PHR privacy policy study in 2007 conducted by the Department of Health and Human Services (DHHS) stated that “only 3 percent, or one in 30, of PHR privacy policies stated that explicit consumer consent was necessary prior to the vendor sharing any of the data in the PHR” (Gellman, 2008, p. 7). Even though numerous PHRs are free to consumers, a PHR vendor is operating a business, in which revenues are generated primarily through advertising and marketing. Therefore, it is imperative that consumers educate themselves when sharing their information with PHR vendors.

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Categorization of PHR Systems Based on Ownership There are many ways to categorize PHR systems, but for the purpose of this chapter, the concept of ownership is used. This approach was taken to highlight the potential issues that arise from transferring ownership. Most of the existing applications fall into three main categories: individually maintained, “tethered” to a health plan or employer, or comprehensive (Sprague, 2006). A new category, which has also emerged recently and has been added for the purpose of this review, is called health 2.0. It is this new category of PHR applications that is providing serious concerns from a security and ethics perspective. In order to assist readers attempting to compare and contrast among the different categories, we will also highlight the respective advantages and disadvantages associated with each category.

MoBIile HEALTH 2.0 PHRS This category refers to the new generation of Web 2.0 healthcare applications that support mobility. Health 2.0 is just as difficult to define as the Web 2.0 concepts. The Health 2.0 conference definition focuses on user-generated aspects of Web2.0 within health care but not directly interacting with the mainstream health care system. The problem with this definition is that it is very difficult to separate the user generated and mainstream healthcare systems without generating duplication and redundancy. Given that there are several definitions, we have decided to share the approach taken by Scott Shreeve from CrossOver Healthcare due to the pragmatic nature of his definition of a Health 2.0 Company: “Next generation health companies that leverage the principles of openness, standards, and transparency; utilize the technology tools

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of collaboration, information exchange, and knowledge transfer; and focus on delivering value added services that empower health participants (patients, physicians, providers, and payers) with freedom, choice, and accountability for health outcomes. There is currently a lot of debate regarding this new concept. Some believe that the these companies are providing a wider movement to reform the entire US health care system, while others believe that these are merely tools and technologies to support the current system. There are considerable concerns when companies that are not within the health care industry take active roles in storing and maintaining personal health data. Most of the companies that operate in this domain are strong advocates for mobility and are keen to integrate mobile technology into their Web portal solutions. Google is implementing a pilot at Cleveland Clinic hospital in Cleveland as the pilot site for a new personal health records initiative. Between 1,500 and 10,000 patients will partake in the project. Patients will have their current MyChart electronic health records migrated to their Google accounts. Once the PHRs, are shared with Google, patients will have the capability to access them outside of the Cleveland Clinic. Google is not the only technology giant looking to change the healthcare industry. AOL founder, Steve Case, has recently launched a new organization called Revolution Health (http://www.revolutionhealth. com/); InterActiveCorp has also invested in several health-related start-ups (http://www. healthcentral.com/); and Microsoft has been very active with a medical record service called HealthVault. One of the ways that companies are providing customers with additional benefits is by providing secure access to the health records stored on the portals via a mobile device. At this point, we will highlight the respective advantages and disadvantages associated with the Health 2.0 category.


Advantages

Individually Main PHR

• •

The simplest form of a PHR is one that is maintained by the individual. This sort of PHR is created, updated, and controlled strictly by the individual (Sprague, 2006). Such a PHR allows the individual to organize and retrieve their own health information and it captures the patient’s concerns, symptoms, emergency contacts, and other relevant information (Endsley et al., 2006). This type of PHR can be software driven and may reside on a person’s computer or be Web-based. The Wed-based format is maintained by a thirdparty. Other devices such as “smart cards,” USB drives, and CDs can also be utilized for this type of PHR (Endsley et al., 2006).

The data is available from anywhere Interoperability and the use of open interfaces mean that data can be imported directly from the healthcare provider in some cases.

Disadvantages •

•

• •

This approach has already raised serious privacy concerns due to the migration of private data into the commercial domain, and critics of such projects have already begun to make themselves heard. These third-party PHR systems are not covered by the HIPPA, which has been in effect since 1996 and requires individuals to be notified when a party other than a patient's doctor wants to access confidential medical information. There will be some costs associated with the business model. Access to the Internet is required to access data via the mobile device.

Advantages The individually maintained PHR has a limited number of advantages, aside from it being controlled by the individual; the security may be the biggest advantage of this type of PHR. The individual PHR provides more control over access to the data contained within the PHR (Tang et al., 2006).

Figure 4. Personal health records categories

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Disadvantages The question regarding the individual PHR is how often will the individual update their PHR? The individual PHR, as maintained by the individual, may not be updated as often as they should. Another disadvantage is that it may not be considered a trusted conduit for transmission of medical information among clinical offices or health care institutions. Another aspect is the fact that the individual PHR may not have enough back-up systems in place in case of any technical malfunction (Tang et al., 2006). As we discussed the back-up system, we need to also take into consideration the literacy of the individual. How well versed is the individual with regards to medical and technical information? Does the individual understand and comprehend the information and can they relay the information technically (Tang et al., 2006).

who changes jobs or insurance companies may lose access to the personal health information (Sprague et al., 2006). Other disadvantages include security and privacy issues and the question of standardized language (Tang et al., 2006).

Compehen PHR

The “tethered” PHR, populated with claims data and typically available to the consumer through a secure Web portal, is created by a health plan or an employer Web.

A more sophisticated PHR is made available through the electronic health record (EHR). A care provider or organization, such as a hospital, physician, or an integrated delivery system, usually maintains the EHR. The EHR is designed to be a repository of clinical information on a patient and to accept information from a variety of sources. The sources of information may include physicians, laboratories, and consumers. The capability of this type of PHR allows the consumer access to some portion of his or her clinical data, under rules set by the provider; it may also allow secure e-mail messaging, access to condition-specific information, appointment scheduling, and many other functions (Sprague, 2006). Different organizations will maintain a range of somewhat differing policies and procedures with respect to availability, accessibility, portability, release and use of personal health information captured in the PHR.

Advantages

Advantages

The main advantage of the “tethered” PHR is the fact that this type of integrated PHR can provide the patient with much more relevant data. It may also provide the patient with a better back-up system, due to the fact that the integrated PHRs have a larger back-up system (Sprague, 2006).

The biggest advantage is the access that patients will have to a wide array of credible health information, data, and knowledge (Tang et al., 2006). A secondary but still very important advantage is the potential to lower communication barriers between the patient and health care provider (Tang et al., 2006). Integrated or comprehensive PHRs provide an ongoing connection between patient and physician (Tang et al., 2006).

“Teed a HEALTH Plan omploye

Disadvantages A major disadvantage to the “tethered” PHR is the lack of portability; for example, the individual

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Disadvantages Many of the disadvantages are the same for the comprehensive as for the “tethered” PHR. The issues of security and privacy and the use of standardized language are being raised with regards to the comprehensive PHR. The World Privacy Forum recently issues a report on why many PHRs can actually threaten security. The next section will discuss some of the important issues surrounding privacy and security of PHR systems

Categorization Based on Storage Medium There are now organizations that collected, organized, summarized, and then make available electronically copies of all of the medical information. In some cases, the information is provided to user in a wallet or regular CD, or it could be accessed through the Internet. Typically an electronic PHR can be maintained in various formats including paper-based, PC, Internet or

portable (flash drive, CDROM, DVD or smart card) or on mobile devices.

PHRon Peonal Compu PHR products that involved health information residing on one’s own computer is one of the most common types of PHR service. . The user typically uses a CD or downloads the PHR template from the Internet to the personal computer. Information about one’s health would then be entered and maintained by the user on his or her computer.

PHR on UNIVERSAal SERIial BUS (USB) DRIVES A USB, (Universal Serial Bus), also known as thumb drive or jump drive, is an inexpensive, portable, electronic device used to store information. The USB is a NAND flash device, with memory stored in chips. This architecture allows for low

Figure 5. Categorization of PHRs based on storage medium

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power consumption, fast speed of use, and high density, allowing for storage of large amounts of information (Axelson, 2006). Device memory is “nonvolatile”, easily allowing the user to write and erase information. Moreover, these devices are designed to spread the write-erase cycles evenly across the components of the device in order to prolong the device life.

Advantages Utilization of a USB device for storage of the PHR promotes an important goal, which is, encouraging individuals to become actively involved in their healthcare. With a USB device, the individual manages health information data input and storage (Ball, Smith, & Bakalar, 2007). Once health information is stored on a portable USB device, this information can be quickly accessed anywhere, a factor especially important during emergencies (Shetty, 2007). USB access is a standard feature on all computer systems, and the USB devices allow easy insertion and disconnection without interruption of the system (www.intel.com). Individuals who advocate for development and utilization of PHRs stress that portability of the PHR is important as individuals move through the healthcare system. A USB would allow individuals to input and integrate data from many different sources, such as electronic health records, laboratories, radiology departments, and pharmacies. With increased consumer awareness of the importance of privacy, confidentiality and information security, a USB device that is individually carried and controlled can be an ideal solution (Ball et al., 2007). One design characteristic of USB devices that makes them desirable for information storage is durability, as the devices have no moving parts with the casing protecting the components (Axelson, 2006). Research of various USB vendors provided information on features available, including continuously increasing storage capacity, overwrite/modification prevention, display of remaining storage capacity, display of owner

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identity, and ability to perform on-line back up support. Security technology available includes encryption, password protection, requirements for complex passwords, password reset capability, biometric identification verification, and “lost and found” features.

Disadvantages USB device issues that can be problematic for PHR utilization include potential for device damage and data corruption. In addition, the costs of USB devices increase as the technological and security features become more advanced and complex. Healthcare information is secure on the USB device when in possession of the owner, however, if the device is lost and data on the device is not encrypted or password protected, privacy can become jeopardized (Tang et al., 2006). While the goal of a PHR is to increase an individual’s engagement in their own healthcare, those involved in the evaluation and promotion of PHRs question whether consumers are capable and willing to assume the tasks and costs involved in maintaining a stand-alone/USB PHR (National Committee on Vital and Health Statistics, 2006). The ongoing work involved in keeping the USB PHR up to date will most likely be seen as a burden to many consumers. Many providers would not likely see a PHR that contains information stored and maintained solely by individual consumers as a trusted or valid source. Similarly, providers may deem the information stored by consumers as “clinically irrelevant”, and if the information were excessive, it would be overwhelming for providers to review (Tang et al., 2006). If providers decide that information contained in the PHR is unimportant, the value of the PHR is adversely affected (Ball et al., 2007). Finally, providers may be concerned with the legal issues involved in the utilization of PHR data, that is, when the treatment decisions they made are based on inaccurate or invalid patient-entered information (Tang et al., 2006).


While utilization of USB devices for PHRs promotes individual’s engagement in their healthcare, the ability to interface and integrate healthcare information over time is a priority that an USB/stand-alone record cannot provide as the sole PHR source.

PHRon WORLld WIDE WEB Personal health records on the Internet are a growing phenomenon. These systems typically consist of a patient’s personal health information on the Internet, entered by them and/or possibly by their caregivers. Patients can record their personal information, demographic information, emergency contacts, insurance, medications, allergies, immunizations, tests, hospitalizations, surgeries, advance directive, spiritual affiliation, and even their care plan (Colorafi, 2006). There are several Web sites on the Internet promoting personal health records. In 2001, if you performed a general search on the Internet for personal health records revealed over 19 sites, some of the examples found included Dr. I-Net, HealthCompass, MedicalEdge, MedicalRecord. com, MedicData, Medscape, AboutMyHealth, and many more (Kim, & Johnson, 2001), this number has grown significantly. The Web services begin with a registration process that involves the user choosing a username and password. Through a Web interface, users then complete information about their (or a family member’s) health that is stored in a secure server maintained by the PHR company. Users can then access that information (and/or authorize access to others such as emergency contacts, physicians, or ER departments) by logging-in and providing their password. At this point not too many patients are aware of personal health records. First, many of them are not computer savvy; or since some of the Web sites are subscription based. However, there are some companies that are willing to help out their employees, and are encouraging the use of a PHR.

A number of U.S. companies are working together to develop “Dossia”, a Web-based framework to assist employees and retirees to create and maintain lifelong personal health records, of themselves and their dependents.

Advantages There are many advantages to having PHR on the Web for patients, physicians, employers, and pharmaceutical companies. “Technology can allow the use of personal health records that patients themselves can maintain, can allow and promote telehealth systems, and can actually enhance consumer choice” (Colorafi, 2006) Page 3. Several of these sites “allow patients and physicians to share patient-charted information and diagnostic test results. The benefit to the physician and office staff is that it enhances the physician’s teaching efficiency and reduces communication bottlenecks when the telephone is the sole communication tool” (Smithline & Christenson, 2001). Another push towards the Internet for physicians is the ease of use with their handheld devices. They are able to research medications, diseases, treatments, as well as patient labs, test results, and even billing, coding, and dictation abilities. Electronic prescribing systems are also on the rise and a great advantage to the physician and patient. These systems increase patient safety and physician efficiency (Smithline & Christenson, 2001). There are many benefits to the use of the Internet and personal health record. “The most important benefit the Internet will bring to health care will be the integration of information” (Smithline & Christenson, 2001).

Disadvantages 1.

With all the positive aspects of PHR and the Internet, some major disadvantages includecost and training. In addition, with the increased use of computers and the Internet,

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2.

3.

there is an increase of technical issues and system downtime. Training staff can be tricky, especially since a large number of health care providers did not grow up with the internet or e-mail. There will be a large cost for training staff in addition to hardware costs, software costs, implementation, maintenance, and productivity improvements (Colorafi, 2006).

Advantages 1.

2.

3.

Patients can create and maintain comprehensive online PHR accounts via the mobile devices. The mobile devices provide the capability to easily update and manage that PHR at any time, and from any location. Easy and convenient to use for wellness and health monitoring.

PHRS on Mo devi

Disadvantages

There is a new trend that is enabling patients to use their mobile devices to access details from their PHR. Patients can use the mobile device for a variety of functions including: maintaining a real-time health diary, and tracking vital health measures such as blood glucose levels, blood pressure, carbohydrate intake, height, weight. This approach can also be used to record and monitor physical activity such as diet, calorie intake or exercise. As people are becoming more reliant on mobile technology to organize and manage day to day routines, accessing and maintaining personal health information on wireless devices is a natural progression. One approach taken by system developers is to provide functionality that will allow emergency details along with important data such as immunization records, insurance details, and allergy information from a PHR portal to be downloaded to a secure module within a mobile devices operating system. Another approach is to provide access to the PHR via Smartphones / mobile device using the Internet and the mobile network to view and update the records held in the PHR portal. The benefit of the latter approach is the ability to gain access to more detailed information such as clinical records, medical history and interventions.

1.

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2.

3.

If the device is lost and the health data is stored on the mobile device, there could be serious security implications for the patient. The screen for the mobile device may not display all the information clearly due to the size and those users who are not used to mobile displays may find it difficult to navigate and access information. Only limited information can be viewed, while images and notes may be difficult to understand. The mobile version is not a replacement for the Web portal, and the role is to provide an interface to the portal.

Creating a Framework for Mobile PHR Systems Evaluation The Mobile PHRS framework presented here has been inspired by the Personal Health Record System Functional Model (PHR-S FM), a model proposed by the Health Level Seven (HL7), along with the Evaluation model proposed by (Kim & K. Johnson, 2002). The approach by Kim (2002) provides a comprehensive view of the PHR functions, and identified five prospective functions of PHRs. The model outlined requirements for accurate entry of information and verification of reported test and study results. The criteria also outlined requirements for the provision of different


routes of access, links to consumer health care information, functions to process and interpret information, and functions to provide secure communication between patients and providers. This evaluation was constrained by Web based criteria and does not take mobility into consideration. Another important element omitted from the Kim model is the data storage medium. HL7 is a premier health care information technology standards development organization boasting an extensive national and international representation. The main purpose of the PHR-S FM is to define the set of functions that may be present in PHR systems. The PHR-S also presents a set of guidelines that “facilitate health information exchange among different PHR systems and between PHR and EHR (electronic health records) systems,” The HL7 group advocates that “The PHR-S FM can be applied to specific PHR models (stand-alone, Internet-based, providerbased, payer-based, or employer-based models). At the same time, the Functional Model is flexible enough to encourage product innovation.” The mobile model presented here also takes into consideration the mobility aspects. The model is currently not an American National Standards Institute (ANSI)-accredited standard. The ANSI accreditation process will take 2 years. This means that the PHR-S FM will become a U.S. standard for PHRs at around 2010. Once the PHR-S FM is finalized by HL7, it will ensure that standards are available to the health care industry and the general public for use in PHR development. There is currently a profusion of PHR systems in existence but there is a lack of a functional standard to which these systems should conform. HL7’s PHR-S FM will be the first major industry standard to specify functionality for PHR systems. HL7 proclaims that the development of standardized, interoperable PHRs is a major component in the U.S1. DHHS plans, which is to make health information available electronically through a National Health Information Network (NHIN) by the year 2014.

Portability of Records: The HL7 EHR Technical Committee was created in 2005 by the PHR Working Group - the group has members from heathcare providers, consumers, vendors, and payers. The group recently announced that it had entered into a memorandum of understanding (MOU) with America’s Health Insurance Plans (AHIP) and the Blue Cross and Blue Shield Association (BCBSA) to create a collaborative process for the maintenance of portability standards for PHRs. AHIP and the BCBSA have already developed an implementation guide (Implementation Guide for the Personal Health Record Data Transfer Between Health Plans) containing technical standards, a data dictionary, and operating rules for the transfer of PHR data elements between health insurance plans. Under the MOU, AHIP and the BCBSA have agreed to turn over the maintenance of the technical standards components of the Implementation Guide to HL7 and ASC X12 to ensure long-term maintenance of the standards. With considerable research and investment into personal health records over the past decade, and the growing number of publications and patents registered in this areas, our review unveiled that the main areas of PHR functionality or concern can be grouped into four functional areas as illustrated in Figure 6 and Figure 7. Personal health records are maturing rapidly and several diverse actors’ have fullscale implementation of PHRs, - these groups include subscribers, employer groups such as DOSSIA, and commercial vendors such as Google. There are few Web based systems that are fully integrated into ambulatory or hospital-based EMR systems. There are considerable challenges to implement the ideal PHR system, and there are important lessons that can be learned from the early adopters. A study by Halmaka et al (2008) identified a set of unique implementation issues and concerns from three case studies MyChart at Palo Alto Medical Foundation, PatientSite at Beth Israel Deaconess Medical Center, and Indivo at

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Figure 6. Mobile personal health record functional overview

Children’s Hospital Boston. They identified the following implementation challenges from 1999 to 2007, postulating that further challenges are likely to evolve over the next five years. Current challenging questions facing implemented PHR systems include: • • • • • •

•

Should the entire problem list be shared? Should the entire medication list and allergy list be shared? Should all laboratory and diagnostic test results be shared with the patient? Should clinical notes be shared with the patient? How should patients be authenticated to access the PHR? Should minors be able to have their own private PHR and should patients be able to share access to their PHR via proxies? Should PHR include secure clinician/patient messaging?

Future challenging questions that may arise in 2008 and Beyond include:

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•

• •

•

•

•

PHRs are institution-based and patients will want a single PHR that works with all their sites of care – how can this be achieved? Should PHRs support electronic data input from outside institutions? How do you allow patients to integrate knowledge sources on the Internet with their PHRs? How do you connect patients using social networking tools? Patients with specific diseases may want to connect to communities of others with similar diagnoses Patients may wish to participate in clinical trials, post market pharmaceutical vigilance, or public health surveillance via their PHR – how is this possible without compromising security? How do you securely incorporate the concept of mobility in a PHR system?

The next section will discuss the wireless and information management element of the evaluation framework, which are important evaluation components of a mobile PHR.


Figure 7. Mobile personal health record functionality

Mobile PHR Information Infrastructure There are no clear guarantees that the use of any IT applications in healthcare is going to be effective due to the technical complexity of Health IT systems. In the past healthcare software and hardware markets were considered to be less mature than other Industries and for medical technologies (Chiasson & Davidson, 2004). This notion is changing due to the development of new innovative software applications and availability of hardware specifically targeted to the growing healthcare market. The key to the growth in this area has been the launch of software that improves effectiveness by providing functionality to manage the administrative and support functions of healthcare. It is important that any PHRS system should take into consideration Information Infrastructure from the following Dimensions.

1.

2.

3.

4.

Interoperability: Interconnectivity among systems is important and managing relationships with various healthcare providers in a seamless and efficient manner along with providing user-friendly processes and interfaces to perform administrative functions are key features that must be considered in the design of a PHR. Information management: Question about how the data is to be stored, how often will it be backed up, and what storage medium is in place are key to successful information management. Record security: A variety of options may be available such as password protection, biometrics, and encryption, but the challenge is to fit the best mechanism to the purpose and design of the PHRs. Audit capabilities: With growing security and privacy concerns, measures must be in place to provide detailed audit of access to the records.

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It is vital that any MPHRS is evaluated on the audit capabilities. Clear and comprehensive audit policies must be defined that describe the use of patient medical records within the system. The policies should not only aim to protect the confidentiality and integrity of data but also protect the patient. One of the important features of the new generation of PHR systems is their ability to interconnect electronically using predefined interfaces or XML based interfaces. All sub systems

Figure 8. Information infrastructure components

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transferring data need to provide secure interconnection capabilities between the host systems and the PHR database. The data must be protected in terms of data integrity and patient privacy. Security policies for personal health information must be carefully designed in order to limit the number of people, clinical physicians, insurance companies, nurses, and others, that can access the patient record, and to control the operations that may be applied to the record itself (Anderson 2006).


Jelena (2007) defines clear policies that are appropriate for wirless clinical information systems, these policies have been adapted to a PHR. These policies are discussed below as a number of security procedures: •

•

•

•

Each record must have an associated access control list - a list that restrict access to the records other than those individuals and groups identified on the access control list. There must be an individual on the access control list that must have administrator privileges and/or rights, i.e., the power to add other users to the access control list. It is critical that the administrator notifies the patient of any changes of names on the access control list to any part of the patient records. An audit log of usage activity must be presented to the user. Each time the record is accessed the following information must be presented - the name of the user performing the access, the date and time of access, and the manner of access (including records read, updated, stored, and/or deleted) - and has to be recorded. When the patient is incapacitated, the ownership of the records should belong with the legal guardian or another person with the appropriate power of attorney, not the person with the patients’ mobile device.

WieleETWORK Componen The adoption of mobile technologies in healthcare is on the increase and technologies such as Wireless Local Area Networks (WLAN) that use different protocols from the standard digital mobile technologies such as 2G, 2.5 and 3G technologies. A summary of these technologies are presented below along with the speeds and range covered.

It is important to understand how the device will connect to the Internet to access private health information as some networks are more secure than others. The ease of access that wireless networks offer is matched by the security and privacy challenges presented by the networks. One of the key concerns surrounding the implementation of Mobile PHRS is the issue of security. Moving a PHR into the mobile realm compounds these security fears. There are four types of security breaches that can occur. 1.

2.

3.

4.

Data duplication: PHRs raise the possibility of storing health data in multiple storage locations. For example in the EMR, Hospitals databases, and the PHR. In a mobile scenario this issue is compounded because the data may also exist on the mobile device. Data transmission issues: Using open unsecured networks such as wireless local area networks (wifi) to transmit personal health information will leave users open to security vulnerabilities. The system must detect when using an unsecured network and prevent the release of information in that scenario. Lost devices: If the device is lost, the data server must prevent that device from accessing medical information from that device. If the functionality is embedded within the SIM card the device must prevent the embedded functionality as well. Virus and malware: There is a growing trend of Viruses being targeted at mobile devices due to the proliferation of mobile devices.

MoBIile PHR SYSTEemReview There are a variety of configurations that can be employed for a mobile PHR system. Each of the approaches provides a variety of benefits along with potential security vulnerabilities. The section

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Figure 9. Wireless system components

will use the evaluation framework defined in the previous section to review three commercial PHR systems that support mobile phone access.

System 1: In Case of Emergency (IC) PHR Mobile This PHR product is a combination of CapMed products, icePHR and Microsoft HealthVault. The aim here is to make appropriate medical records freely available “In Case of Emergency (ICE)”.

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The user has the capabilities to modify what pieces of their information will be accessible in an emergency situation by using a Web application. The full medical record resides within the HealthVault eco-system and is transferred into the icePHR. The icePHR emergency data are viewed by three methods. 1. 2.

displayed through a personalized icePHR Web site, wallet-size emergency card,


Table 1. Wireless networks Networks 2nd Generation GSM

Speed 9.6 kilobits per second (KBPS)

Range and Coverage

Main Issues for M-Health

World wide coverage, dependent on network operators roaming agreements.

Bandwidth limitation, Interference.

High Speed Circuit Between 28.8 KBPS and 57.6 KBPS. Not global, only supported by Switched Data (HSCSD) service providers network.

Not widely available, scarcity of devices.

General Packet Radio Service (GPRS)

171.2 KBPS

Not global, only supported by service providers network.

Not widely available.

EDGE

384 KBPS

Not global, only supported by service providers network.

Not widely available, scarcity of devices

UMTS

144 KBPS - 2 MBPS depending on mobility

When fully implemented should offer interoperability between networks, global coverage.

Device battery life, operational costs.

Wireless Local Area

54 MBPS

30–50 m indoors and 100–500 m outdoors. Must be in the vicinity of hot spot.

Privacy, security.

Personal Area Networks 400 KBPS symmetrically – Bluetooth 150 -700 KBPS asymmetrically

10 – 100m

Privacy, security, low bandwidth.

Personal Area Networks 20 kb/s – 250 KBPS depending on – Zigbee band

30m

Security, privacy, low bandwidth.

WiMAX

Up to 70MBPS

Approx. 40m from base station.

Currently no devices and networks cards.

RFID

100 KBPS

1m Non line-of-sight and contact less transfer of data between a tag and Reader.

Security, privacy.

Satellite Networks

400 to 512 KBPS new satellites have potential of 155MBPS.

Global coverage.

Data costs, shortage of devices with roaming capabilities. Bandwidth limitations.

3.

Mobile client side wallet.

The Wallet is the client-side portion of the application and resides on the mobile phone or personal computer. The personal health information located externally or privilege information is accessed right to the wallet. The mobile device would need to receive a software installation which can be delivered over-the-air. Vault Server: the Wallet communicates with the MobiSecure Vault Server for data synchronization and management. The role of Server-based software is to securely host, manage and retrieve personal user data from external data sources. The Personal Health record

application must be hosted on the server side of the provider of the Vault Server software. The user has the capability to view, submit or send, via fax, email or SMS, the user’s selected data. icePHR Mobile is a mobile device accessible software application that provides the capability to store and manage emergency medical information and contact numbers in a mobile phone. This product requires that the user subscribes to icePHR subscription. Mobile devices must meet the requirements of installing and running the custom icePHR Mobile application to allow the data to be stored on the phone negating the need for a data link connection. The application will

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allow subscribers the editing capability to insert, update and delete medical information directly on the icePHR server when Internet access is available. The icePHR application is only supported on specified networks and on a number of certified handsets, which includes smartphone, blackberries and standard mobile phone handsets that run java Midlet. The phones that do not incorporate the technology to install and run the icePHR Mobile™ application can use the WAP to view medical information stored on the server. To use WAP, the handset must have a built in Web browser such as Media Net. Unlike the Java icePHR Mobile

application where medical data are stored on the device, using the WAP access method no information is stored on the device nor can the user edit the records over WAP.

System 2: No More Clipboard M-PHR NoMoreClipboard.com is an online, patient-controlled personal health record management system designed to consolidate medical information in one convenient and secure location for easy retrieval and updates. NoMoreClipboard.com provides Web-based solutions to maintain an online personal health record (PHR). The system

Table 2. Evaluting three commercial MPHR Systems Criteria

System 1

System 2

System 3

System Name

ICE PHR

No More Clipboard

allOne Mobile

System Description

software application that provides the capability to store and manage emergency medical information and contact numbers the a mobile phone.

Mobile Web based patientcontrolled personal health record management system designed to consolidate medical information

AllOne Mobile cell phone-based application. to manage personal health information. Patient information

Network

2G or 3G Cellular requires data plan and SMS Plan

Access via mobile Internet browser using WiFi

Access via mobile Internet browser using WiFi

Device - OS

RIM 3.6, J2ME 1.1, Windows mobile for Pocket PC and Smart phone 2003+

Any device with Internet access

Blackberry RIM Java, Windows mobile for Pocket PC and Smart phone 2003+

Devise - Type

Mobile Phone (singular, sprint devices), Smartphone or PC

Any mobile device with Internet capabilities

Any mobile device with Internet capabilities

Information management

Application is downloaded and stored on the device

Application is run from the server. The user accesses the information via navigating to the home page on the mobile device

Application is downloaded and stored on the device

Data Storage

Stored on Device. (There is also an option to store data on server for non supported handsets

All the data is stored on the server

Data is stored on both the server and device

PHR Capabilities

Provided by ICEPHR and Microsoft Health Vault

Provided by Nomoreclipboard. com. Can integrate with Microsoft health vault

Integrates with online PHRs via community of Care (CCR) standards

Business Model

Add on to the ICE PHR subscription

Subscription only

Subscription only

Access technology

Uses WAP for non supported devices

Via mobile browser. Future enhancement will allow cellphone technology to access sever

Via mobile browser.

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also provides cell phone integration capabilities. Patients with Internet-enabled Smartphones such as the Palm Treo or Apple iPhone can interact with a PHR if they have an account with NoMoreClipboard. This approach requires the user to be connected to the Internet as no data is downloaded to the device. To gain access to the Web portal from a mobile device the users points their Smartphone browser to the PHR portal address and a version of the PHR application optimized for mobile devices is presented. Future functionality will include the capability to send reminders, receive appointment alerts, medications reminders, or follow care plan directives. The Patients will also have the capability to capture pain levels or illness symptoms as they occur and insert this information directly into the PHR. Patients will also be able to capture and store images from their phone into their PHR. From a security perspective the site uses Secure Socket Layer (SSL) account security. Although the information is secured behind a firewall, accessing the information over non-secured network can lead to security vulnerabilities. An application under development that uses Binary Runtime Environment for Wireless (BREW)will allow patients with standard Code division multiple access (CDMA) based wireless devices to access their PHRs. A Short Messaging Service (SMS) interface is also being developed to enable communications between patients and the PHR system.

System 3: AllOne Mobile MPHR AllOne Mobile cell phone-based application was created by the AllOne Health Group Inc. to manage personal health information. Patient information is stored behind a password-protected, encrypted channel using Diversinet wireless security application. AllOne Mobile uses mobile technology to facilitate the exchange of critical health information between individuals, providers, and health plans. This application downloads wirelessly a small application to the mobile device, which

accesses the patients PHR stored securely on the Internet. The PHR system allows updates from the mobile device. The application supports all smartphones and the majority of non-business mobile consumer devices. The AllOne Mobile application supports the following features. •

• • •

Storage of confidential personal information, including provider and insurance information, allergies, immunizations, and medications Synchronize their mobile device with Web based PHR. Fax PHR information from a Mobile device Control access to receipt of relevant and timely communications on health care–related topics

AllOne Mobile integrates with existing health care information systems and applications, including existing online PHRs.

Conlu The goal of the MPHR system is to provide secure and controlled access to personal health informationat anytime via a mobile device to improve health outcomes. Mobile PHRs can provide patients with a variety of functions including: maintaining a real-time health diary, and tracking of vital health measures such as blood glucose levels, blood pressure, carbohydrate intake, height, weight. This chapter presented a framework adapted from the HL7 PHR functional model and PHR literature.The purpose of the framework is to define the set of functions that may be present in Mobile PHR systems and to highlight important components that must be taken into consideration when evaluating Mobile PHR systems. The growing number of large technology organizations entering the PHR landscape will

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eventually help the industry by driving standards forward, developing open interfaces and generating awareness of the products and implementing solutions that incorporate mobility on the one hand, and satisfying standards requirements and regulatory policies such as HIPAA privacy and security rulings on the other hand. The key challenge that is likely to evolve from the influx of non healthcare organization delivering PHR systems runs parallel with Electronic Medical Records (EMR) and EHRs prior to HIPPA rules and regulations, enacted by the USA congress in 1996. These problems included the denial of health care coverage to individuals with chronic and genetic predispositions to diseases and the release of personal health information. Ultimately consumers must exercise extreme caution when utilizing and implementing a mobile or Web based PHR.

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scopus.com/scopus/inward/record.url?eid=2s2.0-39049183450&partnerID=40&rel=R7.0.0. Tang, P., Ash, J., Bates, D., Overhage, J., & Sands, D. (2006). Personal health records: Definitions, benefits, and strategies for overcoming barriers to adoption. Journal of the American Medical Informatics Association, 13(2), 121-126. Retrieved from http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-33644682163&partnerID=4 0&rel=R7.0.0. Thielst, C. B. (2007). The New Frontier of Electronic, Personal, and Virtual Health Records. Journal of Healthcare Management, 52(2), 75-78.

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Chapter IV

Medical Information Representation Framework for Mobile Healthcare Ing Widya University of Twente, The Netherlands

Jacqueline Wijsman University of Twente, The Netherlands

HaiLiang Mei University of Twente, The Netherlands

Hermie J. Hermens University of Twente, The Netherlands

Bert-Jan van Beijnum University of Twente, The Netherlands

ABSTRACT In mobile healthcare, medical information are often expressed in different formats due to the local policies and regulations and the heterogeneity of the applications, systems, and the adopted Information and communication technology. This chapter describes a framework which enables medical information, in particular clinical vital signs and professional annotations, be processed, exchanged, stored and managed modularly and flexibly in a mobile, distributed and heterogeneous environment despite the diversity of the formats used to represent the information. To deal with medical information represented in multiple formats the authors adopt techniques and constructs similar to the ones used on the Internet, in particular, the authors are inspired by the constructs used in multi-media e-mail and audio-visual data streaming standards. They additionally make a distinction of the syntax for data transfer and store from the syntax for expressing medical domain concepts. In this way, they separate the concerns of what to process, exchange and store from how the information can be encoded or transcoded for transfer over the internet. The authors use an object oriented information model to express the domain concepts and their relations while briefly illustrate how framework tools can be used to encode vital sign data for exchange and store in a distributed and heterogeneous environment. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Medical Information Representation Framework for Mobile Healthcare

Inoduion Mobile healthcare applications receive more and more attention due to the ability to reshape healthcare delivery, for example, enabling selfmanagement of patients whilst they pursue their daily activity. Information and communication (ICT) technology and infrastructures which provide the necessary ubiquitous connectivity enable these applications. Competitive value-add ICT providers moreover facilitate these applications with alternatives to computation and communication services. Today’s environment for networked applications is therefore rich in ICT services which are accessible anywhere and anytime, for example by prepaid or subscription contracts between users and ICT service providers or by collaboration contracts between these providers. Such environment enables applications to select (wireless) connections of required quality and technology which are considered best for their purpose. A mobile application may for instance seamlessly switch over between GSM, UMTS or WiFi 802.11 (Schiller, 2003) connections that are offered by competing providers. These developments enable mobile healthcare applications in choosing the appropriate situations with adequate ICT support that permit healthcare to be delivered where previously it was difficult or impossible to do so (Wootton, 2006). Due to these ICT and business advancements, a travelling patient with a chronic disorder can be monitored continuously everywhere in the country of residence as well as abroad. If his health condition requires, he may be examined at a care centre abroad that uses equipment different than at his country of residence. This may further imply that the format of the processed healthcare data differs from the format used at his residential care centre. Local care centre’s policy or local governmental health regulations may also impose the use of a different healthcare data format standard. In (near) future mobile healthcare therefore, we typically need to deal

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with healthcare data which are represented in multiple format standards due to the different policy or regulations and the heterogeneity of applications, systems and ICT technology. This chapter describes a framework which enables healthcare data, in particular (digitized) continuous-time patient’s vital signs and professional annotations, be processed, exchanged, stored and managed modularly and flexibly in a mobile, distributed and heterogeneous environment. A framework is often described as a basic conceptual structure to compose something from fitting parts. In the context of this chapter, a framework is an integrative (standardized) conceptual structure which brings together a set of components which themselves may be standards such as vital signs format & encoding standards (Blair & Stefani, 1998). It therefore addresses questions like: •

•

•

How to deal with healthcare data expressed in accordance with several data format standards and how to encode the data to fit to the characteristics of the provided connections to enable effective and efficient data transfers; How to deal with professional (textual, graphical or multimodal) annotations and derived (i.e. trend) signs in sync with the analyzed vital sign segments; How to manage vital sign data sets of a patient that originate from the same measurement session in a (distributed) study, which typically process data in several steps using processing tools with specific parameter settings. Similarly, how to manage vital sign data sets (of the same patient and the same measurement session) in different formats, e.g. if the returning traveling patient, who has been monitored and diagnosed in a care centre abroad, consults his general practitioner, who then inspects the annotations and the vital signs measured and processed using a locally certified system to confirm


the annotations, the diagnosis and treatment of his colleague abroad. The proposed framework should furthermore fit to the practices used in ICT to manage the use of multiple format and encoding standards, as discussed in the next sections. In the next section, we discuss some of the issues of information exchange using computer networks and illustrate the need for a framework which flexibly supports exchange of healthcare data, in particular digitized continuous-time vital signs and professional annotations, in a distributed and heterogeneous environment. Thereafter, we analyze the functional requirements of mobile healthcare stakeholders on the framework. We address only those stakeholders that influence the functional aspects of a framework for multiple formatted vital signs for use in a heterogeneous distributed environment. Stakeholders addressing financial aspects like insurance companies are therefore beyond the scope of this chapter. In the section thereafter, we address the representational model, which distinguishes between the syntax for data transfer and store from the syntax for expressing medical domain concepts. Then, we discuss the information model of the framework and some ECG standards. Thereafter, we address some other syntax notations and briefly discuss tool based translations of conceptual or abstract syntax to transfer syntax. The last section presents our conclusions.

Ba One of the issues of transferring information in an ICT environment is to preserve meaning despites the dynamic property of the data transfer characteristics of the connections and the different ways of representing information at the computer systems at the connection endpoints. A connection in this environment can be modeled by a bit or a character pipe (i.e. a model which supports

the transfers of sequences of bits or characters, respectively). For example, an echocardiogram needs to be formatted as a sequence of pictures, serialized, and encoded further to suit the pipe, transferred via the pipe, and at the receiving end reconstructed (i.e. decoded). This chain of data formatting and encoding steps requires a suitable end-to-end quality to preserve the clinical interpretations of the echocardiogram. Mechanisms and techniques for data formatting and encoding have been widely investigated and developed in the area of computer networking. In this chapter, we present the data representation model of the Open Systems Interconnections (OSI) of International Organization for Standardization (ISO) (MacKinnon, 1990). This model provides clarity to and better understanding in the structures of many format and encoding standards like MPEG, JPEG, H.261, or DICOM (Le Gall, 1991; NEMA, 2007a). This is due to the distinction between abstract syntax representation, which is suitable for the entities that exchange information, and transfer syntax representation, which is suitable for the pipe that transfers the serialized and encoded data. This distinction therefore separates the concerns of exchanging concepts of the domain ontology from the concerns of serializing and transferring the encoded concepts in a meaning preserving way. As in multimedia, several formats and encodings have been proposed or developed for vital signs, in particular for electrocardiograms (ECGs). We may identify de-jure standards developed by standardization bodies, such as the CEN/SCPECG (CEN/TC251 prEN 1064, 2002), which is developed by the European Committee for Standardization CEN (CEN/TC251, 2007) and defined specifically for ECGs, or HL7 (Hinchley, 2005), which is developed by an organization cooperating with standardization bodies and accredited by the accrediting organization for US national standards, but which has a larger scope than only addressing monitored healthcare data like ECGs. Another example of a de-jure standard that can be

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used to represent ECGs, or vital signs in general, is VITAL (Weigand, 2005). We may also identify de-facto standards, i.e. standards that were developed by industrial or research consortia, or proprietary standards used by vendors of medical equipment or proposed by a research institute, e.g. ecgML (Wang, 2003). For our convenience, we denote ECG data format and encoding proposals found in the literature as (proprietary) standards. ECG data representation standards vary in their semantic expression levels. Some of these standards focus only on the waveform representation, some others additionally provide heart physiological or bioelectrical domain concepts like the notion of P or ST waves. These differences may imply loss of interpretation power when ECG data has to be converted from one onto another standard (lossy conversion). In this chapter, we show how the OSI data representation model (MacKinnon, 1990), in particular the abstract syntax, can be used to identify these differences in semantic expression level and how to associate equivalent ECG segments formatted in different standards. We also discuss how the abstract syntax can be used to specify professional annotations or derived (/trend) signs like heart-beats such that rendering tools are able to visualize these annotations or trend signs in sync with the associated data segment. We apply the Unified Modeling Language (UML) (Booch, 1999) as a (graphical) abstract syntax language to express the concepts of vital signs, in particular ECGs; this results in an information model of the framework. Some of the ECG standards format ECGs as sampled timedomain bio-signals (e.g. the format described in (Browns, 2002)), others include bio-electrical or heart physiological concepts like the notion of P-waves and QRS complexes (e.g. the standard described in (CEN/TC251 prEN 1064, 2002)). The specification of ECGs using UML, addressed in (Concalves, 2007), has elaborated several ECG ontological models from different perspectives like the heart physiological, bio-electrical, including the recording session perspectives. UML is

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also used by other standards, for example HL7, to capture the association semantics between the healthcare domain concepts. In this chapter, however, we specify the vital signs information model from the perspective of the different format and encoding standards. This approach fits to our objective to develop a framework for processing, transferring and storing vital signs in a multiple formats environment. Our information model therefore includes multiple structures for (replicated) ECG data that are specified by the different standards and it includes structures to express their relations, for example the applied conversion tools, the settings of the tool and the actor in charge of the conversion, or the processing algorithm and settings that derive a trend sign. To deal with the exchange of medical information represented in multiple formats we adopt similar techniques and constructs as are used on the internet. In particular, we are inspired by MIME (Multipurpose Internet Mail Extensions) (Freed, 1996) which enables users to exchange text, pictures, video clips, excel sheets, etc. independently of the computing devices, software packages or the operating systems involved. For example, the MIME construct “multipart/alternative” can be used to express the relation between two or more ECG segments of the same measurements of a patient but formatted differently, e.g. one in the CEN/SCP-ECG format and the other in the DICOM waveform format (NEMA, 2007b). The latter can be a conversion of the first to match the format of the data to the software or the equipment of the professional, for example in the earlier illustrated case of the travelling patients. As this construct specifies that the multiple parts are alternatives of one another, an ECG viewer tool can select the part that is encoded in a preferred format as indicated by a profile of preferences. Moreover, a policy that regulates tools to ignore parts that are encoded in a format unknown to the tool provides flexibility when introducing new formats without influencing existing systems (upwards compatibility and open-endedness with respect to new features or new functionality).


Furthermore, to enable synchronization of care professional’s (textual or graphical) annotations with segments of analyzed vital signs, we adopt a construct similar to MIME “multipart/parallel” to inform a rendering tool that the annotations could better be visualized together with the corresponding vital sign segments. We additionally adopt a similar technique as is applied in MPEG (Le Gall, 1991) for joining and splitting types of media, e.g. synchronized under-titles with video, to merge professional annotations, trend signs or other auxiliary data on the fly. As in MPEG, the framework includes identifiers to distinct between the data types at abstract as well as at transfer syntax level. Besides the discussed facilities for healthcare data processing, transfer and store, this chapter also addresses the facilities to manage the dynamics experienced by mobile healthcare applications due to changes in patient’s health conditions or fluctuations of the ICT infrastructural resources due to environment data traffic or roaming patients. A framework for multiple formatted vital signs therefore needs to adopt the discussed techniques or constructs. In the next section, we justify these needs by analyzing the requirements of healthcare stakeholders that are relevant for the framework’s functionality.

SakeholdeConain and RReemen We analyze the needs of three mobile healthcare stakeholders to identify the functional needs that have to be accommodated by the framework. For this analysis we use our experiences collected during several mobile healthcare projects (MobiHealth, 2002; HealthService24, 2005; Myotel, 2008) and our study of several healthcare systems reported in the literature. Some of the identified needs were examined during the development of

Extensible Markup Language - XML (Bray, 2004) constructs for vital signs representations. These constructs were discussed in (Mei, 2006) and several simplified scenarios were used in (Mei, 2006) to illustrate the benefits of the framework which accommodates these constructs. We distinguish three mobile healthcare stakeholders who typically influence the vital sign representations and their use (Figure 1): •

•

•

End-users; stakeholders who use the services provided by the mobile healthcare systems. End-users include both patients and the healthcare professionals, for example the medical specialists, nurses, physiotherapists; Mobile healthcare system providers; stakeholders who are involved in the provisioning of mobile healthcare systems for clinical remote monitoring and treatment. In the context of this chapter, these providers are assumed to be aware of the applied information and communication technologies; Care centers, such as the primary care centers, healthcare call centers (also called healthcare portals), and the secondary care centre’s (e.g. corporate hospitals with their departments of different specialties). For our convenience, regulatory bodies as well as medical ethical committees are categorized as this stakeholder. That is, the care centers are assumed aware of the healthcare regulations that influence the way of handling patient’s vital signs.

Requirements from End-Users From the healthcare professional’s point of view, the vital sign representation should be suitable for effective clinical interpretation as required by the health condition of the patient and in accordance with the working practices of these professionals:

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Figure 1. Stakeholders of vital sign representation framework

•

•

•

• •

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Healthcare professionals typically access units of interpretable segments of vital signs in a quality appropriate for the purpose of the clinical task, e.g. patient’s ECG filtered from noise and movement artifacts and visualized in a resolution necessary to inspect ventricular contraction; Healthcare professionals may need to correlate signs that belong to a group of coherent vital signs, e.g. patient’s oxygen saturation, heart beat, blood pressure, and respiration that together form an indicator of the oxygenation of the patient’s brain in trauma care; Healthcare professionals may have priorities regarding the importance of vital signs, e.g. doctors may prefer to see trend signs and only in case of abnormalities, they need the underlying vital signs; Healthcare professionals may need to annotate vital sign segments; Healthcare professionals may need to know how vital sign data was measured and processed for evidence based treatment.

In some mobile healthcare applications, patients typically generate vital signs by attaching sensors on their body and initializing the sensing devices. These patients, especially mobile patients, may need to check and calibrate the sensors’ readings from time to time to ensure accurate (local) monitoring and treatment feedback. For example, patients may need to re-attach sensors in case of bad skin contacts. For this, vital signs visualization or other feedback modality has to have a resolution suitable for patient’s interpretation. Moreover, medical and sensor technologies are evolving and new vital signs or sensors may be developed for measuring patient’s health condition in mobile environments. Therefore, vital sign representations should be extensible to enable the introduction of new vital signs or the integration with new data like professional annotations.

Requirements from System Providers Mobile healthcare system providers have the mission to facilitate the computation and communication needs of the patient’s care process.


In a remote monitoring and supervised treatment session, the healthcare system regularly matches the computation and communication needs of the supported care process with the resource capability and capacity of the ICT infrastructure. These systems often apply a hunting strategy to collect the available ICT resources of the contracted ICT providers. They often apply an adaptation strategy to control the vital sign data processing and transmission. For example, by down-sampling, prioritizing or discarding some of the vital sign packets, a system may improve the utility of transferring vital signs in a meaning preserving and adequate way. Therefore, a vital sign framework should enable prioritized transfers of important signs and deferred transfers of remaining signs, which may traverse other delivery routes and at cheap data communication hours. Consequently, the framework should further support aggregation and resynchronization to reconstruct the set of vital signs.

Rquirements from Care Centers Care centers, especially corporate hospitals, often accommodate a diversity of specialized systems, each of which may apply specific vital sign formats. If furthermore, these centers also treat travelling patients, interoperability between these remote systems needs to be supported. In such cross-platform environments, vital sign representations require an open environment to facilitate multiple vital signs formats. Healthcare data is considered private and has to be subjected to privacy rules. Monitoring and treatment protocols described in the trial designs which were proposed to the Medical Ethical Committees in the earlier mentioned healthcare projects address healthcare data privacy, such as password protected and role based access to recorded and processed data, vital signs are also made anonymous. The framework should enable transferring, processing and storing of vital signs subjected to privacy rules.

InfoRMmaRepenion Model A model suitable for information transfer in a heterogeneous environment, which accommodates different (wireless) communication technologies and qualities and different computer systems, is the OSI Presentation Layer model (MacKinnon, 1990) (Figure 2). This model uses three kinds of syntaxes to represent information. The earlier described abstract syntax represents the domain ontological structure of the information in respect of the entities exchanging the information. It is therefore the vocabulary and the structuring rules used to represent the information. This syntax is considered useful in a meaningful meaning preserving transfer, in which the sending and the receiving entities share a common universe of discourse. An abstract syntax enables these entities to interpret the exchanged information in the same way. The earlier described transfer syntax is the syntax used to represent data in transfer. Information expressed in a transfer syntax is therefore represented as sequential groups of bits or characters sequences. Groups, in turn, associate to terms of the abstract syntax vocabulary. The third kind is the local syntax which is the syntax used to represent stored data at the involved computer systems. In a heterogeneous environment, the local syntaxes used by the communicating computer systems can be different, e.g. one uses a Java based local syntax and the other a C based syntax in a Unix system. An abstract syntax is therefore not a concrete syntax as are transfer and the local syntaxes. ECGs specified from a specific perspective in an abstract syntax result in a conceptual model of the ECGs. This model can be used to reason about the elements of the ECG, for instance the bio electrical properties of the heart or the heart condition if the model is defined from those perspectives. In the perspective of interoperability in an environment that uses multiple standards, the ECG model at abstract syntax level should en-

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Figure 2. Information representational model

able the identification of the same ECG segments which are formatted using different standards and should further enable conversion from one format to another. An abstract syntax moreover enables the development of information exchange techniques and mechanisms for a heterogeneous environment. Information conceptually represented in an abstract syntax can be encoded to different transfer syntaxes. Information encoding from abstract syntax to a transfer syntax is virtual, because in reality the information is represented at a computer system in a local syntax. Information encoding in reality is therefore the conversion from a local syntax to a transfer syntax. The rules needed for the conversion can be derived from the encoding rules from abstract syntax to transfer syntax. As mentioned earlier, information represented in an abstract syntax can be encoded in several transfer syntaxes, each of them binary or character sequence oriented. Moreover, some transfer syntaxes are more suitable for efficient processing rather then generating compact codes; others generate compact codes but are not processing

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efficient. In an e-mail application, a plain text message can be encoded amongst others as an ASCII characters sequence or a base64 character sequence (Freed, 1996). Base64 encodes 6 bits of the abstract syntax representation to one base64 transfer syntax character. Three (8 bits) characters of the plain text message will therefore be encoded to 4 base64 characters. However, binary data can be encoded using the base64 encoding to fit to a character pipe as used by internet e-mail. The benefit of binary data encoded in base64 is the availability of many internet protocols to convey the data using computer networks. On the other hand, conversions of digitized ECGs to base64 and back to a digital form at the receiving end point consume processing capacity and a lot of time, a bit oriented transfer syntax is much more efficient in such cases.

Vi-SIGNS INFORMmaion Model In this section, we discuss the information model of the conceptual structure that binds together the


abstract syntax level structures of vital signs, in particular ECGs, as defined in the various vital sign standards. In particular, the model specifies the different kinds of relations between the vital sign structures as identified in the stakeholder’s analysis section. As discussed in that section, several kinds of relations need to be addressed: •

Similarity relation: This relation expresses that the related segments of vital signs are similar to one another in respect of a defined context, such as the context of their use which reflects the purpose of the vital signs. Similarity is used here to associate vital signs that reflect the same (physiological) phenomena but are represented and structured in different ways in the different standards. For example, an ECG P-wave may be represented as sampled amplitude values of the wave and parameterized by a sample distant variable specified in another part of the ECG standard. This wave may similarly be represented in terms of the wave onset, duration and peak value. A converted ECG segment, which is formatted in a standard other than the original one but considered having the same interpretation and quality in the perspective of the addressed context, is defined here as being similar to the original source segment. This similarity relation therefore needs to contain the context of the similarity; it for example includes the identity of the conversion tool or algorithm, the parameter settings, the actor in charge of this conversion and the actor’s comments for example to further detail the context of similarity. This similarity relation originates from the need of the care center stakeholder to enable a multi standards environment and the policies of the regulatory bodies at the different points of care.

In many cases, this relation associates one source segment to one other converted segment.

In general, a many to many, many to one or a one to many association may exists, for example in the case of multiple vital signs types or in the case that the abstract syntax of the source vital sign segment standard is much richer than the abstract syntax of each of the destination standards, but together these destination abstract syntaxes span the source abstract syntax. This is for example useful in a case in which an annotated ECG segment which includes both a time based signal representation and the physiological phenomena like P-wave and QRS complexes is converted to standards that support time based signal representations, but only one of them is able to represent physiological phenomena but, on the other hand, does not support annotations. In this example, annotations but not physiological phenomena are supported by the other destination standards. Vital sign segments which are similar are also equivalent. That is, segments which are similar also have the reflexive, symmetry and transitivity property of an equivalency relation because the related vital signs are supposed to reflect the same (physiological) phenomena. As discussed earlier, these properties are defined in the sense of the applied conversion tools and settings. That is, similar ECG segments reflect the same heart condition in respect of the resolution of the applied tool. For example, an ECG formatted in the CEN/SCP-ECG standard, which is converted to the DICOM wave form standard and the latter converted again in ecgML (Wang, 2003), is considered equivalent and even similar to the ECG representation in ecgML in the context of the applied tools and parameter settings. Remark that one of the applied tools needs only to convert the wave form of the ECG and can be unaware of the physiological phenomena expressed by the data. Therefore, the resulting ECG formatted in ecgML is not necessarily completely identical to the CEN/SCP-ECP formatted ECG, but in the context of use, which is reflected by the applied tools and settings, they are considered equally useful for the clinical purpose because at the

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resolution of the cascaded tools they both reflect the same heart condition. This cascade of conversions is usually called lossy if a CEN/SCP piece of data representing an abstract syntax concept is not represented in ecgML. •

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Enhancing relation: This relation expresses that the enhanced segments of vital signs are better conditioned in respect of the context of use. For example, ECG segments filtered from undesirable noise or EMG movement artifacts are enhanced if compared against the source ECG segments. Although the enhanced segments are more appealing for use, the originating source segments essentially have the same effectiveness in respect of the context of use. This enhancing relation is meant to express vital sign segments which contain the same bio-electrical or physiological phenomena relevant for the medical purpose, but the enhanced segments are considered better conditioned for the medical purpose, for example more efficient for use. As in the case of the similarity relation, this relation needs to contain the specification of the context of use, for example it needs to include the identity of the vital sign enhancing tool or algorithm, the parameter settings, the actor in charge of this enhancement and the actor’s comments, for example, to further detail the intended context. In this perspective, the enhanced and the source segments are equivalent in the specified context, that is one may replace the other without influencing the interpretation of the clinical data in the addressed monitoring and treatment context. This enhancing relation originates from the need of the professional stakeholder to provide adequate vital sign units of interpretations. Priority relation: This relation expresses the inter vital signs degree of importance. It is a means for the mobile healthcare applications to ensure continuity of processing or

•

transfer of vital signs which are considered important for the diagnosis or treatment tasks. In case of severe bandwidth degradation, vital signs which are considered less important may for example be stalled; Aggregation and splitting relations: these relations express that the related segments of vital signs are aggregated or split, respectively, from the others. As discussed in the previous cases, especially the similarity relation, the aggregated segments are equivalent to the source segments in the sense of the aggregation tool resolution. This equivalence is therefore specified by the aggregation tool and parameter settings. We may apply the same justifications for the splitting relations. However, in the latter relation, we additionally may deal with the downscaling of vital signs, for example to fit the data onto an available transmission channel of a specific quality that otherwise is not able to transfer the vital signs. Although the quality may be reduced, the resulting vital signs are considered useful for the professional; otherwise the downscaling was meaningless, thus not executed. In this context of use, the related vital signs are also considered equivalent. As in the other cases, it is therefore necessary to specify the aggregation or splitting tool, tool settings, the actor in charge of the aggregation and the splitting or the splitting strategy and the actor’s comments. The tool that splits vital signs may use the priority of the vital sign discussed earlier to determine the splitting. Aggregation of vital signs, on the other hand, may also be used to concatenate (digitized continuous-time) vital signs which otherwise are located remotely; this improves availability or efficiency of the processing or the vital sign analysis by a professional. These aggregation and splitting relations come mainly from the system provider’s requirements analysis and partly from the professional needs.


Concepts of vital signs at abstract syntax level can be expressed by languages like ASN.1 (ASN.1, 2008; MacKinnon, 1990), XML schemas (Malik, 2008) or UML (Booch, 1999). Figure 3 describes the information model of the framework expressed in the Unified Modeling Language (UML) class diagram. A class is symbolized by a rectangular with a class name at the top, attributes in the cell in the middle, and operations at the bottom. In this chapter, we do not detail the operations of a class and only provide those attributes that are relevant to explain the framework. The associations between the classes are represented by the lines between the related classes.

Clinical Data of Patients Figure 3 shows that patient’s clinical data (represented by the UML class PatientClinicalData) is a collection of vital signs data (represented by the abstract class VitalSignData explained in the next section). In Figure 3, the set of vital sign data is represented as a (genuine) part of patient’s clinical data by the black diamond composition symbol. The clinical data is anonymous, because it is identified by some patient identification number. Via the patient’s electronic health record PatientEHR, however, a patient’s clinical data can be associated to the patient, but the other way around is not specified (in UML, this unidirectional association

Figure 3. Vital sign information model

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is symbolized by the arrow, which arrow-head denotes the navigation direction between the involved classes). The 1 to 1 multiplicity of this association indicates further that patient’s clinical data represents the whole collection of measured, processed and stored vital signs of this anonymous patient. Alternatively, one may replace the left value “1” with “1 .. *”, which indicates a range of one or more collections of vital signs of the patient identified by patient_id. In the context of this chapter, we assume the availability of one set of clinical data per patient.

Vital Sign Data As mentioned earlier, the vital sign data is represented by the abstract class VitalSignData in Figure 3. The class is a UML abstract class, because the class is only conceptually defined, other (nonabstract) classes will refine (i.e. specialize) this class. In UML, an abstract class can be identified by the class name written in italics. For example, the abstract class ECG_Data is a specialization of the abstract class VitalSignData (specialization is an “is-a” relation and is symbolized by the open triangular symbol in UML). This abstract class ECG-Data may be specialized further for example by the classes DICOMStudy and SCPECG_Record. In this chapter, the class DICOMStudy represents ECG data formatted in accordance with the DICOM waveform standard and the class SCPECG_Record represents ECG segments formatted in accordance with the CEN/SCP ECG standard. The information model can be further extended with other ECG data formatted in other (de jure, de facto or proprietary) standards.

Vital Sign Relations Via the abstract class Equivalency, Figure 3 also shows that some source vital sign data can be related to some other destination vital sign data. In the figure, the similarity relation discussed earlier is represented by the class Similarity, which

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is a specialization of the class Equivalency. As discussed earlier, the equivalence between vital sign data is defined in a specific contextual setting. In the model, this context for equivalence is specified by the attributes actor_id, which identifies the responsible actor for the relation between the vital sign data, actor_comment, which denotes the comments of the actor, and also the time and date information. As discussed earlier, the context is also defined by the applied tool and its settings, both are represented by the class Tool. An additional design choice is that we define the similarity relation only for vital signs that are encoded in different standardized formats. This constraint is not shown in the figure, however, it can be expressed by a UML note or specified in Object Constraint Language (OCL) (OMG, 2003). Analogous to the similarity relation, the enhancing relation, which is expressed by the class Enhancing (Figure 3), is a specialization of the class Equivalency. In this model, we define an enhancing relation only for vital signs that are formatted in the same standard. Aggregation and split relations are also specialization of the class Equivalency. Aggregation is a many to one relation between vital signs formatted in conformance with the same standard and the other way around, the split relation is a one to many relation.

Derived Vital Signs Trend signs or, in general, derived vital signs are frequently used in care programs as first indicators of the condition of the patients. Instead of a plethysmogram, care programs like emergency services or COPD programs use the derived oxygen saturation O2sat (or SpO2) parameter as a measure for the oxygenation of blood. Heart Rate and Heart Rate Variability are other examples of trend signs, typically derived from one of the ECG leads. In contrast to the similarity and the enhancing relations, we specify derived signs as specializations of the abstract class Extracted-


Feature, which in turn is specified as a component of the class VitalSignData (in UML symbolized by a black diamond (cf. Figure 3)). As the case for equivalence relations, the applied tool, tool setting, actor in charge of the trend data processing and the actor’s comments refines ExtractedFeature even further. We model trend or derived signs as a component of the original vital signs, rather than modeling them via the equivalence relation, because it better fits to the way vital sign standards deal with derived signs and because of the complexity of the required constraints due to the transitivity of equivalency relations. For example, Heart Rate and Heart Rate Variability are derived signs but they represent different concepts; therefore they are not equivalent. Other features which can be extracted from ECG leads are for instance the high and the low frequency components, including their ratio.

Care Program Dependent Priority of Vital Signs As discussed earlier, in mobile healthcare, data transfer bandwidth especially from wireless communication channels like GPRS may fluctuate. If available bandwidth drops below the required level, less important vital signs can be stored locally in favor of the transmission of the more important ones. The management modules of the mobile healthcare applications may (semi) automatically decide which type of vital signs to stall and which to transfer or process further if these vital signs are prioritized. Sophisticated prioritizing structures which are care program or clinical task dependent can be developed, but in this chapter we use a simple priority attribute. If necessary, this attribute can be extended with a reference to the professional actor in charge of prioritizing vital signs for the care program. We specify the attribute priority in the abstract class VitalSignData to enable priority based selec-

tion at the level of the types of vital signs rather than at a more detailed level, for example at the level of digitized vital sign samples. This choice has the additional benefit that vital sign sets formatted in a specific standard can be treated as a black box; an approach which intends to preserve the structures defined in standards as atomic units. This could be necessary in case of handling vital signs formatted in proprietary standards whose internal structures are unknown to the application developers of the mobile healthcare system provider stakeholder. In this kind of cases, third party tools that have knowledge of these structures are needed to enable processing, rendering or conversion of the vital sign sets. This black-box approach is for example supported by MIME via the “-x” constructs. In case of multi-valued or multi-channeled vital signs (e.g. the leads of ECGs) or in case of (multiple) trend signs, the earlier mentioned priority attribute can be refined further to priority of these values, channels or trends (e.g. represented by the attribute t_priority in the class TrendSign). Consequently, these intra vital sign priorities depend on the attribute priority of the class VitalSignData. This dependency is represented by the dashed arrow in Figure 3.

ECanda Several de-jure, proprietary and de-facto format and encoding standards are suitable for ECGs, amongst others CEN/SCP-ECG, DICOM waveform, ecgML, FDA-ECG, HL7 and VITAL. We express some of them in UML class diagrams to illustrate the use of the information model of the framework. It is not in the scope of this chapter to provide a complete list of ECG standards neither to provide detailed UML class diagrams of all these standards.

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CEN SCP-ECG STAanda The Standard Communication Protocol for computer-assisted Electrocardiography (SCP-ECG) is a standard developed by CEN/Technical Committee (TC) 251 (CEN/TC251, 2007). Besides ECG data, SCP-ECG additionally defines ECG related data to enable the specification of patient’s demographic data, the measurement settings, the performed signal processing on the ECG data, the compression and manufacturer specific information. In SCP-ECG, the entire ECG data set is called a record. A record is further decomposed into “section” parts (indicated with section numbers from 0 to 11), each of which carrying a specific aspect like patient (demographic) information, compression tables, the ECG lead definitions, the ECG lead data, the reference ECG beat(s) of the leads, including the physiological complexes like QRS, and also interpretive annotations. Eleven types of sections are defined in SCPECG. Table 1 presents the eleven sections and a brief description. Some sections are mandatory (e.g. Section 0 or Section 1), others are optional

Figure 4. CEN/SCP-ECG model

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(e.g. Section 11). Sections have a common header structure, in the figure represented by the generalized class CEN/SCP Section. A high level SCP-ECG structure, expressed in a UML class diagram, is given in Figure 4. Section_6 contains a black-box of ECG data. To render the individual ECG leads from Section_6, attributes of Section_3, which represent the metadata specifying the number of leads and the leads description, have to be accessed first. This dependency of Section_6 from Section_3 is represented in UML by the dashed arrow between these two classes.

DICOM ECG Wavefo Supplemen DICOM (Digital Imaging and Communications in Medicine) standards (NEMA, 2007a) are developed by a joint committee of the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA), often in liaison with other organisations like CEN TC251, JIRA in Japan, IEEE and the American


Table 1. CEN/SCP-ECG sections Section No.

Title

Description

0

Pointer

the sections and their locations in the data set record

1

Header Information

patient and acquisition related information

2

Huffman tables

the Huffman compression tables

3

ECG lead definition

the leads, the sample numbers and their relativity to a reference beat (cf. Section 4)

4

QRS location and Reference beat

the location of the QRS complexes and the position of the reference beat

5

Reference beat encodings

parameters like encoding flag, sample distance, gain.

6

Rhythm data

the ECG data

7

Global measurements

info pacemaker spikes and QRS complexes like the P-, QRS-, T- on-/offsets, QT intervals

8

Interpretive statements

text based (diagnostic) annotations

9

Manufacture specific statements

manufacturer specific diagnostic annotations

10

Lead measurement

leads information and fields reserved for manufacturer data

11

Universal ECG interpretive statements

universal statement codes (cf. SCP-ECG standard) and most recent annotations which have to be consistent with annotations in other sections

National Standards Institute (ANSI). Although the DICOM organisation originally addresses imaging standards, it also developed a standard to exchange waveforms. This latter is therefore suitable for ECGs. DICOM uses an object based model, therefore not only specifying the structure of the medical data content as information objects, but also the operations on the data (i.e. services). The functional units in DICOM define the classes of the information objects and the corresponding services, the so-called Service-Object Pair classes (SOP classes). One of the SOP classes is for instance meant for a waveform store. In DICOM, a waveform information object is decomposed into information entities, each of which stored in data modules. Examples of information entities are patient, (clinical or patient) study, clinical data series within a study, equipment which creates the series, and waveforms as part of the series. Figure 5 presents a simplified UML model of DICOM’s waveform related information entities.

The figure reflects the clinical procedure by using terms like studies (class DICOMStudy) and series of clinical data (class Series). Although not shown in the figure, these terms include the specification of the responsible professional, the clinical protocols, the waveform identifications (incl. the acquisition time), the annotations, the waveform data (which may be multiplexed bio signals, therefore also includes the multiplexing parameters, the sampling rate, etc.) and also the corresponding equipment used to generate the data. That is, the class Waveform may contain several multiplexed vital sign channels (represented by the classes MultiplexGroup and Channel in Figure 5)

FDA EC SPECIifiion As observed in the previous section, the DICOM waveform standard is to some extent based on clinical procedures and accordingly the data is represented in terms of studies, the FDA format for waveforms (Browns, 2002) is based on the 2D

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Figure 5. DICOM waveform model

property of sampled waveforms; it emphasizes the viewing representation of waveforms. Figure 6 shows the FDA waveform information model. As in the earlier discussed standards, the FDA model also provides manufacturer information, patient (/subject) identification, and annotations. In the figure, the class PlotGroup models an ECG data set and aggregates data of the class XYPlot, each of which representing a piece of ECG data of a particular lead.

FRAamewo IMPLEMENTATIion ASPpe In this section, we discuss some of the implementation aspects that illustrate the use and the benefit of the framework. First we discuss refinements of the information models presented earlier. These refinements enable the translation and serialization of the abstract syntax to the transfer syntax.

Refinement of the Specialization Constructs In the earlier discussed information models, we apply the object oriented specialization construct, for 86


Figure 6. FDA Waveform model

example, to distinguish between vital signs that are formatted in accordance with different standards. This specialization can be refined by additional discriminating attributes, for example, an attribute identifying the vital sign type (i.e. vitalsign_type in Figure 7b) and an attribute identifying the format and encoding standard (i.e. standard_id in Figure 7b). The advantage of this refinement is that the encoded attributes in the transfer syntax can be used as header fields in the transfer syntax, for example to indicate that the subsequent payload block of data contains vital sign data formatted in the transfer syntax of the identified standard. This

code enables (de-)multiplexing of serialized pieces of vital signs, for example necessary for a 24/7 continuous monitoring of patients. Together with the attribute priority (Figure 7), these discriminating attributes can be used to split a vital sign set, for example necessary in case of severe bandwidth degradation along the healthcare delivery path. This (de-)multiplexing technique is proven useful in multimedia communication using MPEG, which analogously applies process identifiers to join or remove language channels and to merge and split television program channels on the fly.

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Figure 7. Refinement with discriminating attributes

Refinement of Many to Many Relations

Abstract and Transfer Syntax Notations

The information model can cope with one to many or many to many associations between sets of vital sign data, for example the similarity between a source set of ECG data formatted in CEN/SCPECG and destination data sets formatted in FDAECG and in DICOM waveform standard. In this case, a cardiologist may want to visualize both FDA and DICOM sets simultaneously in case that the CEN/SCP-ECG data set is not available on the premises or a rendering tool for the latter format is not available either. As discussed earlier, the MIME type and subtype construct informs applications which tools to use and how tools should render the data. For example, in e-mail applications, the MIME value “multipart/parallel” indicates that the aggregated data sets have to be visualized simultaneously. Similar to MIME, we can refine the equivalency class with additional attributes that specify which vital sign sets need to be rendered simultaneously.

Other languages are available to express concepts at abstract syntax level, for example XML Schema and ASN.1. Using XML tools a character-based XML document can be derived that contains the vital sign data specified in accordance with the XML Schema. This document can then be serialized by reading it from left to right and from top to bottom, yielding a sequence of characters (transfer syntax) suitable for transfer using internet protocols. Tools are also available to visualize XML Schemas as XML Schema diagrams. However, tool based development kits are also available to develop XML Schemas from UML (Malik, 2008). Abstract Syntax Notation One (ASN.1) is defined by the International Organization for Standardization ISO (ISO 8824, 1994). It is a notation for specifying data at abstract syntax level. Associated with ASN.1 are encoding rules for generating binary transfer syntaxes from the abstract syntax (ISO 8825, 1994). ASN.1 and its encoding rules provide compact transfer syntax code.

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A joint committee of ISO and International Telecommunication Union (ITU-T) has produced several standards on the mapping of XML Schema to ASN.1 and vice versa. A web accessible on-line tool which translates XML Schemas to ASN.1 is for example available (ASN.1, 2008). A framework that accommodates the collection of the previously described tools provides a development environment that enables the translation of vital signs specified via UML class diagrams to concise binary transfer syntax code.

Conlu We propose a framework for flexible and modular processing, storing and transferring (segments of) medical information in a mobile, distributed and heterogeneous environment. The framework adopts an ICT information representation model, which separates the concerns of information transfer and store from the concerns of expressing, converting, splitting, synchronizing and joining information. The abstract syntax level methods, techniques and mechanisms, which address the latter mentioned concerns, provide the necessary support for processing medical information in an environment that contains multiple standards for data format and encoding. On the other hand, the transfer and local syntax level methods, techniques and tools, associated to the first mentioned concerns, enable transfer and store of medical information in an efficient and dependable way. The framework, which also contains the vital sign information model discussed in this chapter, therefore supports the exchange of medical information in a meaning preserving way despite the use of different format and encoding standards and the fluctuations of the property of the end to end data transfer connections. This chapter discusses the framework at conceptual level. It provides a generic approach to deal with multiple formats and fluctuating proper-

ties of connections. This approach is considered useful for healthcare delivery in which patients are mobile and self managing. It is expected useful for new clinical pathways for mobile and distributed healthcare delivery that involves collaborating actors of different medical specialty, possibly acting in new roles and each of them needing medical information that are represented in accordance with the (new) working practices of their specialty. The proposed framework, which amongst others is an integrative conceptual structure that binds methods, techniques and mechanisms for interoperability of different format and encodings of medical information, needs to be supplemented further with other ECG and vital sign standards. That is, the framework needs to be populated by relevant format and encoding standards. Consequently, the information model described in this chapter needs to be refined further, for example, to provide tool developers the necessary hooks (e.g. class attributes, object methods and dependency relations) to design medical information conversion, splitting and joining tools. Such refinements not only require details of the format and encoding standards which populate the framework but may also need abstract syntax level knowledge of the ontology of the corresponding bio physiological or bio electrical phenomena, for example, to specify in details the transitivity constraints of the similarity relation. Another topic for future work is for example the specification of guidelines or rules to up- or down-scale digitized continuous-time vital signs in transfer automatically to match to the fluctuations of the properties of the end to end connections within the tolerance specified by care programs or professionals. These guidelines supplement the framework further and improve its use for mobile healthcare delivery in a heterogeneous environment.

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ACKknowledgmen The authors thank Bayu Erfianto, who did his MSc project on XML and ASN.1 based representations of ECGs. His project and results have been a starting point for our continued research in this area. This work is part of the Freeband AWARENESS project (http://awareness.freeband.nl). Freeband is sponsored by the Dutch government under contract BSIK 03025.

Refeen ASN.1 Information site. (2008). Retrieved March 28, 2008, from http://asn1.elibel.tm.fr. Blair, G., & Stefani, J-B. (1998). Open Distributed Processing and Multimedia. Addison-Wesley. Booch, G., Rumbaugh, J., & Jacobson, I. (1999). The Unified Modeling Language: User Guide. Addison-Wesley. Bray, T. et al. (2004). Extensible Markup Language (XML) 1.0. 3rd edition. Retrieved 2005, from http:// www.w3.org/TR/2004/REC-xml-20040204. Browns, B., Kohls, M. & Stockbridge, N. (2002), FDA XML data format design specification. Draft of the US Food and Drug Administration. CEN/TC251 prEN 1064 (2002). Health Informatics – Standard Communication Protocol – Computer-assisted Electrocardiography. CEN/TC251 prEN 1064. CEN/TC251 (2007). CEN website. Retrieved June 8, 2007, from http:// www.centc251.org. Concalves, B., Guizzardi, G., & Pereira Filho, J. G. (2007). An Electrocardiogram (ECG) Domain Ontology. In Proceedings of the Second Brazilian Workshop on Ontologies and Metamodels for Software and Data Engineering (WOMSDE’07). 22nd Brazilian Symposium on Databases (SBBD)/21st

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Brazilian Symposium on Software Engineering (SBES). João Pessoa, Brazil. Freed, N., & Borenstein, N. (1996). Multipurpose Internet Mail Extensions (MIME) Part One: Format of Internet Message Bodies. IETF RFC 2045. From http://www.rfc-editor.org/rfc/ rfc2045.txt. HealthService24 project eTEN-C517352 (2005). EC eTEN Programme. Retrieved Feb. 24, 2006, from http://www.healthservice24.com. Hinchley, A. (2005). Understanding Version 3, A primer on the HL7 Version 3 Communication Standard. Munich: Alexander Mönch Publishing. ISO 8824 (1994). Information Processing System – Open Systems Interconnection – Abstract Syntax Notation 1 Specification. ISO/IEC JTC1/SC21. ISO 8825 (1994). Information Processing System – Open Systems Interconnection – Basic Encoding Rules for Abstract Syntax Notation 1 (ASN.1). ISO/IEC JTC1/SC21. Le Gall, D. (1991). MPEG: A Video Compression Standard for Multimedia Applications. Communications of the ACM, 4(34). MacKinnon, D., McCrum, W., & Sheppard, D. (1990). An Introduction to Open Systems Interconnection. New York: Computer Science Press. Malik, A. (2008). Design XML schemas using UML. Retrieved March 28, 2008, from http:// www.ibm.com/developerworks/xml/library/xumlschem/. Mei, H., Widya, I., Halteren, A. van, & Erfianto, B. (2006). A Flexible Vital Sign Representation Framework for Mobile Healthcare. 1st International Conference on Pervasive Computing Technologies for Healthcare 2006. Nov. 29th – Dec 1st, 2006. Innsbruck, Austria. MobiHealth project IST-2001-36006 (2002). EC programme IST. Retrieved Feb. 24, 2006, from http://www.mobihealth.org.


Myotel (2008). Myofeedback based Teletreatment Service Project. EU programme eTEN – 046230. Retrieved Feb. 2008, from http://www. myotel.eu. NEMA (2007a). Digital Imaging and Communications in Medicine (DICOM), Part 1: Introduction and Overview. Virginia: NEMA. NEMA (2007b). Digital Imaging and Communications in Medicine (DICOM), Part 3: Information Object Definitions. Virginia: NEMA. OMG ptc/03-10-14 (2003). UML 2.0 OCL Specification. OMG Adopted Specification (ptc/0310-14). Retrieved in 2007, from http://www. omg.org.

Schiller, J. (2003). Mobile Communications. Addison-Wesley. Wang, H., Jung, B., Anuaje, F., & Black, N. (2003). ecgML: Tools and Technologies for multimedia ECG Presentations. Proceedings of XML Europe Conference. London. Weigand, C. (2005). VITAL: Use and Implementation of a Medical Communication Standard in Practice. Computers in Cardiology, 32, 319322. Wootton, R., Craig, J., & Patterson, V. (Ed.). (2006). Introduction to Telemedicine. 2nd edition. London: Royal Society of Medicine Press Ltd.

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Chapter V

A Distributed Approach of a Clinical Decision Support System Based on Cooperation Daniel Ruiz-Fernández University of Alicante, Spain Antonio Soriano-Payá University of Alicante, Spain

ABSTRACT The incorporation of computer engineering into medicine has meant significant improvements in the diagnosis-related tasks. This chapter presents an architecture for diagnosis support based on the collaboration among different diagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinical meetings in hospitals in which the members of a medical team share their opinions in order to analyze complicated diagnoses. A system that combines availability, cooperation and harmonization of all contributions in a diagnosis process will bring more confidence in healthcare for the physicians. They have tested the architecture proposed in two different diagnosis, melanoma, and urological dysfunctions.

INTRODUCTION Medicine has been one of the most important disciplines in society since mingled with magic and religion in the Egyptian era. The importance that medicine represents in society makes it one of the major destinations of technological advances:

from elements that provide proofs of diagnosis such as medical image acquisition systems, for example, radiographies, echographies, CAT, PET images, etc. (Rangayyan, 2004); till technical support applied to treatments, for example, electro-stimulation in rehabilitation or prosthesis (Vitenzon, Mironov, & Petrushanskaya, 2005) or


A Distributed Approach of a Clinical Decision Support System Based on Cooperation

telecommunications applied to medicine (Moore, 1999; Wootton, Craig, & Patterson, 2006). Although technology is used to apply certain treatments or to make diagnosis tests, it is still not considered as a real aid to the main medical task: the diagnosis decision process. On the other hand, medical diagnosis is defined as “the discovery and identification of diseases from the examination of symptoms” (Collins, 2003). This definition involves two steps in any act of medical diagnosis. Firstly, the “research” task in which the specialist tries to determine the symptoms of a patient by using his medical record and diagnostic tests. Secondly, a task of analysis of these symptoms and the decision, based on the medical knowledge, of which illness is associated to the symptoms with the greatest probability. An important detail is noting that medical diagnosis is essentially a decision-making process based on the lesser or greater probability of a patient’s symptoms of being related to specific information. Medicine has evolved since the days of Esculapio, when the physician was a wise expert on all the medical knowledge, problems and treatments; research and discoveries have broadened the field of medical knowledge, making necessary the creation of specialities: neurology, traumatology, rheumatology, urology or gerontology (one of the last specialities incorporated). Moreover, most of these specialities are divided into two groups: adult and paediatric specialities (Weisz, 2005). Have you ever wondered how many known diseases are presently now? We might have a slight idea of the number of known diseases by checking the International Classification of Diseases proposed by the World Health Organization in its last revision (ICD-10) (WHO, 2005): the group of infectious and parasitic diseases is divided into 21 subgroups (and each subgroup includes dozens of disease families), the group of tumours is divided into 19 subgroups, the group of nervous system diseases has 11 subgroups, the group of circulatory dysfunctions is subdivided in 10 subgroups, etc. Along with this enormous amount of diseases,

we find the corresponding symptoms: physicians must not only know the name and treatment for diseases, they must also be to identify their diagnostic signs and distinguish them from others corresponding to similar diseases. The evolution of medicine has also led to the gradual change in diagnosis techniques (Adler, 2004; Porter, 2006). In the early days of medicine, diagnosis was based exclusively on clinical data, that is to say, on the symptoms and the physical examination of the patient. With medical advances and the application of technology, new diagnosis tests and laboratory analysis were incorporated. The discovery of new diseases and their grouping into families and specialities has facilitated the development of differential diagnosis, which consists of determining the different illnesses that could affect a patient, after a comparative study of the symptoms and injuries suffered. The large number of diseases and organic dysfunctions coupled with the growing number of diagnostic signs (that increase thanks to new diagnostic tests) are paradoxically hindering the process of diagnosis. Computer engineering has techniques for the treatment of knowledge that may be useful for the processes of medical diagnosis (Burstein & Holsapple, 2008; Greenes, 2007). Most of these techniques are based on artificial intelligence and have been drawn from biology to be applied to computer science as neural networks or genetic algorithms (Haas & Burnham, 2008; Morbiducci, Tura, & Grigioni, 2005; Rakus-Anderson, 2007). These techniques can classify patients into groups according to whether or not they have certain diagnostic signs. There are many examples of researching applications of these techniques to diagnosis support: in (Roberts, 2000) a system based on Bayesian networks is proposed to assist the diagnosis of breast cancer; (Georgopoulos & Malandraki, 2005) shows a soft computing system to help in the differential diagnosis of dysarthrias and apraxia of speech which is able to distinguish among six types of disarthria and apraxia; systems of clinical

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decision support are also applied with diagnostic tests based on images, for example in diuresis renography (Taylor, Manatunga, & Garcia, 2007) or in radiographs for diagnosing lung diseases (Katsuragawa & Doi, 2007). A good example of the importance of the clinical decision support systems is the OpenClinical project. OpenClinical is an international nonprofit organization created and maintained as a public service with support from Cancer Research UK. One of the objectives of OpenClinical is to promote decision support and other knowledge management technologies in patient care and clinical research. This organization, through its web site, presents an interesting group of resources related with support systems. There are several clinical decision support systems that have gone beyond the field of research and are being continually applied in health centres (Coiera, 2003; Greenes, 2007). Examples of these systems are GIDEON®, ERA and Isabel. GIDEON® (Berger, 2001) is a system designed to diagnose infectious diseases, based on symptoms, signs, laboratory testing and dermatological profiles. It is made up of four basic modules: diagnosis, which enables the user to introduce all the signs and symptoms and provides a ranked list of differential diagnosis; the epidemiological module lets the user retrieve epidemiological parameters or access a list of the world wide distribution of any disease; the therapy module provides detailed information about choices in drug therapy; the microbiology module provides full laboratory characteristics for almost 900 organisms. The Early Referrals Application (ERA) (Coiera, 2003) is a system to support physicians in identifying those patients with suspected cancer that should be referred to a specialist in a short time period. ERA is intended to be used within the consultation so the workflow has been designed to be as simple as possible: during a typical user session just four web pages, clear and concise, will be encountered. ERA was developed by the Advanced Computation Laboratory of Cancer

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Research UK at London in collaboration with Infermed Ltd. Isabel (Ramnarayan et al., 2006; Ramnarayan et al., 2003) is a web system created by physicians to offer diagnosis decision support at the point of care. Isabel covers all ages and specialities as Internal Medicine, Surgery, Gynaecology, Paediatrics, Geriatrics, Oncology, etc. Isabel gives the physician an instant list of diagnoses for a given set of clinical features (symptoms, signs, results of tests, etc.). Isabel consists of a proprietary database of medical content and a tutored taxonomy of over 11000 diagnoses and 4000 drugs and heuristics. Medicine is a context where the massive quantity of knowledge makes it perfect to use distributed knowledge; in fact, as it has been previously explained, clinical knowledge is divided into medical specialities: oncology, cardiology, urology, etc. In computer science, distributed knowledge is implemented in different ways and levels. On a basic level, distributed databases are used (Luo, Jiang, & Zhuang, 2008; Waraporn, 2007); information can be stored in different databases according to a location criterion (store data near the location where it is going to be used) or a homogeneity criterion (store information about the same topic in the same location). One important advantage of distributed databases is that each instance (of the database) can be managed by a different group of experts, so there is an implicit sharing of knowledge (apart from sharing data). Distributed data mining techniques can be used to extract knowledge implicit in a distributed database (Cannataro, Congiusta, Pugliese, Talia, & Trunfio, 2004; Han & Kamber, 2005). In a higher level of distributed of knowledge we have the agents paradigm (Lin, 2007; Pop, Negru, & Sandru, 2006). In this paradigm, the knowledge is shared in an explicit way between different entities called agents. An agent analyzes external information and makes actions (according to its internal logic) to achieve an objective. There are different types of agents but all of them


are based in three main components: perception, decision and execution. With the perception, the agent obtains information from the environment; in the process of decision, the agent uses its “knowledge” (represented internally for example with an artificial intelligence method: neural networks, rules, fuzzy logic, etc.) to decide what action to take in order to achieve an objective; finally, with the execution, the agent tries to influence the environment to achieve the objective. An important characteristic of the agents is their ability to interact with other agents. Thanks to this interaction, agents can exchange information or share knowledge. This characteristic of the agents paradigm is the basis of the idea used in the cooperative approach presented in this chapter. We can find an example of cooperation between agents in business processes in which software agents must negotiate in order to reach an objective, for example, the best price. In our scheme, several entities with different knowledge about the same topic, collaborate to get a better diagnosis. In the following sections, we are going to introduce the concept of clinical decision support systems, detailing their features, advantages and disadvantages. Then, we will introduce the natural evolution of these systems into cooperative systems, propose their design and explain its performance in a scenario. Next, we will show some examples and results of the application of cooperative systems to diagnosis support. Finally, we will present different future lines regarding this subject.

CINICAL DECISION SUPPORT SYSTEMS A decision support system (DSS) can be defined as a multi-model, interactive system used by a decision maker to perform an exploration (Berner, 2007; Pomerol, 1997). The main objective of a DSS is to provide an aid to make unstructured

decisions thanks to its capacity to extract and manage information. It is important to note that, many times, the information provided by a DSS becomes another variable of the group of variables involved in the problem; therefore, the experts should determine the value of the decision automatically provided by the DSS. Decision support systems are used in a wide range of fields such as economics (Modarres & Beheshtian-Ardekani, 2005), industry (Delen & Pratt, 2005), medicine (Vihinen & Samarghitean, 2008), etc. DSS can be classified according to different criteria such as the mechanism used to represent knowledge; this is the case of rule-based DSS, DSS based on decision trees, etc. (Burstein & Holsapple, 2008). Another criterion used to classify DSS is their operation autonomy: solicited advice, when the system helps the user when requested; unsolicited advice, when the system provides diagnosis information without any request from the user. In the latter group, there are, for example, intelligent alarm systems that analyse constants and warn about a possible diagnosis that requires an emergency treatment. When we use a DSS to support the medical diagnosis task, we have what is known as Clinical Decision Support System (CDSS), that could be defined as “active knowledge systems which use two or more items of patient data to generate case-specific advice” (van Bemmel & Musen, 1997). In this definition, we have the three main elements of a DSS: the knowledge of medical diagnosis, the patient data or the information to be analysed, and the results provided by the DSS as a recommendation. A result coming from a DSS applied to diagnosis will usually indicate the probability that the symptoms will correspond to a particular illness. The operation of a traditional CDSS is as follows: a user inputs data associated to any illness; the system analyses the information and provides one or more results along with their corresponding probabilities of success. Difficulties found in traditional medical diagnosis can be divided into two main groups:

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sources of error in the examination of the symptoms and problems in forming hypotheses. The first group covers the problems stemming from confused information provided by the patient, low reliability of this information or deliberate concealment of the true cause of consultation (due to cultural reasons or personal embarrassment). The second group includes those situations associated with direct problems of the diagnosis task: patients that present incomplete signs or common illnesses with unusual manifestations, low frequency of diseases (rare diseases), mimicking diseases, and, in general, the difficulty the specialist has in associating the symptoms to a specific disease. Although a CDSS can be useful for both groups, in the first one, its support capacity is more limited, as problems are found in the input data. Additionally, the same difficulties found by the specialist are going to be found by the CDSS. In the second group, the problems are related to the enormous amount of symptoms and diseases (impossible to remember for a specialist) coupled with human diversity (the same illness can be manifested in different ways in different patients). These problems are directly associated with the ability to analyze and compute, a question on which a computer system can be particularly helpful. Two parameters are used to validate a diagnosis system in medicine, sensitivity and specificity (van Bemmel & Musen, 1997). Sensitivity is the probability of classifying a diseased individual correctly (as diseased); that is to say, a high sensitivity of a CDSS implies a low number of false negatives. On the other hand, specificity is the probability of classifying a healthy individual as healthy and is directly related to false positives. In general, it is a priority that a CDSS has a high sensitivity and, therefore, a low number of false negatives (diseased individuals classified incorrectly as healthy). A CDSS may be helpful to a medical specialist in several aspects:

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•

•

•

To support a clinical decision. Sometimes, if the symptoms are not well defined, the physician may have doubts about several diagnoses. The CDSS may help increase the physician’s confidence in a particular diagnosis. To propose alternatives. The large number of existing illnesses can cause a physician overlooks diagnostic tests for less common diseases. A CDSS is able to propose an alternative diagnostic that could be easily refuted or accepted by means of another diagnostic test or symptom the physician had not initially thought about. To question a clinical decision. A CDSS can provide a more objective diagnosis support than a medical specialist. This can cause that sometimes the CDSS issues, as a first option, a diagnosis result relegated by the physician due to subjective reasons (for example, an emotional feeling).

Advantages and Disadvantages The use of computer technology in the medical field, particularly in diagnosis, involves both advantages and disadvantages. Next we will describe some of the advantages related to the use of a CDSS: •

•

•

Permanence. A computer system does not age or lose power over the course of time. It may need maintenance and updating as well as specialists must constantly update their knowledge, but once it stores a specific knowledge, this information lasts the duration of its working life (without lost of reliability). Duplication. Initially, the cost of developing a CDSS may be high, but once it is implemented, duplication is simple and inexpensive. Reliability. The reliability of a computer system is independent of external condi-


•

•

tions such as fatigue, personal affinity or pressure. Ubiquity. A computer system can be accessed via communication networks from anywhere; computers can work in environments that are hostile or dangerous for a human being. Availability. Access can be permanently available, achieving an availability of 24 hours a day, 7 days a week.

The limitations of an expert system that provides diagnosis support are directly associated to the limitations of any computer system when it is responsible for tasks that may require human skills. Some of these limitations are: •

•

•

Lack of common sense or limitation of knowledge. Without the proper knowledge, a male could be diagnosed 80 weeks pregnant by a computer system. Inability to maintain an informal conversation in natural language. There are subtle differences when a patient is expressing his symptoms that could be essential for the diagnosis. Flexibility. When issuing a diagnosis, a human specialist can be flexible because he is

•

•

•

aware of his limitations; this flexibility is not possible with a computer system. Need for structured knowledge. Any knowledge that is incorporated in a computer system requires a task of structuring. Lack of feelings. Its operation is governed by strict rules that have nothing to do with human sensitivity, often necessary in the relationship between patient and doctor. Ethical problems. This limitation especially arises when computer systems are applied in health sciences. As computer systems cannot accept responsibility for their own decisions, their operations should be supervised by a medical specialist at all times.

Classical Design The typical architecture of a decision support system consists of three main elements: the user interface, the reasoning module and the knowledge database on which the reasoning is based. This architecture is schematically shown in figure 1. The user interface has evolved over the course of time, from the input of data and questions via console until the most modern graphical systems that give access ubiquity properties thanks to interconnection networks. Additionally, inter-

Figure 1. DSS Architecture

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Figure 2. Interface of a DSS for urologists

faces have been adapted to the main users, the physicians, who are not technical experts. Figure 2 shows a screenshot of a user interface for clinical decision support system in urology that has been adapted to the clinical methodology used by the urologists. The knowledge database is the part of the system that contains the basic information for the diagnosis issuing. This information may include both structured knowledge and data used to extract information or to train the reasoning module. Finally, the reasoning module implements an algorithm that analyses the input data and provides a result that basically consists of a classification: in the case of medical diagnosis, certain symptoms are classified as belonging to a certain disease with a degree of certainty. The main problem of the classical design for a CDSS is the limitation to tackle problems inside the domain of the CDSS but not well represented in the knowledge database. Another problem is that the only way to improve the functioning of the CDSS is to increase the knowledge database and to improve the reasoning module. This increases the complexity of the CDSS and, therefore, increases

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the possibility of failure; moreover, maintenance tasks become more difficult. Cooperation between simple CDSS can manage both problems, the lack of a complete knowledge of the domain and the way to improve the reliability easily.

COOPERATIVE CLINICAL DECISION SPPORT SYSTEM In the previous section, we have explained what a clinical diagnosis support system consists of and how it can be a tool to help medical specialists. The evolution of the CDSS causes them to meet the same limitations that the physicians have: the large amount of medical knowledge makes a CDSS specialise in just one kind of disease. The approach presented in this point is the design of a cooperative clinical decision support system (CCDSS). The cornerstone of this architecture is the improvement of the system reliability thanks to the participation of several entities, with diagnosis functions, which work together to provide a single diagnosis. Such cooperation can be oriented towards various directions; different


entities can be specialised in different illnesses or organ systems affected by the same illness and their collaboration facilitates the diagnosis. Additionally, the use of several diagnosis entities operating in parallel increases the global system availability. Cooperation in diagnostic tasks, thanks to the participation of several physicians, is a common working manner in many medical teams. When faced with cases of difficult diagnosis, several physicians work together to issue different diagnosis ideas. They all together discuss ideas, propose diagnostic tests and, finally, reach a consensus. The architecture of a CCDSS wants to automate this working manner. The architecture of a CCDSS is primarily meant to satisfy the reliability requirements. Maintaining a high availability of the system is also very important. To meet these criteria, we define a distributed architecture based on the paradigm of intelligent agents. The components of the architecture are: the user interface, the diagnosis entities, the system core and the communications subsystem (figure 3).

The user interface will be the means through which healthcare professionals will be able to request the CCDSS opinion. It will also be in charge of presenting the consensus information that must be evaluated by the specialist that requested it. The diagnosis entities may be physicians, and they will be human entities, or decision-making support software, and they will be then software entities. The software entities will receive the symptoms and diagnostic tests and, next, they will issue a set of possible diagnoses along with their corresponding probabilities of certainty. The system core will be responsible for receiving diagnosis requests and distributing them to the appropriate diagnosis entities. It must keep the system security, preventing intrusions of false entities that will destabilize the system. In addition, it will also be responsible for collecting the results from the different entities and providing a consensus. The communications subsystem consists of both the communications network and the protocol to be used to transmit information. The

Figure 3. Distribution of the components in the CCDSS

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structure of the communications network must preserve the security of the communications and the system integrity.

User Interface This is a key module of the system, as it will influence the degree of user acceptance. The interface must be friendly and adapted not only to the user but also to the device from which is being operated. Authentication will be the first contact with the system, as it is necessary to maintain the appropriate security levels and to provide each user with the most suitable interface. After the authentication process (figure 4), the system core will be responsible for adapting the data according to the device from which the system is being accessed; this involves the design of different environments to facilitate the access from the most common devices used to access a data network: PC, Tablet PC and PDA. These three kinds of devices include the main differences between working environments. For the PC environment, there are no graphical limitations and the size of objects can be small, as the commonly used screens allow it. For the Table PC environment, the touch screen function implies the adaptation of the graphical interface to this working man-

Figure 4. System access process scheme

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ner; in addition, the size of the screens is often smaller than that in the PC environment. Finally, the PDA environment involves working with a device with graphical limitations, touch screens and a very small size. Once the device environment has been selected, the system will need to format the information according to the role and user permissions as a last step. The system, as a part of the security mechanisms, will distinguish three levels of accessibility according to a set of roles and individualized permissions. There will be six roles: •

•

•

•

Healthcare reviewer. This will be an accredited professional in charge of reviewing the ethics of the system, both the operation of the artificial entities and the activities of consulting physicians and medical officers. It will be responsible for supervising the consensus operations. Medical officer. This is the role for those professionals who are working together in a diagnosis, giving their opinion on certain symptoms. Consulting physician. Users that use the system to get a possible diagnosis will be authenticated under this role. Practice. If users use the system under the practice role, only artificial entities will


•

•

participate and the results of clinical cases will not be used to update the system. Student. This role allows the system to serve as a teacher. The operation will be the opposite: the system will provide some symptoms and the user will issue the diagnosis. Thus, the CCDS becomes a system that could help with the task of teaching future physicians, thanks to the enormous amount of clinical cases stored in the database. Administrator. This will be the engineer responsible for the proper operation of the technical side of the system. The activities involved will be coordinated with those corresponding to the healthcare reviewer in charge of the medical supervision.

If a consulting physician accesses the system, the data input will depend on the speciality related to the issue he wants to consult. This does not imply that the information will also be analysed by entities with different specialities if deemed necessary. Moreover, the consulting physician is more familiarised with a certain kind of information related to his speciality and this is why this feature will be taken into account. After introducing the symptoms and diagnostic tests, a diagnosis will be required from the system, indicating a time limit for issuing this diagnosis. This time will allow the adjustment of the accuracy level of some artificial entities as well as indicate the possibility of participation or not of human diagnosis entities (when available during that time limit). Once time has expired, the system will provide a list of possible diagnoses along with their corresponding probabilities of confidence. Furthermore, the listing will show which entities have supported each diagnosis, indicating whether they are artificial entities or physicians and their confidence level within the system.

Diagnostic Entities The second component of a CCDSS is a set of diagnostic entities, each one understood as an element that, after receiving information about the symptoms of a patient, issues a possible diagnosis with a certain degree of certainty. These entities may be either human or artificial. Diagnostic human entities are consulting physicians who want to participate voluntarily in diagnosis activities within the system. Artificial entities are software that can propose a classification and, therefore, a possible diagnosis, thanks to statistical or artificial intelligence techniques. Diagnostic entities are distributed and get the symptoms and report their results to the system core through the communications subsystem. There is a quality value associated to each diagnostic entity. This quality value will be used by the system core to make a decision on the final diagnosis. We can distinguish between human and artificial entities to assign these quality values. Since it is difficult to automatically evaluate the quality of a diagnosis from a physician participating in the system, the healthcare reviewer will be the person who will assign the quality value of a particular doctor when a physician is registered in the system. This value may be altered at any time by the healthcare reviewer. In the case of diagnostic artificial entities, the quality value can be automatically calculated. In order to do this, artificial entities must pass an initial testing phase through which the probability of diagnosis success is calculated as well as sensitivity and specificity. Taking into account these three variables, a value of certainty for the entities can be calculated, for example, calculating the average. Moreover, this value can be updated with the performance of the artificial entity so that the diagnosis successes improve its value of certainty and the failures decrease this value. This

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process of constant updating gives the system an added value, because as new artificial entities are joined to the system (with improved artificial classification techniques), old entities go obsolete automatically and their diagnoses lose importance with regard to the final consensus. For the proper operation of a CCDSS, it is very important that all artificial entities follow the same method to calculate their value of certainty, because if this value is obtained by different means the consensus process will lose reliability. Differences between human and artificial entities should not affect the consensus negatively thanks to the control by the healthcare reviewer; additionally, the final consensus will include the influences of both kinds of entity. However, different CCDSS can use different methods to evaluate the value of certainty because, if we wanted to combine the results of several CCDSS, we would take the final consensus results and not the particular ones corresponding to the entities working together in the consensus. As explained, medical officers are identified with the diagnostic entities. Every medical officer, when accessing the system by the authentication, will find a set of diagnosis possibilities related to his speciality and will be able to choose which ones he wants to use to issue a diagnosis. It is important to say that, with regard to the consulting physician (who is requesting a diagnosis for certain symptoms), the detail of diagnosis will not include any identification of the medical officers who have participated in the diagnosis. If he needs any explanation about the diagnosis, he should ask the healthcare reviewer. Despite this, due to legal and ethical reasons, detailed information of every diagnosis from a medical officer will be stored and only accessible to the healthcare reviewer. Depending on the CCDSS, the medical officer may act in an altruistic way or receive remunerations for his contributions. Figure 5 shows the design of the diagnostic artificial entities. Each artificial entity consists of a diagnostic module, a local storage and a module

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of perception or interaction with the environment. The diagnostic module has the algorithm to classify the information received (diagnostic data) and decides whether this information corresponds to a healthy individual or to a diseased person. The local storage will be in charge of storing the information relevant to the diagnosis process; furthermore, it will store the data related to the entity itself such us the entity designer, the last update date, the achieved percentages of classification, sensitivity and specificity, etc. The module of interaction with the environment is responsible for receiving the data, adapting them to the analysis to be done by the diagnostic module and, afterwards, formatting the results in a form suitable for transmission; besides, this module will also update the diagnosis algorithm and provide information about the entity itself when required by the system core of the CCDSS. This structure is similar to that corresponding to software agents (Ferber, 1999; Lin, 2007), which are able to perceive changes in the environment, to consider what actions to take and to try to influence the environment to achieve an objective. In the case of a medical agent, the perceptions correspond to the symptoms; the actions would be the diagnoses (that will give rise to treatments), and the main objective would be to give an accurate diagnosis in order to cure the patient.

System Core The system core is a key element in the CCDSS management. This module is responsible for distributing the diagnosis requests to the entities, for reaching a consensus of the different diagnoses and for controlling accesses and information transmitted through the system. The structure of the system core has three different parts: the consensus module, the security control and the request manager. The consensus module is, after receiving the diagnoses from the different entities and taking its values of certainty as a reference, elaborates


Figure 5. Diagnostic artificial entity

a consensus listing possible diagnoses and their corresponding probabilities. This is one of the most complex tasks of the CCDSS, as there are numerous factors involved and always surrounded by degrees of uncertainty. The consensus algorithm must be able to solve extreme situations; for example, a case in which several artificial entities with a high degree of certainty agree on a diagnosis and the opinion from a medical officer differs completely. Moreover, depending on the organization that manages the CCDSS, there can be other factors that may influence the decision such us the economic cost of the treatment or whether a diagnosis has a favourable prognosis; thus, faced with two diagnoses, being the second more likely to has a better prognosis, the decision algorithm could consider this factor and change the diagnoses order, keeping an optimistic approach. There are a lot of decision algorithms that can be used and a lot of studies done about the decision problems, especially in economics (White, 2006). Another aspect that must be controlled by the consensus module is whether the request is related to several specialities. In this case, maybe the decision from all entities has the same value or maybe there is a major speciality and secondary specialities; in this case, the diagnosis from the entities belonging to the major speciality

will have more value that those coming from the remaining entities. Security control is mainly performed in two ways: user authentication and entities control. User authentication involves not only the access control and the selection of the appropriate role but also a complete monitoring of their actions within the system. It is important to prevent a malicious user that has taken a medical officer role from modifying a diagnosis, causing a malfunction of the CCDSS. On the other hand, the system core also manages the artificial entities, which must be registered in a database containing information about the entity, the engineer responsible for the development, the speciality, the initial degree of certainty, etc. All this information will be used by the system to control to which artificial entities it is possible to request a diagnosis. The healthcare reviewer is not only in charge of distributing the profiles among the physicians with access permissions, he is also in charge, along with the administrator, of allowing an artificial entity to join the system (and of deciding the deleting). The request manager is responsible for distributing the diagnosis requests and for collecting the results before transmitting them to the decision module. In order to make the distribution, the database with the register data of the entities (both artificial and human) is taken into account, and

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the symptoms input by the consulting physician are sent to those entities that correspond to the speciality or specialities related to the request (which have been input by the consulting physician as well). Along with the transmission of information necessary for the diagnosis, it should be included the time limit for the entities to issue the diagnosis. Once the time has expired, the manager will submit all diagnoses received with their corresponding probabilities, stating the entity from which they come in order to consider the relative importance of each diagnosis in the consensus task.

Communications Subsystem and Protocols The communications subsystem is the basis for the operation of a CCDSS. The information flows between different components through secure channels and standardised protocols. Artificial entities work like web services and they use Hyper Text Transfer Protocol (HTTP) and Simple Object Access Protocol (SOAP) to inter-component communication. In order to ensure a secure channel, HTTP is secured by using the Secure Socket Layer (SSL) protocol, which makes communication secure on the transmission level. Moreover, better security levels can be achieved by encrypting the information on the implementation level. The artificial entities secure the messages they send and receive with the XML-Encryption specification of the World Wide Web Consortium (W3C). They also sign and validate the messages with the XML-signature specification, also of the W3C. The structure of the messages transmitted within the system should be based on a standard. For this project, we intend to use the HL7 standard, so that the XML messages are consistent with the Reference Information Model (RIM) of the HL7 version 3 specification (Hinchley, 2005). The data considered to be managed are only diagnostic data, as the aim of the posed system is the diagnosis process. In accordance with the

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RIM specifications, data are modelled as observations, both the data coming from the medical examination (joined to the diagnostic tests) and the diagnosis.

SCNARIOS The first proposed scenario is located in the surgery of a family doctor from a city; we assume that there are no limitations with regard to the resources for making diagnostic tests. The physician, after checking the medical record of the patient and examining him, concludes that the patient suffers from an intestinal problem. In order to determine the diagnosis, the physician will order urine and blood tests and ask the patient to come back with the results after three days. During this period, the physician may use the system to consult experts on the digestive system, sending the patient symptoms and waiting for a result within three days, before the patient comes back. The results provided by the system can confirm the physician’s diagnosis, but also can help him look for alternative diagnoses when checking the laboratory results that the patient will bring on his next visit. In this case, the use of the system has avoided the referral of the patient to a specialist on the digestive system, if the laboratory tests did not confirm the hypothesis of the family doctor, and he had not thought about any alternatives. In this scenario, the system has helped reduce waiting lists for specialists. Another scenario could consist of a patient living in a rural environment with a widely disseminated population, away from the city and from the most sophisticated healthcare resources. Faced with the case of a patient with an acute backache, the physician should decide whether to order more tests and, therefore, refer the patient to a medical centre (perhaps, many kilometres away), where the resources necessary for the tests are available. One of the ways of interacting with the system in this case could be to determine which diagnostic


tests to do, thus reducing the number of trips the patient should make to the hospital. The physician would check the patient’s symptoms with the system while he prescribes an analgesic treatment to see if the pain goes away. When the patient comes back to the medical centre, the physician will already have a listing of possible diagnoses provided by the entities connected to the system and will be able to order diagnostic tests at the hospital, which confirm any diagnosis. In this case, the system has helped the physician select the diagnostic tests needed to obtain a differential diagnosis; at the same time, inconveniences for the patient due to the several trips to the hospital, far away from his home, has been avoided. Finally, we propose a different scenario that does not involve problems related neither to specialised knowledge (as in the first case) nor to resources (as in the second case). A patient goes to the hospital with strange symptoms that do not correspond to any illness. In this case, a team of physicians takes responsibility for the patient and, usually, begins to study the case in order to get a differential diagnosis. In this scenario, the system could be regarded as one more expert opinion (with the value that the team leader wants to give it), taking into account that this opinion is a consensus of many diagnoses; therefore, it is as if the team of physicians at the hospital had at its disposal another team of external physicians, also taking part in the same differential diagnosis. With these three scenarios, we want to show the reader several possible uses of a CCDSS, in situations with some kind of restriction as well as in cases in which the systems becomes one more opinion to be considered depending on the evaluation of a medical director. Although we have provided only three scenarios, there are many more cases in which the system could be useful; for example, in a prison or war the location characteristics make it difficult for a complete medical team to work together, so a CCDSS would be a viable option.

Although the architecture proposed is focused on the diagnosis of human diseases, it could be used in any decision-making support in which a consensus among different opinions is useful. If we stay in the healthcare field, we have the same situation in veterinary medicine. A CCDSS could be also used to propose economical questions (maybe related to decisions on stock market values) or auto mechanics.

EXPERMENTATION In the research group of bioinspired engineering and healthcare computing, we have developed a prototype of this architecture. The main objectives for this first development were, on the one hand, to confirm that more accurate diagnoses can be obtained thanks to a consensus among the diagnoses provided by different CDSS. On the other hand, we wanted to study the overall performance of the system with the incorporation of medical opinions and to know what the physicians’ acceptance level was. In order to study whether the consensus among several diagnostic artificial entities improved the individual diagnosis provided by each entity, we selected the diagnosis of melanoma as an experimental test. Two classification algorithms were implemented: a Bayesian classifier and a multilayer perceptron (Greenes, 2007). To increase the number of diagnostic artificial entities, multiple instances of the multilayer perceptron were created by modifying the number of hidden neurons and the training set. Finally, in addition to the entity based on the Bayesian classifier, we also obtained four entities based on the multilayer perceptron (with 3, 4, 5, 6, 7 and 8 hidden neurons), which had acceptable classification rates. Table 1 shows the performance of the multilayer perceptron with regard to the number of neurons of the hidden layer. Instead of working with a list of symptoms, the prototype was adapted to work with vector

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features taken from a pre-processing of the melanoma images. Regarding the consensus module, we used a simple voting system based on the classification rate of the entities: the importance of one entity was equivalent to its classification rate. Thus, if an entity with a classification rate of 85% diagnosed a skin injury as healthy and two more entities with rates of 80% and 82% diagnosed the injury as melanoma, the final consensus would be melanoma. This consensus mechanism can involve the following problem: two entities with very low classification rates (45% and 47%) can induce a wrong diagnosis within the system, over the diagnosis provided by an entity with a classification rate of 90% (45+47>90). In order

to solve this problem, we have selected, from all possible artificial entities, only those with classification rates over 75%. Figure 6 shows a comparison of the classification rate between the Bayesian algorithm, the best multilayer perceptron entity (7 neurons in the hidden layer) and the CCDSS (obtaining a consensus from all the artificial entities). The classification rate of the CCDSS (91.32%) is very similar to the one obtained with the multilayer perceptron entity. Regarding the specificity and the sensibility, both measures are higher in the CCDSS: 93.58% and 78.04%, respectively. The Bayesian entity has a classification rate of 81.25%, a sensibility of 93.15% and a specificity of 76.70%.

Table 1. Performance of the multilayer perceptron entities Hidden neurons

Classification Rate (%)

Specificity (%)

Sensibility (%)

3

70.59

22.54

46.08

4

74.22

55.02

52.94

5

81.00

70.89

55.00

6

84.37

92.00

70.58

7

90.62

93.20

77.35

8

87.50

91.15

76.47

Figure 6. Comparison between CCDSS and other DSS diagnosing melanoma

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Figure 7. Classification rates for different artificial entities and CCDSS in urological dysfunctions

In the other experiment, we developed artificial entities to diagnose different urological disorders based on urodynamic tests (Ruiz Fernández, Garcia Chamizo, Maciá Pérez, & Soriano Payá, 2005). In this experiment, we used a urology expert and several family doctors interested in participating as consulting physicians. The experimentation outcome is shown in figure 7. In particular, we used two different multilayer perceptron (MLP1 and MLP2, in which we modify the hidden layer and the training data set) and a Kohonen SelfOrganising Map (SOM) to classify three types of urological dysfunctions: obstruction, hyperreflexia and effort incontinence. As it is possible to observe in the graph, the classification rate obtained with the consensus is higher than the individual rates for each entity for all the dysfunctions studied. In the consensus we used the same voting system as in the other experiment, so the weight of one entity in the final consensus was equivalent to its classification rate (obtained in a previous test stage). The most important conclusion of this second experiment was the physicians’ opinions with regard to the system; they found the system very useful in supporting those diagnoses involving very specific fields in which they do not have deep knowledge.

CONCLUSION Throughout this chapter, we have tried to introduce the reader to the relevance that the decision-making support systems may have in the healthcare field, particularly in medical diagnosis. Moreover, given the characteristics of the medical environment, we have proposed a new kind of system that involves the cooperation between different entities as the core of the diagnosis support process. Thanks to cooperation among diagnostic entities, physicians and artificial entities, we want to achieve higher precision in the final decision, as well as provide wider system availability. The proposed architecture is based on the paradigm of intelligent agents and distributed computing. The diagnostic entities are distributed to the system and act as web services when asked by a control element (system core); consequently, a lot of diagnosis processes can be performed in parallel. These processes are carried out by physicians and software programs. Finally, the system performs a consensus process among the overall results in order to give the user a set of possible diagnoses with their corresponding probabilities of occurrence.

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The distribution of knowledge is a powerful tool in a clinical decision support system. By adapting the system to a distributed architecture, any number of future sources of knowledge could be integrated into the network, generating an expanding knowledge-base. In addition, different knowledge sources specialized in the same problem can provide different points of view, a key factor in decision support. The experimentation carried out by our research group has confirmed that cooperation between different diagnostic entities can determine a diagnosis from a set of symptoms, improving the individual performance of each entity. We have presented two examples, one related to the melanoma diagnosis and another based in the diagnosis of urological dysfunctions. In both cases, diagnosis results obtained with the cooperative approach are better than individual results. It is important to say that at no time did we propose that a CCDSS replace a qualified professional at the diagnosis activity; the CCDSS operation cannot be understood without the supervision of physicians that check the proper operation of the artificial entities and evaluate the quality of the consensus diagnoses provided by the system.

FUTURE TRENDS One problem with implementing a CDSS is the acquisition of the knowledge related with the diagnosis. It is necessary to contact a group of experts and organize their knowledge, before including the diagnosis rules or heuristics in the system; all these actions take a lot of work and time. In the future, the knowledge should be extracted by more automatic means which take less time. Journals and conference proceedings are an easy alternative way to collect useful information about diagnosis; on the other hand, in hospital information systems there are huge databases with data related to diseases and their diagnosis evaluation. Furthermore, the use of data from

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journals or electronic health records in hospitals allows for an automatic and constant update of the knowledge rules of the clinical decision support systems. An important future goal is the interaction between different decision support systems. These systems should evolve in the same way as medicine. Each CDSS should be an artificial expert in one speciality or even in just one group of diseases. This implies not only incorporation of information about the diagnosis and therapy of diseases but also including information about others organic systems that can be affected by a disease (in order to interact with the CDSS expert in these organic systems). The consensus between the different artificial entities will be more difficult because each entity will be an expert in just one topic so each entity will have a relative importance in the final diagnosis. It will be necessary to improve the consensus algorithms. As a summary we can suggest three specific objectives for the future. First, it will be necessary to involve a higher number of healthcare professionals in order to validate the system when collaboration of entities expert on different specialities is required. Another objective will be to improve the diagnostic capability of the artificial entities, particularly during the self-learning process, based on their performance within the CCDSS and the feedback which is available after the physician approves the consensus. Finally, it will be find necessary to do an in depth study of the consensus algorithms used, in order to ensure more precise results and to ensure that they can take into account additional variables like the morbidity of the diagnosed illness.

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Chapter VI

Managing Mobile Healthcare Knowledge:

Physicians’ Perceptions on Knowledge Creation and Reuse Teppo Räisänen University of Oulu, Finland Harri Oinas-Kukkonen University of Oulu, Finland Katja Leiviskä University of Oulu, Finland Matti Seppänen The Finnish Medical Society Duodecim, Finland Markku Kallio The Finnish Medical Society Duodecim, Finland

ABSTRACT Incorporating healthcare information systems into clinical settings has been shown to reduce medication errors and improve the quality of work in general by improving medical decision making and by saving time. This chapter aims to demonstrate that mobile healthcare information system may also help physicians to communicate and collaborate as well as learn and share their experiences within their work community. Physicians’ usage of a mobile system is analyzed through a knowledge management framework known as the 7C model. The data was collected through the Internet among all of the 352 users of the mobile system. The results indicate that frequent use of the system seemed to improve individual physicians’ knowledge work as well as the collective intelligence of a work community. The guide for


Managing Mobile Healthcare Knowledge

acute care, evidence-based medicine guidelines and information related to drug interactions supported the knowledge creation to a large extent. As such, mobile healthcare information systems may be capable of supporting the different sub-processes of knowledge creation and the knowledge work of individual physicians, and through this also improving the collective intelligence of the work community. Overall, knowledge management seems to be a prominent approach for studying healthcare information systems and their impact on the work of physicians.

InTROoduion Personal digital assistants and mobile applications are promising tools for managing medical information and accessing it at the point of care (Ebell et al., 1997). They have been shown to assist in evidence-based practice in a clinical setting and support the educational needs of physicians, nurses and other clinical staff, while drug information, medical calculations, guideline information and administrative tasks have been identified as the most useful resources (Honeybourne et al., 2006). Topics such as e-prescribing (Kushniruk et al., 2005) and patient tracking (Lapinsky et al., 2001) have gained a lot of attention recently. On the other hand, access to medical literature and electronic pharmacopoeias, i.e. drug information, seem to be excellent tools for providing physicians with knowledge at the point of care (Fischer et al., 2003). Incorporating healthcare information systems into clinical settings has been also shown to reduce medication errors (Grasso & Genest, 2001) and improve the quality of work in general by improving medical decision making and by saving time. Mobile versions of these systems are relatively easily incorporated into the workflow of the physicians (Rothschild et al., 2002) as they can be carried around and used when ever needed, for example during home visits or ward rounds. In the information systems field the topic of knowledge management has received a lot of attention recently (for an excellent review on the subject, see (Alavi & Leidner, 2001)). Our view on knowledge management is through a

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conceptual framework known as the 7C model (Oinas-Kukkonen, 2004). It suggests that the seven Cs or knowledge creation sub-processes, namely Connection, Concurrency, Comprehension, Communication, Conceptualization, Collaboration, and Collective intelligence, play a central role in knowledge management. According to the 7C model, going through the key phases of Comprehension, Communication, Conceptualization and Collaboration repeatedly, in a seamless and spiral-like way leads into the growth of the organizational or social knowledge, i.e. Collective intelligence. This paper focuses on mobile access to medical literature and electronic pharmacopoeias, aiming to demonstrate that these may help physicians to communicate and collaborate as well as learn and share their experiences within their user community. There are relatively few scientific studies on managing knowledge with mobile healthcare information systems. Moreover, only a small number of articles provide knowledge about the actual use of mobile medical applications (Fischer et al., 2003). We will present a case study among the users of Duodecim mobile healthcare information system. The data was collected through the Internet among all of the 352 physicians (with the response rate of 66.5%, n=234), who were users the case system. The article is organized as follows. Chapter II describes the background for the study. Chapter III presents the 7C model for knowledge creation and management, Chapter IV introduces the case system, Chapter V discusses the results, and finally Chapter VI concludes the article.


Bakg Systematic processing of health-related data, information and knowledge focusing on the study of information processing principles and solutions in healthcare is referred to as health informatics, while the scientific discipline related to it is called medical informatics (Hasman et al., 1995). The terms are often used synonymously, even though some differences exist in their use between countries. For instance, medical informatics in Germany also includes nursing informatics and dental informatics, while in other countries medical informatics primarily focuses on solutions from the physicians’ viewpoint (Hasman et al., 1996). In general, health informatics is often examined from different perspectives such as information technology or user needs. A healthcare information system (HIS) is defined as an application of information technology in healthcare, encompassing a wide range of applications from disciplines such as medicine, computer science, management science and statistics (Raghupathi, 1997). Based on the interaction between the medical personnel and the patient, HISs may be categorized as customer supporting systems, interaction supporting systems, consultation systems, decision support systems, process supporting systems, economic systems, preparation tools and administrative tools (Suomi, 2001). According to Siau (2003), mobile healthcare information systems are among the basic tools employed in the healthcare industry, the other two being Internet applications and enterprise systems. Mobile HISs offer two distinct advantages for the healthcare sector: firstly, they are important for the success of telemedicine, and secondly they enable physicians to access information whenever and wherever needed. Access to real-time information is important for physicians and hospitals because information is often needed immediately to enable accurate decision making (Siau, 2003).

Fischer et al. (2003) classify the main uses of mobile applications for medicine as: accessing medical literature, electronic pharmacopoeias, patient tracking, medical education, research data collection, e-prescribing, business management and specialty-specific applications. Other classifications have also been suggested, such as those ones by Adatia and Bedard (2003) and Embi (2001). Providing access to medical literature increases the extent to which evidence will be sought and incorporated into patient care decisions (Sackett et al., 1998). The access to medical literature through a mobile application will allow decisions to be made by the bedside or at the point of care. This could further improve the decisions made by physicians (Sackett et al., 1998). Mobile devices containing decision-making tools and summaries of evidence may also improve learning in evidence-based medicine (Honeybourne et al., 2006) and reduce patients’ length of stay in hospitals (Sintchenko et al., 2005). Usually, evidence-based information is presented in a guideline form to further support decision-making. Pharmacopoeias are drug information databases and drug interaction guides. Drug information refers to information such as adult and paediatric dosing guidelines or common side effects, while drug interaction guides contain information about possible interactions that two or more drugs used together can have (e.g. drug A may influence the absorption of drug B). Access to drug information may reduce medication errors (Grasso and Genest, 2001) as it is impossible in practise to know all conceivable drug interactions by heart. Thus providing an easy manner to double-check these interactions should indeed help the work of physicians at the point of care. The survey conducted by Rothschild et al. (2002) with palmtop drug information guide users suggests that mobile systems may save time in information retrieval and improve drug-related decision making and can be relatively easily incorporated

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into the workflow of physicians. This is important, as it could improve technology acceptance and save time. Patient tracking systems aid medical staff in ward rounds by keeping them informed about the patient’s condition. Medical education applications are designed to help medical students in their studies, e.g. by monitoring their clinical experience or by finding gaps in their education. Mobile applications used for data collection have also been found promising for research purposes (Fischer et al., 2003). Using mobile applications for electronic prescribing has been found to decrease medication error rates (Grasso & Genest, 2001) and business management applications help the efficiency of hospitals, e.g. by improving coordination and billing (Fischer et al, 2003). Finally, different specialties (for example family medicine) have their own specific mobile applications. Usually medical literature includes not only descriptions of treatment methods but also evidence supporting each method. This form of decision making is referred as evidence-based medicine and is defined as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients” (Sackett et al., 1996). For example, mobile decision support systems have contributed a significant reduction in antibiotic prescribing (Sintchenko et al., 2005), i.e. physicians’ have been able to see better when to prescribe antibiotics and when not to. There are two approaches to applying evidence to medicine (Eddy, 2005). The first approach is to use evidence-based guidelines. Twenty years ago medical guidelines were based on experts’ consensus but over the years most of the guidelines have changed into evidence-based. Quite interestingly the new guidelines have been dramatically different than the previous ones (Eddy, 2005). The second approach is to apply evidence in individual decision making (Evidence-based Medicine Working Group, 1992). The difference to evidence-based guidelines is that this

114

approach aims to “educating physicians to help them bring more research and evidence into their individual decisions about individual patients” (Eddy, 2005). Knowledge management is “a business concept which includes concerted, coordinated and deliberate efforts to manage the organization’s knowledge […] and applying it to enhance organizational performance” (Bose, 2003). Because of the growing costs of healthcare various knowledge management solutions have been applied in hospitals and medical centers to enhance performance and e.g. provide better care. Yet the healthcare sector has been said to be data rich but knowledge poor (Abidi, 2001). That is to say different healthcare organizations generate huge amounts of data from hospital reports to clinical trials but not much of the data is transformed into strategic decision-support knowledge (Abidi, 2001). Thus most of the knowledge management solutions in healthcare have been concentrating on transferring data into knowledge. One way of doing this is to utilize data mining (Fayyad et al., 1996). By using data mining we can find e.g. correlations or dependencies from the vast amounts of data available to us. This way the data could be transformed into a more usable form (i.e. knowledge) for e.g. evidence-based medicine. An example could be to use data mining to predict patients’ length of stay in hospital (Kraft et al., 2003). Clinical decision support systems (Teich & Wrinn, 2000) have also been used to utilize the data. Usually, they combine population statistics with existing knowledge to offer real-time information to support physicians’ daily work (Teich & Wrinn, 2000). They can also facilitate evidencebased medicine (Jadad et al., 2000). Recently, information systems focusing on the knowledge and relationships between patients and hospitals have also been introduced to healthcare settings. This solution is called patient relationship management (Siau, 2003) and through it healthcare


unit can provide better care to patients by allowing the unit to get an increased understanding of patients’ needs. Besides the abovementioned the use of knowledge management based solutions on healthcare offers other benefits, too. For example if we had knowledge management based healthcare systems we could better analyze different types of knowledge found in healthcare organizations (e.g. clinical knowledge stored in repositories) as well as achieve significant organizational productivity improvements (Bose, 2003). Our goal is not to define new ways to facilitate knowledge creation through healthcare information systems but rather to investigate knowledge reuse. Using the 7C model of knowledge creation and management we argue that over time through knowledge reuse healthcare organization do not only generate new knowledge but get better at their work, too. The next chapter will present the 7C model used in this study.

7C model of KNOWLEDGE CREea and managemen The 7C model suggests that the following seven Cs play a critical role in the creation of organizational or social knowledge: Connectivity, Concurrency, Comprehension, Communication, Conceptualization, Collaboration, and Collective intelligence (Oinas-Kukkonen, 2004). The 7C model follows Nonaka and Takeuchi (Nonaka & Takeuchi, 1995) in that the integration of individual and social orientations (individual and organizational in their terminology) is emphasized, and that knowledge is assumed to be created through interaction between tacit and explicit knowledge. The model follows Engelbart (1992) in the outcomes of the Comprehension, Communication and Conceptualization sub-processes. The framework assumes that connectivity of all stakeholders with the joint information space (the 1st C) and with people potentially concurrently

(the 2nd C) is provided in a technologically sound manner, e.g. through the Web, Internet, wireless, mobile and other technologies. These may promote options and allow freedom of choice with contextual support, providing users with a rich environment for comprehending (the 3rd C) and communicating (the 4th C) the information they find. Knowledge is conceptualized (the 5th C) as artifacts, which serve as a vehicle for collaboration (the 6th C) through interaction between information producers and consumers, within a team of co‑workers or among other stakeholders. All of these six preceding Cs contribute to the growth of “togetherness” or collective intelligence (the 7th C). The creation of organizational knowledge is not a linear process, but rather a multi-cycle spiral process (Oinas-Kukkonen, 2004). See Figure 1. The four central sub-processes in knowledge creation are comprehension, communication, conceptualization and collaboration (Oinas-Kukkonen, 2004). Comprehension is a process of embodying explicit knowledge in tacit knowledge by surveying and interacting with the external environment on an ongoing basis in order to identify problems, needs and opportunities (e.g. learn by doing or re-experiencing). Communication is a process of sharing experiences between people and thereby creating tacit knowledge in the form of mental models and technical skills, producing dialogue records which emphasize needs and opportunities, and integrating the dialogue and resulting decisions with other project knowledge on an ongoing basis. Conceptualization is a collective reflection process articulating tacit knowledge to form explicit concepts and rationales and systematizing these into a knowledge system, which are iteratively and collaboratively developed, possibly including proposals, specifications, descriptions, work breakdown structures, etc., but rarely a one-shot effort. Collaboration is a team interaction process of using the resulting conceptualizations within teamwork and other organizational and social processes. 115


Each of the sub-processes may also be regarded as the building of an artifact and reasoning over why it has been built in the way it has, i.e. capturing the knowledge rationale. Repeatedly going through these phases in a seamless and spiral-like way leads to the growth of collective intelligence. Support for capturing deep individual thinking and recording the dialogue between team members may help create truly innovative knowledge products. The learning involved in the comprehension and communication processes is closely related to the attitudes of the participants, i.e. whether they understand their weak points in the sense of individual learning styles, for example. It is important to notice that the 7C model does not try to define how information systems should manage knowledge. Rather, it models the processes of how individuals interact with information and knowledge (and with each other) to increase the collective intelligence of the organization. In a hospital physicians and nurses can learn and understand new things (Comprehension) while they perform their daily work. They can then share their work related experiences with

their colleagues (Communication). As they share they can collectively add to the knowledge of the group and create e.g. best practice guidelines (Conceptualization) to help them perform their work better in the future (Collaboration). Over time, as these processes go around over and over again the hospital unit could get better at providing care for its patients (Collective intelligence). In spite of receiving a lot of attention recently among practitioners, relatively little knowledge management research has discussed the evaluation of the solutions suggested (Oinas-Kukkonen, 2005). This kind of evaluation may be carried out at the individual, work unit (group, team, or department), or overall organizational levels. The increase in the sharing and dissemination of information and the increase in varied interpretations are obvious and in any case by no means the most important measures of the success of knowledge management solutions. The truly important measure is the identification of underlying non-obvious, complex problems and issues (Oinas-Kukkonen, 2005).

Figure 1. Knowledge creation sub-processes (Oinas-Kukkonen 2004) Tacit

Communication

Individual

Comprehension

Collective Intelligence

Collaboration

Explicit

116

Conceptualization

Social


Evaluation of the Comprehension and Communication sub-processes means, for instance, assessment of whether the following goals are achieved: better understanding of current and potential future customers, the key organizational business processes, the product portfolio, product features and potential future products and markets in general. Quite naturally, an improvement in any of these will lead to either an increase in new ideas or the achievement of better ideas for future business, and it may also help solve some of the problems that the organizations are faced with over time (by being more capable of defining the core processes and their key challenges) or even avoid some of the pitfalls they might suffer from. (Oinas-Kukkonen, 2004).

Ca DUODECIM The mobile healthcare information system under investigation is evaluated at the individual and work unit levels. The system comprises a set of medical information and knowledge databases developed by Duodecim Publications Ltd (the Finnish Medical Society Duodecim is a scientific society with over 18,000 of Finnish doctors and medical students - almost 90% - as its members.). The system emphasizes the role of evidence-based medical guidelines (EBMG), i.e. a

collection of clinical guidelines for primary care combined with the best available evidence. The collection includes almost 1,000 concise primary care practice guidelines covering a wide range of medical conditions and including both diagnosis and treatment, over 2,700 high-quality evidence summaries supporting the recommendations, a library of 900 high-quality photographs and images of all common and many rare dermatological conditions, electrocardiograms and eye pictures as well as abstracts from the Cochrane Library (which is a collection of databases in medicine and other healthcare specialties). The system also contains the pharmacology database Pharmaca Fennica with a wireless update service for a complete medicine price list, a drug interaction database for drug-related decision making, the international diagnosis code guide known as The International Classification of Diseases ICD-10, an acute care guide, a medical dictionary of over 57,000 terms, and a comprehensive database of healthcare-related addresses and contact information for pharmacies, hospitals and health centres. The system is described in Table 1. It is typically used through smartphones and it is delivered to users as a memory card that includes a search engine, user interface software and the core databases. The knowledge base is planned to support physicians in their day-to-day activities. It can

Table 1. The Duodecim mobile HIS under study Duodecim database

Description / functionality

Evidence-based medical guidelines

Search for evidence-based guidelines including literature references and abstracts from the Cochrane Library.

Pharmaca Fennica

Drug lists, adult and paediatric dosing guidelines, common side effects.

ICD-10

International Statistical Classification of Diseases and Related Health Problems. Codes for classifying diseases and a wide variety of signs and symptoms.

Acute Care Guide

Pathogenesis, causes, symptoms, differential diagnosis.

Drug Interaction Database

Possible interaction effects of selected drugs.

Medical Picture Database

Descriptions of symptoms and pictures.

Contact Information

Search for contact information on pharmacies, hospitals and health centres.

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be carried around and applied in the bedside or at the point of care. As physicians apply and reuse the knowledge they may be better equipped with the tasks at hand. This knowledge may also support their medical decision making as well as help them to learn new things. Some earlier studies of the system (see Han et al., 2004a, Han et al., 2005) have demonstrated that physicians have a positive perception of it and intend to use it, and that the most frequently requested mobile content entities were EBMG, Pharmaca Fennica and ICD-10.

Data Collection The data were collected through the Internet during a two-week period from January 23 to February 7, 2007. The key knowledge creation issues under investigation were Comprehension, Communication, Conceptualization, Collaboration and Collective Intelligence. The technological viewpoints of the 7C model (Connection and Concurrency) were omitted as they are beyond the scope of this research. Medical performance was not measured either. The questionnaire contained 18 questions. See Appendix 1. Five-point Likert scale from “Completely disagree” to “Completely agree” with the choice “I don’t know” in the middle was utilized. Physicians were very familiar with this scale, as it had already been used in previous studies of the same system (cf. Han et al., 2004b, Han et al., 2006). The respondents were approached by email with a link to the online questionnaire. The questionnaire was sent to all of the 352 users of the mobile system. They were all physicians who had a smartphone of their own and the software installed in it, donated by a large international medical company. It should be mentioned that while all of the users were members of the Finnish Medical Society Duodecim, they were not necessarily working at the same hospital. The response rate was 66.5% (n=234). Two responses were deleted from the data set because the respondents did

118

not actually use the system, and one was deleted because the respondent did not answer any of the main questions related to this study. Thus the final data set consisted of 231 replies.

Re 62.3% (n=144) of the respondents were men and 37.2% (n=86) women. 61.9% (n=143) were specialists, 27.3% (n=63) general practitioners, and 10.4% (n=24) researchers or working in administrative positions. Most of the respondents had more than 20 years of experience of working as a physician (55.8%, n=129), while 32.0% (n=74) had over ten years of experience and only 12.1% (n=28) had less. The majority of the physicians worked daily with patients (80.5%, n=186), nurses (86.6%, n=200) and other physicians (85.3%, n=197). 45.9% (n=106) of the physicians used the information system daily, 37.7% (n=87) several times a week, 11.7% (n=27) once a week, 3.9% (n=9) once a month and only two used it less often than once a month. The two most frequently used parts were Pharmaca Fennica drug information (96.5%) and EBMG (88.7%). The least used was the Picture Database (n=46, 19.9%). It was the most recent addition to the system and not all physicians had access to it yet which at least to some extent explains its low usage (the medical society estimated that about half of the users had the picture database installed). Besides using the mobile HIS, 27.7% (n=64) of the physicians read emails through the mobile device and 36.4% (n=84) used it for other Internet services. Quite naturally, the less experienced physicians more often felt that the system helped them to learn new things, and they also found it more useful to some extent than did the more experienced physicians (see Table 2). Interestingly, there was a slight difference in how specialists and general practitioners felt about the system’s ability to support learning of new things. 62.9% (n=39) of the general practi-


Table 2. Experience affected the perceived usefulness and learning Experience

Learning (Chi-Square=15.445, p=0.000)

Usefulness (Chi-Square=7.459, p=0.024)

under 10 y (n=28)

81.5%

92.9%

10-20 y (n=74)

58.1%

86.3%

over 20 y (n=129)

42.2%

74.2%

Table 3. A mobile HIS may improve all key knowledge creation sub-processes Independent s amples t est Lev en e's T est for

C o llec tiv e Intelligenc e

C o mpr ehen sion

C o mm unic atio n

C o nce ptualiz atio n

C o llabo ratio n

S um V ar ia ble

E qual v arianc es as s ume d E qual v arianc es not as su me d E qual v arianc es as s ume d E qual v arianc es not as su me d E qual v arianc es as s ume d E qual v arianc es not as su me d E qual v arianc es as s ume d E qual v arianc es not as su me d E qual v arianc es as s ume d E qual v arianc es not as su me d E qual v arianc es as s ume d E qual v arianc es not as su me d

M ean ,001

,972

-4,63 0 -4,60 2

3,246

,073

,074

,786

-5,50 4 -5,55 8 -3,30 6 -3,28 1

6,197

,014

-2,77 5 -2,73 6

,035

,851

-2,84 0 -2,83 0

,672

,413

-4,42 4 -4,39 3

tioners felt that the mobile HIS helped them to learn, while 47.9% (n=68) of the specialists felt that way (Chi-Square=3.902, p=0.048). The explanation may be simple fact that the specialists’ area of expertise is more focused while general practitioners have to treat patients with wide variety of symptoms. The four Cs of the knowledge creation spiral correlated with each other strongly. Interestingly, the highest correlations were between Comprehension and Communication, i.e. the individual side of the model (r=0.626) and Conceptualization and Collaboration, i.e. the social side of the model (r=0.675). This supports the individual-social

229 216 ,8 01 226 224 ,0 10 227 211 ,9 89 229 205 ,8 83 229 219 ,7 74 229 215 ,6 31

S td. E rr or

95% C on fide nce Interv al o f the

,000

-,576

,124

-,821

-,331

,000

-,576

,125

-,823

-,329

,000

-,692

,126

-,939

-,444

,000

-,692

,124

-,937

-,447

,001

-,427

,129

-,682

-,173

,001

-,427

,130

-,684

-,171

,006

-,334

,120

-,571

-,097

,007

-,334

,122

-,574

-,093

,005

-,347

,122

-,587

-,106

,005

-,347

,122

-,588

-,105

,000

-,445 45

,1006 9

-,643 86

-,247 05

,000

-,445 45

,1014 0

-,645 31

-,245 60

dichotomy in the knowledge creation model. The next strongest correlation were between Communication and Conceptualization (r=0.554), Comprehension and Conceptualization (r=0.538), and Communication and Collaboration (r=0.534) while the lowest correlation was between Comprehension and Collaboration (r=0.514). To investigate the knowledge creation spiral a sum variable was constructed, representing the Comprehension, Communication, Conceptualization and Collaboration sub-processes (referred to later simply as the “sum variable”). We used the sum approach as each of the 7C sub-processes may be treated equally important. Since five responses

119


had one or more missing data items related to these, the missing data were replaced by means from similar respondents. The sum variable has a high reliability (Cronbach’s α=0.841) and correlates strongly with Collective Intelligence (0.629). This seems to confirm the interplay among the four Cs, i.e. the spiral, indeed leads to the growth of Collective Intelligence. A comparison between those who used the system daily and those who used it less frequently indicates that the daily use improves all knowledge creation sub-processes as well as the sum variable (see Table 3). This seems to indicate that it actually helps physicians to perform their jobs better and it eventually may increase the Collective intelligence of the whole work community. This is an important finding and provides some empirical evidence for the usefulness of mobile information systems in healthcare in general. Thus, a mobile healthcare information system would be of benefit not only for patient safety

(Honeybourne et al., 2006) but for the professional skills of the physicians as well. We also analysed the different parts of the system to find out which functionalities had on effect on different knowledge creation subprocesses. As 96.5% of the physicians used the drug information (only eight did not use it) we could not compare users and non-users with it. The Independent Samples T-tests between those who used EBMG and those who did not suggest that EMBG use improves user perception on the mobile system’s help to Communicate (F=1.813, p=0.001), Conceptualize (F=0.538, p=0.001) and Collaborate (F=0.035, p=0.001). See Table 4. It improves the spiral (F=2.195, p=0.000), and to some extent it also affects Comprehension (F=4.949, p=0.022). The ICD-10 improved Collective Intelligence (F=1.550, p=0.000) and the spiral (F=0.084, p=0.003). Whereas ICD-10 is packed with fourletter abbreviations of various diseases and it

Table 4. Use of EBM Guidelines improves physicians’ Communication, Conceptualization and Collaboration Independent samples test Levene's Test for Equality of Variances

Collective Intelligence

Comprehension

Communication

Conceptualization

Collaboration

SumVariable

120

Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed

F

,285

Sig. ,594

4,949

,027

1,813

,179

,538

,464

,035

,852

2,195

,140

t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper -,220 ,587

229

Sig. (2-tailed) ,371

Mean Difference ,184

Std. Error Difference ,205

1,003

34,055

,323

,184

,183

-,189

,556

-2,731

226

,007

-,563

,206

-,970

-,157

-2,410

29,842

,022

-,563

,234

-1,040

-,086

-3,357

227

,001

-,681

,203

-1,080

-,281

-2,996

29,968

,005

-,681

,227

-1,144

-,217

-3,424

229

,001

-,644

,188

-1,014

-,273

-3,193

30,537

,003

-,644

,202

-1,055

-,232

-3,341

229

,001

-,639

,191

-1,016

-,262

-3,046

30,213

,005

-,639

,210

-1,067

-,211

-3,941

229

,000

-,63088

,16007

-,94627

-,31548

-3,569

30,118

,001

-,63088

,17676

-,99182

-,26994

t

,897

df


seems impossible for anyone to know all diseases and their codes by heart, it is surprising that there was no correlation with it and Comprehension. It may well be that these abbreviations are really used only for healthcare management and they do not involve diagnosing or modelling the groups of diseases. Use of Acute Care Guide improved all knowledge creation sub-processes: Collaboration (F=10.312, p=0.000), Comprehension (F=6.067, p=0.000), Collective Intelligence (F=0.339, p=0.000), Communication (F=1.730, p=0.001), Conceptualization (F=0.001, p=0.008), as well as the sum variable (F=1.560, p=0.000). As such, the use of the Acute Care Guide seems to improve each of the knowledge creation sub-processes. Use of Drug Interaction Database improved Collaboration (F=1.218, p=0.000), Conceptualization (F=0.979, p=0.001), Comprehension (F=0.095, p=0.001), Collective Intelligence (F=0.073, p=0.010), as well as the sum variable (F=0.922, p=0.000). Quite interestingly, it did not affect Communication. Maybe the drug interaction information is useful in places where communication is not required, e.g. the physicians has already decided that he will prescribe drug A and he uses the system to check out possible interactions with existing medication. Use of Medical Picture Database improved only the sum variable (F=0.000, p=0.009). The results concerning the Picture Database may be

little inconclusive due to its low usage. Contact Information improved Collaboration (F=1.724, p=0.002) and the sum variable (F=0.025, p=0.004). Interestingly it did not improve Communication, even if it provided contact information. Table 5 summarizes the correlations between parts of the system and knowledge creation subprocesses.

DiSCUSSIion From the five Cs addressed in this study Comprehension was improved by the use of the Acute Care Guide and Drug Interaction Database. The Acute Care Guide was used slightly more often by the less experienced physicians, as 33.3% (n=43) of those who had more than 20 years of experience used it, compared with 48.0% (n=49) of the rest of the physicians (Chi-Square=5.140, p=0.023). Quite obviously, the less experienced physicians still have more to learn and comprehend. Maybe this is especially true in acute medical situations. The fact that Drub Interaction Database improves Comprehension seems feasible too, since there are a large number of different drugs and their combinations that it is practically impossible to know all of their interactions. Thus an easy way of checking these interactions should indeed help physicians and over time they may comprehend something new. Interestingly, EBMGs did not

Table 5. Usage of the different parts of the system and their impact on the 7C processes Duodecim database

Frequencies

CI

Comp

Comm

Conc

Coll

Sum var

Acute Care Guide

39.8%

X

X

X

X

X

X

Drug Interaction Database

54.5%

X

X

X

X

X

Evidence-based medical guidelines

88.7%

X

X

X

Contact Information

74.5%

X

X

ICD-10

57.6%

Medical Picture Database

19.9%

Pharmaca Fennica

96.5%

X

X

X X

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affect Comprehension. This might be because most of the physicians were experienced and thus familiar with the guideline information. On the other hand, most of the users (61.9%, n=143) had specialized in certain medical domains, which implies that their knowledge needs might have been more specialized than what is provided through the evidence-based medical guidelines. Communication was improved by the EBMGs and the Acute Care Guide, which both are well-structured and evidence-based. Thus, they contain guideline information that is relatively easy to deliver. For example, all guidelines in the Acute care guide are organized in the same format, i.e. pathogenesis, causes, symptoms and differential diagnosis. Conceptualization was improved by the EBMGs, Acute Care Guide and Drug Interaction Database. Indeed, evidence-based information may help a group of physicians to reach a consensus in making medical decisions. Collaboration was improved by the EBMGs, Acute Care Guide, Drug Interaction Database and Contact Information. It seems natural that guidelines help physicians to collaborate. Similarly providing Contact Information helps find the right people. Collective Intelligence was improved by ICD10, Acute Care Guide and Drug Interactions. Interestingly, 65.0% (n=93) of the specialists used ICD-10, but only 39.7% (n=25) of the general practitioners. This could mean that specialists have a greater need for the ICD-10 than general practitioners, but as such it does not explain why the use of ICD-10 improves Collective Intelligence. One reason for this could be that hospitals are very bureaucratic by nature and these classifications of diseases are needed in many situations, e.g. when a patient checks in, when a patient’s treatments are entered into hospital records, or when a patient is discharged. The use of a mobile ICD-10 application can provide practical support in these situations.

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From the different subsystems the Acute Care Guide improved all knowledge creation sub-processes. Mobile applications such as the Acute Care Guide combine the “any time, anywhere” possibilities of mobile applications with actual needs in acute medical situations, where knowledge must be acquired and applied swiftly. Thus, instead of concentrating on any time and anywhere mobile applications in knowledge work context might need to concentrate more on exact situations where knowledge is needed, e.g. in healthcare at the point of care. Of the other subsystems Evidence-Based Medical Guidelines and Drug Interactions seem to support the knowledge creation sub-processes to a greater extent. Overall, guideline information seems to provide a good fit with knowledge creation. Guidelines contain information about diagnostic procedures that may be used with certain symptoms as well as suggestions for which drugs might work best for different illnesses. Physicians may also find them helpful when consulting other physicians, as the guideline information may provide a basis for communicating and collaborating. A general practitioner may first check the information found in the mobile HIS, for example, and then use it as a reference when consulting a specialist.

FuTURE RESEARCH Future research should be directed towards empirical testing of the knowledge processes in more detail, e.g. what type of communication do the evidence-based medical guidelines really support and how can the transfer of tacit knowledge into explicit be better supported. Also multiple sources of data would help obtain deeper understanding. We are also planning a longitudinal study on the case system described in this paper. With the longitudinal data we can see e.g. how the regu-


lar use of the system affects the 7C knowledge creation processes. For example the collective intelligence of the hospital units should increase over time. We would also like to compare the case system to other ways of obtaining the same information and knowledge. For example, how does the usage of the mobile system compare to e.g. books or desktop information systems (Duodecim also has the desktop version of the case system). We are especially interested in finding out does the case system really offer better support at the point of care? Another line of research we are interested in is what kind of features of functionalities would support the processes of the 7C model? We are especially interested in the comprehension and conceptualization processes as they have received less attention in the scientific literature than communication and collaboration.

benefit not only for patient safety (Honeybourne et al., 2006) but for the professional skills of the physicians as well. A limitation of our study is that we were not able to go deeper with studying the differences between experienced and less experienced physicians. Another limitation would be that the picture database was not in use by all of the physicians so the results concerning it are not conclusive. Also one limitation on our study could be that most of the physicians participating in the study had more than 20 years of work experience. It could be argued that the less experience the physician has, the more he has to learn and more he could benefit from the use of a mobile healthcare information system. In sum, knowledge management seems to be a prominent approach for studying healthcare information systems and their impact on physicians’ work.

Conlu

Refeen

This article discusses physicians’ usage of a mobile healthcare information system. This was studied through the 7C knowledge management framework. The usage of the system seemed to improve the knowledge work of individual physicians as well as the collective intelligence of work community. The easiest sub-process to support through the system seemed to be collaboration between the physicians. Comprehension and, quite surprisingly, Communication were the most difficult aspects to support. All parts of the case system helped improve the knowledge creation spiral. These findings go hand in hand with some of the previous findings (e.g. Ebell et al., 1997, Honeybourne et al., 2006) of the usefulness of healthcare information systems, especially in acute medical situations where decisions have to be made swiftly. The findings also hint that the daily use of such a system may indeed over time be of

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Appendix A Demographics 1. Gender

Male / Female

2. Experience

Less than 1 years / 1-5 years / 5-10 years / 10-20 years / over 20 years

3. Occupation

General practitioner / Specialist / Researcher / Management position

4. I use the mobile databases

Daily / A few times a week / Once a week / Once a month/ Less than once a month

5. I use the following parts of the system

EBM guidelines

Pharmaca Fennica

ICD-10

Acute care guide

Drug interactions

Picture database

Connection information

6. I work with hospital management

Daily/A few times a week/Once a week/Once a month/Less than once a month/Never

7. I work with physicians


8. I work with nurses


9. I work with patients


The medical databases Please, answer using these criteria: 1 = Completely disagree, 2 = Partially disagree, 3 = I don’t know 4 = Partially agree, 5 = Completely agree 10. The mobile medical databases increase the professional capabilities of my work community.

1

2

3

4

5

11. The mobile medical databases help me better comprehend issues relatedto work at hand.

1

2

3

4

5

12. The mobile medical databases help me communicate better.

1

2

3

4

5

related to work at hand.

1

2

3

4

5

14. The mobile medical databases support collaboration.

1

2

3

4

5

15. This mobile service makes me to learn new things.

1

2

3

4

5

16. In my opinion, this is a useful mobile service.

1

2

3

4

5

13. The mobile medical databases help the working community to reach a consensus in issues

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The use of mobile Internet 17. Do you read email with you mobile phone

Yes / no

18. Do you use your mobile phone for other internet services.

Yes / no

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Section II

Patient Monitoring and Wearable Devices

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Chapter VII

Patient Monitoring in Diverse Environments Yousef Jasemian Engineering College of Aarhus, Denmark

ABSTRACT Recording of physiological vital signs in patients’ real-life environment could be especially useful in management of chronic disorders; for example for heart failure, hypertension, diabetes, anorexia nervosa, chronic pain, or severe obesity. Thus, monitoring patients in diverse environments, by a mobile health system, is one of the major benefits of this approach, however at the same time the demands and challenges for improving safety, security and integrity increase. Top priorities for patients under recovery of health and elderly under care are the feeling of being cared securely and safely in there home and its surroundings. Solving these issues will elevate users’ compliance and trust to mobile health services. Most research activities have been focused on achieving common platform for medical records, monitoring health status of the patients in a real-time manner, improving the concept of online diagnosis, developing or enhancing telemedicine solutions, which deals with remote delivery of health care services applying telecommunications, etc.This chapter intends to explore the issues and limitations concerning application of mobile health system in diverse environments, trying to emphasize the advantages and drawbacks, data security and integrity suggesting approaches for enhancements. These issues will be explored in successive subsections by introducing two studies which were undertaken by the author.

INTRODUCTION In recent years, initiatives have been taken both from academia and by the industries with a view for improving the health care and safety of the

public by taking use of information and communication technologies. Most research activities have been focused on achieving common platform for medical records, monitoring health status of the patients in a real-time manner, improving the con-


Patient Monitoring in Diverse Environments

cept of online diagnosis, enhancing security and integrity of the patients, developing or enhancing telemedicine solutions, which deals with remote delivery of health care services applying telecommunications, etc. (Freedman, 1999; Shimizu, 1999; Woodward, Istepanian & Richards,2001; Jasemian & Arendt-Nielsen, 2005a; Bønes, Hasvold, Henriksen & Strandenaes, 2006; Sax, Kohane & Mandl, 2005). Recent advances in embedded computing systems have led to the evolution of wireless and mobile health services, consisting of small battery-powered entities with computation and radio communication capabilities. This permits data gathering and computation to be deeply integrated in the patients’ daily environment. The technology has also the potential of automatically collecting vital signs to be fully integrated into the patient care record and used for real-time triage, correlation with hospital records, and long-term observation. During the past few years, advances in sensor technology have enabled the development of small, lightweight medical sensors such as pulse oximeters and electrocardiogram leads that can be worn by the patient while wirelessly transmitting data. This frees the patient from the confinement of traditional wired sensors, allowing him or her to move at leisure and increasing comfort in daily environment. It is foreseen that with the help of these enhanced mobile health systems, better health care and services can be delivered to users, and hospitals can also benefit a better information management and administration. Also, it will provide the users the ability to access their medical records anywhere, anytime. As the patients in a mobile heath system are monitored in diverse environments, several challenges are present. First, Mobile health network may contain a large number of mobile sensors due to the mobility of patients. Second, the timeliness and reliability of data delivery is crucial in mobile health services. Third, wireless and mobile communication media generally have limited

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bandwidth and relatively high loss rate. Finally, the most fundamental challenge is the security and privacy of sensitive patient data. Because the data is transmitted wirelessly, it is easy for an eavesdropper with a properly tuned receiver to intercept the data. Hence, mechanisms must exist for data authenticity and integrity. Moreover, patients’ compliance concerned in a mobile health is an important issue in focus. The present chapter intends to explore the issues and limitations concerning application of mobile health system in diverse environments, trying to emphasize the advantages and drawbacks, data security and integrity suggesting approaches for enhancements. These issues will be explored in successive subsections by introducing two studies which were undertaken by the author of the present chapter.

BACKGROUND AND MOTIVATIONS The number of people with chronic diseases such as heart arrhythmia, diabetes, cancer and chronic obstructive pulmonary disease (COPD) is increasing in most Western countries, and the majority are elderly. Chronic diseases are the leading causes of death and disability, and these accounts for 70 % of all deaths in the U.S., which is 1.7 million each year (National Centers for Chronic Disease Control and Prevention, 2008). Almost 25 million people have major limitations in daily living in the United States (National Centers for Chronic Disease Control and Prevention, 2008). Chronic disease is a growing problem in the United States. More than 125 million Americans had at least 1 chronic care condition in 2000, and this number is expected to grow to 157 million by the year 2020 (Marchibroda, 2008).Some of the challenges associated with chronic care management approaches are the use of telemedicine and mobile health services. Nowadays, in USA, Canada, Australia and many European countries, the health authorities


tend to optimise the resources most effectively. By introducing e-health, telemedicine and mobile health services, it became possible to treat/monitor as many patients as possible at remote areas (BeWell Mobile Technology Inc, 2006; Farmer, Gibson, Hayton, Bryden, Dudley et al., 2005, Friedewald, & Pion, 2001; Logan, McIsaac, Tisler, Irvine, Saunders et al., 2007). Thanks to emerging technologies the elderly now have the opportunity to stay longer in their homes and manage everyday tasks without significant burden for their caregivers. Improving the quality of life for patients is also an essential task in these countries (Sneha & Varshney, 2007; Cocosila, Coursaris & Yuan, 2004; Jasemian, 2006; Jasemian, 2008; McLean, Mendis, Harris & Canalese, 2007). Cardiovascular disease is the main cause of death in the UK and it accounts for 39% of all death each year. Among patients who had heart attacks, about 30% of them died even before reaching to the hospital (Petersen, Peto & Rayner, 2004). Although heart attack can happen suddenly without apparent indications, cardiac arrhythmia can often be found before the event. They can potentially be used as the precursor to major cardiac episodes (Panidis & Morganroth, 1983). In Aalborg hospital, Denmark, for instance, 40 % of the heart arrhythmia patients are hospitalized for disease monitoring and control purpose. They are all monitored by short range telemetry in the hospital for one to five days (patient registration section, Aalborg Hospital, Denmark). Non-risky heart patients are referred for monitoring at home by HOLTER or event recording devices. Currently, electrocardiogram (ECG) Holter monitoring is the most widely used technique for providing ambulatory cardiac monitoring for capturing rhythm disturbances. A traditional Holter monitor can record up to 24 hours of ECG signals, and the recorded data is subsequently retrieved and analyzed by a clinician. Due to the short duration involved and the unknown context within which the ECG signal is captured, reliable interpretation of the recorded data is always a

challenge. Not all patients show high compliance with the application of these home monitoring devices. Nor this monitoring arrangement provides comfort to them. Because, these devices do not function on-line and the arrangement requires a number of hospital visits in order to deliver the recorded data on a tape or memory card to the specialists, which is sometimes a stressful and time consuming process for the patients. To address these drawbacks, some more advanced ECG monitoring systems are emerging. An online mobile health service has been suggested as an alternative to the above mentioned monitoring methods (Jasemian & Arendt-Nielsen, 2005c; Clarke, Bratan, Kulkarni & Jones, 2007; Gostin, 1965). Some can also detect and signal a warning in real-time if any adverse event is captured (Standing, Dent, Craig & Glenville, 2001). The trend of providing more and more wireless health care solutions is especially visible, because going wireless is supported by the telecommunications service providers as well as by the end-users. For users, wireless means being free from inconvenient cables and thereby more mobility plus easier and more flexible access to healthcare services. For operators and providers, wireless means cheaper access, more users on the network and more benefit. In many situations different telecommunication systems coexist thus forming a heterogeneous telecommunications environment. This, however, does not exhaust the problem of heterogeneity. The term applies also to the coexistence of different operators and service providers. Finally, different users’ need can also be defined as heterogeneous. Due to these aspects, addressing variety of systems, environments, services, technologies and needs has already now become a big problem for the telecommunications technology and it is very likely to continue gaining importance. Wireless technology has its own advantages and drawbacks. Among the advantages, mobility and flexibility are important characteristics. Among the drawbacks, major problems with a

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mobile health system are safety, data security and integrity. These concerns increase, as the patients are monitored in diverse environments. Research for optimization of security has been done and in this relation a number of security arrangements have been suggested (Elmufti, K., Weerasinghe, D., Rajarajan, M., Rakocevic, V., Khan, S. 2008; MacDonald, JA. 2008). The remote monitoring of patients, for chronic diseases or to follow up elderly people at home, is a particular application of the promising mobile healthcare services in home environment. The follow up of patients at home must satisfy the same security standard as it does in the hospital. The mobile healthcare device plays the role of the human to machine interface. This communicates with a home care station that should contain the following features: sufficient local data storage (memory), a local processing facility (real time alarm agents), a communication mediator, an authentication agent (PIN), and a “fire wall” to preserve data from piracy. Thus, top priorities for patients under recovery of health and elderly under care are the feeling of being cared securely and safely in there home and its surroundings. Solving these issues will elevate users’ compliance and trust to mobile health services.

SECURITY AND INTEGRITY ARRANGEMENTS IN M-HEALTH SOLUTIONS Recording of physiological vital signs in patients’ real-life environment could be especially useful in management of chronic disorders; e.g. for heart failure, hypertension, diabetes, anorexia nervosa, chronic pain, or severe obesity. This could also be used to provide feedback about someone’s health in the form of behavioural feedback in order to prevent diseases. Thus, monitoring patients in diverse environments, by a mobile health system, is one of the major benefits of this

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approach, however at the same time the demands and challenges for improving safety, security and integrity increase. Security relates to the means by which an entity protects the privacy of any information, and it depends very much on the applied communication technology and data processing. Privacy refers to the individual’s right to keep certain data or information private, unless that information will be used or disclosed with his/her permission (Jasemian, 2006). The rapidly emerging infrastructure of health care information and its relation to patient privacy have been described in the literature (Gostin, Brezina, Powers, Kozloff, Faden et al., 1993; Gostin, Lazzarini, Neslund & Osterholm, 1996; Boncella, 2002). Authentication, confidentiality and integrity of the transferred information are minimum requirements any patient will demand (Boncella, 2002). Security and privacy are very much intertwined; indeed this is security that assures the privacy.

Security and Privacy in a Wireless Remote Medical System for Home Healthcare Purpose (Study 1) The study explores data security and patients’ privacy in a wireless remote patient monitoring system which has been designed, implemented and tested in a clinical setup by the author (Jasemian et al., 2005a; Jasemian et al., 2005c; Jasemian & Arendt-Nielsen, 2005b). A wireless remote patient monitoring system (Figure 1) consisting of a patient-unit (an ECG device, a Bluetooth module and a Mobile phone), public GSM/GPRS network, a GSM/GPRS modem server, and a graphical monitoring station were arranged and setup. To make the setup functioning in a reliable manner with god performance a generic communication platform based on Bluetooth and GSM/GPRS protocols were designed, developed and integrated (Jasemian et al., 2005a; Jasemian et al., 2005c; Jasemian et al., 2005b).


System Functionality (Study 1) The ECG signal is collected, via 4 disposable electrodes, by an ECG device. The ECG device is connected to a Bluetooth module, which transfers data via Bluetooth connection to a mobile phone (Figure 1). The Bluetooth module invokes the mobile phone as soon as the ECG device has detected any electric activity of the heart. The mobile phone establishes a GSM or a GPRS connection to the public mobile network automatically. The transmission of data, from mobile phone to Modem Server at the hospital, is carried out in real time and continuously in packet format. The Modem Server receives the data and converts it to a predefined format. The data are sent to the central monitoring station via a serial cable. Central monitoring station interoperates and converts the received data to graphical ECG (Jasemian et al., 2005a; Jasemian et al., 2005c; Jasemian et al., 2005b). The mobile phone is connected in the course of the real-time monitoring period. In case the network connection fails or no GSM/GPRS network coverage, the Bluetooth module automatically, via the mobile phone, repeatedly attempts for connection reestablishment until a complete connection is established. When the connection is established, the GSM mobile phone is functioning as a mobile modem to the ECG device.

Health care personal at the hospital have the possibility to communicate with the patient by sending him/her text message (in packet format). In case an audio conversation is needed, either a mobile phone (an extra one) which is on the patient outdoors, or a fixed telephone at home is used.

The Benefit of the System (Study 1) Most of the existing telemetry devices are offline (Store and Forward Telemedicine) and rely on wired telecommunication network such as Digital Subscriber Line, Public Switched Telephone Network and Integrated Services Digital Network. Even though, very few devices/systems applying wireless and cellular technologies, those are most off-line, and the majority use Wireless Local Access Network and Internet connection, which make these telemetry devices dependent on a fixed access point and fixed infrastructure. Although, these telemedicine models have a reasonable performance but need a great deal of preparation from the network provider side before any application, as regards installation and logistics. Moreover, these models limit users’ movement freedom and bound them only to their home environment and very close surroundings. This telemedicine setup needs also sophisticated security management, as hackers can easily

Figure 1. A principal sketch of the wireless remote monitoring system consisting of an ECG device, Bluetooth module attached to ECG device, mobile phone, GSM/GPRS network, mobile modem server and a central monitoring station.

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intrude the internet and access vital patient’s information. One of the main factors that make a telemedicine system a success is the use of a secure modern communication The designed and implemented telemedicine model in the present study employs advanced wireless and mobile technologies (Bluetooth protocol, and TCP/IP connection over GSM and GPRS) utilising the existing public cellular network (Jasemian et al., 2005a). One the main benefits of this model is that there is no need for any preparation regarding installation and logistics from the network provider side, and the patients need only a short instruction in the employment of the telemedicine device at the hospital/health centre before using it (Jasemian et al., 2005c), and application of a mobile phone is more common in these days. The second benefit is that the patients are not bounded to their home environment and surroundings, and they can move wherever there is network coverage, thus the telemedicine device is not dependent on a fixed infrastructure. Moreover, the system takes advantage of using the solid security arrangement build in the Bluetooth, GSM and GPRS security protocols. Hence, the present model guarantees as well the Portability as the Accountability of the system.

Method and Material (Study 1) Fifteen non risky heart patients (n = 15), aged (49±14) years (6 males and 9 females) were recruited. The patients’ ECGs were continuously monitored (72 h), while they were performing their every day’s indoors and outdoors activities. Following the instructions, the patients wore the patient-unit, mount the disposable electrodes by own self. For safety reasons, the patients had a fixed telephone line at home and were equipped with an extra mobile phone when they were outdoors, and they were promptly contacted in case there was any technical or health problem (Jasemian et al., 2005c). Taking shower or swimming, while

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wearing the patient unit, was not permitted. The ECG data was transferred anonymously via Bluetooth-GSM/GPRS connection in packet format, assigning each patient an id-number, start monitoring time and date. No name, personal idnumber, age, or address, were transferred along with the ECGs. Only healthcare personal knew whom each ECG was belonging to. The data were collected and processed by a modem server at destination side. The ECGs in the graphical interface, on the server side, were identified by patients’ id-numbers. The server was assigned a user-name and a password, which were known only by the in charge health care personal. Data security from the technological point of view was investigated (Jasemian et al., 2005a). The applied telecommunication technologies and services (Bluetooth, GSM and GPRS) offered Access Control, Authentication, Data Encryption, and User Anonymity. The privacy and security of the transferred ECGs were judged by a committee consisting of three competent persons who were blind to the experiments. The wireless remote patient monitoring system was inspected and examined in order to explore any possible intrusion from unauthorised persons and to unveil any possible impersonalised ECG data. In this relation the authentication, confidentiality and integrity of the data were tested for the risk of Insertion attacks, Client-to-client attacks and Misconfiguration.

Results (Study 1) Without knowing user-id and password of the patients on the patient side, no access was possible; the Bluetooth module which controls connection establishment and termination, data flow and dial-up connection could not be accessed by any unauthorised person as well. No received ECG could be personalized at the server side and only the authorized healthcare personnel could access the data on the server side. However, the achieved results could not be generalised, since the pres-


ent remote patient monitoring system was tested on limited number of patients (n = 15), only few health care providers were involved (n = 4), and the system was tested only within one specific healthcare environment with specific security and privacy policies. However, the applied approach for security and privacy measurement and evaluation were basic and fundamental; hence the used method is valid.

COMFORT, COMPLIANCE AND TR A MOBILE HEALTH SM The success of integration and adaptation of a mobile health technology depends on the patients’ compliance and trust to the introduced system. Trust is a composition of many different attributes; reliability, dependability, honesty, truthfulness, security, competence, timeliness and comfort, which may have to be considered depending on the environment in which trust is being specified. According to The Compact Oxford English Dictionary (Compact Oxford English Dictionary, 2007), trust is “firm belief in the reliability, truth, ability, or strength of someone or something”. A trustworthy entity will typically have a high reliability and so will not fail during the course of an interaction, will perform a service or action within a reasonable period of time, will tell the truth and be honest with respect to interactions and will not disclose confidential information.

Elderly Comfort and Compliance to Modern Telemedicine System (Study 2) The aims of the present study are to investigate, verify and evaluate elderly patients’ compliance, trust and comfort in relation to a real-time wireless telemedicine system at home.

A telemedicine system composed of a patientunit (an ECG device, a Bluetooth module and a Sony Ericsson T610 mobile phone), GSM/ GPRS network, a router, a data interpreter and a monitoring system were used (Figure 2). The system is designed implemented and tested by the author (Jasemian et al., 2005a; Jasemian et al., 2005b).

System Functionality (Study 2) A telemetry device collects the ECGs from the patient’s chest via 4 disposable electrodes. The telemetry device is connected to a Bluetooth module via a serial interface. The Bluetooth module is wirelessly connected to a mobile phone (Figure 2). The Bluetooth module is designed to invoke the mobile phone to establish either a GSM or a GPRS connection automatically. The transmission of the data, from the mobile phone to the server at the hospital, is carried out via GSM/GPRS network. On hospital side the interpreter receives the data through a router and converts it to pre-defined format. The data are sent to the monitoring system via serial cable. The monitoring system converts the received data to graphical ECG (Jasemian et al., 2005a; Jasemian et al., 2005b). The mobile phone is connected in the course of the real-time monitoring period. In case of network failure or no GSM/GPRS network coverage, the Bluetooth module via mobile phone, repeatedly attempts for connection reestablishment until a complete connection is established. The system is equipped with alarm procedure, and gets benefit of the integrated data security arrangements in Bluetooth, GSM and GPRS (Jasemian, 2006).

Method and Material (Study 2) Twenty four non risky elderly heart patients, aged (60±5) years (12 males and 12 females), were included. A week of continuous ECGs for each of the elderly was recorded. The experiments were carried out while the elderly were wearing the

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Figure 2. A principal sketch of the wireless remote patient monitoring system containing ECG device, Bluetooth module, mobile phone, GSM/GPRS network, router, data interpreter and a graphical ECG monitoring system.

patient-unit, performing their every day’s indoors and outdoors activities. They were instructed how to mount the disposable electrodes, how to operate the patient unit and how and when they should change/ recharge the batteries. They were asked not to shower while wearing the patient-unit. The elderly had a fixed telephone line at home and were equipped with an extra mobile phone when they were outdoors. They were contacted in case there was any problem. The patients were asked to keep a diary of their daily activities. To evaluate the elderly compliance, trust and comfort in respect to the present telemedicine system, three questionnaires were designed and prepared. The first one was for the evaluation of the system’s degree of user-friendly, usability and reliability, the second one was for the evaluation of the patients’ privacy, freedom and mobility during monitoring period, the third one for the evaluation of the patients’ degree of confidence and trust in respect of using the present wireless remote monitoring system at home.

Results (Study 2) The elderly spent (15 ± 3) minutes to learn how to use the patient-unit. In average, 80 % of the

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elderly scored the user friendly and usability of the system as good. The majority (92 %) could easily manage employing the system. Only 16 % of the elderly sought help from the healthcare personnel in relation to employment of the patient-unit, and few (4 % - 8%) had problem with changing or recharging batteries for the mobile phone and ECG device as well. 76 % of the elderly (n = 24) scored the reliability of the system as “reasonable”, 20 % as “only now and then”, and only 4 % scored the reliability as “excellent”. The majority of the elderly believed that their expectation to privacy was in 84 % fulfilled. And only 4% believed that their expectation to privacy was only now and then fulfilled. Almost 52 % of the elderly patients (n = 24) scored for a reasonable mobility and freedom, and the rest 20 % scored for not complete fulfilment of freedom and mobility. Only 12 % of the elderly patients do not trust the present wireless remote monitoring system at all, whereas 72 % trust the system. More than 60 % of the elderly are used to employ a mobile phone in daily life and 50 % have a reasonable understanding of the system application. 80 % believe that their comfort is satisfied. Eighteen out of twenty four elderly patients (76 %) preferred to


be monitored from their home (in more natural environments).

DISCON AND CONCLUSION Ubiquitous computing environment is the fundamental of a mobile health care system. In this environment multiple mobile devices and healthcare personal are combined to provide an all-pervasive computing and communications service to end-users. Advanced medical technologies provide solutions for distant home care in form of specialist consultations and home monitoring. This requires automatic configuration of certain aspects of some devices, since there is no global management infrastructure, yet. Ubiquitous computing is still at an early stage of research and development, and very few work environments have been described. Many threats existing in a mobile health system are the same as those arising in a more conventional environment. However, there are also new threats in an M-health setup, e.g. mobile devices typically offer less physical security and it may need to communicate with other devices where a single security infrastructure is not present, e.g. in a hospital. For instance, a mobile device may capture personal/medical information without requiring user consent or knowledge. While Information Technology (IT) enables the use of security arrangement in medical remote monitoring system to limit access to confidential information, it also introduces some real vulnerability. Unless proper controls and procedures are implemented, these kinds of applications also invite unauthorized users to access the data. If the concerns are not sufficiently addressed, the health care consumers will hesitate to share information. Therefore, IT application development and use of that in remote monitoring system must be done in the midst of maintaining confidentiality, privacy, and security.

Data security, patients’ trust and compliance, in relation to a mobile healthcare system were the central concern in these two studies. In this connection, the patient’s mobility, freedom, privacy, and comfort in addition to the user-friendly and reliability of the system, were verified and evaluated. The results in first study showed that the system was reliable, functioning with a clinically acceptable performance, and transferred medical data with clinically acceptable quality, even though the system was tested under totally uncontrolled circumstances during the patients’ daily activities (Jasemian et al., 2005c). A number of important design techniques that were tightly coupled with the real-time patients’ monitoring were elaborated, in order to enhance the system performance. The ECG data were transferred anonymously via Bluetooth-GSM/GPRS connection in packet format, assigning each patient an id-number, time and date of monitoring onset. No name, personal id-number, age, or address, were transferred along with the ECGs. Only healthcare personnel knew whom each ECG was belonging to. On the server side on the graphical interface, the ECGs were identified by patients’ id-numbers. This was a secure way for providing anonymity, and was practical only because the number of the patients was limited and the study was fully controlled. However, when the setup is applied in a larger scale in medical practice a very careful and precise Id-number assignment system should be designed and elaborated, otherwise a little mistake can cause confusion, some data mismatches and lost of some data identification. To evaluate the safety and security of the transferred data in the proposed system a number of tests and control were worked out with great care. Although in the first study the clinical application of the system was implemented in a small scale, the ECG data was secured and patients’ privacy was achieved, though was not guaranteed. However, if the setup should be tested and evaluated in a large scale, where larger number

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of patients is involved, several health care providers are in charge, data magnitude is huge, and the setup is tested in several health care centres with deferent infrastructure and different security policy, then there will be a need for development of security standards for the management of authority access and coding structure. In the second study the patients’ compliance, trust and comfort in relation to a real-time wireless telemedicine system at home were investigated. In this connection, the patient’s mobility, freedom, trust and compliance in addition to the system’s user friendly and reliability were verified and evaluated. The results showed that the system in second study was reliable, functioned with a clinically acceptable performance, even though the system was tested under totally uncontrolled circumstances while the patients’ were performing their daily activities indoors and outdoors. The patients have expressed reasonable compliance and trust to the application of the system at home; the more natural environment. The majority believed that their comfort was satisfied. However, a few numbers of the elderly were not satisfied with the weight and user interface of the ECG device. These issues bring up an important principal approach in a system design and development, namely patients’ satisfaction relies on a more user-driven design and development. The ECG sensors and the corresponding leads malfunction have been the cause of signal deterioration in some cases. This can be enhanced by introducing wireless body-sensor network. The results could not be generalized, as the number (n = 24) of the recruited elderly patients were not representing heart patients’ among the elderly population. Furthermore, the present investigation has been performed only in respect with one specific telemedicine system and should be applied on a number of similar systems in order to have a better picture of the general attitude of the elderly patients’ in the population. Finally, it could be concluded, that the system is applicable for patient monitoring and aftercare in elderly

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patients’ home and the elderly are reasonably confident in using it. However, the patient-unit should be designed more user-friendly (small and lightweight), and the ECG sensors should be enhanced. These subjects also need further investigation in a larger scale. Although reasonable and clinical acceptable results have been achieved, the studies had some limitations. Some confounding factors such as age, social status, education and gender difference were also presented and should have been treated and analyzed. But this was not possible because of the small number of patients in the study. Hence, the results of both studies could not be generalized. However, the evaluated parameters are essential key issues in mobile health services hence these should be explored, investigated and evaluated in a large scale and multidimensional environments.

FUT ON AN M-HEALTH SYSTEM Before trying to decide how to provide and support privacy in a mobile health environment, we need to explore the issues that privacy can arise. This requires identifying where Personally Identifiable Information is at risk of access or disclosure. Disclosure of such vital information can occur in a variety of ways (one way is e.g. linking of sensitive information to a unique identifier, which may eventually be linked to a particular individual). It is important to distinguish between security and privacy. Privacy is not just a special case of security – there are interesting interactions between security and privacy. Indeed the two sometimes conflict. For instance there is a conflict between accountability and anonymity e.g. conflict of denial of service resistance versus anonymity. It is nevertheless true that supporting privacy requires the provision of security services, e.g. confidentiality for stored and transmitted data, and access control.


In a mobile environment, for health information handling, there are a number of possible point where the sensitive information is at risk. The following are just a representative number of those listed. • •

•

•

•

When a communication between two devices are established. At the point of capture of information on both side of communicating side, e.g. by individuals, physicians, paramedics or automatically when a sensor is used. When the information is stored/used in personal devices (e.g. mobile phone, PDA, smart card or sensor equipment). When the information is stored in fixed databases (e.g. in a hospital or in a network provider agency). When the information is stored or used in mobile third party devices (e.g. a healthcare mobile device belonging to a physician or paramedic).

To secure the information and provide privacy and anonymity some action and arrangement are necessary. The following are some key issues that must be consider improving the quality of mobile healthcare devices or services. • • • • •

Identity Credentials Authorisation Anonymity Consent

General Problem •

•

An ad hoc network is a collection of communicating devices with no pre-existing relationships or infrastructure. A typical scenario for use of such a network is an emergency situation, e.g. a major transport accident.

•

Many security issues arise in establishing working relationships in such a network, e.g.: ° Initial trust setting; ° Managing collaborative activities (e.g. routing); ° Authentication, authorisation, …

Identity A user may have many identities with associated identifiers for use when communicating with different third parties. For instance, we all have a name (although this is not a unique identifier); an employee may have an employee number for use with his/her employer; a citizen has one or more numbers for interactions with government; a health care user may have a government ID, and one or more health insurance identifiers. These must be arranged or defined as uniquely as possible.

Credentials When a service provider wants to authenticate a user as a legitimate holder of an identity, the user may be required to provide one or more credentials. Possible credentials include: a password; a biometric sample; a public key certificate; or a signature on a challenge provided by the service provider. These must be assigned in a secure way and must be protected against intruders.

Authorisation Once an entity has been authenticated, the provider needs to decide whether or not to grant the requested service. This is refereed to as authorisation. It means that the network provider has the responsibility to insure whether the holder of this identity authorised to access this service. This could, for instance, be supported using server-held Access Control Lists (ACLs).

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Anonymity A user may wish to be able to access a service in an anonymous way. Anonymity means that no party will learn any of the identities of the user. If a service requires using stored data, then some level of identification to the stored data might be required by the provider. If payment is needed, then an anonymous payment system is needed, e.g. cash or e-cash. However, absolute anonymity is difficult to achieve, since even revealing an IP address or a MAC layer address to some extent compromises it.

Consent In many medical scenarios, the subject may be required to give implicit or explicit consent for treatment to be given, and information to be passed to a practitioner or insurance company. In case of treatment, some measure of nonrepudiability may be required; In case of passing information, the information source will need to authenticate the subject. This is potentially problematic since the information source may be remote and only communicating with a device belonging to the practitioner.

An Ad Hoc Network An ad hoc network is a collection of communicating devices with no pre-existing relationships or infrastructure. Such network is one of the fundamental components in mobile healthcare systems/services. A typical scenario for use of such a network is an emergency situation, e.g. a major transport accident. Many security issues arise in establishing working relationships in such a network, e.g.: initial trust setting; managing collaborative activities (e.g. routing); authentication, authorisation, etc. One fundamental issue for two devices in an ad hoc network is deciding whether to trust one another and in what extend, and what resources

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or services one node should make available to another. Then the question is: Can another node be trusted to provide a communications service without eavesdropping, manipulating messages, and/or selectively dropping packets?

Diionfou Reea In this section, the fundamental problems that need to be solved to realise full potential of mobile healthcare system, have been identified; these are: need for a ubiquitous security infrastructure to support secure communications between mobile devices; need for one device to be able to verify the conditions under which data will be stored, handled, and retransmitted by another device. Thus, the questions are: can trusted computing systems realise all the security infrastructure needs of future pervasive computing environment? Who will be the trusted third parties to support the trusted computing based security infrastructure? What if some mobile devices are trusted computing enabled and others are not? What other solutions are there? Devices collecting, storing and/or using health data may need to share this data with other devices. For instance a wireless heart monitor may need to pass data to a portable device used by a physician which integrates and displays the data to the physician. Such data transfers should not take place without restriction for privacy/security reasons. What if the devices interacting do not all belong to the same individual? We need trusted computing (TC) technologies to be implemented as a combination of hardware and software enhancements to a computing platform such as PC, PDA, server, or mobile phone. The root of most of these problems is the potential lack of a single pre-existing managed security infrastructure. When such an infrastructure is established, many of the problems become much less fundamental.


Thus, the future research direct towards development of trusted computing (TC) technologies and security infrastructure. Providing a trusted computing platform can help to provide security infrastructure. In the other hand provision of a security infrastructure enables one device to determine its level of trust in another device.

Refeen BeWell Mobile Technology Inc. (2006). Better health well in hand: Cell phones have the capacity to more frequently and efficiently connect chronically ill patients with caregivers. Healthc Inform. 23(4), 56-57. Boncella, R. J. (2002). Wireless Security: an overview. Communication of the Association for Information System, 9, 269-282. Bønes, E., Hasvold, P., Henriksen, E., & Strandenaes, T. (2007). Risk analysis of information security in a mobile instant messaging and presence system for healthcare. Int J Med Inform, Sep. 76(9), 677-87. Epub 2006 Aug 23. Clarke, M., Bratan, T., Kulkarni, S., & Jones, R. (2007). The impact of remote patient monitoring in managing silent myocardial infarction in a residential home setting. Anadolu Kardiyol Derg., 7(Suppl. 1), 86-88. Cocosila, M., Coursaris, C., & Yuan, Y. (2004). Mhealthcare for patient self-management: a case for diabetics. Int J Electron Healthc, 1(2), 221-41. Compact Oxford English Dictionary, free internet access. Retrieved March 5, 2008, from: http://www.askoxford.com/concise_oed/ trust?view=uk. Elmufti, K., Weerasinghe, D., Rajarajan, M., Rakocevic V., & Khan, S. (2008). Timestamp Authentication Protocol for Remote Monitoring in eHealth. 2nd International Conference on Pervasive Computing Technologies for Healthcare,

Proceedings, peer reviewed conference article, IEEE Xplore. Retrieved June 15, 2008, from: http:// www.google.dk/earch?hl=da&q=Timestamp+Au thentication+Protocol+for+Remote&meta= Farmer, A., Gibson, O., Hayton, P., Bryden, K., Dudley, C., Neil A., et al. (2005). A real-time, mobile phone-based telemedicine system to support young adults with type 1 diabetes. Inform Prim Care, 13(3), 171-177. Freedman, S. B. (1999). Direct Transmission of Electrocardiograms to a Mobile Phone for Management of a Patient with Acute Myocardial Infarction. Journal of Telemedicine and Telecare, 5, 67-69. Friedewald, V. E., & Pion, R. J. (2001). Telemedicine/home care. Returning home. Health Manag Technol, 22(9), 22-24, 26. Gostin, L. O. (1965). Health information privacy. Cornell Law Review, 80, 451-528. Gostin, L. O., Brezina, J. T., Powers, M., Kozloff, R., Faden, R., & Steinauer, D. D. (1993). Privacy and security of personal information in a new health care system. JAMA, 270, 2487-2493. Gostin, L. O., Lazzarini, L., Neslund, V. S., & Osterholm, M.T. (1996). The public health information infrastructure: A national review of the law on health information privacy. JAMA, 275, 1921-1927. Jasemian, Y., & Arendt-Nielsen, L. (2005a). Design and implementation of a telemedicine system using BLUETOOTH and GSM/GPRS, for real time remote patient monitoring. The International Journal of Health Care Engineering, 13, 199-219. Jasemian, Y., & Arendt-Nielsen, L. (2005b). Evaluation of a real-time, remote monitoring telemedicine system, using the Bluetooth protocol and a mobile phone network. J Telemed Telecare, 11(5), 256- 260.

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Jasemian, Y., & Arendt-Nielsen, L. (2005c). Validation of a real-time wireless telemedicine system, using Bluetooth protocol and a mobile phone, for remote monitoring patient in medical practice. European Journal of Medical Research, 1(6, 2)54-262. Jasemian, Y. (2008). Elderly Comfort and Compliance to Modern Telemedicine System at home, 2nd International Conference on Pervasive Computing Technologies for Healthcare, Proceedings, peer reviewed conference article, ISBN 978-9639799-15-8. Jasemian, Y. (2006). Security and privacy in a wireless remote medical system for home healthcare purpose. 1st International Conference on Pervasive Computing Technologies for Healthcare [CD-ROM],. s. 3, & Proceedings in IEEE Xplore, peer reviewed conference article, 29 November-1 December 2006, Innsbruck, Austria. Logan, A.G., McIsaac, W.J., Tisler, A., Irvine, M.J., Saunders, A., Dunai, A., et al. (2007). Mobile phone-based remote patient monitoring system for management of hypertension in diabetic patients. Am J Hypertens, 20(9), 942. MacDonald, J. A. (2008). Cellular Authentication & Key Agreement for Service Providers. 2nd International Conference on Pervasive Computing Technologies for Healthcare, Proceedings, peer reviewed conference article, IEEE Xplore, Retrieved June 25, 2008, from: http://ieeexplore. ieee.org/Xplore/guesthome.jsp Marchibroda, J. M. (2008). The impact of health information technology on collaborative chronic care management. J Manag Care Pharm., 14(2 Suppl), S3-11. McLean, R., Mendis, K., Harris, B. & Canalese, J. (2007). Retrospective bibliometric review of rural health research: Australia’s contribution and other trends. Rural and Remote Health 7:

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767. Retrieved April 15, 2008, from: http://www. rrh.org.au. National Centers for Chronic Disease Control and Prevention, opdated June 2008, Retrieved April 15, 2008, from: http://www.cdc.gov/nccdphp/ Panidis, I. P., & Morganroth, J. (1983). Sudden death in hospitalized patients: cardiac rhythm disturbances detected by ambulatory electrocardiographic monitoring. J Am Coll Cardiol, 2(5), 798-805. Petersen, S., Peto, V.v & Rayner, M. (2004). Coronary heart disease statistics, British Heart Foundation statistics website. Retrieved June 30, 2008, from http://www.heartstats.org/datapage. asp?id=1652. Sax, U., Kohane, I., & Mandl, K. D. (2005). Wireless technology infrastructures for authentication of patients: PKI that rings. J Am Med Inform Assoc. 2005 May-Jun, 12(3), 263-8. Epub 2005 Jan 31. Shimizu, K. (1999). Telemedicine by Mobile Communication. IEEE Engineering in Medicine and Biology, 32-44. Sneha, S., & Varshney, U. (2007). A wireless ECG monitoring system for pervasive healthcare. International Journal of Electronic Healthcare, 3(1), 32-50. Standing, P., Dent, M., Craig, A., & Glenville, B. (2001). Changes in referral patterns to cardiac out-patient clinics with ambulatory ECG monitoring in general practice. The British Journal of Cardiology, 8(6), 396-398. Woodward, B., Istepanian, R. S. H., & Richards, C. I. (2001). Design of a Telemedicine System Using a Mobile Telephone, IEEE, 13-arch?hl=d a&q=Timestamp+Authentication+Protocol+for +Remote&meta=

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Chapter VIII

Monitoring Hospital Patients Using Ambient Displays Monica Tentori CICESE, Mexico Daniela Segura CICESE, Mexico Jesus Favela CICESE, Mexico

ABSTRACT Hospital work is characterized by intense mobility, a frequent switching between tasks, and the need to collaborate and coordinate activities among specialists. These working conditions impose important demands on hospital staff, whose attention becomes a limited and important resource to administer. Nurses in particular, need to constantly monitor the status of patients in order to assess their condition, assist them and/or notify physicians or specialists. Given their work load, it is not rare for them to miss important events, such as a catheter being disconnected due to the patient movement or the need to change a urine bag that has been filled. Pervasive technologies by being able to continuously monitor patients could provide awareness of the patients’ health condition. This awareness must be subtle, expressive, and unobtrusive without intruding on hospital workers’ focal activity. In this chapter the authors explore the use of ambient displays to adequately monitor patient’s health status and promptly and opportunistically notify hospital workers of those changes. To show the feasibility and applicability of ambient displays in hospitals they designed and developed two ambient displays that can be used to provide awareness patients’ health status to hospital workers. The first display takes into account the mobility experienced by nurses during their work to supervise the activities of daily living (ADL) conducted by patients. The second display is a flower vase that notifies nurses the urine output of patients and the status of their urine bag.


Monitoring Hospital Patients Using Ambient Displays

InTROoduion Hospital staff face working conditions that are substantially different from those of office workers, for which traditional desktop computers were developed (Bardram, J. E., & Bossen, C., 2003; Bardram, J. E., & Bossen, C., 2005; Moran, E. B., Tentori, M., González, V. M., Martinez-Garcia, A. I., & Favela, J., 2006). Most hospital staff need to move continuously around the premises to access people, knowledge, and resources in order to perform their work effectively (Bardram, J. E., et al, 2005). Thus, mobility characterizes work in these environments. For instance, physicians make daily rounds to assess and diagnose patients, changing their location to find colleagues or locate artifacts (patient records, x-ray images, medications) placed in bed wards, laboratories or offices. Therefore, information in hospitals is not generally concentrated in a single place, but distributed among a collection of artifacts in different locations. Consequently, hospitals can be seen as an information space and it is by “navigating” this space that hospital staff can access the information required to support their goal (Bossen, C., 2002). Indeed, nowadays highly mobile hospital workers spend more than 50% of their time onthe-move, making it difficult for them to be aware of the status of the patients they are responsible for (Moran, E. B., et al., 2006). For instance, sometimes hospital workers have patients placed in different rooms or even in different areas of the hospital. Consequently, hospital workers have been held liable for their failure to monitor and promptly respond to patients needs (Smith, K. S., & Ziel, S. E., 1997). This has motivated the introduction of pervasive technologies in hospitals to allow hospital workers to closely monitor patients. For instance, a hospital in Boston is testing an ultrasound tracking system that can determine the location and vital signs of patients (O’Connor, M. C., 2006). These pervasive technologies being introduced range from wireless networks, PDAs

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(Chin, T., 2005), RFID tags for patient tracking (O’Connor, M. C., 2006), voice-activated communication devices (Stanford, V., 2003), and sensors for patient monitoring (Pentland, A., 2004). Indeed, widespread adoption of sensors that monitor the patients’ vital signs and other indicators promise to improve care and reduce medical costs. Thus, pervasive technologies for hospitals are increasingly supporting heterogeneous devices that range from handheld computers that can be used to capture and access limited amounts of information, to PCs that can be used at fixed sites for longer periods of time, and semi-public displays located at convenient places that can be used to share and discuss information with colleagues (Favela, J., Rodríguez, M. D., Preciado, A., & Gonzalez, V. M., 2004; Markarian, A., Favela, J., Tentori, M., & Castro, L. A., 2006). Hence, hospital workers today need to interact with different devices with a wide range of functionality (Bardram, J. E., 2005). Consequently, carrying out a single activity typically involves the use of several systems that call for the user’s undivided attention where several information sources battle for a piece of space in the already limited screen real state. One way to overcome such difficulties is to develop ambient displays that could be embedded into the environment to provide a getaway for that information that could be displayed by objects already placed in the physical space instead of the traditional computer displays. Objects already known and used by hospital workers could be augmented with pervasive sensors making them capable of extending their capabilities beyond its primary role while still constituting a part of the user’s environment. For instance, a mirror augmented with infrared sensors and an acrylic panel could detect human presence and act as a message board to display relevant information when a user faces the mirror. Hence, hospital environments could be augmented with such displays that unobtrusively convey information


to hospital workers without requiring their full attention, while at the same time, allowing an implicit and natural interaction. Indeed, the notion of what constitutes a computer display is changing. No longer is a display confined to the typical CRT monitor with a single user paying focused attention while interacting with virtual objects on the screen (Lund, A., & Wilberg. M., 2007). Rather, computer displays are found in such diverse forms as small screens in mobile phones or handheld computers, to ambient displays that provide peripheral awareness to the presence and status of people, objects or information. Such ambient displays could be located throughout hospital premises providing hospital workers awareness of relevant events associated to their patients while they are on-the-move. In this chapter we explore the use of ambient displays to adequately monitor patient’s health status and promptly and opportunistically notify hospital workers of those changes. To show the feasibility and applicability of ambient displays in hospitals we developed two ambient displays aimed at creating a wearable ambient connection between patients and hospital workers –particularly nurses. The rest of the chapter is organized as follows: In Section II we describe our vision of a hospital as an interactive smart environment saturated with heterogeneous computing devices and specialized services –the iHospital. Section III describes the results of a workplace study conducted in a public hospital to understand the way hospital workers monitor and assess patients. In section IV we describe the design of two ambient displays in support of patient monitoring. Finally, in section V we present our conclusions and directions for future work.

The ipial: TThe HOSPpi a SMmaenvionmen The iHospital is our vision of a highly interactive smart environment saturated with heterogeneous

computing devices where hospital workers use a set of specialized services that account for contextual information (Camacho, J., Galicia, L., Gonzalez, V., & Favela, J., 2008; Favela, J., et al., 2006; Markarian, A., et al., 2006; Munoz, M., Rodriguez, M. D., Favela, J., Martinez-Garcia, A. I., & Gonzalez, V. M.). To help realize this vision, we have developed several ubiquitous and context-aware applications which provide support for the following functionality:

Providing Awareness of People and Artifacts In a hospital, artifacts and people are distributed in space and time. Hence, hospital workers must navigate hospital premises in order to gather the information they need to conduct their work. Unlike others processes, gathering information is a necessary task but adds overtime to the already busy day of hospital workers. Consequently, the iHospital, to reduce the time hospital workers spend searching and gathering information, provides the means for them to be aware of the presence, location and status or artifacts and other people by showing this information through a floor map or a list of users as reported in (Bardram, J. E., & Bossen, C., 2005).

Spporting Collaboration through Context-Aware Communication and the Seamless Interaction among Heterogeneous Devices Hospital staff can send messages that depend on environmental conditions. As an example, a physician can send a message that will be delivered to the doctor responsible for a patient in the next shift when laboratory results are ready. The sender does not need to know a-priori the identity of the doctor that will be attending the patient nor the time when the laboratory results will become available. In addition, hospital staff can transfer information from public spaces to

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personal devices, share information between heterogeneous devices, remotely monitor other computers, and share handheld applications. For instance, two colleagues carrying their PDAs and discussing a clinical case using a public display, could seamlessly transfer information between their personal information space (PDA) and the shared space (public display). Mobility and collaboration create a need to contact colleagues within the hospital, either to discuss a case with a specialist or request help to transfer a patient. Several mechanisms are used for these purposes and technology has been developed to assist in this task; such as the Vocera communication system which enables users to contact a fellow hospital worker either by name, role or location using a hands-free voice communication system (Stanford, V., 2003).

Using Context to Adapt and Personalize the Information To provide relevant information to users, the iHospital takes into account contextual information, such as the user’s identity, role, location, time, or status of an information artifact (e.g. availability of laboratory results). Thus, when a physician, carrying a PDA, is near a patient, the system offers to display the clinical record of the patient. Contextual information such as identity or role is also taken into account to adapt and personalize the presentation of information to the user. Thus, when a physician approaches a public display, it shows only the physician’s patients, personnel calendar, messages, and the location of others with whom s/he may need to interact.

Supporting Multitasking To assist mobile users in the management of their multiple activities and collaborations, the iHospital allows hospital staff to easily manage their activities and their associated resources helping them preserve and recover the necessary context

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when switching between them (Camacho, J., 2008). Using the mobileSJ application, the user can define each of his activities and associate to them, information resources, contacts relevant to the activity, emails related to the activity and pending issues. When a user switches between spheres, each sphere is enabled to quickly gather and retrieve its own workspace state (windows positions, status and overlay order) and context information like open documents or idle time, in a silent manner. In addition, mobileSJ allows sharing activities and resources, as well as, communicating with colleagues through either SMS messages or phone calls. Although these pervasive technologies use contextual information to provide opportunistic services and information to hospital staff they aren’t appropriate mechanisms to convey information about the health status of a patient. This type of information changes very fast and is highly sensitive. Thus using this type of mechanisms of sending messages to a display or a PDA each time an event occurs with a patient could be extremely disruptive to the task the hospital worker is engaged in. For instance, suppose that a nurse is monitoring the urine and evacuation habits of the patient in room 240. While she is inserting a catheter to the patient in room 226 she doesn’t want to receive a sound or message alert in her PDA notifying to her each time such patient has urinated or evacuated. Although the nurse wants to be aware of this information she wants to receive this information in a subtle and unobtrusive manner. One way to overcome this is to show this information in a public display. While this is less disruptive to the nurse’s current goal, it attempts to the patient’s privacy. What we need, thus, is to develop mechanisms to provide another type of ambient awareness. Another class of displays that unlike ordinary computer displays does not call for its users’ undivided attention. Rather, a kind of display that alongside furniture and other fixtures serves its role by constituting a part of the user’s background environment by


proving hospital workers with peripheral cues undistinguishable by others.

Amien DISPplay The physical environments where we live and work are saturated with ambient information, such as sounds or lights, that we use as peripheral cues to discover contextual information relevant or that we manipulate to convey information to others. For instance we can leave our door’s office open, semi-open or closed to communicate to others our availability. In addition, we can become aware of the activity of our peers through passing sounds and shadows at the periphery of our attention. Indeed ambient information exists to provide us with continuous information in a subtle, peripheral and expressive manner without intruding on our focal activity. Research in pervasive computing has focused on developing ambient devices that can become a part of our background while acting as a digital interface to this ambient information. As stated by Mankoff: “Ambient displays are aesthetically pleasing displays of information which sit on the periphery of a user’s attention. They generally support the monitor of information and have the ambitious goal of presenting information without distracting or burdening the user” (Mankoff, J., Dey, A. K., Hsieh, G., Kientz, J., Lederer, S., & Ames, M., 2003). For instance, the artist Natalie Jermijenko at Xerox Parc augmented a string with a motor and spin to convey the traffic’s status to a user –the Dangling String (Weiser, M., & Brown, J. S., 1995). The device rotates at a speed that depends on the amount of traffic in the highway captured through analog sensors. During periods of intense traffic, the string’s movements are slightly audible as well. Thus, ambient displays are, unlike ordinary computer displays, designed not to distract people from their tasks at hand, but to be subtle reminders that can be occasionally noticed. In addition to presenting information,

the displays also frequently contribute to the aesthetics of the locale where they are deployed (Lund, A., & Wilberg. M., 2007). Many types of ambient displays have been built to augment physical objects allowing users to manipulate or access information. For instance, as part of the AmbientRoom project, several displays using light, sound or motion have been developed to augment a user’s office. Such displays provide users with awareness of the activity executed by a distant loved one and the physical presence of others (Ishii, H., Wisneski, C., Brave, S., Dahley, A., Gorbet, M., Ullmer, B., & Yarin, P., 1998). Users of this AmbientRoom can also use these displays to manage ambient activity sources. For instance, a small glass bottle is employed as a graspable “container” for digital content, such that uncorking the bottle “releases” information into the room (Ishii, H., et al., 1998). Similarly, a pair of interactive pillows were instrumented to allow users to stay connected with their relatives who live abroad (Nack, F., Schiphorst, T., Obrenovic, Z., Tjoe, M. K., Bakker, S., Perez, A., & Aroyo, L., 2007). Users can interact with a pillow in a specific location activating dynamic textile patterns that makes another pillow located elsewhere glows. These ambient patterns expand the vocabulary for remote communication through tangible and aesthetic interactions (Nack, F., et al., 2007). The breakway ambient display is a sculpture that bends up or down based on the amount of time a user has been seated (Jafarinaimi, N., Forlizzi, J., Hurst, A., & Zimmerman, A., 2005). The user can place this sculpture on his desk to be aware of the how much time he has been immobile to persuade him in improving his posture. Likewise, an ambient display in the form of a lamp has been developed with the aim at informing smokers the level of contamination in our environment as a result of their smoke habits–the Smoking lamp (Hehe, H., 2007). This lamp is a funnel that terminates with a ring of light. The lamp changes from a bright white to a warm pink if it detects nicotine smoke beneath it.

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Although, these projects have shown the feasibility and applicability of ambient displays in support of varied goals, designers face many issues in their development and evaluation. These issues range from the understanding of the information that must be notified to users to the design of such displays that have to work on many levels –aesthetics, utility and integration with the user’s environment. For instance, most ambient displays monitor non-critical information; however, this information should be useful and relevant to the users. It is important to involve the users in the design of the display, in order to obtain the information that will be useful to them. It is also necessary to consider that too much information cramps the display, and too little could restrict the usefulness of the display. In addition, users do not have experience in interacting with ambient displays, yet. Hence, novel metaphors must be used to make such ambient displays intuitive reducing the cognitive load that users could experience in learning how they work (Gross, T., 2003). Finally, an ambient display that changes too fast can distract the user, whiles a display that changes too slow can pass unnoticed (Johan, R., Skog, T., & Hallnäs, L.). Hence, we must develop mechanisms to balance this tradeoff. As design issues remain unsolved, evaluation techniques to assess the impact of these displays are not available. Consequently, the majority of ambient displays have not been tested, some only report brief evaluations with few details or informal feedback; instead they focus on technology and design innovation. This is because the evaluation of ambient displays can be costly, difficult and time consuming.

UndeRSTAanding how hopial STAaff monipaien A CASE STUudy We conducted a field study to understand the way hospital workers monitor and assess patients and

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discover new unseen opportunities in the development of ambient displays for hospitals. The field study was conducted in the Internal Medicine area of a mid-size hospital. There are approximately fifteen patients registered, supervised by three or four nurses, one attending physician and five medical interns. The patients attended in this area, are generally elder people, senile and alone. They normally have a chronic or a terminal disease, such as diabetes, renal failure, cerebral lesions or cardiac problems. Hence these patients are normally immobile and incapable of performing alone the activities of daily living (ADLs), such as eating or dressing. For nine months, we used mobile structured observation to shadow five nurses, five medical interns, and five physicians for two complete working shifts. Mobile structured observation requires researchers to shadow individuals, annotating and time-stamping their actions as executed. We later transcribed and analyzed these handmade detailed records using grounded theory—a systematic research methodology for generating theory from data (Strauss, A., & Corbin, J., 1998). As a result of this analysis, we identified the patient’s behavioral patterns that are monitored by hospital workers. Since nurses are the ones who closely monitor patients, for now on, we will focus in discussing only the results obtained by the analysis of the nurses’ behaviors.

Activities of Daily Care Nurses are responsible for providing integral and specialized care for patients. As part of the integral care, nurses monitor the activities of the daily living (ADL) conducted by patients, such as, if a patient has taken his medicine, if he has walked, eaten, felt from the bed, evacuated, etcetera. As a part of specialized care, nurses need to monitor the behavioral patterns in the activities that put at risk the patients’ health or that indicate an internal failure which might evolve into a more serious disease (e.g., pneumonia, an apoplexy


or a stroke), such as, if a patient is agitated, if a patient is bleeding or if the patient has respiratory insufficiency. These behavioral patterns associated to risk activities (RA) are monitored through the vital signs. Nurses, thus, have incorporated to their work strategies that allow to them be aware of the status of the patients they are taken care off. In this regard, nurses execute activities on a daily basis which are critical in providing quality of attention to patients. We call these actions the Activities of Daily Care (ADC). The goal of an ADC is to allow hospital workers to maintain or establish a connection with patients by being aware of their needs and their progress. Such goal is impel by the nature of the role who is executing such ADC. For instance, while the ADCs executed by nurses have a more technical and specific goal, aimed at providing integral care to patients; physicians and interns, on the other hand, are engaged in assessing the progress of a patient and his diagnosis. As an example of the nature of the ADCs conducted in hospitals we present a scenario that depicts the ADCs performed by a nurse during a working day (Figure 1). At 7:00 am at the bed wards in the Internal Medicine Area, Rita, a nurse, begins her shift by taking the vital signs of all her patients (i.e.,

the patients she is taken care off). During this activity Rita asks her patients for their symptoms and explains to them their progress. Later, Rita administers medicines to her patients and monitors their diet. During this activity, often she interacts with patients and/or relatives by handing over the medicine to them or she, injects the medicine directly to a serum bag attached to the patient. Once Rita finishes the administration of medicines to all of her patients, which occasionally lasts until 10 am, she computes the liquid balance by weighting each patient’s urine bag and measuring their liquid inputs. During this activity Rita cleans and baths some of the patients that can not perform this activity without help. While Rita is performing some of these activities, physicians and medical interns start the ward round. When the ward round has started, Rita makes sure that the information generated through the execution of the ADC (e.g, the heart rate of a patient or his liquid balance) is ready before physicians and interns start the evaluation of a patient. In addition, during this period of time, Rita helps physicians and interns in basic or surgical procedures and discusses with them information relevant to the patient being evaluated. When the ward round finishes, at 12 pm approximately, Rita initiates her ADC routine all over again by consulting the new indications stated by physicians during the ward round.

Figure 1. A nurse performing the activities of daily care using a variety of devices and interacting with several people

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As shown in the scenario, an ADC routine is a coherent and chronological history of the execution of several ADCs. In a similar way, physicians and interns conduct ADCs routines with the aim of evaluating clinical cases. From a particular clinical case physicians and interns start their assessment by exploring the patient and asking about his symptoms, after that they consult clinical evidence, such as the medical records, nurse charts, study results and reference material, to support their discussion towards a new diagnostic of the patient. Table 1 shows the activities of daily care performed by nurses, and how much time they spent in performing such ADCs. We identified common characteristics among the ADC’s conducted by nurses on a daily basis. These characteristics shape the way activities are experienced, monitored and understood by hospital staff.

Activities Monitored are Classified Nurses assign three levels of urgency (i.e., low, medium, high) to each of the activities monitored. For instance, the ADCs are generally classified with a low level of urgency while the RAs are classified as being of medium urgency, and the possibility of evolving to a high urgency. In addition, nurses make exceptions based on each patient’s disease. For instance, for a patient who has cirrhosis hepatic

and that is immobile, monitoring his evacuations is very important since his liver does not function and nurses must avoid the formation of sores in his skin. Hence the classification of an activity might depend on the state of the patient.

Activities are Monitored Based on Cntextual Information Over time, measurements associated to activities performed and/or the patient’s state or disease, are used to determine what needs to be monitored. For instance, a nurse explained during an interview “some activities, at a particular moment, might not be urgent but they could become urgent after a period of time or depending of the patient status or disease”. Moreover, the status of the patient sometimes indicates activities that patients should not perform. For instance, if a patient has a cardiac problem he should not get up from bed. Hence, nurses must monitor when a patient with such disease is out of his bed or they need to be aware of his disease to bring him a wheel chair instead of to take him for a walk if the patient needs to move to another area.

Activities Monitored are Distributed Nurses attend patients distributed in space; hence, it is hard for nurses to select which patient must

Table 1. Time nurses spent performing activities of daily care Nurses Nurses ADCs (time per day per subject) Average time

%

Monitoring diet and medicines administration

00:44:56

36.73

Providing quality of attention

00:33:33

27.43

Cleaning patients

00:19:40

16.08

Taking vital signs

00:15:34

12.73

Performing basic care or surgical procedures

00:06:54

5.64

00:01:42

1.39

02:02:19

100

Computing the liquid balance and monitoring evacuations All

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be monitored closely or to be at the location when an emergency occurs. For instance, a nurse commented: “sometimes, I have patients that are placed in different areas of the hospital and if I am looking for a patient I am locked up in his room and I do not realize what is happening with my other patients, I am totally disconnected from those patients and in one or two minutes a thousand things could happen because I am not there I am over here”

Activities are Monitored to Collect Information Nurses need to compute and manage the information associated with the activities monitored. Therefore, the need for this information under several circumstances determines which activities must be monitored. For instance, a nurse explained: “Sometimes, I do not need to monitor when the patient has eaten or drank instead I need to monitor how much he has eaten or drank. Mostly, when I am controlling the amount of liquids he evacuates”. Hence, in this case the activity being executed by a patient only acts as a trigger alerting the nurse that she must gather the information associated to such activity.

AUGMENTIing naal o wih digial SERVvi and am DISPplay fo paien moniing The data from our study helped us to identify the problems currently faced by hospital workers when monitoring patients. In particular, issues related to hospital workers being on the move and due to the distributed and dynamic nature of the activities being monitored by them, include maintaining awareness of their patients’ status, being easily accessible when an emergency occurs, and prioritizing patient care on the basis

of the patient’s health condition and the activity executed. To cope with these issues we designed and implemented two ambient displays aimed at creating a wearable ambient connection between patients and nurses. The first display takes into account the mobility experienced by nurses during their work to supervise the activities of daily living (ADL) conducted by patients (Tentori, M., & Favela, J.). The second display is a flower vase that takes into account contextual information of the nurses’ presence to adapt its behavior notifying nurses whenever a patient she is taken care off has urinated, as well as, the state of the urine bag wore by him. In the following section we describe the motivation, the sensing technologies required, and the design of both ambient displays.

A Mobile ADL Monitor The mobile ADL monitor uses an ambient display (digital bracelet) and a smart phone to notify events related to the activity being executed by a patient to nurses. The device is a two-layered vinyl bracelet containing five buttons with embedded lights (Figure 2a). Each button represents a patient under the nurse’s care. Adapted from the medical model utilized in the emergency unit, each light in the button turns on with colors analogous to a traffic light. The lights turn on when a patient is executing an activity, when particular actions occur, or after a series of events take place. Nurses can press the button to consult information associated to the activity a particular patient is executing. This information is displayed in the nurse’s smart phone, which can show a more complex representation of the activity being executed by the patient (Figure 2b). Nurses can also use their phone to assign priorities by selecting colors (Figure 2c) or setting contextual information to act as a trigger for the activities being monitored (Figure 2d). The following scenario illustrates the use of the display in the hospital.

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Figure 2. The mobile activity monitor. (a) A nurse uses the activity-aware bracelet; (b) the mobile activity-aware assistant shows information related to an activity being executed by a patient; (c) a nurse uses her cell phone to assign colors; and (d) a nurse associates contextual information with an activity.

Scenario of Use: Monitoring the ADLs Conducted by Patients Juan is a 60 years old man who just had a neurological surgery. Juan is partially immobile so he must perform physical exercises on his bed to improve his motors skills and blood circulation. Since the surgery, he has been very anxious performing abrupt movements resulting in falls from the bed –especially during the night. Consequently, Carmen, the nurse in charge of Juan, wants to monitor Juan’s movements to find out: (1) if Juan has performed his exercises, (2) if he is abruptly moving and (3) if he has fallen from the bed. Therefore, Carmen uses the activity-aware mobile assistant in her smart phone to specify that the light that represents Juan in her bracelet must turn green if Juan is performing his exercises, yellow if Juan is abruptly moving and red if Juan’s has fell from the bed. To do this, Carmen selects from her smart phone a light color and then specifies the contextual information that will trigger a notification to her. For instance, Carmen specifies that the light that represents Juan in her bracelet must turn yellow (Figure 2c) when he moves abruptly, and must change to red if Juan does so at least 3 times over a period of 2 hours (Figure 2d). Later, while Carmen is preparing the medicines to be delivered to the patients, Juan’s light (i.e., the light that represents Juan in Carmen’s bracelet) turns green (Figure 2a). Carmen presses

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the button that represents Juan in her bracelet and the activity being executed by Juan is displayed in Carmen’s smart phone (Figure 2b). Carmen realizes that Juan is performing his physical exercises. Carmen updates this information in Juan’s nurse chart. Throughout the day, Juan’s light in Carmen’s bracelet constantly turns yellow indicating to her that Juan is anxious. An hour later, while Carmen is discussing with a patient his symptoms her bracelet turns red. Carmen consults her smart phone and realizes that Juan has been frequently moving for the last two hours. Carmen moves to Juan’s room and she realizes that Juan is about to fall down. Carmen holds Juan and gives him medication to calm him down.

Sensing the Contextual Information Required to Monitor Patients The system requires to measure users’ movements by monitoring how movements are gradually being change from stationary state to an abrupt one. In particular, when patients are still we must distinguish between when they are lying or have fallen from bed. In this regard, several methods and technologies have been proposed to capture and monitor users’ patterns of movement, ranging from the use of simple sensors such as accelerometers to complex systems that fusion the information from different sources. Research in wearable computing has shown that users’ move-


ment pattern can effectively be inferred from body-worn standalone accelerometers or those wirelessly connected to motes Aminian, K., Robert, P., Jequier, E., & Schutz, Y., 1995; Bao, L., & Intille, S., 2004; Foerster, F., Smeja, M., & Fahrenberg, J., 1999; Minnen, D., & Starner, T., Essa, I., & Isbell, C., 2006; Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., & Korhonen, I., 2006). These devices can be embedded in wrist bands, bracelets, adhesive patches, or belts and later placed on various parts of users’ body. The findings of these studies indicate that the detection of posture and motion based on accelerometers is highly reliable with overall recognition accuracy of up to 95%. Several efforts have been made to use accelerometers to detect differences in users’ patterns of movements, such as if they are gradually changing their movements from uniform to abrupt. For instance, acceleration data of the wrist and arm have been used to estimate users’ upper body activities such as martial arts movements (Foerster, F., Smeja, M., & Fahrenberg, J., 1999). Similarly, others have place accelerometers in a user chest and thigh to detect his posture and ambulatory movements (Aminian, K., et al., 1995). Although these approaches can also estimate if a user is in a steady state, several projects have explored how to combine accelerometers with motes to infer when a user is lying or when he has fallen from bed. For instance, the Ivy Project uses a small device worn on the waist of a user and a network of fixed motes to detect the occurrence of a fall and the location of the victim. Low-cost and low-power accelerometers are used to detect the fall while RF signal strength is used to locate the person (Chen, J., Kwong, K., Chang, D., Luk, J., & Bajcsy, R.). Another project uses a tri-axial accelerometer embedded in a cellphone and wirelessly connected to Internet that uses a pattern recognition algorithm to accurately detect falls (Zhang, T., Wang, J., Liu, P., & Hou, J., 2006).

Designing the Mobile ADL The system uses e-activities as its core units allowing it to react when an event occurs with a patient; display activities in different devices such as in a smart phone or in the bracelet; and, store activities to determine when an ADL is evolving into a RA. An e-activity is the computational representation of a human activity, and it stores attributes depicting the activity’s execution context, such as who owns it, other participants, its location, and the artifacts or applications used. An e-activity can also store a set of rules to inform the system how to adapt the smart environment or infer other attributes, such as a person’s availability. E-activities are: reactive, they can act as a trigger; sequential, they can form histories; mobile, they’re executed across devices; and, persistent, their state is stored over long time periods. The system uses an activity-aware assistant as client and an activity-aware server as a basis for its implementation. The activity-aware server is composed of three layers that are responsible for creating e-activities and histories on the basis of the information sensed. The lower layer recognizes the activity by reading contextual information from sensors; the middle layer defines the activity’s computational equivalence by either extracting a similar activity from its activity knowledge base or creating a new activity from scratch on the basis of information the lower layer provides and; the upper layer uses the e-activity definition to create a history of activities using the activities stored in the history knowledge base. This layer also analyzes such history to infer the next step that should be executed or how such e-activity attributes change as the user course of action evolves. The activity-aware assistant uses a device and a smart phone to notify the nurse of the events related to the activity being executed by a patient. The display that we designed it’s a two-layered vinyl bracelet containing five buttons with embedded lights (Figure 2a). When a nurse presses a button

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Figure 3. The bracelet electrical components. (a) The transmitter used in the activity-aware server; (b) the receiver embedded in the bracelet.

on the bracelet, a message is sent back to the activity-aware server, specifying a patient and bracelet ID. This ID is used by the activity-aware server to determine which activity should be displayed on which smart phone. Communication between the phone and the server occurs wirelessly. We developed our own components to communicate the bracelet with the server (Figure 3). A transmitter is responsible for sending and receiving messages from the bracelet at frequencies under 27 Mhz (Figure 3a). This avoids interference between the bracelet and equipment placed in the hospital or worn by patients. This transmitter is internally connected to the CPU through a RS232 port and uses a remote control to send messages. The transmitter has embedded a receptor circuit that manages the radioelectrical signals from the bracelet and translates them into pulses. The circuit uses a 7805 regulator to decrease volts greater than 5VDc forcing it to work under a TTL range. The circuit also uses a Max232 component that converts signals of the serial port from +12/-12V to 5V+. These volts are the outputs of the circuit and activate the circuit’s remote control for sending messages. In contrast, the bracelet has embedded a receptor circuit which converts radioelectrical signals into electrical pulses (Figure 2b). This circuit enforces a NOT operation over such pulses, using the 74ls04 floodgate, to turn such pulses

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into zero. This will allow the bracelet to either receive or send messages.

The Flower Vase The flower vase is an ambient display that notifies nurses the urine output of patients and the status of their urine bag (Figure 4). The flower is a wooden box containing twenty four artificial flowers: twelve emergency flowers and twelve situation flowers. The flowers are composed of a two-layered felt that enclose pistils covered with insulating tape. In each pistil a red or yellow led is embedded. The emergency flowers are have stems with an embedded yellow light in their pistils (Figure 4a). All emergency flowers blink whenever an event or an emergency occur with a urine bag wore by a patient –if a urine bag is full. In contrast, situation flowers are flowers without stems with a red light embedded in their pistils. This situation flowers are arranged in a matrix to represent the location of the patients in the area. The columns in the matrix represent rooms and the rows represent patient’ beds (Figure 4b) –each room has three beds for patients. This arrangement allows nurses to quickly discover the bed where a patient is located. Situation flowers turn on whenever a nurse approaches the flower vase or if the emergency flowers are blinking. While


Figure 4. The flower vase placed in the nurse pavilion (a) The flowers that notify of emergency events (b) The flowers that personalized their color based on the nurse’s presence

emergency flowers are blinking a situation flower turns on, indicating to a nurse the location of the patient related to that event. If emergency flowers are not blinking and a nurse approaches the vase, the situation flowers personalize their color by turning on only those flowers that represent the patients assigned to that nurse and for which an event has been associated. The following scenario illustrates the use of the display in the hospital.

Senario of Use: Monitoring Patients’ Urine Outputs Carmen, the nurse in charge of Pedro, explains Refugio, the nurse whose shift has just started, that Dr. Perez, the attending physician, has changed Pedro’s medication to include cyclosporine. Pedro is a 56 years old man, who has a chronic renal failure and just had a renal transplant. Hence, to monitor Pedro’s reaction to the new transplanted kidney, Refugio needs to supervise the frequency and quantity of Pedro’s urine. When Pedro’s urine bag reaches a threshold the emergency lights in the flower base start to blink (Figure 5). She approaches the flower vase and realizes that is Pedro’s bag is almost full. She moves to the warehouse and gathers the medical equipment she needs to change the urine bag. Then, she updates the patient’s liquid balance. A couple of hours

later, while Rita is discussing the evolution of a patient with Dr. Perez she approaches the flower vase. The flower vase personalizes the situation flowers and turns on Pedro’s light indicating that he has been urinating. Rita discusses with the physician Pedro’s case, who decides to change the medication to avoid damaging the newly transplanted kidney.

Sensing the Contextual Information Required to Monitor Patients The flower vase requires to monitor the weight of a urine bag wore by a patient and the presence of the nurses who are in front of the vase. To monitor the status of the urine bag we developed a weight sensor that measures the amount of urine in a bag. This weight sensor is attached to a urine bag wore by a patient and connected to a mote (Figure 5a). This sensor is composed of two acrylic pieces which are separated through a spring and a push button. We calibrated the required separation between both pieces. When the urine reaches a threshold (i.e., when the urine has filled 80% of the urine bag) the button is pressed. Once the button gets pressed, the sensor generates an electronic pulse. This pulse is read by the mote that is responsible for the transmission of this information wirelessly. When the bag is replaced

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the button goes back to its normal position. We use motes to avoid saturating the rooms with wires that could be obtrusive to nurses and patients. To monitor nurses’ proximity, several approaches have been proposed to infer user proximity based on location sensing, proximity measurement, and device discovery (Hightower, J., & Borriello, G., 2001; Krumm, J., & Hinckle, J., 2004; Krumm, J. & E. Horvitz; Naya, F., Noma, H., Ohmura, R., & Kogure, K., 2005).

Designing the Flower Vase The flower vase consists of two parts: a weight sensor attached to the urine and a set of flowers that display the status of the urine bag of the patient (Figure 5). When the base station receives the information, it identifies the sensor that sent it, thereby identifying the location of the patient, and then turning on the red light of the corresponding flower (Figure 5b). At the same time the flowers with stems begin to blink, trying to get the attention of nurses. We use the phidgets toolkit (Greenberg, S., & Fitchett, C., 2001) to implement the flower base (Figure 5a). We embedded in the box a communication interface with 8 analog inputs, 8 digital inputs, and 8 digital outputs. We used the digital outputs to directly control substantial devices by switching up to 30VDC at up to 2 Amps. The output acts as a switch to ground voltages, and it

is protected from transient voltages typical when switching inductive devices - relays, solenoids, motors. The outputs can be used to directly control devices requiring substantial power such as incandescent lights, high power LEDs, relays, solenoids and motors, making it possible to control flowers’ light.

Conlu In this chapter, we discuss how ambient displays can support of hospital work. To illustrate this, we designed and implemented two ambient displays that monitor patients’ health status and provide continuous awareness of this information to hospital workers while they are on-the-move. This awareness is expressive, subtle and unobtrusive to the activity being conducted by hospital staff. This awareness will allow hospital workers to promptly identify patient’ needs, save time and avoid errors. The ambient displays presented here are just preliminary designs that give a hint of the potential of this technology in healthcare. We plan to conduct an in situ evaluation of the displays developed to assess their impact within the hospital. In addition, we plan to explore a new setting where this type of technology could be useful –in particular, in nursing homes. Workers at nursing homes specialized in the care of elders

Figure 5. The flower vase components (a) The sensor device; (b) The flower vase as an ambient display

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with cognitive disabilities face working conditions that are similar to those in hospitals. Such workers also use common strategies to monitor patients’ status. This monitoring is done manually, making it time consuming and error prone. This is another healthcare scenario in which ambient displays can prove useful.

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Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., & Korhonen, I. (2006). Activity Classification Using Realistic Data From Wearable Sensors. IEEE Transactions on Information Technology in Biomedicine, 10(1), 119–128.

Markarian, A., Favela, J., Tentori, M., & Castro, L. A. (2006). Seamless Interaction among Heterogeneous Devices in Support for Co-located Collaboration. In CRIWG. Valladolid, Spain: Springer-Verlag.

Pentland, A. (2004). Healthwear: Medical Technology Becomes Wearable. IEE Computer, 37(5), 42-49.

Minnen, D. and T. Starner, Essa, I., Isbell, C. Discovering characteristicactions from on-body sensor data. In International Semantic Web Conference (ISWC). Athens, GA, USA: IEEE. Moran, E. B., Tentori, M., González, V. M., Martinez-Garcia, A. I., & Favela, J. (2006). Mobility in Hospital Work: Towards a Pervasive Computing Hospital Environment. International Journal of Electronic Healthcare, 3(1), 72-89. Munoz, M., Rodriguez, M. D., Favela, J., Martinez-Garcia, A. I., & Gonzalez, V. M. ContextAware Mobile Communication in Hospitals. IEEE Computer, 36(9), 38-46. Nack, F., Schiphorst, T., Obrenovic, Z., Tjoe, M. K., Bakker, S., Perez, A., & Aroyo, L. (2007). Pillows as adaptive interfaces in ambient environments. in Proceedings of the international workshop

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Chapter IX

Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks Javier Espina Philips Research Europe, The Netherlands Heribert Baldus Philips Research Europe, The Netherlands Thomas Falck Philips Research Europe, The Netherlands Oscar Garcia Philips Research Europe, The Netherlands Karin Klabunde Philips Research Europe, The Netherlands

ABSTRACT Wireless body sensor networks (BSNs) are an indispensable building stone for any pervasive healthcare system. Although suitable wireless technologies are available and standardization dedicated to BSN communication has been initiated, the authors identify key challenges in the areas of easy-of-use, safety, and security that hinder a quick adoption of BSNs. To address the identified issues they propose using body-coupled communication (BCC) for the automatic formation of BSNs and for user identification. They also present a lightweight mechanism that enables a transparent security setup for BSNs used in pervasive healthcare systems. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks

INTRODUCTION Wireless Body Sensor Networks (BSNs) are an enabling technology for the paradigm shift towards pervasive health monitoring. Instead of the traditional event-driven model where patients go to their doctor only when they are sick, we envision that a person’s state of health is continuously monitored through the use of smart body-worn medical sensors to detect changes to the worse before a critical condition arises. Thereby emergency cases are prevented and both patients and care givers are empowered to act more proactive in general. However, pervasive health monitoring systems will be widely adopted only if users experience them as easy-to-use and trustworthy, which is a challenging task as we illustrate in this chapter. Although several wireless technologies are available to allow body-worn sensors to communicate with one another, they all presume that the user has connected all her body sensors to a single network. But since existing wireless standards fail to provide support for setting up a network, this turns out to be a difficult job, asking too much of normal users. We propose a mechanism for making wireless body-worn medical sensors aware of the person they belong to by combining body-coupled with wireless communication. This enables a user to create a wireless BSN by just sticking the sensors to her body. We exploit Body-Coupled Communication (BCC) to also eliminate the mixing up of medical data from different patients, which is a common source of errors. For this the user wears a personal identifier that broadcasts her unique user ID around her body. Thereby all her body-worn sensors are able to unambiguously annotate their readings with her ID. Protecting the user’s privacy and ensuring confidentiality of medical data is essential for the acceptance of any pervasive healthcare system. To this end we describe a security system for BSNs that takes the resource constraints of tiny

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sensors into account and can be rolled out in an easy and unobtrusive way. The remainder of the chapter is organized as follows. Section 1 introduces the concept of wireless medical BSNs by describing some sample applications and suitable available and upcoming wireless technologies. In Section 2 we present our approach for automatic network formation and user identification based on BCC. A lightweight security system for pervasive BSNs is described in detail in Section 3. In Section 4 we identify some future trends relevant for the deployment of BSNs followed by our conclusions in Section 5.

WIRELESS MEDICAL BODY SNSOR NETWORKS Concept and Applications Wireless medical BSNs are an enabling technology for the application domain of unobtrusive health monitoring. This field includes continuous cable-free monitoring of vital signs in hospitals (Philips Medical Systems, 2005), remote monitoring of chronically ill patients (Herzog, 2004; Kraemer, 2006; Lo, 2005), monitoring of patients in mass casualty situations (Malan, 2004), monitoring people in their everyday lives to provide early detection and intervention for various types of disease (Habetha, 2008), computer-assisted physical rehabilitation in ambulatory settings (Jovanov, 2005), and assisted living of elderly at home (Eklund, 2005). A wireless medical BSN consists of smart wireless sensors measuring for example electrocardiogram (ECG), non-invasive blood pressure and blood oxygen saturation. By means of advanced low-power radios the body-worn sensors can communicate with one another or with nearby devices (e.g. stationary Internet gateways or mobile phones) within a range of typically 5 to 10 meters.


BSNs are an indispensible building stone for any pervasive healthcare system because they are unobtrusive and allow the patient to perform her normal daily activities while being cared for. Moreover they open up completely new medical perspectives. To illustrate this, we briefly describe two sample systems consisting of several bodyworn sensors for: (1) continuous blood pressure monitoring and (2) motor rehabilitation of stroke patients.

Continuous Cuff-less Blood Pressure Monitoring Arterial blood pressure has long been one of the most important vital parameters for determining a patient’s state of health. Nevertheless its diagnostic potential is not fully exploited yet. Blood pressure shows a high variability during the day due to physical or mental stress, which cannot be monitored effectively and comfortably with usual blood pressure devices. Such traditional devices are mainly based on a sphygmo-manometric occlusive cuff, which is clumsy, uncomfortable

and allows only for intermittent measurements at intervals of several minutes. An alternative is to infer the arterial blood pressure using the pulse wave velocity methodology. This can be done by using a wireless sensor configuration with an ECG measured from the chest and a photoplethysmogram (PPG) measured at the ear or finger (Muehlsteff, 2006; Espina, 2008). With this setup the Pulse Arrival Time (PAT) can be measured, which is defined as the time delay between the R-peak of the QRS wave from the ECG and the arrival of the arterial pulse wave at the periphery, i.e. the foot of the PPG wave. Systolic blood pressure can then be estimated from the PAT with an accuracy of 6.9 mmHg (root mean square error) (Muehlsteff, 2006). Figure 1 shows an overview of a BSN for continuous blood pressure monitoring according to the aforementioned pulse wave velocity methodology. The system consists of an ECG and a 3D-acceleration patch sensor, a finger/ear PPG sensor, and a personal digital assistant (PDA). The wireless sensors are accurately time-synchronized and transmit their data over an IEEE

Figure 1. Left—BSN devices: PDA, finger-PPG sensor, and ECG/3D-accelerometer sensor. Right—Device position on the patient’s body

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802.15.4 wireless link to the PDA, which aligns their data streams and performs the PAT measurement and blood pressure estimation. All data are displayed on the PDA, from where they can be sent to a clinical site via GPRS or UMTS. This BSN application is described in (Espina, 2008) in greater detail.

Home Stroke Rehabilitation System Stroke is the third common cause of death in USA (American Heart Association, 2007) and also results in serious long-term disability for its survivors. In an ageing society, the incidence of stroke is expected to grow in the coming years. Out of the various deficits deriving from strokes, unilateral sensorimotor deficits are very prominent ones, with more than 80% of stroke patients suffering from them (Parker, 1986). Stroke rehabilitation programs allow patients to improve on their sensorimotor capabilities. However, stroke patients have not reached their full potential when they are discharged from the rehabilitation program (Malouin, 2005). Home rehabilitation can increase the intensity of training, which is known to have a huge positive impact on the rehabilitation outcome (Willmann, 2007). Figure 2 shows several components of a novel home stroke rehabilitation system (Acht, 2007; Willman, 2007) consisting of a motion captur-

ing BSN, a user interface, and a therapist station. Upon release from the rehabilitation clinic, patient and therapist agree on rehabilitation goals and the therapist selects exercises and compiles a training plan. The system compares the patient movements to motion targets defined by the therapist and presents the patient with meaningful feedback. Furthermore the therapist can access all data remotely and tracks the patient’s therapy progress and compliance with the exercise plan. The BSN is formed by several match box-sized sensor nodes (Acht, 2007)—from 2 to 10—attached by garments to the parts of the patient’s body that need to be tracked, usually both upper arms, lower arms, shoulders, and chest. Each sensor measures 3D-acceleration, 3D-magnetization (Earth magnetic field), and 3D-angular speed (MEMS gyroscopes) and transmits those data streams to a PC via an IEEE 802.15.4 wireless link. From the raw sensor data the PC calculates the 3D-orientations of all sensors. Since the sensors are time-synchronized with an accuracy better than 1 ms, their 3D-orientations can be combined to successfully reconstruct the patient’s posture and 3D-limb position.

Wireless Technologies To allow for low-cost transceiver implementations as well as low body-induced RF attenuation

Figure 2. Left—Attachment of wireless inertial nodes to the body. Right—Home rehabilitation

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(i.e. good link robustness) medical BSNs should ideally transmit data in frequency bands below 1 GHz. However the available bands are either crowded—as is the case in the 433 MHz spectrum range—too application-specific—e.g. Medical Implant Communication Service (MICS) and Wireless Medical Telemetry Systems (WMTS)— or not usable on a global basis—e.g. the North American 915 MHz Industrial, Scientific, and Medical (ISM) band or the European 86x MHz bands. The worldwide available 2.4 GHz ISM band appears to be today the most appropriate portion of spectrum for wireless medical BSNs. Moreover wireless medical BSNs should communicate using standard-based wireless technologies. This enables device and systems interoperability, low bill of materials, and reduces the medical device manufacturer’s dependency on RF transceiver manufacturers. The most

suitable wireless communication standards are presented below.

Available Communication Standards The 2.4 GHz ISM band accommodates a number of concurrent connectivity standards such as IEEE 802.15.1 (2002) (Bluetooth), IEEE 802.11b/g (2003) (basis of WiFi) and IEEE 802.15.4 (2006), the basis of ZigBee (2007). IEEE 802.11 standardizes Wireless Local Area Networks (WLANs), which refers to systems with a coverage of 10 to 100 meters that often interact with a wired infrastructure (LANs). In contrast, IEEE 802.15 standardizes Wireless Personal Networks (WPANs)—a.k.a. Wireless Body Area Networks (WBANs)—which refers to systems with a coverage of less than 10 meters for highly mobile devices, such as wireless I/O peripherals with very limited power resources. The

Figure 3. Radios are agnostic about persons

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IEEE 802.15 standardization body issued three major WPAN standards so far: IEEE 802.15.1 medium-rate WPAN (derived from the Bluetooth® standard), IEEE P802.15.3 (n.d.) high-rate WPAN (never reaching a product status in the market), and IEEE 802.15.4 (2006) low-rate WPAN (aiming at sensor/actuator networks).The latter was amended in 2007 with the IEEE 802.15.4a (2007) standard (no products available yet). Most existing BSN implementations are based on IEEE 802.15.1 or IEEE 802.15.4 since (compared to the popular WLAN standards IEEE 802.11b/g) they feature low power consumption, low complexity, and a small form factor. Table 1 shows a comparison of such standards. For medical BSNs we endorse the use of IEEE 802.15.4. Compared to it, IEEE 802.15.1 exhibits less flexible networking concepts, limited usability for uninterrupted real-time data transfers, and higher average power consumption. Furthermore most medical BSNs do not require data rates higher than 200

kbps. The BSNs presented in this section were built with the Philips IEEE 802.15.4-compliant AquisGrain sensor platform (Espina, 2006).

Upcoming Communication Standards IEEE 802.15.4a (2007) is an amendment to the IEEE 802.15.4 standard that defines a pulse-based Ultra Wide Band (UWB) physical layer. Despite the high body-induced RF attenuation at the frequencies in which it operates (3 GHz to 10 GHz), IEEE 802.15.4a is a promising communication standard for BSNs. Its implementations are expected to enable very low-power communication while being especially robust to multipath fading. Moreover IEEE 802.15.4a supports sub-meter ranging to enable fully wireless device positioning. Although the standard was published in 2007 there are no compliant implementations so far. The IEEE 802.15 Task Group 6 (2007)—a.k.a. Body Area Networks (BAN) Task Group—is de-

Table 1. Comparison of wireless communication standards

ISM band

Air interface #Channels/schemes Data rate (aggregated) Range Network topology Network size Network join time Real-time support Protocol complexity Stack size Security Typical power consumption

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IEEE 802.11b WiFi

IEEE 802.15.1 Bluetooth

2.4 GHz

2.4 GHz

Directsequence spread spectrum (DSSS) 11 (US) 13 (EU)

Frequencyhopping spread spectrum (FHSS) 10

11 Mbps (50 Mbps)

1 Mbps ( to corresponds to an assignment of processing tasks to devices fulfilling the type constraint and reachability constraint (e.g. the

thick path in Figure 10 which corresponds to the assignment shown in Figure 9). Each arc of this layered assignment graph is then labeled with a weight representing the sum of processing delays and transmission delays such that: (1) the arcs connecting node <S> to node till have weight 0; (2) In layer i (except for the last layer), the arc connecting node to node has a weight equal to the sum of processing delay (the delay for “pi” to process one frame at device resource “dj”, i.e de pipr,dj) and the transmission delay (the delay caused the transmission over the channel connecting di and dk);. (3) For the last layer, each arc connecting node to node the weight equals the processing delay (for the delay for “pm” to process one frame pr at device “dj”, hence de pm , dj). As an illustration, the weights associated with the thick edges are shown in Figure 10. In the last step, by applying a shortest-path search algorithm (e.g. Dijkstra’s algorithm), we

Figure 10. The 6-task-4-device assignment graph. The thick path corresponds to the assignment illustrated in Figure 9

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Context-Aware Task Redistribution for Enhanced M-Health Application Performance

can identify a path connecting <S> and in this weighted graph that corresponds to the optimal assignment Ψopt. The space complexity (defined as the number of nodes in the assignment graph) of this method is O(mn). Thus the time complexity of shortest-path search step is O(m2n2) if the Dijkstra algorithm is used. If a labeling method as proposed by Bokhari (Bokhari, 1988) is used for search, the time complexity can be reduced to O(mn).

Windows XP machine with Intel Pentium 2.4G CPU and 1.5G RAM. 10 different pairs of m and n, e.g. (20,10), (40,10), … (100,20), were tested and the number of assignment graph nodes ranges from 200 to 2000.

5. Tas k Redi str ib ut ion Infr astr uct ur e

As presented earlier (Mei et al., 2007), we design the bio-signal processing tasks as software components, named BSPUs (Bio-Signal Processing We implemented a Java prototype of the proUnits), whose execution location may vary durposed assignment. We recorded the CPU time of ing the life-time of the m-health application. A 4.3.3 Performance analysis two steps in this method: the assignment graph possible architecture of our proposed MADE construction (includes weighting) and shortestmiddleware consists of a Coordinator, a BSPU We implemented a Java prototype of the proposed assignment. We recorded the CPU path search. For the “search” step, we used an Repository and a set of Facilitators (Figure 12). time of two steps in this method: the assignment graph construction (includes weighting) open source library7 which implemented Di(which hosts the assignment and shortest-path search. For the “search” step,The weCoordinator used an open source library6 whichaljkstra algorithm. The program was tested on a gorithm), can make decisions reconfigure implemented Dijkstra algorithm. The program was tested on a WindowstoXP machinethe

4.3.3 Performance Analysis

with Intel Pentium 2.4G CPU and 1.5G RAM. 10 different pairs of m and n, e.g. (20,10), (40,10), … (100,20), were tested and the number of assignment graph nodes ranges from 200 to 2000.

Figure 11. The CPU time of the proposed optimal assignment algorithm for task-chain to device-chain assignment 00 construction search total

00

time (milliseconds)

00

00

00

00

0 00

00

00

00 000 00 00 00 number of assignment graph nodes

00

000

Figure 11: The CPU time of the proposed optimal assignment algorithm for task-chain to device302

chain assignment


Figure 12. Architectural components of BSPU redistribution infrastructure

m-health system, e.g. redistribute some BSPUs across the Facilitators. The reconfiguration can be either stateful (transferring state information) or stateless (discarding state information), depending on application specific requirements. Each node has a Facilitator to manage the resident BSPUs. It can be instructed by the Coordinator to execute the BSPU configuration commands. The BSPU Repository stores BSPU implementations that can be provided on request. To realize the Facilitator, we have adopted the OSGi technology8, which is a service oriented, component-based software container that, as apposed to other container platforms such as J2EE, standardizes the lifecycle management of components and services. In addition, OSGi technology is lightweight and can run on for instance mobile devices such as Smart Phones and PDAs. For networked communication between OSGi containers we have used the so called ROSGi9 service. Each BSPU is implemented as a single OSGi bundle, which we name a BSPU bundle. This means that we can benefit from the life-cycle

management facilities in the OSGi framework: the bundled BSPU can be installed, started, stopped and uninstalled by OSGi bundle management commands. Both the Facilitator and the Coordinator are also implemented as OSGi bundles. As shown in Figure 13, the Coordinator bundle can connect to various remote Facilitators using the “R-OSGi” service. At the moment, we adopted a “break-before-make” strategy in the implementation: the current configuration is terminated first and then new BSPUs are distributed and formed into a new configuration. In addition, the reconfiguration is assumed stateless. To test the performance of the task redistribution infrastructure, we built an experimental system containing 4 computers (PC1, PC2, PC3 and PC4) within the same LAN. Each of PC1 and PC2 hosts one Facilitator, PC3 is the Coordinator node and PC4 is the BSPU repository. The measured application interruption time resulted by the “break-before-make” BSPU redistribution ranges from tens to hundreds of milliseconds depending on the code size of the BSPU and network conditions. Further experi-

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Figure 13. Coordinator node connecting Facilitator node through R-OSGi service interface

ments are planned on mobile systems with the aim to analyze the adaptation’s agility according to the targeted scenario.

6. DiSCUSSIion With the development of mobile and high capacity personal computing devices, miniature wearable sensors and ever improving wireless communication infrastructures, m-health is becoming a realistic prospect from the technical point of view. However, challenges remain for providing timely and adequate services in real world mhealth applications (Jones, Incardona, Tristram, Virtuoso, & Lymberis, 2006). We argue in this chapter that context awareness should be an essential feature in the design and implementation of m-health systems. Typical scenarios that we investigate address the remote monitoring and treatment of patients. In particular, we have addressed an epileptic seizure detection application based on m-health systems. Patient monitoring sessions are often long lasting, in extreme cases it may be 24/7. To cope with the dynamics of the ICT infrastructure under consideration, we proposed a task redistribution based middleware, MADE, and investigated several assignment algorithms to obtain the optimal system configuration. Our research on task assignment algorithm benefited from earlier research in the area of

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parallel and distributed computing (Norman & Thanisch, 1993). In particular, we have adopted graph-based techniques to model the assignment problem. We have identified three different categories of constraints (type, local and reachability constraints) that allow us to build realistic models of the assignment problem. Because of the generic nature of our model, it may be very well applicable to distributed stream processing in general, for instance (Amini, Jain, Sehgal, Silber, & Verscheure, 2006; Gu & Nahrstedt, 2006; Pietzuch et al., 2006; Xing, Hwang, Çetintemel, & Zdonik, 2006).

7. Conlu In this chapter, we propose a task redistribution based adaptation middleware (MADE). It consists of a task assignment decision mechanism and a task redistribution infrastructure. The task assignment decision mechanism models the task assignment as a graph mapping problem and searches for the optimal assignment given the latest context information. In particular, we identify three assignment constraints in the mobile healthcare system: the type constraint distinguishing between computation and communication, the local constraint capturing the computation heterogeneity and the reachability constraint capturing the communication heterogeneity. Although the problem in its general


form is NP-hard, some cases with specific topologies can be tackled efficiently in polynomial time. We illustrate a general approach to solving “DAG-tree” problems and a polynomial-time algorithm for “chain-chain” problems. Once a new assignment is found, the member tasks are distributed accordingly by the redistribution infrastructure. A prototype implementation based on the OSGi framework, which has been used to validate the task redistribution infrastructure, is also reported. The future research agenda for improving the MADE middleware may consist of: (1) applying various techniques, e.g. A* and branch and bound, to design more efficient task assignment algorithms, (2) proposing performance metrics (including reconfiguration time, power consumed by reconfiguration) on the redistribution infrastructure to evaluate the reconfiguration, and (3) experimenting with the MADE middleware in a real world mobile healthcare system to evaluate the feasibility of task redistribution based adaptation.

ACKknowledgmen This work is part of the Freeband AWARENESS Project. Freeband is sponsored by the Dutch government under contract BSIK 03025. (http:// awareness.freeband.nl)

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http://www.mobihealth.org/ http://www.healthservice24.com/ http://awareness.freeband.nl/ The value can be zero, i.e. meaning it is not possible to run a task on certain resource due to a violation of a hard QoS requirement,

5

8 6 7

e.g. device CPU type or channel’s bandwidth cannot support the to-be-assigned task. If a device has a fixed line power supply, then its eto has a value of +∞. http://rollerjm.free.fr/pro/graphs.html http://www.osgi.org http://r-osgi.sourceforge.net/

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Wood, A., Virone, G., Doan, T., Cao, Q., Selavo, L., Wu, Y., Fang, L., He, Z., Lin, S., & Stankovic, J. (2006). ALARM-NET: Wireless Sensor Networks for AssistedLiving and Residential Monitoring. Technical Report CS-2006-1. Charlottesville, VA: University of Virginia, Department of Computer Science. Woodward, B., Istepanian, R. S. H., & Richards, C. I. (2001). Design of a Telemedicine System Using a Mobile Telephone, IEEE, (pp. 13-15). Wootton, R., Craig, J., & Patterson, V. (Ed.). (2006). Introduction to Telemedicine. 2nd edition. London: Royal Society of Medicine Press Ltd. Wootton, R., Craig, J., & Patterson, V. (Eds.). (2006). Introduction to Telemedicine (2nd ed.): Rittenhouse Book Distributors. World Health Organization WHO (1993). Environmental Health Criteria 137, Electromagnetic Fields (300 Hz to 300 GHz), Geneva. World Wide Web (July 2002). Retrieved March 25, 2008, from: http://www.conformity.com/0207specific.pdf). Wpa . Retrieved February 8, 2008, from http://www. wi-fi.org/knowledge_center/wpa/. Wullems, C., Tham, K., Smith, J., & Looi, M. (2004). A trivial denial of service attack on ieee 802.11 direct

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About the Contributors

Dr Phillip Olla is the endowed Phillips Chair of Management and Professor of MIS at the school of business at Madonna University in Michigan USA, and he is also a Visiting Research Fellow at Brunel University, London, UK. His research interests include Knowledge Management, Mobile telecommunication, and health informatics. In addition to University level teaching, Dr. Phillip Olla is also a Chartered Engineer and has over 10 years experience as an independent Consultant and has worked in the telecommunications, space, financial and healthcare sectors. He was contracted to perform a variety of roles including Chief Technical Architect, Program Manager, and Director. Dr Olla is the Associate Editor for the Journal of Information Technology Research and the Software / Book Review Editor for the International Journal of Healthcare Information Systems and Informatics, and is also a member of the Editorial Advisory & Review Board for the Journal of Knowledge Management Practice. Dr. Phillip Olla has a PhD in Mobile Telecommunications from Brunel University in the UK, he is an accredited Press member of the British Association of Journalism, Chartered IT Professional with the British Computing Society and a member of the IEEE society. Joseph Tan, Dip., BA, MS, PhD, holds a professional diploma in civil engineering from Singapore Polytechnic, an undergraduate degree in Mathematics and Computer Science from Wartburg College, IA, a Masters Degree in Industrial & Management Engineering from the University of Iowa, and a PhD. in Management Information Systems from the University of British Columbia (UBC). He has been a tenured associate professor, teaching in the Department of HealthCare & Epidemiology at UBC for many years prior to serving as Professor and Head of Information System and Manufacturing (ISM) department, School of Business, Wayne State University.Joseph publishes widely in numerous computing, ergonomics, information systems, health informatics, health education, e-health and e-business journals and has served as guest editor and member of various journal editorial boards. He sits on key organizing committees for local, national, and international meetings and conferences. Professor Tan’s research, which has enjoyed significant support in the last several years from local, national and international funding agencies and other sources, has also been widely cited and applied across a number of major disciplines, including healthcare informatics and clinical decision support, health technology management research, human processing of graphical representations, ergonomics, health administration education, telehealth, mobile health, and e-health promotion programming. *** Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.


Luís Antunes obtained a PhD in Computer Science at University of Porto. Currently he is an Auxiliary Professor at the Computer Science Department at University of Porto. Most of his research is on Computational Complexity and Cryptography. He is in the Coordination Committee of the first Health Informatics Master course in Portugal and has a strong collaboration with the Medical School of Porto University. He supervises several Master and PhD students in areas such as Access Control and Information Measures for Cryptography protocols. Heribert Baldus is principal scientist at Philips Research Europe in Eindhoven (The Netherlands). He studied electrical engineering and did his PhD at RWTH Aachen University of Technology. He is with Philips Research since 1989, in Hamburg, Aachen, and Eindhoven. Since then he has been leading various internal and international research projects on different areas of communication technology, especially public networks and network management, home-networking and wireless medical systems. He has various publications and more than 20 patents and patent applications. Furthermore, he is on the Technical Program Committee of several conferences on communication technology, services, and security. Luís Barreto received the BA degree in Electrotechnical and Telecommunications Engineering from the Oporto University, Portugal, and the Master degree in networking systems and security by the same University. He is a PhD. student in Electronical Engineering at the Aveiro University, Portugal. He is currently a Professor, Vice-President of the direction board of Business Science Superior School, Polytechnic University of Viana do Castelo (Escola Superior de Ciências Empresariais- Instituto Politécnico de Viana do Castelo) and Coordinator of the Business Computing Course. He is, also, the manager of different R&D projects, namely: NetStart (http://www.netstart.pt), CSI- Cooperation Servicing Innovation and SeeLe (Seeking Learning Evaluation- http://seele.ipvc.pt). The following subjects are Luís Barreto main interests: Networking Protocols, Wireless Security, Ad-hoc and Wireless Networks, E-learning, Web 2.0, Informal and Formal Learning. Pedro Brandão is an assistant lecturer at the Faculdade de Ciências da Universidade do Porto and a member of the Instituto de Telecomunicações – Porto group NIP. Currently, he is at the University of Cambridge on the Opera Group pursuing a PhD on body sensor networks . He is partially funded by Fundação para a Ciência e a Tecnologia from Portugal. In 2002 he received an ECE MsC from the Engineering School of University of Porto, where he also got his diploma. In his past he has worked on network security and ad hoc networks research, subjects that still drive some of his interests. As part of this work he was involved in the identity management system of the European FP6 IP Daidalos. His main research areas are sensor/actuators networks and protocols; middleware for sensor networks and network security. Beenish Chaudry is a doctorate student in the Computer Science Department at Indiana University. She is working on developing a Dietary Input Monitoring Application on PDA for low literacy dialysis population under the guidance of Prof. Kay Connelly. Kay H. Connelly is an Assistant Professor in the Computer Science Department at Indiana University where she leads the Security for Ubiquitous Resources Group (SURG). She is the Associate Director of IU’s Center for Applied Cybersecurity Research and a member of the Pervasive Technology Labs.

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Dr. Connelly’s research focuses on user acceptance of ubiquitous and mobile computing technologies where there is a delicate balance between such factors as convenience, control and privacy. Ricardo João Cruz Correia is an assistant lecturer and researcher at the Department of Biostatistics and Medical Informatics at the Faculty of Medicine of the University of Porto. His research interest is the electronic patient records and on the integration of heterogeneous healthcare information systems. He received his MSc in computer science from the University of Porto, and is currently a PhD student at the University of Porto. Rodrigo de Oliveira received a BSc degree in Computer Science from Federal University of Lavras, Brazil in 2002, and both MSc and DSc degrees in Computer Science from State University of Campinas, Brazil in 2004 and 2008 respectively. In 2007, he was awarded a Microsoft Research Fellowship and worked as a Research Intern in Microsoft Research, Redmond, USA. His research interests include Human-Computer Interaction, Mobile Computing, Machine Learning, and Health Monitoring Systems. He can be contacted at [email protected]. Changyu Dong received his BSc from Fudan University, China, in 1999 and his MSc from Imperial College London, UK, in 2005. Since then, he has worked as a research assistant in Imperial College London, on the project CareGrid. His research interests include trust and privacy management, applied cryptography, access control and policy-based management. Naranker Dulay is a senior lecturer in the Department of Computing at Imperial College London were he obtained his PhD. His research interests include architectures and runtime systems for security, privacy, trust, and context-awareness in distributed, mobile and pervasive systems. Dr. Dulay has over 60 publications in international conferences and journals and has served on numerous program committees. He currently leads research projects in autonomous trust domains for healthcare applications, validation of ubiquitous systems, urban design and pervasive systems, and pervasive workflows. Javier Espina is a research scientist at Philips Research Europe. He studied electrical engineering, specializing in telecommunications, at UPC in Barcelona (Spain) and graduated in 2002. Since then he has been with Philips Research Europe, first in Aachen (Germany) and currently in Eindhoven (The Netherlands), where he has worked on wireless connectivity middleware in the fields of mobile ad-hoc networks, wireless sensor networks and body area networks. His current research interests include body-centric wireless communications as well as time synchronization, interference management and medical applications of wireless sensor networks. Javier holds 9 patents and patent applications and has published several papers, and two book chapters. Thomas Falck studied computer science and graduated summa cum laude from RWTH Aachen University of Technology. Since 1992 he is with Philips Research Europe working on connectivity solutions for business communication, consumer electronics and medical systems. His current research interests include wireless body sensor networks, mobile ad hoc networks, and pervasive healthcare. Thomas is member of the Technical Program Committee of several conferences on communication technology for medical applications and acts as reviewer and guest editor for leading journals. He organized the 4th International Workshop on Wearable and Implantable Body Sensor Networks in 2007 in cooperation with RWTH Aachen. Thomas is author of various papers, book chapters, and patents. 334


Jesús Favela is a professor of computer science at Cicese, where he leads the Mobile and Ubiquitous Healthcare Laboratory and heads the Department of Computer Science. His research interests include ubiquitous computing, medical informatics, and computer-supported cooperative work. He received his PhD from the Massachusetts Institute of Technology. He’s a member of the ACM and the American Medical Informatics Association. Contact him at the Computer Science Dept., Centro de Investigación Científica y de Educación Superior de Ensenada (Cicese), Km. 107 Carretera Tijuana-Ensenada, Ensenada, B.C., 22860, Mexico; [email protected]. Daniel Ruiz Fernández received his Bachelors degree in Computer Science from the University of Alicante (Spain) in 1998 and his Ph degree in Computer Science from the same university in 2003. Between 1999 and 2004 he worked as a lecturer at the University of Alicante. Since 2004, he has been an Associate Professor in the Department of Computer Technology and Information at the University of Alicante. Additionally, he is a founding member of the IBIS research group of the University of Alicante, and a member of IEEE and Computer Society. He has been a leader of several national projects related to medical informatics. His research interests are focused in medical informatics, computer architectures, distributed computing and computer networks. Ana Ferreira is an IT specialist at Porto Faculty of Medicine; she is a CISSP and is pursuing a PhD in Computer Science with a joint supervision between the University of Porto and the University of Kent, aiming at improving access control to healthcare information systems. Other main interests include information security for healthcare, wireless networks, usability, users’ awareness and education. Fernando Flores-Mangas received a double BSE degree in computer Engineering and Telematics Engineering from Instituto Tecnolgico Autnomo de Mexico, Mexico City in 2002 and a MSc degree in Computer Sciences from Universidad Nacional Autnoma de Mexico, in 2005. During that year, he also worked as a Research Intern with Microsoft, Redmond, USA. He is currently pursuing a PhD in Computer Science at University of Toronto, Canada. His research interests include various Computer Vision topics such as segmentation and registration, shape priors and structure from motion, but he is also interested in Machine Learning. His website can be found at http://www.cs.toronto.edu/˜mangas. Oscar Garcia-Morchon is a research scientist at Philips Research Europe. He studied electrical engineering, specializing in telecommunications, at Higher Engineering School in Zaragoza (Spain) and graduated in 2005. Since then he has been with Philips Research in Aachen (Germany) while working towards his PhD. in collaboration with the Distributed Systems Group at RWTH Aachen University of Technology. His research interests focus on networking and systems security, applied cryptography, security for mobile computing, and sensor networks. BjÄorn Gottfried works at the Centre for Computing Technologies in the Department of Mathematics and Computer Science at the University of Bremen, Germany, where he received his doctoral degree in the context of spatial reasoning. He is research scientist and lecturer in the context of artificial intelligence, spatial and diagrammatic reasoning, and image processing. Over the last ten years he published about fifty papers, mainly about spatial and temporal reasoning,image processing, and ambient intelligence. He is member in the programme committees of several workshops about ambient intelligence, smart homes and related fields and organises the annual BMI workshop on behaviour monitoring and interpretation. 335


Hermie J. Hermens did his master in biomedical engineering at the University of Twente, the Netherlands. His PhD was on surface EMG simulation, processing and clinical applications. Currently, he is Professor in neuromuscular control at the University of Twente and cluster manager in the area of non-invasive assessment at Roessingh Research and Development, a research institute in the area of rehabilitation. He became recently head of a research group at the University, focused on remote monitoring and remotely supervised training. He is (co)-author of over 100 peer reviewed scientific journal publications (pubmed), 8 scientific proceedings/text books and over 250 congress contributions. He coordinated three European projects and participated in over 15 other international projects. He is fellow and past president of the International Society of Electrophysiology and Kinesiology (ISEK), editor in chief of the JBMR. His present research is focused especially on combining Biomedical Engineering with Information and Communication Technology to enable remote monitoring and remotely supervised treatment. Markku Kallio, PhD., MBA, is physician at Medical Society Duodecim. He does clinical work as well as helps develop tools to support physicians’ work. Karin Klabunde studied Computer Science and Linguistics at University Koblenz, Germany and University of Edinburgh, Scotland. She received her diploma in Computer Science in 1989. Since 1990 she is with Philips Research Europe, first in Aachen and now in Eindhoven (The Netherlands) working in the field of middleware and protocols for distributed systems. Her current research interests include wireless ad-hoc communication and body area networks, especially focusing on applications in the medical domain like monitoring of people’s vital signs in hospitals or at home. Val Jones received her PhD in Computational Sociolinguistics from the University of Newcastle upon Tyne in 1979. Since then she has conducted research at the Universities of Newcastle, Stirling, Aberdeen and Twente, in the areas of Computational Linguistics, Health Services Research and Computer Science. In 1992 she was appointed project leader of a health telematics evaluation project in Scotland and conducted a randomized controlled trial involving 15000 patients. Her experience in computer science includes research in the areas of Functional Programming, Software Engineering, Formal Methods, Knowledge Based Systems and mobile applications enabled by wireless technologies. In 2001 she initiated a new research theme at Twente relating to the use of Body Area Networks (BANs) for remote health monitoring and treatment. She was jointly responsible for the scientific coordination of the MobiHealth project. Current research interests include: application of modelling approaches to the design and development of e-health and m-health applications, the ICT research challenges raised by these applications and future m-health possibilities enabled by emerging technologies such as Ambient Intelligence and nanotechnologies. Desiree Lambert is a Registered Dietitian who has worked with dialysis patients at the Indiana University Medical Center in Indianapolis. She is currently President of the Indiana Council on Renal Nutrition, which organizes quarterly educational meetings for Renal Dietitians. She is also an active member of the National Kidney Foundation of Indiana. Katja Leiviskä is a doctoral student at the University of Oulu, Finland. Her research focuses on healthcare information systems.

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Hailiang Mei received his BSc degree in 2001 from Beijing University of Technology, China and his MSc degree from the Delft University of Technology, the Netherlands in July, 2003. Both are in Electrical Engineering. Since July 2005, he works towards his PhD degree at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, the Netherlands. His PhD topic is on Smart Distribution of Bio-Signal Process in Mobile Healthcare. His current research interests include task assignment, dynamic reconfiguration and component based software engineering. Filipe Meneses is an invited lecturer of the Department of Information Systems, University of Minho. He received the “Licenciatura” degree in Computer Management, the Master degree in Information Systems, and the PhD. degree respectively in 1998, 2001 and 2008, from the University of Minho – Portugal.His research interests are in location and context management and in mobile and context-aware computing. His research activities have been developed within the ubicom@uminho research group at the University of Minho. He is a member of IEEE, the Computer Society and the Communications Society. Adriano Moreira is an Associate Professor in the Department of Information Systems, University of Minho, since 1996. He received the “Licenciatura” degree in Electronics and Telecommunications Engineering and the PhD. degree in Electrical Engineering, respectively in 1989 and 1997, from the University of Aveiro - Portugal. He has been a voting member of the IEEE 802.11 working group where he participated in the specification of the infrared physical layer. His research interests are in wireless local area networks and mobile and context-aware computing. His research activities have been developed within the ubicom@uminho research group at the University of Minho, which has been focusing its research in the creation of technologies for smart places. In the past few year he participated in many research projects funded by national and European programs, such as Supporting Location Based Internet Services (AROUND, FCT), Easy and friendly access to geographic information for mobile users (HYPERGEO, IST), Value Added Environments for Dynamic Support to Location-Based Services in UMTS Networks (VADE, FCT), Environmental Policy via Sustainability Indicators On a European-wide NUTS-III Level (EPSILON, IST), Location contexts for location-aware applications (LOCAL, FCT), and USability-drivEn open platform for MobilE GOVernment (USE-ME.GOV, IST). He is a member of the IEEE and the Communications Society. Harri Oinas-Kukkonen, PhD., is Professor of information systems at the University of Oulu, Finland. His current research interests include the next generation of the Web, electronic business, social and organizational knowledge management, network awareness and human attitude and behaviour change. His research has been published in journals such as ACM Computing Surveys, Communications of the ACM, The DATA BASE for Advances in Information Systems, European Journal of Information Systems, Information and Software Technology, Information Technology and Management, International Journal of Human-Computer Studies, International Journal of Networking and Virtual Organizations, Journal of Digital Information, Netnomics: Economic Research and Electronic Networking and Software Process Improvement and Practice. In 2005, he was awarded The Outstanding Young Person of Finland award by the Junior Chamber of Commerce. Nuria Oliver received the BSc (honors) and MSc degrees in Electrical Engineering and Computer Science from the ETSIT at the Universidad Politecnica of Madrid (UPM), Spain, in 1992 and 1994

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respectively. She received her PhD degree from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in June 2000. From July 2000 until October 2007, she was a researcher at Microsoft Research in Redmond, WA. In November 2007, she returned to Spain to create a research group in the scientific team of Telefonica R&D in Barcelona. Her research interests –where she has extensively published-- include smart environments, context awareness, statistical machine learning, artificial intelligence, health monitoring and human computer interaction. She has received a number of awards, including MIT’s ‘TR100 Young Innovators Award’ (2004) and the First Spanish Award of EECS graduates (1994). Besides her scientific publications, she is very interested in making science available to the general public. Her work has been featured on newspapers, magazines, radio and TV stations both in Spain and the US. Her website can be found at http://www.nuriaoliver.com. Antonio Soriano Payá received his Bachelors degree in Computer Science from the University Polytechnic of Valencia (Spain) in 1993 and his Ph degree in Computer Science from the University of Alicante (Spain) in 2001. Since 2002 he is an Assistant Professor in the Department of Computer Technology and Information at the University of Alicante. He coordinates the division of medical bioengineering in the Department and he is also a founding member of the IBIS research group of the University of Alicante. His research interests are focused in cybernetics, computer architectures and computer networks. Teppo Räisänen is a doctoral student at the University of Oulu, Finland. His research interests include Web 2.0 functionalities, social and organizational knowledge management and persuasive technology. Currently he is a Visiting Researcher at Stanford University. Giovanni Russello is a research associate in the Department of Computing at Imperial College working on the CareGrid project. He obtained is PhD form the Department of Mathematics and Computer Science at the Eindhoven University of Technology (TUE). Part of his PhD was conducted at the Software and Architecture (SwA) Group of Philips Research Laboratories in Eindhoven. Giovanni’s research interests focus on trust management, access control mechanisms and policy-based management. Daniela Segura is a graduate student in computer science at Cicese. Her research interests include ambient information systems, ambient displays, mobile and ubiquitous computing, human-computer interaction and software engineering.Contact her the Computer Science Dept., Centro de Investigación Científica y de Educación Superior de Ensenada (Cicese), Km. 107 Carretera Tijuana- Ensenada, Ensenada, B.C., 22860, Mexico; [email protected] Susana Sargento graduated in Electronics and Telecommunications Engineering from the University of Aveiro, in 1997 and concluded her PhD in 2003. In September of 2002 she joined the Department of Computer Science in the Sciences Faculty of the University of Porto, where she lead the Computers Networks research group, and in February 2004 she returned to the University of Aveiro. Her main research interests are in the areas of next generation heterogeneous networks (infrastructure and adhoc, including broadcast), more specifically in QoS, mobility, routing and charging issues, with more than 100 papers published in the area. She is currently involved in several European IST projects on

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next generation architectures (Daidalos, EuroNF, WIP, C-Cast). She has leadership responsibilities of self-organizing activities and QoS 4G architectures inside the Daidalos project. Matti Seppänen, MD, is physician at Medical Society Duodecim. Katie A. Siek is an Assistant Professor in Computer Science at the University of Colorado at Boulder. She is interested in integrating pervasive technologies in health and wellness environments to study how technology affects interventions. Her research interests include human computer interaction, ubiquitous computing, health informatics, and performance support systems. Monica Tentori is a doctoral student in computer science at Cicese and a lecturer in computer science at the University of Baja California. Her research interests include ubiquitous computing, human-computer interaction, and medical informatics. She received her MSc in computer science from Cicese. She’s a student member of ACM-Sigchi. Contact her at the Computer Science Dept., Centro de Investigación Científica y de Educación Superior de Ensenada (Cicese), Km. 107 Carretera TijuanaEnsenada, Ensenada, B.C., 22860, Mexico; [email protected]. Bert-Jan van Beijnum received his MSc and PhD in Electrical Engineering from the University of Twente. He is an assitent professor of the Remote Monitoring and Treatment research group at the University of Twente, the Netherlands. His research is embedded in the projects of the Centre for Telematics and Information Technology (CTIT) and the Institute of Biomedical Technology (BMTI). His research interests include Autonomic Computing, Mobile Virtual Communities, Telemedicine information systems, ICT Management, Task Assignment Systems and algorithms, Application layer mobility handover mechanisms and QoS. Janet L. Welch is an Associate Professor and Chair of Adult Health at the Indiana University School of Nursing. Her program of research focuses on self-management of diet and fluid intake among adults receiving hemodialysis. Ing Widya has a PhD in Stochastic Signal Processing and is an assistant professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the University of Twente in the Netherlands. He is a member of the “Remote Monitoring & Treatment” group of the chair Biomedical Signal and Systems. His research is embedded in the projects of the Centre for Telematics and Information Technology (CTIT) and the Institute of Biomedical Technology (BMTI) of the university. His current research is on internet protocols & QoS control for telemedicine system adaptivity. Jacqueline Wijsman has a BSc in Biomedical Engineering and currently is a Master student in Biomedical Engineering at the University of Twente in the Netherlands. She wrote her bachelor thesis in the field of investigation and modeling of different ECG standards, equivalencies and differences among the standards and ways to deal with storage and conversion of ECG records. Jasemian, Yousef, Ass. Prof., project mgr. for knowledge Ctr., Leader of pervasive healthcare lab., Aarhus Engring Coll., Denmark, 2007- ; b. Abadan, Iran, Dec. 20, 1954; Diploma in El. & Telecomm. Engring., Engring. Coll., Iran, 1975; BSc. in Biomed. Engring., Aarhus Engring Coll., Denmark, 1991;

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MS in Biomed. Engring., Denmark Tech. U., 1998; Diploma in TCM, Acupuncture and Manual Therapy, Physical Therapy Sch. Fredericia, Denmark, 2002; PhD in Health Sci. and Tech. Aalborg U., Denmark, 2005; MSc. in Health Sci., Aarhus U., Denmark, 2008; Leader Iranian Oil Refinery and Nat. Iranian Petroleum, Abadan, 1975-83; Iranian Offshore Oil Co., Dep. Telecomm., Lavan Island, Parsian Gulf, 1983-85; lectr. project devel., communication, mktg. and promotion Fedn. Free Info., Aarhus, Denmark, 1992-94; Rsch. Asst. Royal Dental Coll., 1992-94; Rsch. Asst. prof. Dep. Info. Tech., Denmark Tech. U., Lyngby, 1998-99; Software designer Ericsson Mobile Co., Aalborg, 1999-2002; project mgr., Ericsson Mobile Co., Aalborg, 2002-05; acupuncturist and manual therapist, pvt. Practice, 2002-; asst. rsch. Prof. Ctr. Sensory-Motor Interaction, Dep. Health Sci. & Tech., Aalborg U., 2005-07; Achievements include invention of Wireless Telemedicine System applying Bluetooth tech. & Cellular Communication Network: New Approach for Real-time Remote Patient Monitoring; Publishing scientific booklets; received rsch. & travel grants from Ericsson & two U.; Contbr. scientific, articles, abstract to international journals; oral & poster presentations in health sci. & biomedical engineering; organized scientific workshops within pervasive healthcare; Expert reviewer Rsch. Grants diff. Com.; Expert reviewer for international journals; member of diff. scientific communities including IEEE, DSKT, IASP, ANMP, IBRO and SASP.

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Index

Symbols 7C model 111, 112, 115, 116, 118, 123 802.1X 30, 31, 32, 33, 43 α-secure Key Distribution Schemes (α-sKDSs) 170, 171

A abstract syntax 73, 74, 77, 78, 79, 80, 81, 86, 88, 89, 90 access control 29, 30, 31, 35, 41, 42, 43, 63, 103, 138, 192, 247, 248, 249, 251, 252, 255, 259 access control lists (ACLs) 139, 247 access points (APs) 31 access to medical literature 112, 113 active digital aura (ADA) 167 active digital aura (ADA) module 167 activities of daily care 148, 149, 150 activities of daily living (ADL) 143, 151 activity-aware server 153, 154 ad hoc network 139, 140, 166 adopted information 71 advanced encryption standard (AES) 33, 173, 192 Aiemagno, Sonia A. 4, 21 alcoholism 4 ambient displays 143, 144, 145, 147, 148, 151, 156, 157, 158 ambulatory cardiac monitoring 131 America’s Health Insurance Plans (AHIP) 59 American College of Radiology (ACR) 84

American National Standards Institute (ANSI) 59, 84 AMON prototype 210, 211 angle of arrival (AOA) 264 anonymity 134, 139, 140, 200 anorexia nervosa 129, 132 application availability 290, 295 apraxia 93 arp spoofing option 37 artificial entities 92, 100, 101, 102, 103, 104, 105, 106, 107, 108 artificial objects 272 assignment constraint 296 asynchronous connection less (ACL) 191 asynchronous transfer mode (ATM) 185 audio-visual data 71 audit log 63 authentication, authorization and accounting (AAA) protocols 32 authentication header (AH) 34 authorisation policy 247, 256 average heart-rate 219, 220 awareness 145, 158, 212, 269

B back filled entries 11 barcode 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 16, 17, 20, 169 barcode ed application 5, 8, 9, 12 barcode ed skills 8 base station controller (BSC) 193 battery-powered entities 130


Index

battery lifetime 290, 295 blood glucose 58, 67, 182 blood oximetry 211 blood pressure sensor (BPS) 254 Blue Cross and Blue Shield Association (BCBSA) 59 BMA policy model 246, 247 body-coupled communication (BCC) 159, 160, 166 body area network (BAN) 164, 176, 210, 287 body sensor network (BSN) 165, 167 bulimia 280, 281

consumer subscription model 50 context 20, 31, 72, 75, 79, 80, 82, 94, 122, 131, 145, 146, 153, 165, 168, 171, 174, 232, 245, 246, 248, 252, 254, 255, 259, 260, 261, 262, 263, 264, 265, 268, 269, 271, 283, 285, 286, 287, 288, 289, 291, 304 context-aware 157, 261 context management 261, 262 cooperative clinical decision support system (CCDSS) 98 cryptographic functions 33

C

D

cell-ID 264, 268 central repository (CRep) 26 chaining message authentication code protocol (CCMP) 33 chronically ill 1, 2, 4, 13, 19, 141, 160 chronic kidney disease (CKD) 2, 20 chronic obstructive pulmonary disease (COPD) 130, 182 chronic pain 129, 132 cipher protocol 33 circulatory collapse 272 CKD patients 2, 6, 7, 17, 19, 20 clinical decision support system (CCDSS) 95, 98, 108 clinical usage 173 clustering algorithm 266 Cochrane Library 117 code division multiple access (CDMA) 67 collective intelligence 111, 112, 115, 116, 120, 123 competition 208, 209, 210, 211, 212, 220, 221, 222, 223, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 243 Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) 20 computer-based applications 47 computer-based tools 211 conceptual structure 72, 78, 89 consumer purchase 49 consumer purchase model 50 consumer subscription 49

data security 129, 130, 132, 135, 185, 195, 200, 203, 289 decision support system (DSS) 95, 97, 98, 108 denial of service (DoS) 36, 37 departmental information systems (DIS) 26 Department of Health and Human Services (DHHS) 51 Deterministic Key Pre-Distribution Scheme (DPKPS) 172 diagnosis-related tasks 92 diagnosis algorithm 102 diagnostic module 102 digital form 78 Digital Music Library (DML) 212, 218 disarthria 93 disease monitoring 131 drug interaction database 117

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E e-activity 153 ECG model 77, 84 Egyptian era 92 electrocardiogram (ECG) 131, 160, 213 electromagnetic compatibility (EMC) 200 electronic health record (EHR) system 45, 247 electronic monitoring 9 electronic patient record 24 electronic pharmacopoeias 112, 113 emergency flowers 154, 155 employee support model 50 encapsulating security payload (ESP) 34 end-to-end delay 290

Index

end of study compliance 18 enhanced-observed time difference (EOTD) 264 epileptic seizure detection 287, 288, 289, 290, 304 equivalency 82 ettercap tool 36 evaluation model 58 event-condition-action (ECA) 250 event-condition-action (ECA) policies 250 event-driven model 160 evidence-based medical guidelines (EBMG) 117 evidence-based medical procedures 248 evidence-based medicine 112, 113, 114 extensible authentication protocol (EAP) 30

F familiarity 13, 260, 261, 262, 264, 265, 266, 268, 269 free model 50 frequency hop synchronization (FHS) 191 fundamental challenge 130, 203

G general practitioner 72, 122 geographic objects 272 getting lost behaviour 283 glanceable interface 208, 209, 210, 212, 220, 223, 231, 236, 239 global system for mobile communication (GSM) 185 group ad-hoc network (GN) 189 GSM cell 268 GSM networks 264, 267 GSM phone 265

H health-related quality 4 Health 2.0 46, 52 healthcare applications 43, 52, 72, 75, 76, 80, 83, 165, 170, 178, 179, 245, 246, 258 healthcare environment 25, 45, 135

healthcare information 24, 25, 47, 57, 68, 69, 111, 112, 113, 115, 117, 120, 123, 246, 247, 248, 249, 258 healthcare information system 111, 112, 113, 117, 120, 123 healthcare institution 25, 30, 41 healthcare professionals 24, 25, 26, 28, 35, 75, 76, 99, 108, 183, 187, 237, 246, 248 healthcare provider 46, 53, 287 healthcare system 56, 75, 76, 77, 83, 137, 140, 159, 160, 161, 182, 184, 192, 200, 285, 286, 304, 305 healthcare treatment 24 health informatics 5, 113, 124 Health Insurance Portability and Accountability Act (HIPAA) 170 health level seven (HL7) 46, 58 health monitoring 25, 58, 160, 166, 174, 203, 208, 209, 210, 211, 237, 241, 288 healthy lifestyle 2 heart rate 149, 214, 217, 218, 220, 226, 229, 241 Helzer, John E. 4, 20, 21 hospital-based 5, 59, 182 hospital patient record (SAM) 28 host controller interface (HCI) 190 hypertension 129, 132, 142 hyper text transfer protocol (HTTP) 104

I ICT environment 73 iHospital 145, 146 individual-social dichotomy 119 industrial scientific medical (ISM) 189 information model 104 information systems 24, 27, 42, 63, 67, 68, 69, 108, 111, 112, 113, 114, 115, 116, 120, 123, 177, 246, 258, 270, 271, 277, 282, 284 Information technology (IT) 45 infra-red cameras 246 Infrared Data Association standard (IrDA) 186 Institute of Electrical and Electronics Engineers (IEEE) 32, 200

343

Index

integrated services digital network (ISDN) 185 integrated voice response system (IVRS) 1, 2, 12 integration model 27 integrity 28, 29, 34, 41, 44, 62, 100, 129, 130, 132, 134, 170, 173, 186, 203, 252 International Organization for Standardization (ISO) 73 internet key exchange (IKE) 34 intervention 2, 3, 4, 5, 58 IP security 33 IP security (IPSec) 33 IVR-based data collection 5

K key distribution 173 key distribution center (KDC) 171 key management 175 knowledge management 114, 123, 284

L lightweight directory access protocol (LDAP) 32 link control and adaptation protocol (L2CAP) 190, 191 link manager (LM) 190 literacy skills 5, 7, 9, 10, 18 local syntax 77, 78, 89 location-based 261, 263 longitudinal data 122

M m-health 45, 65, 178, 180, 182, 185, 187, 188, 285, 287, 306 m-health system 180, 287, 288, 289, 290, 291, 292, 294, 296, 297, 303 MADE middleware 289, 291, 302, 305 man-in-the-middle (MITM) 36 Medical Implant Communication Service (MICS) 163 medical literature 112, 113, 114 medical records 48, 62, 64, 129, 130, 150, 246, 248, 249, 264, 269 medical system 142, 205, 268

344

medical team 92, 105 Medicine Working Group 114, 124 medium access control (MAC) 31, 176, 177 memorandum of understanding (MOU) 59 message integrity check (MIC) 31 methodology 2, 98, 148, 161, 233, 243 Metronome condition 225, 229, 230 Microsoft 3, 32, 43, 44, 46, 49, 52, 64, 66, 214, 243 mobile computing module 212 mobile healthcare information 111, 112, 113, 117, 120, 123 mobile healthcare information system 111, 112, 117, 120, 123 mobile personal health record (MPHR) 45 mobile PHR 60 mobile PHR system 63 mobility 146, 157, 158, 265, 266, 267 Mobility Index 265, 266, 267 multi-media 71 Mundt, James C. 4, 22 musical feedback 208, 209, 212, 213, 223, 225, 226, 231, 239 music finding module 213

N National Committee on Vital and Health Statistics (NCVHS) 47 National Health Information Network (NHIN) 59 National Health Service (NHS) 247 Network Access Point (NAP) 187, 188, 189, 191, 201 network attacks 25, 36 network connectivity 264 network formation 168 next action module 213 non-intrusive 209, 210 NP-hard 295, 299, 305 nutrition monitoring systems 20

O off-line processing 210 open systems Interconnections (OSI) 73 OSGi framework 286, 303, 305

Index

OSI presentation layer model 77 outpatient dialysis unit 5

P pace information 212 parking lot compliance 18 PDA technology 3 personal area network (PAN) 189, 210 personal digital assistant (PDA) 2, 20, 161, 183, 188 personal health information (PHI) 51 personal health record (PHR) 45, 60 personal health record system functional model (PHR-S FM) 58 personal identifier (PI) 166 personal records of healthiness 282 persuasive interfaces 238 persuasive technologies 211 pervasive 21, 22, 90, 141, 142, 143, 157, 158, 176, 178, 183, 202, 205, 242, 245, 261, 269, 283, 306 pervasive computing systems 245 pervasive healthcare application 258 pervasive technologies 143 Pharmaca Fennica 117, 118, 121, 126 pharmacology database 117 pharmacopoeias 113 photoplethesmography (PPG) 186 photoplethysmogram (PPG) 161 PHR functional model 67 PHR systems 45, 46, 48, 49, 52, 53, 55, 59, 60, 62, 64, 67, 68 physiological state 212 Piette 3, 4, 21, 22 Piette, John D. 3, 4, 21, 22 place lab system 262 plain old telephone service (POTS) 185 policy enforcement point (PEP) 35, 249 Ponder policy language 250 post-study interview 11 professional mobile radio (PMR) 196 public-key cryptography 172, 173 public switched packet data network (PSPDN) 193

Q quality of care 180, 181, 285 quality of service (QoS) 183, 187, 286, 305, 306 Questionnaire for User Interface Satisfaction (QUIS) 8

R radio frequency (RF) 195 Rand, C. S. 18 real-life environment 129, 132 real-time analysis 210 real-time physiological monitoring 208, 209 record managed objects (RMO) 251 remote access dial-in user service (RADIUS) 32 remote monitoring systems 182, 286 research prototype 208 role-based access control (RBAC) 29, 246 rule-based DSS 95

S scanner input 2 score function 221, 222, 240, 243 secure digital (SD) card 213 secure logical network 35 secure socket layer (SSL) 67, 104 secure socket layer (SSL) protocol 104 secure wireless architecture 25, 35 security domain (SD) 170 security protocols 25, 30, 31, 34, 35, 134, 174 sensing module 212 sensor managed objects (SeMO) 251 service discovery protocol (SDP) 191 short message service (SMS) 67, 198 SIM card 63 simple object access protocol (SOAP) 104 situation flowers 154, 155 smart body-worn medical sensors 160 smart environment 145, 153 spatial health system 271, 282 spatial representations 272, 273 spatial scale 274, 283 spatiotemporal behaviour patterns 273

345

Index

spatiotemporal developments 271, 274 spreading of diseases 272 stakeholder 75 Stephen Intille 3 storage capacity 56 Stuart, Gail W. 4, 22 subject-return authorisation (SRA) 252 subject managed objects (SMO) 250 symbolic data 266

T target-return authorisation (TRA) 252 task assignment 296, 297, 306 task redistribution 285, 286, 287, 289, 290, 291, 303, 304, 305 technical committee (TC) 84, 196 telemedicine 91, 110, 133, 135, 141, 142, 178, 181, 186, 187, 204, 205, 206, 238, 243, 286, 306 telemedicine solutions 129, 130 temporal key integrity protocol (TKIP) 31 testbed 36 time difference-of-arrival (TDOA) 264 time division multiple access (TDMA) 193, 194, 196, 200 time division multiplexing frame (TDM) 193, 200 tobacco use 2 training zones 218, 224, 235, 236, 237 transfer syntax 73, 75, 77, 78, 86, 87, 88, 89 transport layer security (TLS) 31 trend sign 74 TripleBeat’s Digital Music Library (DML) 218 TripleBeat system 208, 210, 214, 223, 231, 239 trusted computing (TC) 140, 141 typical running 220

U unified modeling language (UML) 74, 81 United States Department of Agriculture (USDA) 3 universal mobile telecommunications system (UMTS) 185, 201

346

universal serial bus (USB) 55, 187 user-centric systems 210 user identification 29, 42, 159, 160, 167, 168, 171, 173, 175, 179

V VEPR architecture 25 virtual competition 208, 209, 212, 220, 223, 231, 233, 234, 239 virtual electronic patient records (VEPR) 24 virtual LAN access control lists (VACL) 34 virtual LANs (VLANs) 32 virtual private networks (VPNs) 34 vital sign 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 87, 89, 129, 130, 132, 144, 149, 150, 160, 173, 182, 183, 187, 188, 203, 211, 242, 273, 281

W wearable physiological monitoring 210, 211 web-based interface 26 web-services 27 web interfaces 27 Weiler, Kay 4, 22 Wi-Fi protected access 31 wireless architecture 25, 29, 35, 41 wireless body area networks (WBANs) 163 wireless body sensor networks (BSNs) 159 wireless local area network (WLAN) 30 wireless medical telemetry systems (WMTS) 163 wireless personal networks (WPANs) 163 wireless technology 24, 25, 28, 29, 165, 183, 186, 189, 194, 200 wireless version 25 work community 111, 112, 120, 123, 126 workflow 94, 112, 114, 245, 247, 248, 249, 250, 254, 255, 256, 257, 258, 259, 292 workout goal 221, 224, 227, 229, 234 World Wide Web Consortium (W3C) 104

Y YAWL system 250

Index

Z ZigBee 163, 173, 179, 196, 197, 198, 201, 202, 203, 207 ZoneAccur 221, 222, 243

347

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