Improving Mental Health Care: Commitment to Quality

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Author: Barbara Dickey | Lloyd I. | M.D. Sederer

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IMPROVING MENTAL HEALTH CARE Commitment to Quality

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IMPROVING MENTAL HEALTH CARE Commitment to Quality

Edited by

Barbara Dickey, Ph.D. Lloyd I. Sederer, M.D.

Washington, DC London, England

Note: The authors have worked to ensure that all information in this book concerning drug dosages, schedules, and routes of administration is accurate as of the time of publication and consistent with standards set by the U.S. Food and Drug Administration and the general medical community. As medical research and practice advance, however, therapeutic standards may change. For this reason and because human and mechanical errors sometimes occur, we recommend that readers follow the advice of a physician who is directly involved in their care or the care of a member of their family. A product’s current package insert should be consulted for full prescribing and safety information. Books published by American Psychiatric Publishing, Inc., represent the views and opinions of the individual authors and do not necessarily represent the policies and opinions of APPI or the American Psychiatric Association. Copyright © 2001 American Psychiatric Publishing, Inc. ALL RIGHTS RESERVED Manufactured in the United States of America on acid-free paper 04 03 02 01 4 3 2 1 First Edition American Psychiatric Publishing, Inc. 1400 K Street, N.W. Washington, DC 20005 www.appi.org Library of Congress Cataloging-in-Publication Data Commitment to improving quality in mental health care : concepts and case reports / edited by Barbara Dickey, Lloyd I. Sederer. p. ; cm. Includes bibliographical references and index. ISBN 0-88048-963-4 (alk. paper) 1. Mental health services--United States--Standards. 2. Health care reform-United States. I. Dickey, Barbara. II. Sederer, Lloyd I. [DNLM: 1. Mental Health Services--standards. 2. Quality Assurance, Health Care. 3. Health Care Reform. 4. Quality of Health Care--standards. WM 30C7267 2001] RA790.6 .C575 2001 362.2’02’1873--dc21 2001027945 British Library Cataloguing in Publication Data A CIP record is available from the British Library.

To patients and others who have shown us the way.


Contents About the Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xix R. Heather Palmer, M.B.B.Ch. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xxi Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii Barbara Dickey, Ph.D. Lloyd I. Sederer, M.D.

I Challenges Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1 The President’s Advisory Commission on Consumer Protection and Quality in the Health Care Industry . . . . . . . . . . . . . 5 Steven S. Sharfstein, M.D.

2 Maintaining Professional Ethics in a Changing Health Care System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Steven M. Mirin, M.D.

3 Quality of Care in an Era of Wall Street Medicine. . . . . . . . . . . . . . 37 Lloyd I. Sederer, M.D.

4 Can Managed Care Deliver Quality? . . . . . . . . . . . . . . . . . . . . . . . 49 Mary Durham, Ph.D.

5 Role of Academic Medical Centers in Achieving Quality . . . . . . . 61 Linda S. Godleski, M.D. Allan Tasman, M.D.

II Quality Measurement Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

6 Measuring Quality: An Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Barbara Dickey, Ph.D.

7 Risk Adjustment of Clinical Outcome Data . . . . . . . . . . . . . . . . . . . 89 Sebastian Lorenz, M.D., M.P.A Lloyd I. Sederer, M.D.

8 Building Partnerships in Accountability: Consumer Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Jean Campbell, Ph.D. Kimberlee Einspahr

9 Toward a National Report Card: Measuring Consumer Experiences . . . . . . . . . . . . . . . . . . . . . . . . . 115 Susan V. Eisen, Ph.D. Brian Clarridge, Ph.D. Vickie Stringfellow James A. Shaul, M.H.A. Paul D. Cleary, Ph.D.

10 Practical Steps Toward Quality Improvement: A Public-Sector Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Astrid Beigel, Ph.D. Christine Torre

11 The American Psychiatric Association’s Agenda for Evidence-Based Quality . . . . . . . . . . . . . . . . . . . . . . . 151 Deborah A. Zarin, M.D. Joyce C. West, Ph.D., M.P.P. Claudia Hart

12 Status of National Efforts to Improve Accountability for Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Ronald W. Manderscheid, Ph.D. Marilyn J. Henderson, M.P.A. David Y. Brown

III Case Reports Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

13 Intensive Psychiatric Community Care in the Department of Veterans Affairs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Robert A. Rosenheck, M.D. Michael S. Neale, Ph.D.

14 A National Strategy to Reduce Depression and Suicide in England. . . . . . . . . . . . . . . . . . . . . . . . . 193 Rachel Jenkins, M.A., M.B., Bchir, M.D.(Cantab), F.R.C.Psych.

15 The Texas Medication Algorithm Project . . . . . . . . . . . . . . . . . . . . 201 William V. Rago, Ph.D., M.B.A. Steven P. Shon, M.D.

16 Improving the Quality of CommunityBased Services for Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Mary E. Evans, R.N., Ph.D., F.A.A.N.

17 Improved Emergency Access for Medicaid Clients: A Colorado Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Paula M. Dahl, M.B.A., C.P.H.Q. Sandra L. Forquer, Ph.D.

18 Improving Employment Outcomes for People With Severe Psychiatric Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . 235 Deborah R. Becker, M.Ed. Robert E. Drake, M.D., Ph.D.

19 Matching Substance Abuse Patients to Services . . . . . . . . . . . . . 241 Grant R. Grissom, Ph.D.

20 Adverse Drug Reactions: An Interdisciplinary Team Approach . . . . . . . . . . . . . . . . . . . . . . . 251 Carlos A. Zarate Jr., M.D. Arthur Siegel, M.D. Lloyd I. Sederer, M.D.

21 High-Risk Medicaid Enrollees and a Community-Based Support Program . . . . . . . . . . . . . . . . . . . . . . . 265 Deborah Nelson, Ph.D. Donna Zeh, L.I.C.S.W. Kathleen Bennett, M.D.

22 Increasing Consumer Involvement in Treatment . . . . . . . . . . . . . 275 Susan V. Eisen, Ph.D. Barbara Dickey, Ph.D. Lloyd I. Sederer, M.D.

23 Improving the Detection of Mental Disorders by Primary Care Clinicians. . . . . . . . . . . . . . . . . . . . . . . . 285 Richard C. Hermann, M.D., M.S. Paul Erickson, M.D. Robert Joseph, M.D., M.S. David H. Bor, M.D.

24 Reducing the Incidence of Restraint and Seclusion. . . . . . . . . . . 297 Paul Plasky, M.D., Ph.D. Catherine Coakley, M.S., R.N., C.

25 Using Dialectical Behavior Therapy in the Treatment of Borderline Personality Disorder . . . . . . . . . . . . . . . . . 307 Daniel Potenza, M.D. Patricia Carty, M.S.

26 Reducing the Risk of Falls for Elderly Patients . . . . . . . . . . . . . . . . . 315 Geraldine Nelson, M.S., R.N. Afterword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

About the Editors

B

arbara Dickey, Ph.D., is Associate Professor of Psychology in the Department of Psychiatry at Harvard Medical School and The Cambridge Hospital. She has been active in hospital quality improvement efforts and has a long academic career in health services research. She has published widely in the fields of outcome research, homelessness, and the effects of managed care on costs and outcomes for people with serious mental illness. She is leading a research study of the quality of care for adults with schizophrenia, comparing the treatment and outcomes of people in managed care with those of people whose care is not managed. With Dr. Sederer, she coedited Outcomes Assessment in Clinical Practice.

Lloyd I. Sederer, M.D., was Medical Director and Executive Vice President of McLean Hospital at the time this book was being prepared. In this role, he directed the hospital’s clinical services, including its special emphasis on clinical quality assessment and quality improvement. He is now Director of the Division of Clinical Services at the American Psychiatric Association in Washington, DC. Dr. Sederer is Associate Professor of Clinical Psychiatry at Harvard Medical School and has published 10 books and over 125 articles, reviews, chapters, and monographs. He has consulted nationally and internationally on clinical care and service delivery, quality improvement, risk management, and the regulation and reimbursement of medical care.

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Contributors

Deborah R. Becker, M.Ed. Research Assistant Professor of Community and Family Medicine, Dartmouth Medical School, Hanover, New Hampshire Astrid Beigel, Ph.D. Mental Health District Chief, Quality and Outcome Division, Los Angeles County Department of Mental Health, Los Angeles, California Kathleen Bennett, M.D. Senior Medical Director, Boston Medical Center/HealthNet Plan, Boston, Massachusetts David H. Bor, M.D. Associate Professor of Medicine, Harvard Medical School, Boston, Massachusetts; and Chief of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts David Y. Brown Consultant, Center for Mental Health Services, Substance Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services, Rockville, Maryland Jean Campbell, Ph.D. Director, Program in Consumer Studies and Training; and Research Assistant Professor, Missouri Institute of Mental Health, St. Louis, Missouri Patricia Carty, M.S. Director, Community Support Services, Mental Health Center of Greater Manchester, Manchester, New Hampshire Brian Clarridge, Ph.D. Senior Research Fellow, Center for Survey Research, University of Massachusetts, Boston, Massachusetts

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I MP ROVI N G M ENT AL H E ALTH C ARE

Paul D. Cleary, Ph.D. Professor of Medical Sociology, Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts Catherine Coakley, M.S., R.N., C. Nurse Director, Bipolar and Psychotic Disorders Program, McLean Hospital, Belmont, Massachusetts Paula M. Dahl, M.B.A, C.P.H.Q. Executive Director, National Networks Quality Assurance, ValueOptions, Virginia Beach, Virginia Robert E. Drake, M.D., Ph.D. Andrew Thomson Professor of Psychiatry and Community and Family Medicine, Dartmouth Medical School, Hanover, New Hampshire Mary Durham, Ph.D. Vice President/Research, Kaiser Foundation Hospitals; and Director, Center for Health Research, Northwest and Hawaii, Kaiser Permanente, Portland, Oregon Kimberlee Einspahr Multisite Interviewer Coordinator, Consumer Operated Services Project (COSP), and Senior Research Lab Technician, Missouri Institute of Mental Health, St. Louis, Missouri Susan V. Eisen, Ph.D. Director, Department of Mental Health Services Research, McLean Hospital, Belmont, Massachusetts; and Associate Professor of Psychology, Harvard Medical School, Boston, Massachusetts Paul Erickson, M.D. Assistant Professor of Psychiatry, Harvard Medical School, Boston, Massachusetts; and Acting Chief of Psychiatry, Cambridge Health Alliance, Cambridge, Massachusetts Mary E. Evans, R.N., Ph.D., F.A.A.N. Professor, Director of Research, and Director of Doctoral Program, College of Nursing, University of South Florida, Tampa, Florida Sandra L. Forquer, Ph.D. Vice President for Education Services, Comprehensive Neuroscience, Inc., White Plains, New York; and former Executive Director, Colorado Health Networks, Colorado Springs, Colorado

Contributors

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Linda S. Godleski, M.D. Associate Professor, Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine; and Chief of Mental Health and Behavioral Science Services, Mid-South Health Care Network, Department of Veterans Affairs, Louisville, Kentucky Grant R. Grissom, Ph.D. President, PsyberMetrics, Inc., Langhorne, Pennsylvania Claudia Hart Director and former Associate Director, Office of Quality Improvement and Psychiatric Services, American Psychiatric Association, Washington, DC Marilyn J. Henderson, M.P.A. Assistant Branch Chief, Survey and Analysis Branch, Center for Mental Health Services, Substance Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services, Rockville, Maryland Richard C. Hermann, M.D., M.S. Assistant Professor of Psychiatry, Harvard Medical School, Boston, Massachusetts; and Director of Quality Management, Department of Psychiatry, Cambridge Health Alliance, Cambridge, Massachusetts Rachel Jenkins, M.A., M.B., Bchir, M.D.(Cantab), F.R.C.Psych. Professor and Director, World Health Organization Collaborating Center for Research and Training for Mental Health, Institute of Psychiatry, De Crespigny Park, London, United Kingdom Robert Joseph, M.D., M.S. Assistant Professor of Psychiatry, Harvard Medical School, Boston, Massachusetts; and Director of Consultation-Liaison Psychiatry, Department of Psychiatry, Cambridge Health Alliance, Cambridge, Massachusetts Sebastian Lorenz, M.D., M.P.A. Head, Clinical Resource Management Unit, President’s Staff, Inselspital Bern University Hospital, Bern, Switzerland Ronald W. Manderscheid, Ph.D. Chief, Survey and Analysis Branch, Division of State and Community Development, Center for Mental Health Services, Substance Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services, Rockville, Maryland

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Steven M. Mirin, M.D. Medical Director, American Psychiatric Association, Washington, DC Michael S. Neale, Ph.D. Associate Director, Veterans Affairs Northeast Program Evaluation Center, West Haven, Connecticut Deborah Nelson, Ph.D. Vice President of Quality Management and Clinical Services, Beacon Health Strategies, Woburn, Massachusetts Geraldine Nelson, M.S., R.N. Program Director, Behavioral Medicine, Caritas Norwood Hospital, Norwood, Massachusetts; and former Nurse Director, Geriatric Program, McLean Hospital, Belmont, Massachusetts R. Heather Palmer, M.B.B.Ch. Professor of Health Policy and Management and Director of the Center for Quality of Care Research and Education, Harvard School of Public Health, Boston, Massachusetts Paul Plasky, M.D., Ph.D. Associate Psychiatrist, McLean Hospital, Belmont, Massachusetts Daniel Potenza, M.D. Vice President of Medical Services, Mental Health Center of Greater Manchester, Manchester, New Hampshire William V. Rago, Ph.D., M.B.A. Special Assistant to the Commissioner, Texas Department of Mental Health and Mental Retardation, Austin, Texas Robert A. Rosenheck, M.D. Director, Veterans Affairs Northeast Program Evaluation Center, West Haven, Connecticut; and Professor of Psychiatry and Public Health, Yale School of Medicine, New Haven, Connecticut Steven S. Sharfstein, M.D. President and Medical Director, Sheppard Pratt Health System; Clinical Professor of Psychiatry, University of Maryland, Baltimore, Maryland; and Member of the President’s Advisory Commission on Consumer Protection and Quality in the Health Care Industry, Washington, DC James A. Shaul, M.H.A. Project Director, Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts

Contributors

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Steven P. Shon, M.D. Medical Director, Texas Department of Mental Health and Mental Retardation, Austin, Texas Arthur Siegel, M.D. Chief of Internal Medicine and Member of the Pharmacy and Therapeutics Committee, McLean Hospital, Belmont, Massachusetts Vickie Stringfellow Assistant Study Director, Center for Survey Research, University of Massachusetts, Boston, Massachusetts Allan Tasman, M.D. Professor and Chairman, Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, Louisville, Kentucky Christine Torre Mental Health Analyst, Pequic Division, Los Angeles County Department of Mental Health, Los Angeles, California Joyce C. West, Ph.D., M.P.P. Director, American Psychiatric Practice Research Network, American Psychiatric Institute for Research and Education; and former Associate Director for Research, Office of Quality Improvement and Psychiatric Services, American Psychiatric Association, Washington, DC Carlos A. Zarate Jr., M.D. Chief, Mood Disorders Research Program, National Institute of Mental Health, Bethesda, Maryland Deborah A. Zarin, M.D. Director, Technology Assessment Program, Agency for Healthcare Research and Quality, Rockville, Maryland; former Deputy Medical Director, American Psychiatric Association; and Director, Office of Quality Improvement and Psychiatric Services, American Psychiatric Association, Washington, DC Donna Zeh, L.I.C.S.W. Director of Clinical Management, Beacon Health Strategies, Woburn, Massachusetts


Foreword R. Heather Palmer, M.B.B.Ch.

Why would anyone worry about the quality of mental health care in the twenty-first century? Isn’t mental health care better now than ever before? Certainly, significant advances in effective treatments for mental health conditions were made in the twentieth century. We now have the means to help many of those with serious mental health disorders lead productive lives in the mainstreams of society. As medical advances roll out of research laboratories, it is easy to assume that these advances represent improvements in the quality of care. However, translating research findings into clinical practice means learning whether these advances are diffusing rapidly into practice and are actually changing patients’ lives. To put it into the language of quality-of-care researchers, we want the processes of mental health care to change so that health outcomes improve for patients and their families. The existence of new therapies that can reduce suffering spurs our concerns about quality. One challenge currently facing us is ensuring that these benefits reach patients and families. Because some of the new, more effective medications, such as clozapine, are expensive and have dangerous side effects, we must ensure appropriate access to these new treatments in a manner that is acceptable to patients. This raises the problem of juggling the multiple perspectives that rightly demand attention within the health care market: those of health care professionals, state and federal governments, payors, patients, and families. Each of these groups defines and develops expectations about the quality of care delivered. Resolving diverse perspectives is challenging within the current health care environment, in which managed care and competition are shaking up the old mechanisms for delivering services. The dislocation of old practice patterns provokes arguments that quality of care is deteriorating, which is most evident when xix

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a patient is denied a particular treatment. The only way to move beyond arguments about the state of our health care system is to measure quality— to make it visible. Coming in the midst of the health care revolution, this book is a timely guide for those who are brave enough to tackle the complex systemic problems at the root of patient harm or insufficient treatment benefit. It details a range of projects, both small and large, that illustrate that quality measurement and improvement are actively being pursued in the mental health field. Measurement of quality in health care invariably reveals many areas needing improvement. This should not surprise anyone—delivering good health care is difficult. But those who seek to join the cause will find much in this book to guide them.

Acknowledgments

T

he editors gratefully acknowledge the editorial assistance of Lydia Ratcliff and the thoughtful proofreading and diligent manuscript preparation of Monique Wyatt.

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Introduction Barbara Dickey, Ph.D. Lloyd I. Sederer, M.D.

Quality of health care remains a poor second to cost containment in the eyes of the American public. In a survey conducted by Blendon et al. (1999), participants were asked to select the single most important health care issue today. Of the respondents, 26% chose quality of care, up from 14% 6 years ago when this survey was last reported. Cost, still the primary issue for Americans, was down to 34% from 42%. This report points to a shift in public attitude not yet reflected in negotiations between government agencies or corporate employers and health care organizations. In an environment in which price—not quality—remains the deal breaker, clinicians are challenged to improve quality of care. Our goal in this book is to help clinicians respond to this challenge. Any effort to improve quality of mental health care must start with a definition of quality. Many definitions have been put forward in recent years, and virtually all are multidimensional: appropriateness of treatment, technical and interpersonal competence, and patient access and acceptability (Lohr 1997; Roper 1999). Most definitions reflect a particular point of view; the dimensions of care just listed suggest the perspective of the physician– patient dyad that is the conventional view of quality of care. However, there is also a public health perspective that considers the overall health of a population, not just the health of those treated. This public health perspective has gained in importance as the high cost of medical care continues to rise. The emergence of managed care to control costs and the growth in the rate of uninsured people who cannot afford health benefits have led to public concern that health outcomes among the managed and the uninsured may be compromised. Studying the effects of access limitations requires a population-based approach to morbidity and mortality outcomes. For example, health plan xxiii

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members represent a population of individuals who are all covered by the same health benefit. The plan is responsible, within a fixed budget, for the care of all its members. We want to know if all members, not just those members who are treated, are being well served. From the same population perspective, it is important to consider the health of the uninsured, which bears on the well-being of the nation overall. Probably the most conceptually inclusive definition of quality is from Donabedian (1980), who gave us the theoretical foundation for measuring quality: the highest quality of care is that which efficiently maximizes benefits and minimizes risks to the patient. As the amount of treatment increases, there comes a point at which maximum benefit is attained and beyond which further treatment adds risks and costs without proportionate increments of benefit. The net benefit derived from a medical treatment, according to Donabedian, can be viewed as the benefit of the treatment minus its risks and costs. From his perspective, too many services reduce the net benefits of treatment just as too little treatment may fail to achieve potential benefits. It is remarkable that most of Donabedian’s work was published well before managed care became the most prominent aspect of the delivery of health care. Yet we see in his definition of quality the recognition that both benefits and costs must be taken into account. Cost effectiveness is the measure of quality of care for both individuals and populations. His theoretical link between benefits and costs has been lost in the rhetoric surrounding managed care. Instead of seeing managed care as being synonymous with costeffective treatment, plan members have been experiencing increased outof-pocket expenses and restricted access to treatment. From the members’ perspective, managed care has reduced benefits and increased costs, lowering the quality of their care. These two concepts—quality of care and managed care—bear further discussion; they need to be distinguished from one another to demonstrate that “fixing” managed care will not, in itself, improve quality of care. In recent decades, unrestrained resources to pay for care led to the common supposition that more care was better. It is not difficult to see how the corollary, that less care is worse care, became a natural response to cost containment. Managed care is an approach based on a theory about how to best deliver medical treatment. After World War II, a group of physicians who believed in preventive medicine, continuity of care, and responsible stewardship of resources organized the first health management organization, believing that this way of delivering medical care would improve the quality of care (see Chapter 4). Although managed care and high-quality care were closely linked in the minds of those who founded these organizations, today the public has linked managed care with low-quality care.

Introduction

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The definition of quality as maximizing desired health outcomes efficiently does not preclude using managed care to achieve this goal. However, if quality is conceptually independent from how care is paid for, why has managed care become synonymous with poor quality? Plan members, from their perspective, have both higher costs and less care—experiences that lead them to conclude that they are not receiving higher quality of care. Furthermore, some observers maintain that competition has lowered premiums (paid by employers or government agencies) so significantly that health plans can no longer provide adequate services to their members. We know that the fee-for-service system had high costs and, for the most part, good outcomes. Competition under managed care has resulted in lower premiums, but the evidence about outcomes remains equivocal. The pressure to reduce premiums may have put the delivery of high-quality health care in peril. The low premiums paid to managed care organizations in a pricecompetitive world may foster the underuse of medical services that would certainly fail to meet our standard of quality. Financial incentives for clinicians to limit care may also lead to inadequate services and poor outcomes. Cost containment, however, is not the only cause of poor-quality care. Poor-quality care also includes overuse or misuse of medical treatment (Chassin and Galvin 1998). It is difficult to argue that the financial incentives in managed care that limit access are responsible for the overuse of treatment. Misuse, mistakes, or inappropriate treatment may happen under any payment system. The influence of market forces on the health care delivery system has been uneven. Competition has primarily led to lower premiums rather than better value, as market theory would predict. We might expect this to happen when there are few means of determining the quality of services purchased. This may change as the explosive growth of managed care catalyzes efforts to measure quality. Several national groups (e.g., National Committee for Quality Assurance, Joint Commission on Accreditation of Healthcare Organizations, Foundation for Accountability) have developed performance indicators for health plan report cards. These performance indicators typically are population-based rates designed to inform purchasers (and consumers) about the quality of specific health plans, hospitals, or clinics. An assumption behind this quality reporting movement is that competition between plans will lead to improvements in performance indicators that are more general proxies for quality of care. An important limitation of this approach is that these reports do not help health plans improve their performance. Although report cards measure some aspects of quality, they do not specify how to make changes for the better. Needed improvements can be initiated only by the health plans themselves; improvement from within will be the result of continuous quality improvement (CQI) techniques.

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Several authors have written about the problems of CQI as well as its potential. Blumenthal and Kilo (1998) itemized lessons learned in recent years: involving physicians earlier rather than later in the process, emphasizing clinical improvements (not cost containment), investing in professional expertise, avoiding jargon and management language, starting small, recruiting members of the board of directors, and investing in sophisticated information systems. They noted that when these conditions are present, CQI can be a positive force for change. Shortell et al. (1998) reviewed the effectiveness of CQI activities in inpatient and outpatient settings. Despite the paucity of reports, these authors were optimistic about the positive effects of CQI as long as certain conditions were met: activities are focused on areas of real importance, and problems are addressed by clearly formulated interventions; the organization is ready for change and has capable leadership and adequate information systems; and the external environment (i.e., regulation, payment policies, and competition) is conducive to action. Quality improvement efforts by health care organizations fall outside of government regulation of the industry. Although state governments have regulated insurance without controversy, federal regulation has been limited by the Employee Retirement Income Security Act (ERISA) legislation, which preempted state regulation of federal insurance and health maintenance organizations. Regulation aims to protect individuals from wrongdoing and to provide mechanisms to redress wrong if it occurs. In other words, regulation can set a minimum standard for medical care, but it does not provide mechanisms for improving quality. Most of the regulatory mechanisms currently in place relate to administrative, not clinical, aspects of medical care such as marketing, tax status, fraud and abuse, and other market-related functions. It is not surprising that the proposed Patient’s Bill of Rights (see Chapter 1) has created controversy in Congress, because it extends regulation beyond health care administration to clinical management. The debate underlying this controversy is whether market regulation or clinical regulation will provide the more powerful motivation for raising the quality of care. From the perspective of managed care organizations, market regulation provides rules (i.e., predictability) that help keep the playing field level among competitors (Ignagni 1999). On the other hand, clinical regulation exposes these organizations to liability that is unpredictable. From the perspective of patients, especially those denied health benefits, regulatory protection from the abuses of managed care may be a fair tradeoff for higher out-of-pocket expenses. The steps to a successful strategy for quality improvement begin with three fundamental building blocks. First, we must understand the powerful

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dynamics (e.g., political, economic, and clinical) of health care delivery. Second, we must be familiar with the methods of measuring quality and learn how others have implemented these methods in clinical settings. Third, we need examples of quality improvement projects—what methods others have tried successfully—to serve as models for replication and to inspire similar projects in the readers’ clinical setting. The purpose of this book is to encourage and help practitioners and administrators in mental health clinical settings improve the quality of care within their own domain. We divided the book into three parts that mirror the foundations of quality improvement success. The chapters have been written by contributors from many sectors of the mental health community. Part I begins with a review of the report to President Clinton from the Advisory Commission on Consumer Protection and Quality in the Health Care Industry. Chapters follow on professional ethics and managed care, how Wall Street investors are changing the practice of medicine, problems faced by health maintenance organizations, and changes needed in medical education if physicians are to be well prepared to practice medicine in the twenty-first century. Part II reviews methods of quality measurement and describes support by the federal government to improve measurement methods. Chapters in this part also address how consumers are joining the quality of care measurement movement and what one large urban county mental health program is doing to advance quality measurement. Part III offers examples of quality improvement projects done at various clinical levels, ranging from the national health service in Britain to a small geriatric unit in a large hospital. Although the cases are not elaborate research studies, they are samples of work done by clinicians or administrators who were concerned about the quality of care in their own settings. The data may not generalize to other similar settings as they would in research studies, but that is not the point: action taken has been based on data collected. These contributors used data to test, for themselves, whether the interventions they made improved care. Our goal is to encourage physicians and mental health professionals to more fully open the door to quality measurement projects at all levels of care and clinical administration. Leadership must come from the clinical community by setting standards for doing the right thing and doing it well. Only clinicians can appreciate the subtle nuances that maintain or improve quality standards, and only they can make substantive changes in their clinical settings. Even if managed care were to disappear tomorrow, the quality of patient care must continue to be questioned, examined, and improved, with clinicians taking the lead. We hope this book contributes to those who make quality their mission.

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REFERENCES Blendon RJ, Young JT, DesRoches CM: The uninsured, the working uninsured and the public. Health Aff 18:203–211, 1999 Blumenthal D, Kilo CM: A report card on continuous quality improvement. Milbank Q 56:625–648, 1998 Chassin MR, Galvin RW: The urgent need to improve health care quality. JAMA 280:1000–1005, 1998 Donabedian A: The Definition of Quality and Approaches to Its Assessment, Vol 1. Ann Arbor, MI, Health Administration, 1980 Ignagni K: The managed care industry: balancing market forces and regulation, in Regulating Managed Care. Edited by Altman SH, Reinhardt UE, Shactman D. San Francisco, CA, Jossey-Bass, 1999, pp. 239–262 Lohr K: How do we measure quality? Health Aff 16:22–25, 1997 Roper WL: Regulating quality and clinical practice, in Regulating Managed Care. Edited by Altman SH, Reinhardt UE, Shactman D. San Francisco, CA, JosseyBass, 1999, pp 1145–1159 Shortell SM, Bennett CL, Byke GR: Assessing the impact of continuous quality improvement on clinical practice: what it will take to accelerate progress? Milbank Q 56:593–624, 1998

PART I Challenges


Introduction

In the introduction to this book, we sought to help readers distinguish between the effects of managed care on mental health services and the determination of the quality of care provided. However, the burning issues of the day are inextricably linked to managed care: consumer rights, professional ethics, the differences between for-profit and not-for-profit medicine, and medical education. In this part, we asked contributors to consider how these issues are related to quality. The five chapters in Part I set the stage for the quality measurement and case reports parts that follow. Each chapter in Part I offers a different point of view about the state of health care today. Taken together, we think these chapters will help the reader better understand the effort required to focus on quality and the resources needed to continue to improve quality in all types of settings. The chapters go to the heart of the controversies over cost and quality. What is the role of government regulation in the clinical process? How can professionals maintain or improve the quality of care they provide when pressured by payors to reduce the cost of care, and when utilization review intrudes into the patient–doctor relationship? What can physicians do to mitigate the problems stemming from utilization review strategies that undermine professional decision making and fiduciary responsibility? Several writers discuss the nature of the health care market economy, the growth of for-profit medicine, and how these economic forces have become entangled with quality of care. There is concern in practice settings as well as medical schools about the incompatibility of the invisible hand of the market and the caring hands of physicians. For those who want to criticize managed care indiscriminately, readers are reminded of the philosophy that led to the establishment of not-for-profit health maintenance organizations in the middle of the twentieth century and of the potential benefits of managed care when it is not in the grip of Wall Street. Some aspects of managed care can be evaluated separately, on their own merit. In 3

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fact, some quality improvement mechanisms used by managed care (e.g., practice guidelines) can have positive value in the training of medical students and the continuing education of physicians when they have been widely distributed.

1 The President’s Advisory Commission on Consumer Protection and Quality in the Health Care Industry Steven S. Sharfstein, M.D.

The purpose of the health care system must be to continuously reduce the impact and burden of illness, injury, and disability, and to improve the health and functioning of the people of the United States. Advisory Commission 1998, p.1

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n September 5, 1996, President Clinton signed an executive order establishing the President’s Advisory Commission on Consumer Protection and Quality in the Health Care Industry. This 34-member commission, cochaired by the secretary of health and human services and the secretary of labor, received a two-pronged charge from the president: 1) to review the status of consumer information and protection for those enrolled in managed health care plans, making recommendations for improvement through the Patient’s Bill of Rights, and 2) to review definitions promoting the quality of health care, building consensus on the role of federal leadership in ensuring patient quality of care. The appointed commissioners were consumers, business leaders, labor leaders, physicians, other health care professionals, hospital leaders, health plan executives, and state and local government officials from many geographic regions. One ground rule established at the first meeting was that 5

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the commission would endeavor to reach consensus from these diverse perspectives when drafting a patient’s bill of rights and a report on quality— that is, it would strive to unanimously agree on recommendations. I was appointed as a representative of the mental health field in general and psychiatry in particular. There was one other psychiatrist among the commission members, Dr. Herbert Pardes, who represented the academic community as dean of the College of Physicians and Surgeons of Columbia University in New York City. The commission met monthly for 1 year. Each 2-day meeting was divided into a day of subcommittee deliberations and another day of meetings with all commission members present and the two secretaries acting as cochairs. We completed the Consumer Bill of Rights (changed from “patient’s” in our first meeting) in November 1997 (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997) and a final report on quality in April 1998 (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1998). The change from “patient’s” to “consumer” underscored the marketplace economics for health care and highlighted why health care consumers, like consumers of other highly complex and technical products, need protections. All meetings were conducted in public, and there were opportunities for the public at large to give testimony. An Internet Web site was established for all of the commission’s deliberations, and all papers submitted and considered by the commission were distributed to the public. No secret or private meetings were held, which was in sharp contrast to the Clinton administration’s failed health care reform task force 2 years earlier.

THE CONSUMER BILL OF RIGHTS AND RESPONSIBILITIES: NOVEMBER 1997 The commission’s first task, the Consumer Bill of Rights and Responsibilities, focused on managed care and its affect on patients and providers in the marketplace. In response to mounting public pressure that was in part a response to media exposure of “horror stories” about managed care, states had begun to regulate specific managed care practices; these initiatives included setting minimal hospital stays for maternity care and mastectomies and prohibiting gag clauses in managed care provider agreements. Some states also mandated disclosure of managed care procedures, operations, performance measurements, procedural protections (e.g., appeals rights for challenging managed care decisions), and access to specialty care and emergency treatments.

President’s Commission on Consumer Protection and Quality

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In the context of state legislation and regulation, which varies widely from state to state, we felt that the federal government should establish minimum standards for consumer protection that would apply to all payors, including self-insured benefit plans. This was considered necessary not only to fill the large gaps left by some states but also to regulate self-insured plans that avoid state oversight because of the Employment Retirement Income Security Act (ERISA). This act, passed in 1973, provided for federal regulation of employer pension plans but also included protection from state oversight of self-insured health plans provided by employers. In 1997, it was estimated that 120 million Americans received health care coverage through such plans. The federal government itself is a major purchaser of health care through the Medicare program and its contribution to state Medicaid programs. The need for consumer protection under these programs and other federal health programs underscored the commission’s efforts.

GUIDING PRINCIPLES FOR THE CONSUMER BILL OF RIGHTS AND RESPONSIBILITIES All Consumers Are Created Equal. In establishing the Consumer Bill of Rights and Responsibilities, we felt strongly that all people serviced by public programs, those with private insurance (including those who purchased their own insurance and those who had coverage through employer-based health plans), and even those with no health insurance were entitled to certain basic health care rights. Since health insurance was invented in the 1930s, we have endured a twolevel system of coverage, with one set of benefits for all medical conditions and another for so-called nervous and mental disorders, or behavioral health care conditions. In both public and private programs, inside limits (i.e., reduced benefit coverage) for treatment of mental and substance abuse disorders have been the rule rather than the exception. The movement toward parity of mental disorders with the rest of medicine has achieved some success on the state as well as federal level, and the Consumer Bill of Rights and Responsibilities supported these efforts. Certainly, the concept that “all consumers are created equal” was an important principle to us in establishing parity.

Quality Comes First. It was the intention of the commission to shift the health care debate in the managed care marketplace from cost to quality. It was clear to us that the first phase of managed care had focused primarily on reducing health

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care costs and that issues of quality were often neglected in the process. In each area of the Consumer Bill of Rights and Responsibilities, the commission asked itself: “Will this improve the quality of care and the system that delivers that care?” According to the 1999 Hay Report (Hay Group 1999), concerns about quality moved to the forefront as managed care disproportionately cut back on behavioral health benefits. Ultra-short hospital stays, lack of access to outpatient alternatives, lack of coverage for novel and costly psychopharmacologic agents, and severe restrictions on psychotherapy were all creating great concern among patients and providers.

Preserve What Works. In putting together the Consumer Bill of Rights and Responsibilities, the commission looked at the aspects of the health system that seemed to work well, with the intention of preserving them.

Cost Matters. From the commission’s perspective, it was important to assess the impact consumer protection had on the cost of care. We recognized that the problem of underinsurance and lack of insurance would worsen if costs rose because some employers and employees were unwilling or unable to pay additional premiums to improve consumer protection or quality. Yet the commission felt strongly that some additional spending should be promoted in cases in which it represented an investment in higher-quality health care and better health outcomes.

GOALS FOR THE HEALTH CARE SYSTEM The main goal of the Consumer Bill of Rights and Responsibilities was to move the health care system in the direction of obtaining the highest quality and the best outcomes for consumers and their families. It was written at a time when the managed care–driven marketplace had created great concern about access to quality care. The goals were as follows: 1. Health care coverage is the best consumer protection. Lack of health care coverage or inadequate coverage is the biggest barrier to quality care. From the outset, it was clear to the commissioners that our delivery system left too many Americans without any coverage (estimated at 42 million Americans in 1997). The fact that many additional Americans lacked adequate coverage for behavioral health care, even those who had basic health insurance coverage, was difficult to justify and led


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to the movement for parity at the state level as well as to the Federal Mental Health Parity Act of 1996. In addition, the enactment of the Health Insurance Portability and Accountability Act of 1996 was an effort to ensure coverage as individuals changed jobs or moved geographically. 2. Consumers faced with catastrophic illness require assistance. The commissioners recognized that the health care system, with its focus on acute treatment and cost containment, was neglecting the chronic nature of many serious illnesses and the catastrophic costs that can occur in treating persistent life-threatening conditions. Among the most costly are treatments for psychotic disorders, especially bipolar illness and schizophrenia. For more than 100 years, most of these patients have had to rely on state-funded public hospitals and outpatient centers for their mental health services. In the past several decades, states have cut back on their mental health programs as they have deinstitutionalized care from state facilities and cost-shifted much of the care to the federal Medicare and federal/state Medicaid programs. These actions have caused many individuals who need treatment to fall through the cracks, creating a public health disaster by increasing homelessness and inappropriately incarcerating the mentally ill in jails. 3. Coverage must be made affordable for all consumers, employers, and other purchasers. The commission understood that achieving affordable coverage required the ability to spread financial risks over a large population. In addition, cost containment practices needed to continue because the demand for health care, including mental health care, was potentially limitless. Traditionally, the most costly and catastrophically mentally ill patients have been treated through state-funded hospital and clinic programs. If these individuals were brought into health plans, coverage could be offered only if plans spread the risk broadly or received riskadjusted payments. 4. Vulnerable groups require special attention. The commission focused on the most at-risk groups in our society, including low-income individuals, children, non-English-speaking consumers, and individuals with mental or physical disabilities. Patients with severe and persistent mental disorders are an especially vulnerable group that has suffered in the marketplace and public sector. The large number of people who are homeless and the many individuals incarcerated in hospitals and prisons further underscore the extent of the public health crisis. Throughout the development of the Consumer Bill of Rights and Responsibilities as well as the report on quality, these vulnerable groups were a major concern.

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5. Small purchasers need assistance. It was also clear that the owners of small businesses who were self-employed and those who purchased insurance in the individual market needed special assistance to make health benefits affordable. Expanding insurance pools and providing payment systems to reflect greater risks inherit in small group markets were issues discussed by the commission. 6. Consumers must be able to participate in clinical research. Clinical research is critical to the continued advancement and innovation in health care. Therefore, the commission encouraged the efforts of researchers and others to resolve impediments to consumer participation in research. The balancing of patients’ rights against the need for new knowledge was discussed at several important points during the commission’s debates.

CHAPTERS OF THE CONSUMER BILL OF RIGHTS AND RESPONSIBILITIES The commission stated that “the American people should have access to health care that is of high quality, evidence-based, safe, free of errors, and available…regardless of ability to pay” (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 10). In this statement, the commission’s work was visionary in its goals for the future. In drawing up the Consumer Bill of Rights and Responsibilities, we made recommendations that we thought were achievable at the time the report was published.

Chapter One: Information Disclosure Consumers have the right to receive accurate, easily understood information and some require assistance in making informed health care decisions about their health plans, professionals and facilities. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 10)

Because informed consumers are fundamental to the efficiency of any marketplace, the first chapter established that health care information has strategic importance as we move to a managed care–driven marketplace. It is complicated and difficult to provide timely, adequate, clearly understood information to consumers when they choose health plans, select providers of care, and collaborate with their physicians and other clinicians on treatment decisions. There are many obstacles to ensuring this right to information; when consumers do not have adequate information they face major inequities and problems, especially poor quality care.


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Chapter Two: Choice of Providers and Plans Consumers have a right to a choice of health care providers that is sufficient to insure access to appropriate high quality health care. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 17)

The right of choice focused on managed care practices in particular, including the development of restrictive health plan networks and the lack of sufficient access to the number and types of providers to ensure that all covered services would be acceptable without unreasonable delay. In the second chapter of the Consumer Bill of Rights and Responsibilities, emergency services were emphasized as well as the importance of ensuring access to qualified specialists for consumers with complex or serious medical conditions or those requiring women’s health services. We also recommended a 90-day period for transition from one specialty provider to another when an employer or payor switches health plans and the patient’s physician is not in the new plan network. Too often in the managed care marketplace we have seen the disruption of care for seriously and persistently mentally ill individuals who must switch providers in the course of treatment. Whether informed consumers can choose between health plans based on comparative information about quality and cost is an area of major controversy. Most consumers do not know their future treatment needs. If they do not have a mental illness or substance abuse disorder, they tend to think that they will never need mental health services in the future and therefore do not make their plan choice on the basis of information on quality or access to mental health services. Even if a consumer anticipates the use of mental health services and can compare benefits across plans, managed care plans require authorization before payment to a restricted network of providers that makes this benefit information less relevant. Furthermore, consumers needing mental health services have little information that compares the performance of providers because almost no comparative data on clinical outcomes exist. Although standardized patient satisfaction surveys are beginning to be introduced, this right to choose among plans and providers is idealistic given the current conditions of the marketplace.

Chapter Three: Access to Emergency Services Consumers have the right to access emergency health services when and where the need arises. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 22)

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The third chapter emphasized the “prudent lay person” standard: when a consumer presents with acute symptoms of sufficient severity that a lay person believes an emergency room visit is prudent, managed care plans are required to provide payment for the emergency visit. Behavioral health emergencies may be life threatening and require easy access, prompt attention, and common sense. The commission recommended that health plans ensure that their coverage and payment policies are consistent with this “prudent lay person” standard.

Chapter Four: Participation in Treatment Decisions Consumers have the right and responsibility to fully participate in all decisions related to their health care. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 25)

The right to participate in treatment decisions emphasizes the need for patients to have access to easily understood information that describes the risks and benefits of treatment so that they can decide among medically necessary treatment options consistent with informed consent. The fourth chapter of the bill highlighted the role of health plans in disclosing provider economic incentives or personal compensation and the elimination of so-called gag clauses that restrict the health care providers’ ability to communicate openly with their patients about treatment options and benefit limits.

Chapter Five: Respect and Nondiscrimination Consumers have the right to considerate, respectful care from all members of the health care system at all times and under all circumstances. An environment of mutual respect is essential to maintain a high quality health care system. Consumers must not be discriminated against in marketing, enrollment practices, or the delivery of health care services, consistent with the benefits covered in their policy or as required by law, based on race, ethnicity, national origin, religion, sex, age, and mental or physical disability [emphasis added], sexual orientation, genetic information, or source of payment. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 31)

The fifth chapter was of great importance to individuals with mental and substance abuse disorders. Discrimination against behavioral health disorders is widespread and only partially addressed by the Americans with Disabilities Act of 1990. The federal Mental Health Parity Act of 1996 was an important first step in the direction of nondiscrimination by prohibiting differential lifetime or annual caps on coverage for physical and mental ill-


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nesses if an insurance plan covers mental disorders. Even so, full parity needs to be enacted at the federal level if we are to ensure that all consumers receive adequate treatment.

Chapter Six: Confidentiality of Health Information Consumers have the right to communicate with health care providers and to have the confidentiality of their individually identifiable health care information protected. Consumers also have the right to review and copy their own medical records and request amendments to their records. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 34)

The commission recognized that the right to confidentiality, a topic of great concern to the behavioral health field, needed special attention. This age of electronic medical records, combined with requests from utilization reviewers for sensitive clinical information to determine “medical necessity,” has compromised the confidentiality inherent in the doctor–patient relationship. As a result, many individuals are reluctant to seek psychiatric care because they risk disclosure of these details. The privacy of records is compromised further by requests for research, quality oversight, or investigations of fraud.

Chapter Seven: Complaints and Appeals All consumers have the right to a fair and efficient process for resolving differences with their health plans, health care providers, and institutions that serve them, including a rigorous system of internal review and an independent system of external review. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 37)

The commission, concerned about managed care denials on the basis of medical necessity, developed the right of consumers to submit and resolve problems with their health care. The recommendation for an independent external review received a lot of discussion and opposition from the managed care industry. The commission recommended that the external appeals system should apply to any decision by a health plan to deny, reduce or terminate coverage or deny payment for services based on a determination that the treatment is either experimental or investigational in nature; apply when such a decision is based on a determination that such services are not medically necessary and the amount exceeds a significant threshold or the patient’s life or health is jeopardized. (p. 37)

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This is especially important for patients with behavioral health care problems who have experienced arbitrary and capricious denials of care.

Chapter Eight: Consumer Responsibilities In a health care system that protects consumer rights, it is reasonable to expect and encourage consumers to assume reasonable responsibilities. Greater individual involvement by consumers in their care increases the likelihood of achieving the best outcomes and helps support a quality improvement, cost-conscious environment. (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997, p. 42)

The eighth chapter was an important chapter about expectations for consumer responsibility for maximizing healthy habits, collaborating with health care providers in carrying out treatments, disclosing relevant information to providers, and showing respect for other patients and health care workers. A large problem in behavioral health care related to quality and consumer responsibilities is treatment noncompliance and relapse, especially for patients with severe and persistent mental illness complicated by substance abuse.

THE FINAL REPORT ON QUALITY In April 1998, the commission delivered its report, Quality First: Better Health Care for All Americans, to the president and vice president. This report was the first comprehensive statement on the status of the quality of health care in the history of the United States. In contrast to the Consumer Bill of Rights and Responsibilities, which could be construed as critical of managed care practices, the quality report was more broadly concerned with the health care delivery system as a whole: the reduction of avoidable errors, the underuse of effective services, the overuse of some ineffective services, and the wide variation in health care practices. In addition, it was clear to all of the commissioners that evidence-based practice did not exist in this country. Although more than 50 recommendations in the report addressed quality concerns, I highlight in the following section only the key recommendations that affect the quality of care for individuals with mental illness and substance abuse disorders.

Recommendations The commission recognized that a critical obstacle to improving health care quality was the difficulty of measuring accurately the quality of health care and providing easily understood, comparable information about quality


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to consumers and other stakeholders. The absence of a systematic approach has made tracking and sustaining quality improvement efforts difficult. Thus, the commission recommended a series of steps to bring order to the current system of quality measurement while simultaneously investing in the development of new methods: 1. Core sets of quality measures should be applicable to each sector of the industry. For example, health plans, hospitals, and individual clinician practices should be identified for standardized reporting. 2. The health care industry should support the development of quality measures. 3. A major effort should be undertaken to ensure that comparative information is valid, reliable, comprehensive, and available in the public domain. It was clear to the commissioners that the capacity to measure and improve quality needed to be strengthened; it was felt that the proposed Consumer Bill of Rights and Responsibilities would help in that process. Building the capacity to improve quality requires additional investment in research on evidence-based practice, additional training of health care workers in quality improvement, the upgrading of information systems, the collection of valid and reliable data on individuals, the development of comparative performance indicators to help advance evidence-based practice, and the encouragement of health care organizations to move in the direction of improving the quality of care. For mentally ill and substance-abusing populations, specific health care quality measures and measurement methods were also recommended. Although this initiative was meant to be integrated within general quality improvement measurement strategies, it was recognized that specific quality measures and methods tailored to patients and their families would be required.

Risk Adjustment The commission recommended the “payments to health plans and providers should promote quality health care and improved health and functional status for all patients, including vulnerable populations” (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1998, p. 12). Adjusting payments for differences in health or functional status is critical for individuals with severe and persistent illness or disability so that developing high-quality and innovative models of care best serves these individuals. The severely and seriously ill often need a disproportion-

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ate amount of care, and payment systems need to be adjusted accordingly to avoid compromising the quality of such care. The validity of statistical risk adjustment, however, has been questioned. How do we know which patients will relapse, be noncompliant with treatment, or require more intensive intervention strategies such as assertive case management?

Cost of Improving Quality Care Can we measure quality improvement given the current state of knowledge and the willingness of systems of care to invest in quality measurement given the cost pressures of today’s marketplace? Much lip service has been paid to the idea that good quality reduces cost. At the commission meetings, this idea was expressed several times; however, a skeptical few, including myself, felt that quality improvement leads to additional—not lower— costs. One example of this is outpatient cancer treatment, which over the course of time has improved considerably and has led to longer remissions and better life expectancy but is clearly more costly because more patients are “better, but not well.” The dilemma is that major cost savings result only if an illness can be cured or prevented or if the patient dies. Most clinical services provide care for those with chronic illness, which leads to better quality and perhaps longer life, but with increments in quality valued differently by different stakeholders in health care. It is difficult to imagine that quality improvement will not, to some extent, lead to higher costs, especially in behavioral health care. Psychotherapies, psychosocial rehabilitation, and new psychopharmacologies could and should lead to better outcomes for patients with serious and persistent mental illness. However, as patients improve and are able to function better in the community (even though they are not “well”), they will have continued treatment costs. Current approaches to contain costs include deinstitutionalization, defunding of community services, and incarceration of the seriously and persistently mentally ill. Assertive community treatment, a program that has been shown to be cost effective in Wisconsin and elsewhere (Stein 1993), would actually cost more in the short run, however, in places where these programs do not currently exist. As our treatments and interventions are proven to be effective, we are confronted with the ethical dilemma of the millions who do not have coverage and slip through the cracks of our health care system.

ERISA Protection The commission spent many hours in political and legal debate over whether self-funded health benefit programs protected by the ERISA pre-


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emption should lose their protection so that they can be sued when care is arbitrarily denied. Many managed care companies protest that they do not deny care, just reimbursement. This position is difficult to reconcile with the reality that reimbursement denials restrict access to treatment. Basic fairness dictates a level playing field with respect to liability, but there is an argument for maintaining the ERISA protection: fear of litigation should not be the foundation for a quality improvement program. Does such fear lead to costly, defensive medicine without proven effectiveness? How do we decide as a society which clinical risks are acceptable and which are unacceptable? An example is the assessment of suicide. Suicide risk assessment, fraught with clinical uncertainty, in some cases leads to denial of requested hospital days and, in a certain small percentage, a catastrophic outcome. If a lawsuit was filed every time a successful suicide occurred as a result of a denied day (as seen in the eyes of the family and others), would this lead to betterquality care or just to arbitrary length-of-stay requirements similar to those legislated for maternity stays or mastectomies? Surely there is a compelling argument for research into our capacity to predict suicide and the usefulness of hospital stays in reducing the risk of suicide, but who will pay for such research? Moreover, if we are able to demonstrate that a certain small percentage of additional hospital days are effective in preventing suicide, but we cannot decide which of our suicidal patients in fact may benefit, would we be willing to pay the additional costs of increasing lengths of stay for all patients at risk?

Creating Public–Private Relationships The commission wanted to promote the agenda of quality care in contrast to cost containment. Therefore, when the commission itself dissolved after 1 year of work, it recommended creating two complementary entities, one public and one private, to provide ongoing national leadership on health care quality. A Forum for Health Care Quality Measurement and Reporting, privately funded, was recommended. This group would be made up of private and public purchasers of health care services, consumers, health plans, health care practitioners, and others who would implement a comprehensive national plan for measuring health care quality and reporting the results of such measures to the public. Among the first tasks for the forum were fostering demands for quality assessment and standardizing quality measurement. The forum was expected to work closely with an Advisory Council for Health Care Quality if the forum was funded by Congress. The advisory council was to identify national aims and specific objectives

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for quality improvement and track the nation’s progress in meeting these aims and objectives.

CONSUMER PROTECTION AND QUALITY SINCE THE PRESIDENT’S COMMISSION Since the report of the president’s commission was issued, the battle over consumer rights has moved into the legislative arena as Congress debates “patient protection.” Once again, an interesting change in terminology has occurred from consumer back to patient, and the addition of the use of the word “protection.” This issue has fallen victim to strong partisan politics, the impeachment process, and a stalemate between Capitol Hill and the White House. On one hand, very small incremental steps toward patient protection are being recommended by the Republican majority, including the right to emergency services, appeals of adverse decisions, and some consumer choice. For a few Republicans and the Democrats, stronger protections in relation to confidentiality and the ability to bring ERISA-protected plans to court are the key dividing lines between the two. At the same time, legislation has been introduced to improve parity for mental health benefits, and President Clinton issued an executive order to require parity for the Federal Employees Health Benefits Plan. Without meaningful patient protection, however, managed care authorizations and provider network requirements substantially weaken access to appropriate care, especially for individuals with severe and persistent mental illness. On the other hand, the Forum for Health Care Quality Measurement and Reporting recommended by the commission has made some progress. The members of its planning committee included representatives of public and private purchasers, consumers, health plans, and health care professionals. The chair of the committee was a member of the president’s commission. The first meeting was held in June 1998 and six additional meetings took place until the final meeting in March 1999. This private, nonprofit entity represented a significant breakthrough in the effort to try to provide a national system for quality improvement. Four member councils have been formed: one for consumers, one for public and private providers, one for providers and plans, and one for research in quality improvement. The forum also plans to develop a comprehensive quality measurement and public reporting strategy with national aims for quality improvement and health care. Although the forum has raised start-up funds from foundations, there has been no legislative action to fund the Advisory Council for Health Care Quality, the public entity recommended by the commission.


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DOES THE MARKET WORK TO IMPROVE THE QUALITY OF BEHAVIORAL HEALTH CARE? In today’s medical marketplace, quality clearly takes a back seat to cost. Progressive pricing strategies, the economies of scale from mergers and large clinician networks, and the incentives under prospective payment all focus on the cost issue, leaving quality concerns to private regulatory agencies, the news media, and the courts (unless the payor is protected by the ERISA preemption). The president’s commission examined the growing chorus of state regulatory efforts in managed care and proposed a series of federal regulatory initiatives. These proposals are necessary for behavioral health care, which suffers disproportionately under managed care. Discriminatory managed care practices have moved patients rapidly out of hospital-based settings and then cut the numbers of approved outpatient visits (Hay Group 1999). This defunding of care has led to a disproportionate cutting of expenditures for behavioral health care compared with those for general medical care. Mental health clinicians find themselves excluded as networks tighten up and rid themselves of inefficient or high-cost providers. Patients and their families have become highly critical of managed care practice. For over 100 years, much of mental health care has been delivered through government, especially state government. The experiment with privatization and market economics has created great hardship for many patients and families. This is particularly tragic in an era in which many of our treatments are much more effective, our diagnostic competency is much improved, and our public health strategies are designed to reduce suffering and improve the quality of life. Strong action by the government to ensure parity of treatment for mental health as well as substance abuse treatment is urgently needed. A focus on quality improvement will lead to additional expenditures for behavioral health care which, in turn, will likely result in cost offsets in other parts of the medical care and criminal justice systems. The President’s Advisory Commission was a first step in the direction of moving Americans’ health care system from an excessive focus on dollars to a mandate for quality care accessible to all.

REFERENCES Advisory Commission on Consumer Protection and Quality in the Health Care Industry: Consumer Bill of Rights and Responsibilities: Report to the President of the United States, November 1997. Available online at http:// www.hcqualitycommission.gov/final/append_a.html. Accessed December 15, 1999

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Advisory Commission on Consumer Protection and Quality in the Health Care Industry: Quality First: Better Health Care for All Americans: Final Report to the President of the United States. Washington, DC, U.S. Government Printing Office, 1998 Hay Group: Health Care Plan Design and Cost Trends, 1988 Through 1998: Report Prepared for National Association of Psychiatry Health Systems. Arlington, VA, Hay Group, April 1999 Stein L: A system approach to reducing relapse in schizophrenia. J Clin Psychiatry 54(suppl):7–12, 1993

2 Maintaining Professional Ethics in a Changing Health Care System Steven M. Mirin, M.D.

TRENDS IN THE FINANCING AND DELIVERY OF MENTAL HEALTH CARE Response of Payors to Rising Costs Powerful economic forces are reshaping how mental health care is financed and delivered. With the nation’s aggregate health care bill now exceeding $1 trillion (Levit et al. 1996), purchasers of health care in both the public and private sector have embraced various measures designed to contain rising costs. At the same time, caregivers have voiced concern that such measures will impinge on access to care and/or the quality of care delivered. In the ensuing debate, consumers of health care have stood by, bewildered over claims and counterclaims about the relationship between cost and quality. Nowhere has this issue been more prominent than in mental health care. In the past two decades, greater public understanding of the nature of mental illness and the availability of more effective treatments have helped remove some of the constraints that had previously discouraged those in need from seeking care. As demand for services has increased, the number of individual and institutional providers of mental health and substance abuse treatment has multiplied. Consequently, overall mental health care costs rose at a rate of 15% per year between 1980 and 1990 (Rice et al. 1992). As the cost of mental health services rose, payors of benefit claims sought to combat this trend. Initial efforts focused on reducing benefits, 21

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imposing annual and lifetime caps on available coverage, and increasing patient copayments and deductibles. In many cases, employee choice of health plans was sharply limited as more comprehensive plans were deemed unaffordable by employers. However, the emphasis soon turned to actively managing mental health benefits by controlling patients’ access to care, reducing use by imposing medical necessity criteria on treatment decisions, and selectively contracting with those individual and institutional caregivers who agree to accept discounted fees and adhere to managed care treatment philosophies (Rowland and Hanson 1996). The initial success of managed care organizations (MCOs) in reducing service use and associated costs, particularly inpatient care, attracted the attention of employers, state governments, and insurance companies looking for ways to reduce expenditures for mental health and substance abuse treatment. Some began providing economic incentives (e.g., reduced health care premiums and deductibles) to subscribers willing to leave traditional, fee-for-service health plans and shift to health maintenance organizations (HMOs), preferred provider organizations (PPOs), or other plans in which benefits could be more tightly managed. These plans had a network of preferred providers and used disincentives, in the form of higher copayments and deductibles, to discourage members from seeking care outside their plan network. The trend toward managed mental health care for patients with mental illness, including those with substance use disorders, accelerated dramatically during the 1990s. By the end of that decade, almost 160 million individuals in the public and private sectors had their mental health and substance abuse treatment benefits managed in some fashion (Fox 1999). Many of these individuals were in carve-out arrangements (i.e., separate from the general health benefit) that delegated the management of the mental health and substance abuse treatment benefit to MCOs. In many such plans, access to care required prior approval from managed care gatekeepers who determined whether the requested care was medically necessary. Reimbursement for care was restricted to services provided by in-network clinicians. As current and future patients migrated into managed care systems, many mental health clinicians felt compelled to join networks of preferred providers set up by insurers and MCOs. In so doing, they accepted more stringent administrative oversight of their patients’ mental health and substance abuse treatment benefits as well as reduced fees and external review (sometimes by nonclinicians) of their clinical decision making. In addition, these clinicians soon learned that the entry-level requirement for participation as a preferred provider included being willing to conform one’s treatment philosophy to a symptom-focused, short-term treatment model and agree-

Maintaining Professional Ethics

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ing to render only care deemed medically necessary to ensure the patient’s immediate health and safety. Inherent in the medical necessity concept is an emphasis on treating only severe or incapacitating mental illness, with reliance on pharmacologic intervention. By implication, care for patients with personality disorders or problems in living, for whom various forms of psychotherapy may be the mainstay of treatment, is often not considered medically necessary by those paying for the mental health benefits. As managed care systems became more sophisticated, they developed the ability to monitor the frequency and duration of treatment delivered by clinicians participating in their networks. They can now identify clinicians whose practice patterns do not conform to their preferred model of care delivery. Similarly, treatment directed toward enhancing patients’ understanding of themselves or improving relationships with others is generally deemed nonessential and therefore not reimbursable. Finally, clinicians in some managed care networks have experienced contractually defined constraints on their ability to fully inform patients of the treatment options available to them, particularly those options that are deemed too expensive or that are unavailable through the patient’s health plan (American Medical Association Council on Ethical and Judicial Affairs 1994). Some contracts also prohibit clinicians from suggesting to the patient that the plan should cover a particular service or that other plans may provide better coverage for services required by a particular patient. Although such gag rules are now banned in several states, many clinicians still feel that voicing their criticism of a health plan or MCO can adversely affect their referral stream or status as a network provider.

Response of Clinicians to Managed Care For many clinicians, cost containment pursued through managed care has aroused concern and resentment. Clinicians have been troubled by the growing constraints on patient access to needed care, and they resent the intrusion of managed care reviewers into decisions about the type, frequency, and duration of care rendered to each patient. Particularly concerned about the limitations on access to specialists, some psychiatrists have made proposals to identify themselves as the primary physicians for patients with mental illness and substance use disorders. Indeed, for many such patients, particularly those with severe and persistent mental illness disorders, psychiatrists serve as the principal (i.e., most involved) physician. However, most psychiatrists cannot easily retain the competency needed to provide ongoing general medical care. The economic pressures generated by the growing control of HMOs and MCOs over patient referrals and reimbursement have caused many

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mental health professionals to seek sanctuary in MCO provider networks. Others have sought countervailing power in the health care marketplace by joining various types of organized provider groups, such as independent practice associations, physician hospital organizations, and integrated delivery systems. These arrangements are generally designed to increase the bargaining power of clinicians and hospitals in negotiations with health care purchasers over how care is delivered and reimbursed. Some organized groups have also competed directly with HMOs and MCOs through arrangements with payors in which they share financial risks and rewards. In capitated financing arrangements, caregivers agree to render services to a population for a prospectively determined annual fee. Participating clinicians, hospitals, and care systems must then allocate (i.e., ration) care to protect their financial interests (Packer 1997; Pearson et al. 1998).

DILEMMAS IN THE DOCTOR–PATIENT RELATIONSHIP A Case Example The ethical problems raised by clinicians participating in managed care systems include economic incentives or disincentives to provide needed care; restrictions on exercising best clinical judgment in the treatment of patients; and demands by payors and MCOs for personal data about patients on which to base reimbursement decisions, which affects clinicians’ ability to maintain patient confidentiality (American Medical Association Council on Ethical and Judicial Affairs 1995). The following example illustrates some of the ethical pitfalls in a health care system in which there are growing constraints on clinicians’ ability to fulfill their fiduciary obligation to patients. Dr. Jones, a midcareer psychiatrist in a small, urban city noted that growing enrollment in the local HMO and the adoption of managed mental health care by both private insurers and the state Medicaid program had sharply reduced referrals to his practice. Despite excellent working relationships with local nonpsychiatrist physicians and mental health professionals, his caseload had dwindled. Faced with growing uncertainty about the viability of his practice, Dr. Jones applied to become a network provider in several MCOs. In each case, he filled out a lengthy application that asked questions about his training, experience, special clinical expertise, malpractice history, practice patterns, and treatment philosophy. He also answered questions related to his office location, fees and billing practices, academic and hospital affiliations, and availability to see referrals on short notice. After a 6-week wait, Dr. Jones received a brief letter from each MCO informing him that there were no openings in their networks. Calls to


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each MCO were unrevealing, although one administrator suggested that it was not psychiatrists, per se, that were excluded from their network, but those with Dr. Jones’s practice pattern and treatment philosophy. However, colleagues suggested to Dr. Jones that MCOs tended to accept psychiatrists with a preference for medication management carried out in brief and widely spaced visits. Long-term psychotherapy, which he had indicated he used for 80% of his cases, was a modality frowned upon by MCOs. Anxious for new sources of referral, Dr. Jones forwarded an application to yet another MCO. He reshaped his description of his treatment philosophy and practice patterns to conform to what he believed MCOs were seeking. This approach, though disingenuous, resulted in his receiving tentative approval as an in-network provider, contingent upon his signing and returning a lengthy provider agreement and accepting the MCO’s fee schedule, which was approximately 30% below his customary fees. After a cursory review, Dr. Jones signed and returned the agreement, noting that it contained language that specifically prohibited him from sharing any information with patients or others that might in any way disparage the health plan or the MCO overseeing the patients’ psychiatric benefits. He also noted clauses that required him to provide to MCO staff any information deemed necessary to review the quality of his treatment and determine its medical necessity, the latter being a prerequisite for MCO payment. Dr. Jones’s first case was a referral, not from a clinician but from a managed care reviewer. The patient, a 16-year-old adolescent female, recently had been discharged from a residential treatment facility where she had spent a week being detoxified after 1 month of binge drinking. She had also been suspended from school and charged with three counts of shoplifting. Her juvenile court appearance was scheduled for the following week, and she was required to have a psychiatric evaluation from a current treater. Dr. Jones learned that her former treatment facility had reluctantly discharged her when the MCO reviewer ruled that further residential treatment was not medically necessary and therefore not reimbursable. Soon after discharge, problems arose at home. Dr. Jones contacted the MCO and asked why the reviewer had terminated the benefit for residential care when the treatment staff felt it was necessary. In the ensuing discussion, he discovered that the clinical staff had disputed the reviewer’s opinion. He agreed to see the patient that day. She presented in a somewhat confused and disheveled state; he thought he smelled alcohol on her breath. He also met with her mother, who advised him that her daughter had been drinking, although the girl denied it. The mother recounted myriad family problems, some stemming directly from her daughter’s problems and others related to her husband’s alcoholism and periodic physical abuse of her and the patient. She also hinted that her husband sexually abused the patient. Dr. Jones noted all of this in his progress notes along with other details relevant to the evaluation and then phoned the managed care reviewer to

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recommend a return to the residential treatment program. The reviewer asked if he planned to prescribe medication. Dr. Jones replied that although the patient might be suffering from an underlying depression, he was unable to assess this in the presence of heavy drinking. Moreover, he doubted whether the patient and her family were competent to begin a regimen of medication responsibly. The reviewer refused to authorize benefits for a return to residential care. Instead, she suggested that residential treatment could be avoided if the patient and her family were seen daily. Dr. Jones agreed to try this option, but indicated he would again request a residential care referral for the patient should the plan not work. Over the next several days, Dr. Jones saw the patient and her mother. Her father had resumed heavy drinking and was unavailable. The patient was also still drinking and was now threatening to run away. Several physical altercations had taken place between her and her father, and her mother was powerless to intervene. Sensing that he could no longer manage this patient and her family outside a structured setting, Dr. Jones called the managed care reviewer to request approval for residential, or possibly inpatient, care. The reviewer responded that the patient’s intensive outpatient treatment trial had just begun and that Dr. Jones should not panic. She would not approve the benefits for anything but frequent outpatient visits.

Discussion This case illustrates some of the ethical challenges for psychiatrists and other clinicians as they confront the changing economics of mental health care. Given the economic pressure affecting the long-term viability of his practice, Dr. Jones adopted a pragmatic approach, applying to his local MCO network to be a preferred provider. He sought a steady stream of referrals in exchange for deeply discounted fees and a contractual agreement, which seemed reasonable in the context of a business relationship. However, he did not sufficiently consider what adherence to the contract would mean in terms of his ethical responsibility to his patients. For example, he was prohibited from discussing the patient’s health plan with respect to any deficiencies in coverage (i.e., the policies and procedures that limited the patient’s access to necessary care). These constraints interfered with his ability to render advice about what he thought was the best care for his patient; he was unable to prescribe and implement a treatment plan that he believed would be best for the patient. By not conveying this information, he unintentionally misled the patient and her family into believing that he endorsed the treatment they followed. Moreover, when Dr. Jones requested the referral for the patient, he did not share all of the information with the managed care reviewer. Although he suspected that some of the patient’s behavior stemmed from a fear of physical or sexual assault, he felt precluded from conveying this informa-


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tion to the reviewer. He feared that confidential details of the clinical record might eventually be reviewed not only by the MCO but also by the personnel department at the father’s job, through which the insurance coverage had been obtained. The health plan’s requirement that Dr. Jones provide MCO staff with the information needed to carry out prospective, current, or retrospective treatment reviews raised another ethical issue. By adding the payor (in this case the MCO) to the conversation about what is best for the patient, the ultimate authority for treatment is ceded to the managed care reviewer and not to the clinician. In many cases, these reviewers have no clinical training or direct involvement with the patient. Acceptance of these and other common managed care procedures undermines the clinician’s ability to exercise his or her best clinical judgment in caring for patients. These practices foster business standards as the guideposts in the care of patients rather than the traditional, patient-focused standards intrinsic to the ethic of medicine. By agreeing to treat the patient described in the vignette, Dr. Jones knew that previous treaters had recommended a course of treatment deemed not medically necessary by a nurse reviewer acting on behalf of the MCO. He also knew that the reviewer had rendered this clinical judgment without any direct contact with the patient. Yet her opinion was the one that prevailed; he should not have been surprised that his recommendation for residential care was denied. Now, Dr. Jones was clearly faced with a dilemma. His patient had become unmanageable. Family support, shaky from the outset, was collapsing. Although he held medical responsibility for the patient, his treatment plan was subject to the approval of reviewers who had no direct contact with the patient and a very different view about how treatment should proceed. Should there be an adverse outcome in this case (e.g., the patient making a suicide attempt), he feared that he would be held responsible. At the same time, he could not abandon his patient. What should he have done then? Should he have told the patient, the family, and the reviewer that he must uphold his recommendation for residential care and that, in the absence of benefits for such care, he could not continue to treat the patient? Should he have appealed the MCO’s decision? Should he have discussed his views with the family and encouraged them to pursue the appeals process? If so, at what stage in the process should this have occurred? Finally, to what extent was his withholding of sensitive material (i.e., the father’s apparent drinking problem, possibility of sexual abuse) from the managed care reviewer appropriate? Was he right to withhold this information, for fear that the father’s employer would become privy to it? Would revealing this information have altered the reviewer’s decision about coverage for residential treatment? Who should decide what

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personal information is shared and to whom it should be conveyed? These questions are all fundamental issues that are central to the medical ethics debate.

MAINTAINING ETHICAL VALUES UNDER MANAGED CARE Responsibility of Caregivers Ensuring Access to Needed Care As benefits for mental health and substance abuse treatment have become more tightly managed, clinicians practicing within managed care networks increasingly feel pressured to adopt a philosophy designed to control costs (or conserve resources if one adopts a public health perspective). Health plans’ and MCOs’ emphasis on resource allocation has shifted the traditional dyadic relationship between clinician and patient to a triangular relationship in which payors or their agents define the parameters under which care may be reimbursed. As a result, clinical decision making has moved from the clinicians to payors and managers who control access to care and reimbursement. Requiring that proposed treatments be withheld prior to approval unduly inserts the payors into day-to-day clinical decision making and perverts the care delivery process. What are the responsibilities of clinicians in an environment in which economic incentives encourage withholding care rather than ensuring its quality? Current practice and emerging case law suggest that clinicians who disagree with benefit allocation decisions of MCO reviewers have an obligation to appeal those decisions within the framework of the patient’s insurance plan (Wickline v. State 1986). Although some argue that denial of coverage is a payment issue, it does not relieve the clinician of medical responsibility. Therefore, in some instances, clinicians may need to render care at a reduced rate or even at no fee until the patient can be safely transferred to a clinically defensible and economically feasible treatment setting. The situation becomes more complex when clinicians believe that their patients need a type or level of care beyond what is approved for reimbursement (e.g., a hospital or residential setting). Here, the complex interplay between public- and private-sector health plans is relevant. Clinicians have a responsibility to advocate for the continued provision of public-sector services as a safety net for all eligible citizens, regardless of their current source of health care coverage. Furthermore, clinicians should inform patients of all appropriate treatment options, including those not covered by the individual’s health plan.


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Maintaining Patient Confidentiality The extent to which medical records may be used to facilitate nonclinical (i.e., administrative) decision making has been a matter of public debate (Davidson and Davidson 1996). Although medical records help insurers and health plans make reimbursement decisions, some have argued that the information should also be available to those investigating fraud and abuse (by clinicians or patients); those regulating access to driver’s licenses or guns; or those making decisions about individuals in high-risk jobs (e.g., pilots, security officers). This issue is now more acute because of rapidly expanding information technology, but it is still far from decided by Congress and the courts. Until then, clinicians should rely on the traditional dictum that “whatever a patient discloses to me in confidence I will hold sacred.” They should be cautious about entering sensitive information into the medical record that could compromise patients’ interpersonal, occupational, financial, or other interests if widely shared. Providing access to patient records containing sensitive material as required by some health plans may undermine what many mental health clinicians and some courts have viewed as the core of the clinician–patient relationship. Clinicians who evaluate patients entering treatment should also be held responsible for informing them about the level of medical records confidentiality that they can expect. Clinicians should disclose sensitive clinical information verbally or in writing and inform the patient or family that such information will be necessary for treatment reimbursement approval of future care. Ensuring Patient Access to Information The traditional ethic of medicine requires that clinicians accept the fundamental obligation to act in the interest of their patients and to place those interests above their own (Healy 1998). In the context of the doctor– patient relationship, this obligation derives from the recognition that the physician possesses specialized skills in the diagnosis and treatment of disease and is in the best position to predict the likely outcome of providing or not providing specific types of care to patients with particular conditions. In this respect, clinicians are obligated to use their specialized knowledge in the best interest of their patients. This generally includes informing patients of the most likely diagnosis, the range of treatment options available, and the likelihood of each option providing relief or cure. With the patient’s informed consent and cooperation, the physician’s responsibility is then to competently implement a treatment regimen designed to ameliorate symptoms, improve functioning, and, when possible, cure disease.

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Patients should know that a health plan or MCO will use medical necessity (and perhaps other criteria) in making reimbursement decisions that will affect the type, frequency, and duration of treatment available to them. Also, they should be made aware that some treatments may not be covered should the plan decide that the treatment is not medically necessary. Moreover, they should be informed that even approved treatments will be monitored and reviewed, and that continued coverage depends on many different factors, including a determination by the plan or MCO that continued treatment is medically necessary. This knowledge is best imparted at the outset of treatment. Patients will be better able to choose whether to embark on a particular course of treatment and to consider whether they will pay for some or all of the treatment themselves. However, for some patients, especially those with severe or persistent mental illness, their condition may impair cognitive abilities or judgment and render them less able to make sound decisions or to advocate on their own behalf. For other patients, the experience of illness creates great anxiety. When a patient is in this position, the clinician should optimize the patient’s participation, engage significant others, and pursue what the clinician believes is in the patient’s best interest. Physicians should always document their own thinking with respect to treatment plans, the nature of the patient’s participation in that plan, and any pertinent information related to the likely success of the plan. Lastly, some argue that clinicians have an implicit responsibility to assist patients by advising them and, when needed, advocating for them with employers, insurance companies, family members, and others with whom they have important relationships. Thus, denials of reimbursement should be discussed with patients. Also, patients wishing to appeal decisions denying reimbursement for treatment should be supported by their clinician when the clinician believes the treatment is medically necessary. Of course, such support should be provided within the bounds of the doctor–patient relationship and the physician’s scope of expertise. Avoiding Incentives to Ration Care When clinicians are forced to chose between economic imperatives and the interests of their patients, they cannot fulfill patients’ spoken or unspoken expectations for an ethically sound doctor–patient relationship that puts their patients’ interests first (American Medical Association Council on Ethical and Judicial Affairs 1990). Patients need to be informed of any financial incentives that may discourage clinicians from recommending a particular treatment or cause them to withhold it (Levit et al. 1996). Examples of such incentives include bonuses or withheld payment keyed to a


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plan’s overall financial performance. The clinicians may also be subject to provider profiling or may have concerns about recommending a particular treatment that runs counter to the treatment philosophy of the MCO. Incentives to ration care may also exist for clinicians who participate in provider-sponsored networks (PSNs) that compete for or manage capitated contracts. In PSNs, primary care physicians (e.g., internists, family practitioners, and pediatricians) typically function as gatekeepers and stand to benefit financially by practices that limit access to care, especially specialty care. Furthermore, even specialists, including psychiatrists and other mental health professionals, may function as preferred providers, receiving either a fee for service or a prospectively determined, subcapitated fee for providing specialty services to a defined population. Some health plans also offer specialists profit-sharing arrangements, ownership interest, year-end bonuses, or other incentives for holding down the costs of the care they provide. Regardless of the arrangement, specialists who provide services in or to these systems experience considerable pressure to provide those services judiciously. Decisions about the allocation of care are not new to the practice of medicine. Triaging patients in emergency departments, placing patients on waiting lists, or providing care for noncovered services only to those who can afford it are examples of how allocation decisions have been made. These decisions are not made solely on the basis of medical necessity (i.e., who needs treatment the most) but instead are often influenced by social and economic factors. However, organized care systems, particularly those at financial risk, have recently adopted a public health model for allocating limited medical resources (Hall and Berenson 1998). In this model, allocation decisions (i.e., rationing) are defended with the suggestion that medical care is a scarce resource to be tapped judiciously lest there be nothing left for those who really need it. Although it is clearly in the public interest to assess cost effectiveness and avoid rendering costly but ineffective treatment, the public allocation paradigm raises ethical concerns for practitioners in the United States. For those who believe that access to health care is a right rather than a privilege and that doctors must advocate for individual patients, allocation models based on rationing may seem unethical. Regardless of the philosophy of the MCO or incentives employed, the individual clinician must take steps to ensure that he or she is not unduly pressured to withhold needed care in the interest of meeting budgetary targets or enhancing his or her own financial positions. Ideally, patients enrolled in health plans should be involved in defining the criteria and circumstances in which certain types of care, particularly high-cost and/or experimental treatments, are available. Physicians, including representa-

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tives from outside organizations, must play a role in developing the framework for treatment allocation decisions. In this respect, the use of evidence-based practice guidelines and quality indicators, such as those developed by the American Psychiatric Association (American Psychiatric Association 1995a, 1999) and other medical specialty societies (American Academy of Child and Adolescent Psychiatry 1998), may be very useful. These efforts are most effective when tailored to specific patient populations and when they account for the treatment needs of outliers. Nevertheless, there will still be high-severity, high-cost cases that require decision making by specially constituted groups. These should include physicians from within the treatment system and also representatives from the clinical and lay community who have no direct financial stake in the outcome of the decision-making process. If clinicians are to fulfill their ethical obligation to patients, they must first act as caregivers and advocates before taking on the role of owners or employees.

Responsibility of Institutions and Health Care Systems Some feel that the enormous cost containment pressures and the erosion of authority over fundamental treatment decisions are justly deserved. Critics of the traditional medical system charge that the frequency of outpatient visits, the ordering of psychological and biological tests, and the duration of inpatient hospital stays were dictated more by the availability of insurance coverage than by patient needs. From this perspective, mental health and substance abuse care is viewed as a precipitant to the emergence of managed care. In the 1980s, stigma faded and the use of inpatient, outpatient, and partial hospital services for patients with mental illness increased. MCOs and other payors responded quickly with cost containment strategies that resulted in dramatic reductions in inpatient treatment and other services. At the same time, tightly managed outpatient resources made it difficult to develop or expand alternative services for the most seriously ill. The financial impact on institutions has been enormous, and growing deficits have led to cutbacks in staff, mergers, and closures. As institutional losses from poorly negotiated contracts have mounted, so has the burden of free care and bad debt from treating uninsured or underinsured patients. This has caused some not-for-profit institutions to reexamine their charitable mission: how can they sustain their historic commitment to treat all patients regardless of the type or severity of their illness and their ability to pay while at the same time remaining financially viable? What are the ethical responsibilities of institutions and practitioners?


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Role of Professional Organizations Medical associations also have an obligation to enhance the scientific knowledge base of their respective fields, to offer continuing medical education for members, and to provide accurate medical information to payors, policymakers, and the general public. Moreover, associations have a responsibility to develop standards for the training of current and future physicians and to define and enforce the ethical standards of the profession (American Psychiatric Association 1998). Enhancing Access to Care Because managed care has affected medical generalists and specialists alike, medical societies have attempted to address the ethical issues faced by their members in daily practice. The American Psychiatric Association, for example, asked its Council on Psychiatry and the Law to develop a resource document on the professional responsibility of psychiatrists in evolving health care systems (Hoge 1996). The council defined ethical principles that must be preserved in any system of health care delivery regardless of the mechanism used for financing care and also examined the impact of current managed care practices on these principles. Many of the council’s recommendations were instrumental to the principles articulated in this chapter. The American Medical Association Council on Ethical and Judicial Affairs (CEJA; 1990) has also considered the impact of changes in the financing and delivery of health care on the doctor–patient relationship. The council concluded that the welfare of patients is always primary, regardless of the organizational setting in which care is delivered or the contractual agreements governing reimbursement. It emphasized the need for physicians to fully disclose to their patients any financial incentives for withholding care, to avoid developing guidelines that restrict care, and to advocate for treatment that may noticeably benefit their patients. Physicians are also urged to play an active role in discussions about the allocation of care and to initiate appeals when patients are denied essential care. The council recommended that managed care plans fully disclose any limitations in coverage and urged physicians to discuss all relevant treatment options, regardless of whether they are covered by the patient’s health plan. Finally, physicians were advised not to participate in plans in which care does not meet minimum professional standards. Addressing Physician Financial Incentives The American Medical Association council also considered the complicated problem of physician financial incentives, citing the potential con-

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flicts of interest inherent in such arrangements. In so doing, the council noted the difficulty in distinguishing incentives designed to promote the delivery of cost-effective care from those that encourage withholding of medically necessary care. The CEJA guidelines advocate full disclosure to patients of coverage limitations and clinician incentives to limit care to plan subscribers. The council also stressed that there be limits on the magnitude of incentives to individual physicians and that incentives be based on the performance of large groups rather than individual physicians. Finally, and perhaps most important, the CEJA guidelines call for health plans to develop clinician incentives based on measurable benchmarks of quality rather than on clinician or plan financial performance. Defining Quality Care Professional organizations have also addressed the cost/quality debate by working to define minimum standards of care. For example, the American Psychiatric Association (1993, 1994, 1995b, 1997) has developed evidencebased practice guidelines for the treatment of various mental disorders. More recently, the association has begun to distribute evidence-based quality indicators for both adults and children (American Psychiatric Association 1999). The dissemination and adoption of practice guidelines and quality indicators coupled with the growing emphasis on developing objective measures of clinical outcome may help shift the focus from cost cutting to cost effectiveness of care. Guidelines and indicators promise to provide accrediting organizations (e.g., National Committee for Quality Assurance, Joint Commission on Accreditation of Healthcare Organizations) and consumers with the tools they need to assess the quality of care being offered. Unfortunately, both health plans and individual practitioners have been slow to adopt these tools. However, use of these quality measures by health plans is inevitable as accrediting bodies employ an outcomeoriented approach to accreditation and when payors and consumers base their purchasing decisions on quality data. The American Psychiatric Association also has argued that scope of practice issues are relevant to the debate over quality (Lehman 1995). In mental health, the tendency of MCOs to shift responsibility for many services away from psychiatrists to lesser-trained, lower-cost, nonphysician practitioners (e.g., nurses, psychologists, social workers) has raised concern that reducing costs has taken precedence over patient needs. The cost effectiveness of treatment not provided by psychiatrists is questionable, particularly for patients with more severe illnesses for whom pharmacologic and psychosocial intervention should be integrated rather than fragmented.


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SUMMARY The health care system in the United States has come to be dominated by various forms of managed care in which the demand to contain costs is more pressing than the need to improve care. The result, as members of Congress are now hearing from constituents, is a system in which access to appropriate care is not assured and the quality of care delivered is uneven. Although purchasers of mental health services, including employers and federal and state governments, maintain that they want quality, relatively few companies or government agencies have embraced a philosophy in which human capital is viewed as an important investment. For many employers, paying the cost of health insurance is viewed as eroding rather than enhancing shareholder value. Consequently, employers aim to reduce the cost and resist the adoption of state or federal legislative mandates for mental health and substance abuse parity of insurance coverage. Government-sponsored health plans have also turned to MCOs to control the use and costs of mental health and substance abuse care. All too often, however, state contracts for carve-out care are brief (e.g., 2–4 years) and build in little incentive for approaches that may improve the health of the covered population over a longer period of time. Failure to deliver the cost savings demanded by state agencies bodes ill for contract renewal. A shift in focus to an outcome-driven payment methodology is at present a goal, not a reality. The advent of managed care and the growing competition for health care dollars among payors, MCOs, and practitioners are straining the ethical foundations of our field to the limit. Fashioning an ethically responsible future will require the thoughtful participation of clinicians, ethicists, policymakers, and—most important—consumers of care.

REFERENCES American Academy of Child and Adolescent Psychiatry: Practice parameters for the assessment and treatment of children and adolescents with depressive disorders. J Am Acad Child Adolesc Psychiatry 37(suppl):63S–83S, 1998 American Medical Association Council on Ethical and Judicial Affairs: Financial incentives to limit care: financial implications for HMOs and IPAs, in Code of Medical Ethics: Reports of the Council on Ethical and Judicial Affairs of the American Medical Association. Vol 1. Chicago, IL, American Medical Association, 1990, pp 130–135 American Medical Association Council on Ethical and Judicial Affairs: Opinion 8.13: Referral of patients—disclosure of limitations, in Code of Medical Ethics: Current Opinions With Annotations. Chicago, IL, American Medical Association, 1994

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American Medical Association Council on Ethical and Judicial Affairs: Ethical issues in managed care. JAMA 273:330–335, 1995 American Psychiatric Association: Practice guideline for major depressive disorder in adults. Am J Psychiatry 150(suppl):1–26, 1993 American Psychiatric Association: Practice guideline for the treatment of patients with bipolar disorder. Am J Psychiatry 151(suppl):1–36, 1994 American Psychiatric Association: Practice guideline for psychiatric evaluation of adults. Am J Psychiatry 152(suppl):63–80, 1995a American Psychiatric Association: Practice guideline for the treatment of patients with substance use disorders: alcohol, cocaine, opioids. Am J Psychiatry 152(suppl):1–59, 1995b American Psychiatric Association: Practice guideline for the treatment of patients with Alzheimer’s disease and other dementias of late life. Am J Psychiatry 154(suppl):1–39, 1997 American Psychiatric Association: Strategic Goals and Priorities. Washington, DC, American Psychiatric Association, 1998 American Psychiatric Association: Report of the American Psychiatric Association Task Force on Quality Indicators. Washington, DC, American Psychiatric Association, 1999 Davidson JR, Davidson T: Confidentiality and managed care: ethical and legal concerns. Health and Social Work 21:208–215, 1996 Fox A: The Yearbook of Managed Behavioral Health Market Share in the United States, 1999–2000. Gettysburg, PA, Open Minds, 1999 Hall MA, Berenson RA: Ethical practice in managed care: a dose of realism. Ann Intern Med 128:395–402, 1998 Healy GB: Can we be faithful to the Hippocratic Oath in the era of managed care? Arch Otolaryngol Head Neck Surg 124:497–498, 1998 Hoge KS: American Psychiatric Association Resource Document 1: the professional responsibilities of psychiatrists in evolving health care systems. Bull Am Acad Psychiatry Law 24:393–406, 1996 Lehman C: Psychologists press harder to prescribe. Psych News Vol. 30, Number 6, 1995 Levit KR, Lazenby HC, Sivarajan L, et al: National health expenditures, 1994. Health Care Finance Rev 17:205–242, 1996 Packer S: Capitated care is unethical. Arch Opthalmol 115:1194–1996, 1997 Pearson DS, Sabin JE, Emanuel EJ: Ethical guidelines for physician compensation based on capitation. N Engl J Med 339:689–693, 1998 Rice DP, Kelman S, Miller LS: The economic burden of mental illness. Hospital and Community Psychiatry 43:1227–1232, 1992 Rowland D, Hanson K: Medicaid: moving to managed care. Health Aff 15:150– 152, 1996 Wickline v State. 192 Cal App 3d. 1639, 1645;239 Cal Rptr 810, 819 (1986)

3 Quality of Care in an Era of Wall Street Medicine Lloyd I. Sederer, M.D.

INTRODUCTION: GLOBAL CORPORATIONS “Money makes the world go round…,” sang the night club maestro in the show Cabaret. Indeed, that claim, made some years ago, rings ever more true today. One vast market, globalization, embraces industry around the world. Consider: of the largest 100 economies in the world, 51 are corporations (49 are countries); 500 of the world’s largest corporations produce 70% of the world’s trade; 200 of the world’s largest corporations account for 28% of global economic activity (although they employ less than 1% of the international work force) (Kaplan 1997). Money, the terra firma of global industry, is making this planet go around. How might we understand this global market? Thomas Friedman of The New York Times wrote that it is an electronic herd of anonymous stock, bond and currency traders, sitting behind computer screens. The members of this herd live everywhere there is a trading floor, everywhere there is a Bloomberg machine, everywhere that someone with a computer screen and modem can buy and sell…stocks and bonds.…The electronic herd cuts no one any slack…. The herd only recognizes its own rules…the herd feeds in 180 countries, so it doesn’t have time to look…in detail. (Friedman 1997, p. A25)

This is a chilling prospect and a brutal reality in a global market in which money moves electronically at almost the speed of light. But what does it have to do with health care? A great deal. 37

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INVASION OF HEALTH CARE BY WALL STREET Evidence that the stock market has firmly entrenched itself into health care is everywhere. Six key measures quantify Wall Street’s increasing involvement in health care (Abelson 1999; Srinivasan et al. 1998). 1. Initial public offerings (IPOs): health care organizations offering stock for the first time on Wall Street. Funds generated by these offerings are used to capitalize the health care industry. Over $6 billion in IPOs in the past 10 years have supported health maintenance organizations (HMOs) and other corporate offerings, including medical practices, hospitals, clinical laboratories, and home health services. 2. Stock market funds: the degree to which the equity market finances medical care. In the past, bonds were the common instrument used to fund health care institutions, especially nonprofit hospitals. In the 1980s, only 20% of external capital for HMOs was derived from stocks, but that figure soared to 69% by 1997 (Srinivasan et al. 1998). 3. Market capitalization: the total value of particular types of health care stocks in circulation. As the stock market increased fourfold in value during the 1990s, HMO and other health care stocks also exploded; HMO capitalization grew more than 12-fold, from about $3 billion to almost $39 billion. For health care corporations other than HMOs, the increase was sevenfold, with total dollars in the range of $112 billion for the same time frame. 4. Stock market pricing trends: the tendency of health care stocks to rise or fall in value. HMO stocks generally outperformed the Dow Jones index in the decade prior to 1996; HMO investors realized almost twice as much return on investment from their stocks as they would have made from the market index. Although significant declines have occurred since 1999, futurists suggested that investments in the health care industry would again appeal to Wall Street when HMO premiums increased. In 2001, their projections about premium increases became true. 5. Wall Street pundits: financial analysts who specialize in health care. The number of health care investment analysts has grown from 152 in 1987 to 559 in 1997, with most of that increase occurring in the last 4 years of that period. 6. Mergers and acquisitions: the consolidation of health care organizations, which reflects industry growth. Almost 3,000 health care corporate mergers and acquisitions have occurred, peaking in 1996. By this measure, the market in health care corporate activity has been brisk.

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Wall Street has indeed invaded the main streets of health care. Yet with all this health care business going on, there is little, if any, indication that the market is truly interested in the well-being of individuals. The sole measure of success is money, not high-quality care or patient satisfaction.

IMPLICATIONS OF CORPORATE MEDICINE In a summary of the economics of health care for JAMA, Princeton economist Reinhardt (1997) described the securitization of a commodity or service. Securitization is the sale of the future ownership rights of cash likely to be generated by a business, whether its product is a service or a commodity. In effect, a purchaser makes an investment in the prospect that a business will yield significant future cash to make a current investment pay off. This is reasonable if the cash derives from automobiles or computer chips. However, when the value of health care securities comes from limiting patient care expenditures, the patients, their families, and clinicians all suffer (Pear 1998). As Reinhardt explained, “patients can be viewed as biological structures that yield future net cash flows” (1997, p. 1850). Whoever has ownership of the future cash from the activities of a corporation can also sell this share of the prospective pie in the global marketplace. In buying a future cash flow, the purchaser makes a bet that a profit will be achieved. The larger the profit, the greater the return on investment. However, in the case of health insurance and managed care organizations (MCOs), the profit derives from the difference in the premiums paid today and the money expended on care in the future. Securitization is at work when we hear employers refer to insured lives or covered lives. For example, if a person insured by a health plan can be expected to yield a profit of $300 per year over 5 years as a subscriber, the anticipated profit of $1,500 (i.e., at the end of those years) can be sold today on Wall Street for several times its value, because the health plan can take today’s sale price and invest it in other enterprises (where money managers anticipate nearer-term profit). The “so-called present-value equivalent of this future cash flow…happens to be the going market rate of an insured life at this time” (Reinhardt 1997, p. 1850). However, the profit is to be secured (hence, securitization) from patient care. To ensure this profit and have a marketable future, the insurance industry must control health care expenditures by tightening utilization review, limiting referrals, withholding payment, and enacting other financial penalties.

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WHAT DOES THE AMERICAN PUBLIC THINK AND WHAT DOES IT WANT? Americans are worried that they will not receive the care that they believe they need at the time at which they need it. A summary of recent polling data reveals that the American public recognizes that the dramatic changes achieved by managed care have wrought troubles on those trying to obtain care. Many more anticipate difficulties when their health fails and they require more care (Blendon et al. 1998; Enthoven and Singer 1998). For example, Blendon et al. (1998) reported that 45% of Americans think that managed care has decreased the quality of care, and only 32% view managed care as having improved quality. Moreover, 54% report that quality of health care will deteriorate in the future. Of those sampled (under age 65) who are enrolled in a heavily managed care plan, 61% believe that the plan is more concerned with saving money than with providing the best treatment for its subscribers when they are sick. Furthermore, 72% of respondents believe that any money saved is used to increase the profits of the health plan. These authors also found that the recommendation of a health plan by a friend or family member carries more weight than independent measures of a plan’s performance. Not surprisingly, people trust their own experiences and the experiences of those they know when making determinations of quality and when choosing to whom they will turn to for medical care. The public is far from regarding objective “report cards” as reliable measures of quality. Blendon’s group concluded that many Americans are experiencing and reporting problems with managed care. Satisfaction with managed care plans falls below that experienced in fee-for-service plans. Although rare, the increased reports in the media of catastrophic health care events have added to the uneasiness of subscribers and have fueled various governmental task forces and legislative initiatives. Lacking confidence that independent quality measurement organizations (e.g., National Committee for Quality Assurance [NCQA], Joint Commission on Accreditation of Healthcare Organizations [JCAHO]) will ensure good care, Americans do not believe they will receive the type and quality of services they will need when illness strikes. A restive public, with few reassuring alternatives, has increasingly sought regulatory solutions, despite the protestations of the insurance industry. Trust is the cornerstone of all doctor–patient relationships. As new methods of risk-based reimbursement place doctors in financial peril, the public is concerned that doctors will not act in the best interests of their patients. Kao et al. (1998) studied the relationship between payment and patient


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trust. Although they found that most patients trusted their doctors, patients with fee-for-service indemnity insurance had greater trust in their physicians than did those receiving care from salaried or capitated physicians or physicians receiving other methods of reimbursement that offer an incentive to provide less, rather than more, care. The authors also found that certain physician behaviors (e.g., spending more time with patients) reduced the impact of payment type on trust. David Lansky, president of the Foundation for Accountability, has described five principal consumer priorities: (1) The basics: Will I be treated respectfully, understand what is said to me, and have access to needed services and providers? (2) Staying healthy: Will I be able to stay as healthy as possible—through education, health promotion, preventative services, and early detection of disease? (3) Getting better: If I get sick, will I get better and regain normal functioning? (4) Living with illness: If I suffer from a chronic condition, will I be able to maintain the best possible functioning, minimize undesirable symptoms, and learn how to care for myself? (5) Changing needs: As I face death or disability in my family, will we be able to cope, minimize pain and suffering, and maximize spiritual and family comfort? (Lansky 1998, pp. 40–41)

An ideal system of care tries to provide what consumers want: choice, access, quality, and affordability, while also attending to the interpersonal aspects of care valued by patients.

CAN THERE BE CHECKS AND BALANCES BETWEEN WALL STREET AND SOCIALLY RESPONSIBLE MEDICAL CARE? The provision of high-quality, humane, and timely medical care is fundamentally at odds with the business ethos that unequivocally requires financial returns to investors. Three points can orient us to their differences. First, medical care in most industrialized countries is broadly held as a social good, exemplified by the distinction between the provision of care and comfort (the ethical obligations of physicians, nurses, and other health caregivers). In contrast, investor-owned, for-profit corporate enterprises are expected to return a reasonable profit to their investors. Although doing a social good may be desirable, it is not the primary obligation of corporate management. Instead, it is the nonnegotiable hegemony of the profit motive that will, if necessary, subordinate other goals. For example, when some HMOs in the United States could no longer draw a profit from providing care to Medicare recipients, they discontinued selling policies to these beneficiaries. The distinction between the priorities of medicine and

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for-profit business is again illustrated in the case of a psychiatric patient in a hospital who is not clinically safe to leave but lacks health insurance or the ability to pay for services. Medical ethics mandate attention to this person, whereas business mandates rapid disposition (sometimes called “dumping”) of this person in favor of a more lucrative consumer. A second difference is between what might be called human demand and market demand. Patients and families want rapid access, choice, quality, low out-of-pocket expenses, and decent and respectful treatment. These demands are intensified when they are sick. Market demand for products is driven by supply, competition, capital, and distribution channels. Individuals (human demand) have been kept at arm’s length from the health care market because they have not been the principal purchasers of care (Enthoven 1993). Most consumers do not feel the full force of health care costs because benefit plans are paid for by business or the government; cost is experienced only indirectly by individuals and families. As a consequence, businesses, governments, insurers, and investors are the principal agents of market demand. The third difference lies in the processes of decision making. The practice of medicine seeks to make clinical decisions based on empirical evidence of effectiveness. For-profit corporations make decisions on the basis of profit and loss. Of course, layers of admission and continued stay criteria, care paths, and clinical algorithms frame a set of appropriateness standards that help to check the drive to the bottom line. However, insurers and MCOs are in the business of bringing medical costs down, which is fundamental to their contractual obligations to their customers—that is, corporations and government agencies, not patients.

Checks and Balances Can these disparate goals of medicine and business be balanced and checked against one another? Three major areas may help to check or balance the power of Wall Street. Each has strengths and limitations and proponents and detractors. Financial Incentives Designing financial incentives within health care systems is one means of trying to resolve the dilemmas of the business of medicine. Pearson et al. (1998) proposed ethical guidelines to mitigate growing patient distrust of physicians engaged in risk-based contracting. Fee-for-service payment traditionally rewarded physicians for greater amounts of care, which fostered the overuse of some services. Physician ownership of diagnostic centers was an example of a conflict of interest in which the federal government inter-


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vened because of evidence of overuse. Financial incentives that encourage physicians to withhold care or place them at risk for financial loss if they exceed their budget evoke anxiety in patients and families (Abrams 1993; Kassirer 1998). Pearson and associates suggested that certain types of payment systems for physicians might reduce conflicts of interest by limiting the percentage of income at risk, by distributing the risk over large groups of enrollees and physicians, or by building in risk-adjusters. However, their proposal was based on the voluntary adoption of an untested plan. This neither provides a strong check nor offers balance for a system that will be subject to powerful external forces. Regulation Brennan and Berwick (1996) defined regulation as “any set of influences or rules exterior to the practice or administration of medical care that imposes rules of behavior” (pp. 710–711). The influences and rules especially affecting health care are litigation (malpractice suits), legislation, licensure and accreditation requirements, mandatory reporting requirements, and contracting. Each type of regulatory influence has shaped medical practice but may not have had a positive effect on quality (Brennan 1998). Litigation. Litigation, in federal and state jurisdictions, can be a powerful influence on physician behavior and may serve to deter as well as punish negligence. Although malpractice suits against physicians can help identify poor-quality doctors, most errors in medicine are the product of system-wide problems and limitations (Leape et al. 1993). Malpractice suits against HMOs and MCOs have been largely unsuccessful until very recently (Sederer 1992). These organizations have successfully argued that they are not practicing medicine but rather are meeting contractual administrative and financial obligations. Fortunately, the tide is beginning to turn. Courts are beginning to recognize that denial of benefits amounts to denial of care. We are beginning to see court judgments of medical liability against payors and insurers. Litigation is a crucial wedge in forcing businesses to recognize that economic decision making has profound human and medical consequences. Legislation. State and federal legislative actions can serve as powerful regulatory tools. Many states have enacted legislation regulating managed care (Mariner 1996). Nevertheless, legislative remedies are very slow in coming and can be imprecise and cumbersome. Those that have been passed to date have been highly prescriptive (e.g., no drive-through deliveries) or easily eluded (e.g., parity in mental health benefits). Legislation that mandates outcome measurement and quality improvement has yet to pass.

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Ayres and Braithwaite (1992) introduced the concept of responsive regulation. They argued against mindless, bureaucratic, and punitive policies and argued for self-regulation within predetermined parameters, innovation, and quality improvement based on the specific needs and problems of each unique facility and specialty practice. Responsive regulation is an important direction for state government departments of health and mental health, JCAHO, NCQA, and MCOs to consider as they shape their regulatory policies for the future. Consumer education and involvement in legislation and regulation of health care is another promising trend. Senior citizens and other special interest groups know the potential of this approach very well and have had considerable success in influencing Medicare medical entitlements. Others can learn from the organization and advocacy efforts of these groups. Licensure and accreditation. Physicians, nurses, other health professionals, insurers, and MCOs are licensed by state agencies. There is little evidence that licensure requirements ensure quality or foster its improvement (Andrew and Sauer 1996). Although suspension or revocation of licensure is a powerful action, it is seldom executed and then only to weed out a very few gross outliers. On the other hand, accreditation through state agencies or national voluntary accreditation organizations (e.g., NCQA, JCAHO) is becoming a major force in making performance measurement an essential aspect of the provision of many types of medical service. Clinical performance measurement is a relatively new addition to efforts to balance cost and quality. Performance measurement is the use of data on measurable and meaningful service indicators to compare providers, groups, or systems of care and to identify opportunities for quality improvement. Measuring clinical outcome affords an objective and credible way to compare costs and quality control (and draws from the quality movement of other industries) (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1998; Deming 1986; Sederer and Dickey 1996). The holy dyad of accreditors, JCAHO and NCQA, has begun to systematically introduce performance measurement into the accrediting process for hospitals and health plans. The American Medical Association has joined JCAHO and NCQA in establishing a Council on Performance Measures. The American Psychiatric Association created a Committee on Quality Indicators that will help shape the organization’s quality objectives for the years ahead. This quality initiative, along with an expanding set of practice guidelines and publications, will offer methods for implementing and measuring empirically based standards of psychiatric care and, ultimately, a rational means for deciding which cost containment measures have a basis in evidence.


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Patients and practitioners have much to gain from regulatory accreditation initiatives that introduce quality measurement into the delivery of health care. This may over time become a solid method to check and balance business and medicine. Scientifically based quality measurement, properly risk adjusted, has promise to become an important force in protecting patients and our health care system from business exploitation. However, the work to be done in this field, which is in its infancy, is great (Iezzoni 1997; see also Chapter 7). Resources to cover the costs and burden of this work must become a recognized budget item in health care, with adequate funding by business and governmental payors. Mandatory reporting. An increasing number of states are publicly releasing data on the performance of hospitals. Perhaps the best-known clinical example is that of cardiac surgery (Hannan et al. 1994). We can anticipate similar performance measures in psychiatry, such as rates of restraint and seclusion and the occurrence of complications, medication errors, and adverse drug events. Moreover, the mandatory reporting for each managed care company seems to be different, leaving clinical facilities drowning in a sea of unstandardized, burdensome, often meaningless performance demands. Rates are meaningless unless put in their proper context and used to foster quality improvement rather than to mete out punishment. Public pressure to punish poor performance—by individuals or hospitals—is a great danger to the public reporting of data. Contracting. Several states are employing contracting mechanisms to regulate the quality of medical care (Rosenbaum et al. 1998). In Massachusetts, a statewide managed care contract for Medicaid-reimbursed mental health and substance abuse services, issued by the state agencies for Medicaid and mental health, established quality measures that will affect payments to the MCO as well as to participating clinical services. In principle, contracting is a desirable means of regulating quality. Contracts can introduce financial incentives to improve care, be shaped to meet the specific needs of the population being served, be changed when circumstances warrant, and have the force of law. Let the contractee beware, however; in Massachusetts, performance goals for Medicaid mental health/substance abuse managed care were not developed by mental health professionals and underestimated the effort required to meet the goals. Ethics Professional ethics are a critical check on the business of medicine. Being a doctor, a nurse, or a professionally trained clinician is a morally directed service to others—self-interests subordinated to the good of others. The

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ethos of medicine requires that care be provided when possible, comfort be ensured, and that both be given without causing harm. These values must be central to and built into the standard operations of every professionally driven health care system. Leadership must articulate a moral and quality mission. The medical staff must construct, implement, and report on ethical and moral standards and remedy problems when they occur. Without a professionally driven moral compass, health care will never find its way.

CONCLUSIONS When Robert Kuttner (1998a, 1998b) asked “Must good HMOs go bad?” he questioned whether the intense market pressures of managed care would break the will of even the most established and quality-oriented health plans and compel them to compromise good care. Kuttner acknowledged many of the checks and balances articulated in this chapter (i.e., legislation, regulation, and professional ethics) but concluded with uncertainty as to the fate of good health care organizations. Kuttner suggested that the health care system in the United States is on a path of investor-owned or investor-financed services that may preclude other, perhaps superior, paths of innovation. Because financial risk is now a sizable factor in commercial insurance plans (and growing for Medicaid and Medicare) and risk adjustment is more a notion than a reality, the power of Wall Street and corporate medicine threatens to destroy any health care providers who take on the costly care of the most seriously and chronically ill. Kutter’s cautionary advice warrants our attention. Since the introduction of Medicare, medicine in the United States has achieved unprecedented growth and technological dazzle. Clinical care, research, and the education of the next generation of caregivers has never been more sophisticated or promising. No one travels from the United States to another country to obtain medical care, and the provision of care (especially hightech care) in the United States to international patients is limited principally by their ability to afford it. What we have achieved—what serves us and our loved ones—is imperiled by a profit ideology headquartered on the “Wall Streets” of the world. One remarkable goal of recent decades—the creation of a common medical standard of care for rich and poor alike—is especially endangered. When profit drives health care, services become less accessible and require higher out-of-pocket deductibles and copayments, and new drugs, procedures, and technologies are subjected to greater utilization review and management. Growing numbers of uninsured, especially employed uninsured, fall out of the care system. The poor, the powerless, and the unedu-


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cated suffer most. The educated and privileged have a better chance of getting what they need. Any social leveling that has been accomplished may give way to the injustices of the all-too-familiar two-class system. These are injustices that stir unrest, prompt social firestorms, and further class division in this country. Health care services and technologies in the United States remain the envy of the world. But for-profit health care threatens what we have achieved. The jury is out as to whether Wall Street corporate medicine, firmly entrenched since the 1980s, is beyond repair. For now, medicine must not lose sight of its moral mandate built into the standard operations of every clinical care system. If quality is protected by the government, pursued in the courts, and relentlessly demanded by the citizens of this country, then perhaps our legacy of putting patients first will be preserved.

REFERENCES Abelson R: For managed care, free market shock. New York Times Op Ed, January 3, 1999, p 9, Week in Review Abrams FR: The doctor with two heads: the patient versus the costs. N Engl J Med 328:975–976, 1993 Advisory Commission on Consumer Protection and Quality in the Health Care Industry: Quality First: Better Health Care for All Americans. Washington, DC, U.S. Government Printing Office, 1998 Andrew G, Sauer H: Do boards of medicine really matter? Federation Bulletin 83:228–236, 1996 Ayres I, Braithwaithe J: Responsive Regulation: Transcending the De-regulation Debate. New York, Oxford University Press, 1992 Blendon RJ, Brodie M, Benson JM, et al: Understanding the managed care backlash. Health Aff 17:80–94, 1998 Brennan TA: The role of regulation in quality improvement. The Milbank Quarterly 76:745–747, 1998 Brennan TA, Berwick DM: New Rules: Regulation, Markets and the Quality of American Health Care. San Francisco, CA, Jossey-Bass, 1996 Deming WE: Out of the Crisis. Cambridge, MA, Massachusetts Institute of Technology Press, 1986 Enthoven AC: Why managed care has failed to contain costs. Health Aff 12:27–43, 1993 Enthoven AC, Singer SJ: The managed care backlash and the task force in California. Health Aff 17:95–110, 1998 Friedman TL: Foreign affairs. New York Times Op Ed, September 29, 1997, p A25 Hannan EL, Kilburn H, Racz M, et al: Improving the outcome of coronary artery bypass surgery in New York state. JAMA 271:761–776, 1994 Iezzoni LI: Risk Adjustment for Measuring Health Care Outcomes, 2nd Edition. Chicago, IL, Health Administration Press, 1997

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Kao AC, Green DC, Zaslavsky AM, et al: The relationship between method of physician payment and patient trust. JAMA 280:1798–1714, 1998 Kaplan RD: Was democracy just a moment? Atlantic Monthly Dec:55–81, 1997 Kassirer JP: Doctor discontent (editorial). N Engl J Med 339:1543–1545, 1998 Kuttner R: Must good HMOs go bad? First of two parts: the commercialization of prepaid group health care. N Engl J Med 338:1558–1563, 1998a Kuttner R: Must good HMOs go bad? Second of two parts: the search for checks and balances. N Engl J Med 338:1635–1639, 1998b Lansky D: Measuring what matters to the public. Health Aff 17:40, 1998 Leape LL, Lawthers AG, Brennan TA, et al: The preventability of medical injury. Quarterly Review Bulletin 19:144–151, 1993 Mariner WK: State regulation of managed care and the Employee Retirement Income Security Act. N Engl J Med 335:1986–1990, 1996 Pear R: Insurance plans skirt requirement on mental health. The New York Times, December 26, 1998, pp 1, 15 Pearson SD, Sabin JE, Emanuel EJ: Ethical guidelines for physician compensation based on capitation. N Engl J Med 339:689–693, 1998 Reinhardt UE: Economics. JAMA 277:1850–1851, 1997 Rosenbaum S, Smith B, Shin P: Negotiating the New Health System: A Nationwide Study of Medicaid Managed Care Contracts, 2nd Edition. Washington, DC, Center for Health Policy Research, George Washington University Medical Center, 1998 Sederer LI: Judicial and legislative responses to cost containment. Am J Psychiatry 149:1157–1161, 1992 Sederer LI, Dickey B: Outcomes Assessment in Clinical Practice. Baltimore, MD, Williams & Wilkins, 1996 Srinivasan S, Levitt L, Lundy J: Wall Street’s love affair with health care. Health Aff 17:126–131, 1998

4 Can Managed Care Deliver Quality? Mary Durham, Ph.D.

Managed care has become a major topic of public discourse in the United States. From the news media to the halls of the United States Congress, hostility, fear, and anger are directed toward nameless companies and faceless bureaucrats because they have wrested control of medicine from doctors. Angry Americans have united against the evil forces of “managed care,” even though managed care has existed in health maintenance organizations (HMOs) for well over 50 years. Smith (1998) noted that three factors characterize unmanaged care: “(1) a patient who had health insurance could go to ANY doctor he or she wanted; (2) the doctor prescribed ANY drug or performed ANY test that he or she wanted; and (3) the doctor could charge ANYTHING that he or she wanted and get paid!” (p. 1). The term managed care is now being used, according to Smith, to refer to health care practice that changes any one or more of these three characteristics. Indeed, many managed care plans create systems that do not permit patients to go to any doctor or have any drug covered by their health care benefit. Without a doubt, managed care has changed the financial incentives of medical practice in the United States. Under fee-for-service care, providers are paid for virtually any treatment regardless of whether it is proven and/or prudent. Under managed care, there is a penalty for providing unnecessary treatment. Many managed care plans are paid a fixed premium per enrolled individual (capitation) regardless of the number of services provided to each person (Luft 1985), although it is easy to argue that capitation creates an incentive for withholding treatment (Kuttner 1999). These fundamental changes have turned American medicine on its head. Short-term profiteering by investors and entrepreneurs abound. The short49

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comings of fee-for-service care pale by comparison to these get-rich-quick schemes. Overproviding or overcharging for care is much less threatening to consumers than the abuses of profit-driven, managed health care, especially because most Americans still do not pay for their benefit premiums. By definition managed care and its first cousin, capitation, neither prescribe the removal of clinical decision making from providers nor deny needed services or tolerate shortcuts that compromise high-quality patient care. In fact, in well-run managed care plans, declining patient functioning and the need for hospitalization represent financial failure and poor clinical performance (Durham 1999). Capitation rewards providers financially when members are kept well. Expenditures accumulate when preventive care is ignored and expensive hospitalization or other long-term (more expensive) treatment results. In theory, capitation works best when health care providers help members prevent disease and intervene early and continuously to help people with chronic illnesses achieve their maximal level of functioning. Unfortunately, this idealized model has broken down in practice. Short business cycles in which profit must be realized in months rather than years precludes investment in prevention and health. If managed care organizations deny coverage for effective treatments to people with jobs and families who have serious and chronic health problems, then more vulnerable members of society—e.g., poor, disabled, mentally ill—will most certainly fare poorly in those systems (Durham 1994). Clinicians are also encountering problems. They spend less time with their patients and have more difficulty prescribing drugs that are off-formulary. Their longstanding contractual relationships with community providers have been disrupted or change with unpredictable frequency (Durham 1999). We must ask if the managed care or capitated models of today are broken beyond repair and if they are inherently unsuitable for health conditions that are difficult to prevent and expensive to treat.

THREE FUNDAMENTAL PRINCIPLES OF MANAGED CARE Managed care does have the potential for providing cost-effective care that does not skimp on quality. The ideal model requires managing “care” as well as costs. There are three essential principles of managed care that, when properly applied, encourage high-quality, cost-effective care. First, providers receive a fixed income or reimbursement, regardless of the amount of service they offer. Second, providers profit most when enrollees remain healthy. Third, incentives in managed care favor less-expensive treatment alternatives when available (Durham 1994; Stuart and Stockton 1973). The capitation approach was used for years by HMOs such as Har-

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vard Community Health Plan, Group Health Cooperative of Puget Sound, and Kaiser Permanente to successfully provide care.

Fixed Income With a fixed income, providers have no incentive to provide treatment that is not effective or medically necessary. This stands in sharp contrast to a fee-for-service system where even treatments without proven value (and that may present risks to patients) may be reimbursed and encouraged. Capitated systems work best when there is widespread recognition that underuse, overuse, and misuse have negative consequences for cost as well as for the quality of health care (Bodenheimer 1999; Chassin and Galvin 1998). Providing too little care (underuse) fails to meet the treatment needs of the enrolled population. For example, untreated or undertreated depression can significantly escalate the overall cost of medical care (Greenberg et al. 1993; Hirschfield et al. 1997). Unnecessary or ineffective treatments (overuse or misuse) waste limited resources, pose unacceptable risks to patients, and lower patients’ quality of life. Selecting the “best” treatments is not easy, however. Although many new medications hold great promise for treating mental illness, it has been very difficult to establish the cost effectiveness of some treatments. For example, newer antidepressants have been shown to have equal efficacy, fewer adverse effects, and greater safety (Simon et al. 1995; Song et al. 1993; Workman and Short 1993), but they are far more expensive than the older tricyclic medications. When used in actual clinical practice the new selective serotonin reuptake inhibitors have not been more cost effective (Croghan et al. 1998) than older tricyclic medications. Fatal adverse drug reactions and medication errors, which caused the deaths of more than 100,000 hospitalized patients in 1994, are prime examples of overuse and misuse of prescription medications (Bodenheimer 1999). Overuse and misuse of antibiotics cost millions of dollars annually and are leading factors in the alarming acceleration of antimicrobial resistance (Low and Scheld 1998; Williams and Heyman 1998). Most recently, direct-to-consumer (DTC) marketing of drugs has been credited with encouraging misuse and overuse of specific drugs. The public’s demand for specific drugs has increased with DTC advertising. A 1998 national survey revealed that over 12 million consumers received a prescription drug as a result of DTC advertising (Holmer 1999). Physicians, whose salaries are based on patient satisfaction with today’s visit, may yield to patient pressure for a specific prescription whether or not the medication is appropriate. Managed care physicians try to discourage misuse or overuse stemming from DTC advertising, but they should also, in theory,

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value opportunities to talk to patients about conditions or illnesses that were not discussed with them prior to these advertisements (Hollon 1999; Holmer 1999). However, even if Americans eschew waste and unnecessarily high costs for health care services, they want their doctor to prescribe any drug or perform any test that might be remotely helpful to them or their family— regardless of the cost. They object to “population-based medicine” based on risk factor probabilities. Americans want and expect an exhaustive examination of their personal health status and a cure at any cost.

Keeping Enrollees Healthy Capitated financial incentives reward providers for preventing episodes of illness, encouraging early diagnosis, and promoting the active pursuit of proven treatments. Therefore, managed care organizations are highly motivated to identify treatments that improve patient function and prevent future episodes of costly care, even when some portion of member enrollment turns over every year. For example, managed care techniques have been shown to reduce days lost from work and related medical care costs in patients with workers’ compensation claims (Bernacki and Guidera 1998). Also, compelling evidence has demonstrated that HMOs do a better job than fee-for-service providers at early diagnosis and treatment of breast cancer (Riley et al. 1999). However, keeping people healthy pays off only if members remain in a health plan long term. Some people maintain the same health plan for many years, but the “churning” of membership has increased in recent years with the growth in the number of plans and the intense competition for membership. It is becoming less likely that any given member will stay with a single health plan over the years it takes for prevention to provide a return on investment. Health maintenance is an easier objective to achieve if managed care organizations select only the healthiest members of the population for membership. Some observers have claimed that the first 50 years of managing care was not very demanding because the 10 million people who enrolled were blue- and white-collar workers with families who took good care of themselves (Goldsmith 1996). Once Medicare and Medicaid beneficiaries began pouring into managed care plans, HMOs found it difficult to respond to the broader spectrum of patient needs. If capitated payments are properly adjusted for the risk of member illness, this should eliminate the temptation of providers to actively or passively discourage enrollment or encourage disenrollment of people with serious, chronic conditions. For example, the motivation to discourage


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people with severe mental illness from enrolling might be moderated if higher rates of reimbursement are given to HMOs willing to take on this high-risk population. It is wisely assumed that specialized providers can better serve the needs of these vulnerable populations; thus, services for mental health and substance use disorders are sometimes carved out to special contractors rather than mainstreamed into integrated managed care systems. Carve-out companies for mental health care or substance abuse treatment encounter special challenges in managing the risk in capitated contracts. It is more difficult to spread financial risk across a population of individuals that includes many who are disabled. Therefore, contractors must obtain sufficient levels of reimbursement to cover the costs of these disabled members. Capitation that has been risk adjusted can provide additional dollars for companies to use in caring for their severely disabled patient populations. Conversely, unscrupulous vendors can take the money and run.

Less-Expensive Treatment Alternatives Because managed care organizations are at risk financially (i.e., capitated premiums are expected to cover health care costs for members during a given period, and should expenditures exceed the premium revenues, the organization must absorb the loss), they have an incentive to encourage less-expensive treatment alternatives. Plans feel justified in denying claims when evidence of safety and efficacy are unknown. This feature of managed care creates controversy when patients want common treatments that are equally or less effective than their lower-cost counterparts. For example, managed care incentives have contributed to the demise of long-term treatments such as individual psychotherapy. Such practices have enraged psychiatrists whose practices were built around providing long-term therapy, often paid for by insurance carriers. The absence of effectiveness data for mental health treatment is a major problem within managed mental health care today. Most evidence documenting the success of contemporary clinical interventions comes from experimentally controlled clinical trials. Application of these treatments in clinical practice often yields disappointing results; treatment effect is usually much smaller (National Institute of Mental Health 1998; Simon and Katzelnick 1997; Simon et al. 1995; Weisz et al. 1995). Consequently, providers and health systems often make treatment and program-design decisions based on data that overestimate the size of the effect. The limits placed on funding for mental health care, especially psychosocial treatments, have led health plans (and payors) to demand rigorous evidence of treatment and cost effectiveness. Many plans opt instead for

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drug treatment that has been carefully scrutinized by the U.S. Food and Drug Administration. State governments, which are most likely to pay for the extremely expensive psychoactive drugs used by chronically mentally ill people, deserve to see rigorous scientific data evaluating the clinical benefit of those drugs. State sponsors need to review the cost effectiveness of public investments to justify widely used programs such as assertive community training, cognitive-behavioral therapy, or new drug treatments (Essock et al. 1998a, 1998b; Rosenheck 1999). In the current competitive environment, the introduction and widespread use of new treatments will require evidence of cost effectiveness, not just clinical efficacy (National Institute of Mental Health 1998; Simon and Katzelnick 1997). Drug formularies are widely used in managed care to control the cost and use of prescription medications. The most important role of a formulary is to list drugs of proven safety and efficacy. Formularies also list drugs that have been shown to have equal or superior results to others on the market. A new drug that costs more than its competitor should be substituted only if it has been shown to be cost effective. In a managed care environment, the length or intensity of a treatment is not the only factor that determines one treatment choice over another; rather, the cost relative to the effectiveness of the intervention matters most. Treatment ultimately costs more money if an ineffective drug leads to clinical deterioration. However, managed care organizations can create incredible hassles for physicians who want or need to prescribe an offformulary drug or depart from established drug guidelines. Some plans may not allow substitution of a drug that the provider knows is right for an individual patient. Not surprisingly, this angers physicians and pits their independent, professional judgment against inflexible, bureaucratic regulations that may run counter to good patient care.

DOES IT ALL ADD UP? Theory Versus Practice From a theoretical perspective, there is nothing inherently damaging about capitation for the provision of high-quality health care. In fact, if properly applied, the concept of managed care (specifically, capitation) encourages efficient use of resources by creating incentives to reduce waste and keep consumers healthy. Several factors have collided to corrupt, or at least compromise, the value of managed care models in the current health care environment. First, the American public has not embraced evidence-based practice and resents any imposition of financial constraints on the selection of drugs,


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procedures, and coverage for their personal health care. They want and expect aspects of the “unmanaged” health care system described earlier by Smith (1998): any provider, any drug, any test, regardless of cost. Americans prefer these open-ended aspects of fee-for-service care and do not want to give them up. Second, Americans believe that the widespread adoption of managed care has eroded the quality of American health care. Hardly a day passes when horror stories about denials of care, misdiagnoses, and outright incompetence do not appear on the nightly news or in major newspapers around the country. This problem would not be erased by returning to a fee-for-service model. Fee-for-service care has no greater insight into effective treatment than does managed care; rather, providers are simply freer to pursue more ineffective and potentially iatrogenic therapies that consume limited health care dollars. In fact, proponents of managed care argue that managed care providers stand alone in their urgent need to do it right the first time. Fee-for-service providers have more room to experiment but have nothing to lose by trying unproven or worthless treatments as long as they are paid for their efforts. Unfortunately, we often cannot document the cost, quality, and effectiveness of existing and new treatment alternatives, including those for mental health conditions (Durham 1998). This is especially true of primary prevention for mental health disorders. The stock-in-trade of HMOs has been primary prevention to avoid illness and the expense of treating illness. With a shortage of preventive approaches for mental disorders, however, interventions occur after the onset of illness. Luckily, our techniques for secondary and tertiary prevention are more advanced than for primary prevention. Tertiary prevention—i.e., the stabilization and maintenance of the chronically ill—can be an effective tool for avoiding acute episodes that are clinically damaging and costly to treat.

The Arrival of Wall Street The most troubling feature of health care at the turn of the twenty-first century is the increase in the proportion of investor-owned health plans from 42% in 1987 to 62% in 1997 (Kuttner 1999). The rush toward investor ownership has transformed the managed care philosophy. Entrusting health care to for-profit managed care systems adds perverse incentives to the capitation model. The fixed reimbursement feature of capitation can—and does—work against a patient if the health care organization wishes to maximize its shortterm return on investment. A profit motive paired with capitation derives value for shareholders only when health plans hold the line on medical loss ratios (the percentage of premium dollars actually paid out in benefits).

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Both for-profit and not-for-profit businesses must operate efficiently and provide value to customers. As Nudelman and Andrews (1996) pointed out, The major differences between for-profit and not-for-profit health care plans lie in the purpose, values, attitude, and behavior: a for-profit plan provides a service so it can make a profit; a not-for-profit plan makes a profit so it can provide a service. What the two systems do with their profit margins and, more important, why they do what they do constitute the distinctions between them. (p. 1057)

If executives and stockholders exploit the health care industry for personal gain, the profit is subtracted from the welfare of patients. The for-profit status of most of today’s health care giants should make us all skeptical about the willingness of an organization to choose longterm benefit over short-range profit-taking for investors. For-profit health care organizations divert some portion of their revenue to shareholders. If profit is the only motive and health care organizations are not held accountable for improvements (or stabilization) in a patient’s health care status, then no one really cares about effectiveness. As noted by Kuttner (1999), “In an industry driven by investor-owned companies, the original promise of managed care—greater efficiency in the use of available resources and greater integration of preventive and treatment services— has often degenerated into mere avoidance of cost” (p. 668). The best answer to this criticism may be returning savings in the form of rate reductions to those who pay premiums or reimbursing providers on the basis of patients’ improvement in health status. Put bluntly, excessive profiteering is contrary to social justice and is a poor fit with medical ethics. It also fuels the anti–managed care resentment that is pervasive in the United States today. Setting ethical considerations aside, there are still questions about quality differences between for-profit and not-for-profit health plans. Recent data from the National Committee for Quality Assurance (NCQA) indicate likely quality differences. For example, in 1998 NCQA reported that Health Plan Employer Data Information Set (HEDIS) measures revealed higher (better) average scores for not-for-profit compared to for-profit plans. Not-for-profit plans outscored the for-profit plans on treatment with beta-blockers after heart attack (79.8% versus 75.1%), breast cancer screening (75.4% versus 72%), childhood immunizations (72.4% versus 64.2%), eye examinations for people with diabetes (45.7% versus 39.4%), follow-up after hospitalization for mental illness (73.2% versus 64.7%), and prenatal care in the first trimester (86.5% versus 84.7%) (National Committee for Quality Assurance 1998).


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CAN EXISTING MODELS BE IMPROVED? Many managed care companies—especially those motivated by profit— have inserted utilization review managers and administrative staff into the clinical process. These staff scrutinize off-formulary drug use, outpatient visits, and many aspects of hospitalization. Companies sometimes use physician profiling to hire and retain doctors whose practice style reflects an austere, cost-conscious approach to patient care. Taken together, these practices remove decisions from clinicians and place them in the hands of someone outside the health care team. This is a deplorable practice in primary care and can be a disastrous process when providing care for people with chronic severe mental illnesses. A health care team should be able to make medical care decisions with clear clinical—not financial—objectives. In this environment, are there adequate incentives within managed care models to improve quality, especially for vulnerable patients such as those with serious mental disorders? Even within for-profit managed care, the answer to this question is definitely yes. Managed care can provide a distinct advantage over fee-for-service care if clinicians are penalized for not improving the functional status of patients rather than rewarded for reducing the number of patient visits or avoiding hospitalization. Financial rewards must be linked to health status improvements or maintenance of optimal levels of patient functioning. Accountability for improved or stable functional status will motivate providers (in for-profit or not-for-profit health plans) to develop outreach programs that keep people healthy and avoid decompensation or loss of function. Physicians will not mindlessly choose an inexpensive drug if there is a chance it will derail a patient’s progress toward enhanced functional status. Companies will seek out the most effective methods of drug treatment or community-based or group treatments and abandon alternatives that cost more money but do not produce desired results.

VALUES, NOT MONEY The easily achieved cost savings taken out of the health care system during the early 1990s are far behind us. At present, the general unwillingness of employers and other purchasers to provide adequate reimbursement—especially for mental health treatment—has provoked a race to lower premiums. We may have squeezed reimbursement too hard to contain costs. The requirement of for-profit health plans to return wealth to investors has interjected a perverse incentive into a process that should focus on patients, not profits. It is time for a national debate about who should finance health care in America and what services should be provided under the most streamlined

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and carefully monitored systems. Most Americans still pay only a very small portion of the health care services they use because their employers or the government pick up the tab. They may believe that health care costs should be controlled, but they do not want any coverage limitations on their own care. Few seem to realize that employers negotiate a menu of covered services with health plans or insurance companies based on what an employer is willing to pay. If employers want to cover unlimited, long-term psychotherapy, health plans are willing to oblige; if employers wish to pay for the cost of experimental therapies, clinicians know how to find promising clinical trials. It is also time to come clean on the accusation that managed care rations health care whereas fee-for-service does not. Fee-for-service medicine provided a rich, almost unlimited, array of health care services for those individuals fortunate enough to have coverage. Uninsured and underinsured Americans received care from emergency departments or public health agencies or received no care at all—the ultimate in rationing of care. Today’s rationing reduced medical inflation and held the line on health care costs for employers for a brief period of time (roughly 1992 through 1998). Employers have told health plans that they will take their business elsewhere unless health care costs are held as low as possible. Ironically, the uninsured did not benefit from cost reductions: the number of uninsured Americans was higher than at any time in the previous 30 years (Kuttner 1999). In the end, payors and the public must insist on more sophisticated performance standards for quality, satisfaction, and cost control. What employers and state and federal governments will have to decide is the priority they will place on the care of vulnerable populations, such as the severely mentally ill, so that health care organizations can design successful managed care programs. A balance must be struck between profit motives, available resources, and the potential care-enhancing features of managed care.

REFERENCES Bernacki EJ, Guidera JA: The effect of managed care on surgical rates among individuals filing for workers’ compensation. J Occup Environ Med 40:623–631, 1998 Bodenheimer TB: The American health care system: the movement for improved quality in health care. N Engl J Med 340:488–492, 1999 Chassin MR, Galvin RW: The urgent need to improve health care quality. JAMA 280:1000–1005, 1998 Croghan TW, Melfi CA, Crown WE, et al: Cost-effectiveness of antidepressant medications. Journal of Mental Health Policy Economics 1:109–117, 1998


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Durham ML: Healthcare’s greatest challenge: providing services for people with severe mental illness in managed care. Behav Sci Law 12:331–349, 1994 Durham ML: Mental health and managed care. Annu Rev Public Health 19:493– 505, 1998 Durham ML: Managing care for the seriously mentally ill: commentary, in Ethical Challenges in Managed Care. Edited by Gervais KG, Priester R, Vawter DE, et al. Washington, DC, Georgetown University Press, 1999, pp 276–284 Essock SM, Frisman LK, Kontos NJ: Cost-effectiveness of assertive community treatment teams. Am J Orthopsychiatry 68:179–190, 1998a Essock SM, Drake RE, Burns BJ: A research network to evaluate assertive community treatment: introduction. Am J Orthopsychiatry 68:176–178, 1998b Goldsmith J: Managed care in the 21st century: a look ahead. Keynote address, HMO Research Consortium Conference, Minneapolis, MN, June 1996 Greenberg PE, Stiglin LE, Finkelstein SN, et al: Depression: a neglected major illness. J Clin Psychiatry 54:419–424, 1993 Hirschfield RMA, Keller MB, Panico S, et al: The National Depressive and ManicDepressive Association consensus statement on the undertreatment of depression. JAMA 277:333–340, 1997 Hollon MF: Direct-to-consumer marketing of prescription drugs: creating consumer demand. JAMA 281:382–384, 1999 Holmer AF: Direct-to-consumer prescription drug advertising builds bridges between patients and physicians. JAMA 281:380–382, 1999 Kuttner R: The American health care system: Wall Street and health care. N Engl J Med 340:664–668, 1999 Low DE, Scheld WM: Strategies for stemming the tide of antimicrobial resistance. JAMA 279:394–395, 1998 Luft H: Health maintenance organizations: dimensions of performance. New York, John Wiley, 1985 National Committee for Quality Assurance: Quality Compass 98, Health Plan Employer Data Information Set (HEDIS), 1998 National Institute of Mental Health: Bridging science and service: a report by the National Advisory Mental Health Council’s Clinical Treatment and Services Research Workgroup (NIH Publ No 99–4353). Washington, DC, U.S. Government Printing Office, 1998 Nudelman PM, Andrews LM: The "value added" of not-for-profit health plans. N Engl J Med 334:1057–1059, 1996 Riley GF, Potosky AL, Klabunde CN, et al: Stage at diagnosis and treatment patterns among older women with breast cancer: an HMO and fee for service comparison. JAMA 281:720–726, 1999 Rosenheck R, Cramer J, Allan E, et al: Cost-effectiveness of clozapine in patients with high and low levels of hospital use: Department of Veterans Affairs Cooperative Study Group on Clozapine in Refractory Schizophrenia. Arch Gen Psychiatry 56:565–572, 1999 Smith M: Managed care. Keynote address, Managed Care Association, Washington, DC, May 7, 1998

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Simon GE, Katzelnick DJ: Depression, use of medical services and cost-offset effects. J Psychosom Res 42:333–344, 1997 Simon GE, Wagner E, Von Korff M: Cost-effectiveness comparisons using “real world” randomized trials: the case of new antidepressant drugs. J Clin Epidemiol 48:363–373, 1995 Song F, Freemantle N, Sheldon T, et al: Selective serotonin reuptake inhibitors: meta-analysis of efficacy and acceptability. BMJ 306:683–687, 1993 Stuart B, Stockton R: Control over the utilization of medical services. Milbank Mem Fund Q Health Soc 51:341–394, 1973 Weisz JR, Donenberg GR, Weiss B, et al: Bridging the gap between laboratory and clinic in child and adolescent psychotherapy. J Consult Clin Psychol 63:688– 701, 1995 Williams RJ, Heyman DL: Containment of antibiotic resistance. Science 279: 1153–1154, 1998 Workman EA, Short DD: Atypical antidepressants versus imipramine in the treatment of major depression: a meta-analysis. J Clin Psychiatry 54:5–12, 1993

5 Role of Academic Medical Centers in Achieving Quality Linda S. Godleski, M.D. Allan Tasman, M.D.

Academic medical centers are primary health care institutions in which the educational, research, and clinical missions of the schools of medicine are fulfilled. The traditional role of these medical centers has been to educate individual physicians to provide the highest quality of medical care possible to the individual patient. Today, this concept of quality has been expanded to mean total quality improvement and quality management of health care processes. Quality improvement now includes evidence-based medicine, practice guidelines and patient care protocols, risk assessment of populations, and resource allocation. With this evolution of quality, academic medical centers are challenged to broaden their role in achieving the highest quality in health care. The process of quality improvement begins with the complex task of evaluating findings from clinical studies. Based on these findings, guidelines for the most common psychiatric disorders are then developed and implemented. Most controlled clinical psychiatric studies strive to demonstrate treatment efficacy. However, efficacy rarely predicts treatment effectiveness in clinical practice because patients with complicating comorbid Axis I or II disorders, substance abuse, or socioeconomic characteristics are often not admitted into clinical trials and are more likely to have poorer outcomes. Furthermore, even when treatment interventions are clinically effective, cost effectiveness must be substantiated in order to support widespread use (e.g., high-cost selective serotonin reuptake inhibitors and atypical antipsychotics). Outcomes in psychiatry are troublesome to measure, often using subjective rating scales rather than physiologic or laboratory 61

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measurements available in other areas of medicine. Use of meta-analysis in psychiatry is difficult because studies of specific interventions, populations, or outcome measures are often not comparable.

WHY ACADEMIC MEDICAL CENTERS HAVE NOT TAKEN THE LEAD IN ACHIEVING QUALITY Although academic institutions have been at the forefront of most advances in medicine, they have lagged behind in the area of quality improvement in modern medical care. There have been several reasons for this resistance in medicine in general and psychiatry in particular. The notion that total quality improvement is directly related to managed care has led to skepticism and impeded progress in the quality arena. If physicians perceive that quality improvement originated with and remains inexorably tied to managed care, then they believe that they will appear to endorse and support managed care by including quality standards in the educational process. Ronald Kaufman, M.D., director of University of South Florida’s Division of Medical Practice Management, stated that “a lot of people [feel] that by teaching managed care, it [may] be perceived that we [are] supportive of it, and most faculty aren’t” (Kaufman 1999, p. 7). In this respect, it is difficult to mobilize academicians to take a leadership role in the quality improvement process. Commercialization of the medical marketplace has also coincided with the quality improvement initiative, and as Blumenthal and Epstein (1996) noted, “the organizations promoting the use of such quality improvement methods are not infrequently motivated, at least in part, by marketing goals” (p. 1328). Several health service researchers have pointed out the problem of distinguishing the purposes of quality improvement and cost containment (Chassin 1996; Guadagnino 1998). Furthermore, a recent New England Journal of Medicine article that reviewed the attitudes toward managed care among medical students, residents, faculty, and deans found that 73% did not favor managed care (Simon et al. 1999). Many perceive that the development of clinical guidelines reduces the autonomy of the practicing physician and adds an arbitrariness to the practice of medicine that does not allow for the physician’s expertise. Moreover, with academia making continuous advances in the mental health field, virtually no mechanism is in place to update the clinical standards at the rate necessary to maintain accuracy and keep up with the pace of research. When faced with a limited amount of time for teaching psychiatry to students and residents, many academicians also feel that the emphasis should remain on teaching traditional core knowledge rather than adding instruction in quality assessment and improvement. Mental health outcomes are

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difficult to quantify, and clinicians are apprehensive about having their outcomes compared with others, especially when they lack confidence in the measurement process. Academic medical centers also fear that their outcomes will not compare favorably with nonacademic medical centers because their patients often have more clinically complex problems or suffer from economic disadvantage. Without well-defined process and outcomes measures, mental health and substance abuse guidelines are thought of as vague, detailed, cumbersome, and not useful. Even the ones that are comprehensive and well referenced (e.g., the American Psychiatric Association’s “Practice Guideline for the Treatment of Patients With Substance Use Disorders: Alcohol, Cocaine, Opioids” [1995]) are often long and not easily readable. Furthermore, the guidelines developed by academics often are based on a metaanalysis of the literature that may or may not reflect the current practice experience.

WHY ACADEMIC MEDICAL CENTERS SHOULD LEAD QUALITY EFFORTS Academic medical centers have not been successful in addressing health care quality and managed care topics in their curricula. Gerald Lazarus, dean emeritus at the University of California Davis School of Medicine, recently reported that medical professionals are not knowledgeable about today’s health care organizational issues (Lazarus et al. 1998). His survey, which assessed the knowledge of managed care among medical students, residents, and faculty, stated that faculty knowledge barely exceeded that of third-year medical students despite the long history of managed care in California. Furthermore, 60% of medical students surveyed by the Association of American Medical Colleges’ (AAMC) 1999 Medical School Graduate questionnaire thought more time should be devoted to teaching about health systems and managed care (Coleman 1999). To respond to this need, medical schools are now incorporating these concepts into their courses. A New England Journal of Medicine survey (Simon et al. 1999) reported that 75% of deans stated their schools are offering managed care courses including courses covering quality of care; at 66% of schools, such courses are required. Academic medical centers also have access to and can coordinate data across a wide range of psychiatric service delivery systems, including traditional university hospitals and their affiliates, Veterans Affairs facilities, state mental hospitals, and community mental health clinics. These data can be used to investigate quality and generate information that will lead to quality improvements. In addition, academic medical centers are the repos-

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itories of research expertise and often have resources solely devoted to research. Research capability is especially available in universities that include schools of public health, epidemiology, health care administration, information technology, and business. With ready access to research investigators, these centers are able to educate medical students, residents, fellows, faculty, and practicing physicians about the results of health services research through their medical school graduate and postgraduate training as well as continuing education programs.

EXAMPLES OF QUALITY IMPROVEMENT IN ACADEMIC MEDICAL CENTERS More than 25 years ago, Wennberg and Gittelsohn (1973) documented wide variations in delivery of health care. Since that time, data technology has exploded, and the ability to collect data and measure outcomes in clinical practice has become a practical reality. Coinciding with this new technology, insurers are more interested than ever before in reducing the variation of clinical practice to optimize quality and cost effectiveness (Wennberg 1998). Movement toward assessing quality in medical and psychiatric care is growing as university medical centers see benefits in conducting health services research. In a recent article in Academic Psychiatry, faculty in the Department of Psychiatry at Yale advocated that academic departments “should take the lead in addressing the question of which treatments are appropriate, for whom, and at what cost” (Jacobs et al. 1997, p. 82). They stated that because clinical services already collected data for their payors, they can use the same data for their own research foundation and, in so doing, can crosssubsidize research. Furthermore, if academic centers develop a program evaluation product (e.g., a set of performance indicators), they can market this product to purchasers who wish to evaluate the quality of health care services. The Yale Department of Psychiatry established the Center for Mental Health, Clinical Services, and Policy Research, which obtained multisource funding for pharmacologic effectiveness studies, jail diversion programs, peer support interventions, and intensive outreach programs. In association with the Yale School of Epidemiology and Public Health, there are now training programs for health services research in mental health and substance abuse. At the University of Pittsburgh’s Western Psychiatric Institute, Kenneth Thompson, M.D., and Frank Ghinassi, Ph.D., used an academic research approach to develop a model program for mental health treatment pathways and guidelines. For a representative program in their adult mental


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health services, they selected a level of care and developed a best practice approach to standardize a clinical pathway. They examined which clinicians were performing what function, exactly what interventions they were making, at what point in the patient’s care these interventions were made, the likely decision branches the clinicians used, and the outcomes. They demonstrated that academic medical centers can develop clinical guidelines and use trainees of all disciplines to collect and analyze data. Furthermore, they are now beginning to market their guidelines as benchmarks for other health care purchasers. McLean Hospital, a psychiatry teaching facility at Harvard Medical School, is a model for developing clinical quality initiatives to improve psychiatric care (McLean Hospital 1996, 1997, 1998, 1999). McLean designed and validated a Behavior and Symptom Identification Scale, BASIS-32, as an outcomes measurement tool for the mental health care field (Eisen et al. 1994). This 32-item questionnaire assesses patients’ perceptions of their illnesses. It has been requested for use by more than 800 health care organizations and has been approved in a number of Joint Commission on Accreditation of Healthcare Organizations (JCAHO)-endorsed ORYX performance measurement systems. McLean also developed the Perceptions of Care (PoC) survey, an 18-item self-report questionnaire of patients’ perception of care received during their hospital stay. JCAHO approved five indicators for ORYX from McLean’s PoC: access to care, communication received from provider, interpersonal aspects of care, continuity and coordination of care, and global evaluation of care. In addition, McLean instituted a rigorous monitoring system for high-risk events such as restraint and seclusion, medication errors, and severe adverse drug reactions. Its careful monitoring of psychiatric readmissions within 30 days as a quality indicator led to identification of risk factors that predicted readmission. The quality improvement program at McLean has decreased harmful events, established a relapse prevention program, and raised patients’ perceptions of treatment. Psychiatric departments of academic medical centers must aggressively pursue research funding for health services research. With tighter fiscal constraints on clinical services, there will be fewer resources for clinical services to subsidize research. University psychiatry departments must take full advantage of external funding sources. For example, the National Institute of Mental Health K awards bring clinicians into mental health services research and support the further development of experienced investigators. The Veterans Administration has also added a Health Services Research branch of funding in addition to its longstanding Merit Review branch geared toward basic science research. Examples of nonprofit foundation funding include the Robert Wood Johnson Foundation (1998) and the Pew

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Charitable Trusts, which both recently funded the Tufts Managed Care Institute. The Partnership for Quality Education has awarded grants to academic medical centers since 1997 for research on the management of patient populations. Aetna U.S. Healthcare and its corporate partners awarded a total of $6.8 million for quality improvement research at academic medical centers in 1998; recipients included Johns Hopkins University School of Medicine, Yale University School of Medicine, and New York Medical College. Finally, pharmaceutical companies have also supported academic endeavors in quality assurance, especially costeffectiveness studies relating to their products (e.g., Clozaril) (Fitton and Benfield 1993).

ACHIEVING QUALITY THROUGH EDUCATION Medical Student Education The AAMC incorporated a number of new quality-related requirements for medical student education (Association of American Medical Colleges 1998). An example of one such mandate is that students must be able to retrieve (from electronic databases and other resources), manage, and use biomedical information for solving problems and making decisions about the care of individuals and populations; understand various approaches to the organization, financing, and delivery of medical care; and become familiar with quality improvement, risk management, and utilization review. The AAMC further required that medical schools design their own education quality monitoring and outcomes measurement systems, and that they report how well they are teaching this system to students. At the University of Louisville, the medical school established one such quality improvement system. By including components of quality into its curriculum and monitoring the quality of its own curriculum, the medical school gives students a firsthand experience in continuous quality improvement (CQI). In addition, the University of Louisville and other medical schools throughout the country have expanded their curricula to include the design and use of databases and decision analysis to create critical pathways, practice management guidelines, and other quality improvement measures. The University of Louisville Medical School is also integrating psychiatry into the basic science curriculum. For example, the physiological impact of psychiatric disorders (e.g., anxiety) is now part of the physiology teaching modules. When medical students are taught psychiatry, clinical guidelines can be used to inform them about psychiatric conditions and the appropriate treat-


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ment. Guidelines may help convince students who pursue nonpsychiatric specialties that “a considerable knowledge and technical mastery are required for the assessment and management of the many complicated psychiatric patients they may encounter in primary care practice” (Yager et al. 1997, p. 230). This exposure to guidelines will also better educate future nonpsychiatric physicians about mental health referrals.

Psychiatric Residency Education The Tufts Managed Care Institute developed a curriculum to train residents in managed care principles that is being used by at least 16 other residency programs. This curriculum (Tufts Managed Care Institute 1999) addresses in detail how to use practice guidelines in clinical decision making, how to assess and improve residents’ own practice patterns, and how to improve disease-specific outcomes and patient satisfaction. The University of Connecticut also has created a managed care curriculum with grant support from The Partnership for Quality Education. According to Bruce Gould, M.D., associate dean of primary care, “managing the care of patient populations means making choices to optimize the outcome of that population, with the most effective use of resources …when our students graduate, they will have learned what’s involved in quality improvement, and participated in improving the quality of a practice” (Coleman 1999, p. 11). Finally, the University of California Davis Medical Center, with the support of the Sierra Foundation in collaboration with The Advisory Board Company’s (of Washington, DC) Medical Leadership Program, produced a curriculum guide to instruct University of California Davis residents, students, and faculty (Chapman and Lazarus 1998). This curriculum also was designed to be used at other academic institutions. The syllabus of over 100 pages includes sections on evidencebased medicine, outcomes research, best practices, and utilization management. Quality-related training can become part of residency education in the following ways: • Psychiatry residents receive core curriculum training in data collection, outcomes measurement, and continuous quality improvement in medical practice. Academic departments of psychiatry can adapt the core curricula already used by Tufts, the University of Connecticut, or the University of California Davis, either for use exclusively by the department of psychiatry or in conjunction with other specialty training programs within their institution. Furthermore, psychiatry trainees should be required to review literature on quality (e.g., New England

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Journal of Medicine “Quality of Health Care” series published in September and October 1996 [Berwick 1996; Blumenthal 1996; Brook et al. 1996a, 1996b] or the Annals of Internal Medicine “Academia and ClinicPhysicians as Leaders in Improving Health Care” series published between February and June 1998 [Berwick 1998; Berwick and Nolan 1998; Clemmer et al. 1998; Nelson et al. 1998; Wennberg 1998]). • Psychiatric clinical guidelines are used in residency training programs and can be incorporated into teaching conferences, seminars, and tutorials. Guidelines are extremely useful residency education tools because they provide a substantial percentage of a resident’s required knowledge base through their comprehensive, up-to-date references and critically evaluated literature reviews (Yager et al. 1997). • Psychiatric training programs increase training in those modalities found to provide the highest quality in the most cost-effective manner. • Psychiatric training programs involve residents in the quality improvement efforts of practicing physicians. At the University of Michigan, psychiatric residents participate with attending physicians in quality assurance of medical staff committees. • For training unavailable at the academic institution, psychiatric residents are encouraged to attend courses in quality and quality management such as those offered by the American College of Physician Executives, American Medical Review Research Center, Group Health Association of America, JCAHO, and The Health Care Forum.

Postgraduate Training Only a few programs exist for fellowship and postdoctoral training in mental health services research. Therefore, many physicians and psychiatrists pursue master’s-level degrees in public health or business. Tulane University has awarded a Master of Medical Management (M.M.M.) degree to 111 physicians, including seven psychiatrists, between 1997, when this program originated, and 1999. The M.M.M. program was created in cooperation with the American College of Physician Executives and has been replicated at Carnegie-Mellon University. The program is designed exclusively for physicians and covers quality-related topics. At least 17 medical schools now offer combined M.D./M.B.A. programs, including the University of Pennsylvania, Dartmouth, Vanderbilt, the University of Chicago, Georgetown University, the University of California Los Angeles, and the University of Louisville. Most provide for a large portion of the M.B.A. work to be done before the last year of medical


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school to allow the business knowledge gained to be applied to subsequent medical school and residency education.

Continuing Medical Education Academic medical centers can retrain practicing clinicians about the subject of quality through continuing medical education (CME) programs. In addition to the traditional grand rounds and lecture formats, many academic medical centers are expanding their CME programs to include computer-based distance learning through CD-ROM or Internet services. Psychiatric departments of academic medical centers can develop CME programs on mental health quality from the materials developed for their medical students and residents. CME also can provide a potential source of revenue. Finally, academic medical centers can help meet the needs of physicians for CME in topics such as mental health/substance abuse guidelines and outcomes assessment.

Pioneering Innovative Educational Methods Some academic medical centers are at the forefront of developing innovative methods of educating physicians about quality. The University of Washington pioneered methods of continuous quality improvement teams and academic detailing as a means of disseminating practice guidelines (Goldberg et al. 1998). In one scenario, CQI team members facilitated the implementation of outcomes data collection and analysis. In another, two physician leaders were trained to conduct 15-minute academic detailing sessions with practitioners, using pocket cards outlining preferred practices modeled on the methods of pharmaceutical sales representatives. Although the results of the study revealed that neither method was ready for widescale use, the authors did emphasize the importance of effective education in achieving implementation of outcomes assessment and practice guidelines.

CONCLUSION Despite professional resistance to managed care, outcome measures, and practice guidelines, academic medical centers are uniquely positioned to lead the way in achieving quality in mental health and substance abuse services. They possess all of the necessary characteristics: 1) the technology to collect health quality-related and outcomes data; 2) the expertise and research infrastructure to analyze the data and recommend practice guidelines and process improvement methods; and 3) the educational capacity to train students, residents, fellows, and staff physicians. Psychiatry departments in academic medical centers must also aggressively pursue research

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funding if they are to take the lead in addressing the question of what treatments are appropriate, for whom, and at what cost. If necessary, they must restructure the existing curriculum to ensure that psychiatrists are trained to measure outcomes, collect data, and incorporate clinical guidelines into their practices.

REFERENCES American Psychiatric Association: Practice guideline for the treatment of patients with substance use disorders: alcohol, cocaine, opioids. Am J Psychiatry 152:1–59, 1995 Association of American Medical Colleges: Learning objectives for medical student education: AAMC/LCME hot topics. AAMC Medical School Objectives Project, January 1998 Berwick DM: Quality of health care, part 5: payment by capitation and the quality of care. N Engl J Med 335:1227–1230, 1996 Berwick DM: Developing and testing changes in delivery of care. Ann Intern Med 128:651–656, 1998 Berwick DM, Nolan TW: Physicians as leaders in improving health care: a new series in Annals of Internal Medicine. Ann Intern Med 128:289–292, 1998 Blumenthal D: Quality of health care, part 4: the origins of the quality of care debate. N Engl J Med 335:1146–1149, 1996 Blumenthal D, Epstein A: Quality of health care, part 6: the role of physicians in the future of quality management. N Engl J Med 335:1328–1331, 1996 Brook RH, McGlynn EA, Cleary PD: Quality of health care, part 1: what is it? N Engl J Med 335:891–893, 1996a Brook RH, McGlynn EA, Cleary PD: Quality of health care, part 2: measuring quality of care. N Engl J Med 335:966–970, 1996b Chapman RW, Lazarus GS (eds): Understanding Today’s Health Care System: A Curriculum Guide for American Medical Centers. Davis, CA, University of California Davis Press, 1998 Chassin MR: Quality of health care, part 3: improving the quality of care. N Engl J Med 335:1060–1062,1996 Clemmer TP, Spuhler VJ, Berwick DM, et al: Cooperation: the foundation of improvement. Ann Intern Med 128:1004–1009, 1998 Coleman C: Medical schools training for reality of managed care. Association of American Medical Colleges Newsletter 10–11, June 1999 Eisen SV, Dill DL, Grob MC: Reliability and validity of a brief patient-report instrument for psychiatric outcome evaluation. Hospital and Community Psychiatry 45:242–247, 1994 Fitton A, Benfield P: Clozapine: an appraisal of its pharmacoeconomic benefits in the treatment of schizophrenia. Pharmacoeconomics 4:131–156, 1993 Goldberg HI, Wagner EH, Fihn SD, et al: A randomized controlled trial of CQI teams and academic detailing: can they alter compliance with guidelines? Joint Commisson Journal on Quality Improvement 24:130–142, 1998


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Guadagnino C: Does standardization equal quality? Physician’s News Digest XI:1, 1998 Jacobs SC, Hoge MA, Sledge WH: Managed care, health care reform, and academic psychiatry. Academic Psychiatry 21:72–85, 1997 Kaufman RP: Teaching business realities to physicians in training. ACPE News May 15, 1999, p 7 Lazarus GS, Foulke G, Bell RA: The effects of a managed care educational program on faculty and training knowledge, attitudes, and behavioral interventions. Acad Med 73:1107–1113, 1998 McLean Hospital: Readmissions as a quality improvement indicator. McLean Reports 3:1–4, July 1996 McLean Hospital: Tracking harmful events to improve quality of care. McLean Reports 4:1–4, Spring 1997 McLean Hospital: A “global” look at BASIS-32. McLean Reports 5:1–4, Winter 1998 McLean Hospital: A new and improved PoC. McLean Reports 6:1–4, Winter 1999 Nelson EC, Splaine ME, Batalden PB, et al: Building measurement and data collection into medical practice. Ann Intern Med 128:460–466, 1998 Robert Wood Johnson Foundation: Robert Wood Johnson Annual Report. New York, Peake Printers, 1998 Simon S, Pan A, Sullivan N, et al: Views of managed care: a survey of students, residents, faculty, and deans at medical schools in the U.S. N Engl J Med 340:928–936, 1999 Tufts Managed Care Institute: Preparing Residents to Succeed in Managed Care: Teaching Tools for Faculty. Syllabus 1999 Wennberg J: Variation in the delivery of health care: the stakes are high. Ann Intern Med 128:866–868, 1998 Wennberg J, Gittelsohn A: Small area variations in health care delivery. Science 182:1102–1108, 1973 Yager J, Zarin DA, Pincus HA, et al: Practice guidelines and psychiatric education. Academic Psychiatry 21:226–233, 1997


PART II Quality Measurement


Introduction

One purpose of this book is to help readers develop and introduce improvement efforts in their clinical settings. We asked our contributors to provide a variety of approaches to measuring quality, including methods developed for use outside of research settings. This part begins with an overview of the most common measurement approaches used today, ranging widely from clinical trials to determine the efficacy of particular treatments to report cards designed to evaluate health plan performance. Measures of quality cannot be made public when used to compare practice groups or health plans unless the measures have been adjusted for differences in the type of patients treated. This part includes a discussion of the development of risk adjustment models and the challenges they pose when used to assess quality of care. We are especially pleased to include several examples of new consumer satisfaction measures. Because the consumer movement has grown so dramatically in recent years, its influence on what constitutes quality pervades the chapters included in this part. Consumers point out that until now satisfaction has been recorded from the clinician’s point of view. As a result, many aspects of treatment delivery have not been probed and negative findings are rare. We include a chapter on consumer involvement in developing a satisfaction measure that discusses what steps are required to ensure confidence in the psychometric robustness of the instrument. Readers are given specific insights on how to initiate quality-of-care studies in large systems; projects in the real world require great skill in political leadership as well as courage and determination. Two chapters document national efforts to support the development of quality measurement projects in the federal and private spheres.

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6 Measuring Quality An Overview Barbara Dickey, Ph.D.

INTRODUCTION Quality of care is no longer defined by the eye of the beholder. Increasingly, its basis is evidence from research studies, statistical reporting, and data collected from physicians, patients, and insurers. Science is the foundation of evidence-based medicine. It is too soon to determine whether evidence-based medicine is, in fact, a better way to ensure quality of care than by the educated opinion of various observers. Although the jury is still out, increasing interest in evidence-based medicine makes it important to understand the various mechanisms of measuring quality that are brought together under this broad label. There has been strong federal support for evidence-based medicine. This support is expressed through funding for the National Institutes of Health, the Agency for Healthcare Quality and Research, and the Centers for Disease Control and Prevention. The federal government also has supported private–public collaborations between pharmaceutical companies, health plans, and medical societies. Reports produced by these agencies and organizations vary in their usefulness and sophistication, which is to be expected in a field in which the methods for quantifying quality are still under development. Two examples of evidence-based medicine are practice guidelines and disease management. Practice guidelines (i.e., the particular standard of care recommended for a given disorder) have proliferated. Managed care companies have kept their guidelines proprietary. In response, professional 77

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organizations and public agencies have written guidelines of their own. Literally hundreds of guidelines are now available that are substantially the same for any given disease. However, some are far more inclusive of treatment options than others. Because of this variation within disease-specific guidelines, it has been difficult to determine how often or faithfully clinicians follow them or whether they find them useful. A second example of evidence-based medicine is disease management, a program that identifies persons at risk, intervenes with specialized care plans, and measures outcomes. Although implicit in medical practice, the packaging (and marketing) of disease management strategies is a fairly recent innovation ushered in by pharmaceutical companies. It has been argued that these programs provide pharmaceutical companies a wedge into the profitable chronic care market (Bodenheimer 1999). Although such programs hold promise for standardizing (and presumably improving) the care of individuals with mental illness, their cost effectiveness rests on the ability to identify high-risk/high-cost patients for whom the benefits of disease management pay off. Managing low-risk/low-cost patients is simply not cost efficient because the cost of management exceeds the value of the benefit obtained.

MEASURING QUALITY: GATHERING THE EVIDENCE Medical practice today rests on centuries of recorded observations, inferences, and deductions made by clinical practitioners seeking to alleviate the pain and suffering of patients. Trial and error gradually improved the treatment of many disorders. When twentieth-century science introduced more systematic methods, quantification of all aspects of medicine became common. Although the success (or failure) of physicians in treating disease has always been a subject of comment, most observers count Ernest Codman, a surgeon at the Massachusetts General Hospital in the early 1900s, as the first contemporary quality-of-care researcher. Codman wanted his fellow surgeons to keep track of the success and failure rates of their procedures and to follow up with patients after discharge to determine long-term outcomes. He felt that patients would benefit from a more systematic and scientific approach to assessing the outcomes of specific surgical procedures and that teaching young physicians what we now call best practices would improve the overall quality of care. Although he was considered something of a crackpot at the time, his legacy is surprisingly contemporary. The next major contributor to quality assessment was Avedis Donabedian, who developed the theoretical basis for the study of quality in the 1970s. He assumed that the highest quality of care was that which mini-

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mized the risks and maximized the benefits to the patient. As the amount of treatment received by the patient increased, a point was reached at which additional treatment brought risks and costs disproportional to the benefit (Donabedian 1980). This theory has been used to study the cost effectiveness of treatments, the efficiency of delivery systems, and the equity of the distribution of health care across diverse subsectors of society (Brook and Lohr 1985; McGlynn et al. 1988; Povar 1991) Donabedian developed a tripartite framework for assessing quality that conceptualized the delivery of health care in terms of its structure, process, and outcome. Structure refers to the resources of providers needed to deliver appropriate care, such as the type of institution or the credentials of the professional staff. The process of care is the actual care itself and can be assessed in two domains: interpersonal aspects of care, including the quality of communication and the quality of the relationship between clinician and patient, and technical aspects of care, consisting of the application of medical science and technology to the management of illness (e.g., accuracy of diagnosis, appropriateness of treatment). Outcomes of care are the desired results of care in terms of patients’ clinical status, functioning, and quality of life. Although it is useful to apply Donabedian’s theory to research, findings in the literature are too limited to really understand just what contributes to patient outcomes and how outcomes are affected by particular processes of care. This type of research is very difficult for several reasons. We know that health status is more closely related to lifestyle and genetic predisposition to certain disorders than it is to medical interventions. In addition, environmental factors play a strong role in outcomes, such as income distribution, availability of health insurance, cultural beliefs, and geographic distribution, to name just a few (Reinhardt and Cheng 1999a, 1999b). Incorporating all of these factors means that linking outcomes to processes of care (i.e., treatment) is difficult. In fact, investigation of the processes will yield more fruitful results than focusing on outcomes alone to estimate quality of care. In addition to its theoretical contribution, Donabedian’s framework has another distinct advantage: it allows for different perspectives to be taken into account. This is important because the growth of managed care in medicine has illuminated the importance of multiple perspectives, including the views of clinicians, payors, consumers, families, and society (Dickey et al. 1994). Each group brings a unique viewpoint to what constitutes quality and favorable outcomes. For example, clinicians seek to provide their patients with the best possible care, weighing the benefits and risks of each treatment. Purchasers of care seek to spend health care dollars judiciously and focus attention on greater physician accountability for the costs of treatment decisions. Consumers want to be treated with dignity and

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respect and have their preferences considered when choosing among treatment options. Families want the best care for a sick family member, understanding that poor care will increase their burden. Furthermore, the societal perspective, which has been shaped by the disciplines of public health, sociology, and economics, considers the health status of the population as a whole and concerns itself with broad health care priorities (Cochrane 1972). Studying quality was once relegated to research but has evolved into four branches of assessment: research studies, public reporting of population-based indicators, CQI studies, and measures of consumer satisfaction. Each of these approaches to quality measurement has different goals and different audiences. Each also has its own special techniques, although the methods used to collect and report data overlap.

Research: Efficacy and Effectiveness Studies The classic clinical trial is a double-blind experiment in which volunteers who meet criteria set by the principal investigator are enrolled as subjects. Subjects all have the same diagnosis and few, if any, complicating medical problems. They are randomly assigned to either the experimental treatment or the control group. The goal of efficacy studies is to test a new treatment under ideal circumstances; if the treatment does not have the desired effect, it is unlikely to work in the more complex environment in which we all live day to day. This research model has been widely used to assess the efficacy of different treatment modalities such as psychotropic medications, psychotherapy, and electroconvulsive therapy. Although studies of the efficacy of specific interventions cannot be considered studies of quality of care, they are used to draw inferences about quality. We assume that when appropriate care is given, patient outcomes will be maximized (Lohr 1997). In efficacy studies, each subject has been screened carefully for meeting study criteria, including having the particular diagnosis that the experimental treatment is designed to help. Research investigators control the entire testing process. They design the research protocol, enroll subjects, provide or supervise the treatment according to the protocol, collect data, and provide data analysis and interpretation. Measurement of outcomes is likely to be done using a disease-specific measure, chosen because it will focus on the specific symptoms of the particular disorder being studied. If the outcomes are better for those in the experimental treatment group (compared with the control group), the next step is an effectiveness study. The goal of effectiveness studies is to determine the outcomes of new treatments under conditions that replicate the environment in which most

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patients are actually treated. Medications and other treatments that yield a favorable response in clinical trials are often less effective when used more widely within the general population. This occurs because both patients and physicians are less likely to adhere to the treatment protocols used in the efficacy studies. There are substantial differences in how effectiveness studies are carried out compared with efficacy studies. Unlike efficacy studies, effectiveness studies cannot be closely controlled by investigators. Researchers relax the enrollment criteria; individuals are not excluded unless participation in the study is medically contraindicated. There also may not be special diagnostic screening (retrospective chart diagnoses may be used instead). Furthermore, such studies include not only those subjects who get the “full” course of treatment but also those who drop out during the study period (e.g., an “intent to treat” design). The number of patients enrolled in effectiveness studies is often quite large to help overcome the inherent inability to control self-selection bias in the study design. As a result, statistical controls must be used to adjust outcome data for case-mix differences, an approach that requires a much larger number of study subjects. The design of this type of study may be quasiexperimental or observational. Quasiexperimental studies may take advantage of natural groupings of patients and test an intervention with one group and compare the results with another group, hoping to control for patient differences. In observational studies, the actual practice of a treatment is observed and recorded without any attempt on the part of the investigator to intervene in choices about where patients seek treatment or how long they receive the treatment. Effectiveness studies measure outcomes that are much broader than those in efficacy studies and that are important to policymakers, patients, or consumers who are considering treatment choices.

Population-Based Quality Indicators The movement toward population-based reporting of quality indicators may be a new trend in the field of mental health, but there is a long tradition in public health of reporting the health status of nations in terms of infant mortality and average life span. These reports of health status are indicators that allow comparisons of how national health expenditures are related to national health status. Population studies rely on information that is often provided as a rate (e.g., suicides per 100,000 population), calculated by dividing the number specific to the indicator by the population to which this indicator applies. These quality indicators must meet certain criteria to be useful; the indicator values should have clear meaning, and there should be consensus about the

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direction of those values. For example, the percentage of pregnant women who received prenatal care during the first trimester is interpreted as better preventive care if the rate is higher. Determining quality indicators for mental health is more problematic because it is difficult to reach consensus on whether certain suggested indicators, when expressed as a rate, can be interpreted meaningfully. For example, do higher rates of readmission within 30 days of inpatient discharge mean better or worse quality? It is not easy to answer this question. Often, only the most distressed and disturbed persistently ill patients are admitted to hospitals; consequently their readmission rates will be higher (when compared with others) because they are the sickest and most likely to relapse even when their stays are longer than average. This does not necessarily mean poor care was provided. In fact, it may mean just the opposite. Health care report cards are one format for comparing the rates of quality indicators or the satisfaction scores of similar patient populations. Report cards are in the early stages of development and have several drawbacks. It is too early to tell how helpful they will be to consumers in selecting a health plan. It is not clear whether consumers can easily interpret the information and, if they can, whether they will use it to choose a health plan (Jewett and Hibbard 1996). In addition, it has been difficult for researchers to link performance on one indicator to general levels of quality for a particular population (Druss et al. 1999). Physician profiling—focusing on patterns of care instead of specific clinical decisions (Welch et al. 1994)—is perhaps the most controversial type of population-based quality assessment. In this method, the population base consists of all the patients treated by a particular clinician. One important benefit of profiling is that it can serve as an alternative to utilization review, an intrusive and troubling strategy widely disliked by clinicians. Whereas utilization review exploits the case-by-case nature of clinical work, profiling covers all the cases treated by a physician or a group over a fixed period of time. Patterns of care can be quantified as a rate, which can then be compared with a benchmarked standard or with those of other physicians with similar cases. Before physician profiling can become a useful tool, several problems must be solved. Comparisons, whether with other physicians or with a standard, must be fair. One way to do this is risk adjustment, an effort to remove the bias in data caused by patient self-selection (see Chapter 7). Our ability to adjust for differences in the health status of patients before treatment is very poor, creating unfair (negative) bias in the direction of physicians who treat the sickest patients.

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A second drawback is that the number of cases included in profiling analyses must be large enough to ensure that differences in profiles are not due to chance. This point raises a problem that cannot be easily (or inexpensively) solved: if a physician rarely performs a particular procedure, it is unlikely that there will be enough cases to profile; at the same time, it is possible that this physician is also performing below standard because he or she rarely performs the procedure. Finally, focusing on physicians is contrary to the continuous quality improvement (CQI) premise that a team, not an individual, is responsible for quality of care. Profiling and reporting physician outcomes may undermine such quality efforts. If a physician has a profile that suggests poor quality care, he or she may be singled out unfairly if the delivery of health care is considered a function of systems rather than individual performance.

Continuous Quality Improvement CQI has been borrowed from industrial quality-control activities developed by Deming (1986) and Juran (1964). It is an internally controlled effort that examines how work is carried out, which places front-line employees at the center of CQI efforts and imbues them with the values of customer service and quality first. The employees’ role is to identify what may make their work more efficient or effective. Advocates of CQI insist that the institution must develop a quality improvement culture for CQI efforts to flourish and be meaningful. This cornerstone of all CQI efforts starts with top management personnel who issue a clear message that quality is a top priority. Without strong support, CQI efforts will not achieve lasting results. Juran provided a set of steps for quality improvement projects: first, define the project for the organization, set priorities, and select a CQI team; second, diagnose the problem by analyzing the symptoms, formulating theories of causation, testing theories, and identifying root causes; third, implement a remedy (including finding alternative solutions and establishing a plan of action) and take steps to reduce the inevitable resistance to change; and fourth, maintain the gains by checking performance and monitoring the remedy. Donald Berwick, president of the Institute for Healthcare Improvement in Boston and a leader in the CQI movement, also offered suggestions for implementing CQI efforts (Berwick et al. 1990). He urged CQI neophytes to tackle issues that occur frequently, involve substantial cost, and for which there is existing variation in practice. He believed it helpful to choose processes that are clearly defined and have short cycle times with readily available data. One example he provided was the problem of the timely and

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efficient transfer of medical information from emergency departments to primary care physicians. When the information was not readily available, this transfer delay was time consuming for both the patient and doctor and potentially led to costly and inappropriate care. This problem also met the criteria of a clearly defined process for which the cycle time is short: data on transfer time could be collected, an intervention implemented, and more data collected to evaluate the implementation. Problems that are not easily solved in the course of day-to-day work are often those that cross over disciplinary or functional units and tend to be rooted in the way work is organized in an institution. The above example is just such a case. Defining the problem is often an iterative effort, helped along by mapping the processes on a flow diagram. Mapping provides an opportunity to identify and include every aspect of a particular process. Conceptualizing problems in this way helps CQI teams avoid blaming an individual or work group; instead, efforts are focused on understanding how the system works, the root causes of problems, and how processes can be improved. This system failure concept also establishes why CQI is a team effort, not the work of an individual or special unit within the organization. The team must include individuals who are part of the process under examination and also staff members from different parts of the organization that may affect the process in some way. Because CQI activities are based on systematic data collection, it is important to note how they differ in some specific ways from other empirical research. CQI efforts that aim to improve outcome of care must include some measure of patient outcome. CQI teams must not only collect data on outcomes but also be prepared to examine their data with regard to patient differences in sociodemographic background and psychiatric history. There are also questions about the match of outcome instrument to individual patient. Should the outcome measure be diagnosis specific? Will the language of the instrument be clearly understood? Can patients complete it with minimal assistance? Data collection in clinical settings has some special characteristics as well. The standards of precision and rigor that are the hallmarks of research studies are bound to be relaxed. Discharged patients are hard to follow up and nursing staff rarely have the same view as researchers on the need for adherence to data collection protocols, even when they enter into CQI projects enthusiastically. Changes in clinical staff lead to repeated training sessions for new staff who serve as data collectors. Timing of data collection in a pre/post design is often difficult to work out in busy clinical settings: How soon before treatment should the data be collected and how can it be done efficiently? When is the optimal time to follow up?

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The audience for CQI reports is both internal clinical and administrative staff. The project results must be reported in a timely fashion in formats that clinical staff can readily understand. This also means that the data should be specific to the situation and detailed at a level that provides sufficient information to inform decisions about systemic improvements. Some institutions also share their CQI successes with regulatory agencies and purchasers of care.

Satisfaction With Care No quality improvement program is complete without some measure of how patients perceive the care they receive. The questions we ask patients should seek to identify specific aspects of the treatment process that can be improved rather than simply ask about general satisfaction. Three different satisfaction instruments are provided in this book as appendixes to Chapters 9, 10, and 22. Consumer responses to questionnaires are especially important today because collecting data from medical records or clinicians is expensive and time consuming. A major challenge is finding data collection mechanisms that yield the highest possible response rate, because low rates fail to make a convincing case for change.

COST OF POOR QUALITY, COST OF IMPROVING QUALITY It has been said that poor quality of medical care drives away customers and increases treatment costs. Although logic suggests that keeping people healthy (good-quality care) will lower medical expenditures, few attempts have been made to quantify this idea. Preventive medicine and early intervention have been the foundations of health maintenance organizations, but good-quality care does not lower our national expenditures on health care because terminal illness and death continue to rise in cost, no matter what age they occur. Of course, high mortality and morbidity rates are the most visible consequences of poor-quality care. However, lower productivity, diminished quality of life, recurrent psychiatric or substance use disorder problems, and disrupted family life are also important ways in which inadequate care affects individuals with mental illness. All of these social consequences have costs associated with them, most of which are borne by patients and their families. Costs aside, there is another reason for improving the quality of mental health care: we know that meeting certain standards of care will result in better outcomes. The question is how to achieve these higher standards. Regu-

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lation to protect consumers may provide a floor below which quality is not allowed to fall, but it still will not push quality levels as high as they should be. Unwavering clinical leadership and strong support from corporate purchasers of care and from consumers must shape an environment for change. How can research studies pave the way for effective CQI efforts? Research can provide a conceptual template to guide the planning and implementation of quality improvement projects. Published research can be useful to CQI committees by identifying potential trouble spots and inspiring ideas for new solutions to problems. Current research findings help to sharpen questions about quality and can help CQI committees avoid duplicating interventions or changes in practice that have already been shown to be inadequate. Finally, published research describes successful quality improvement activities carried out in real-world settings. A good example of this is a study of interventions designed to improve quality of care for primary care patients with depression (Wells et al. 2000). The interventions resulted in improved mental health outcomes and quality of care for depressed patients. CQI activities are far less resource intensive than research, although they still carry a heavy financial burden at a time when health care organizations are struggling to cut overhead costs. Many quality improvement projects are carried out with few financial resources, but meeting external standards for reporting quality indicators (such as the Joint Commission on Accreditation of Healthcare Organizations’s ORYX performance indicator project) is expensive. Support for these projects must come out of agency or institutional budgets. Although these projects have drawn attention to the quantification of quality, attention must also be paid to mechanisms that redistribute resources from high-cost, low-return quality-of-care initiatives to local, on-site improvement efforts that may have more immediate and lasting effects for patients.

METHODS FOR THE FUTURE If evidence-based medicine is the answer to our quality prayers, significant advances must occur in the following areas: First, we must significantly upgrade our management information systems so that needed clinical data are accurate and available and accessible only on a need-to-know basis that protects patient privacy. Second, we must engage all consumers in the quality process without being intrusive, encouraging them to respond to surveys about their perception of their care, educating them about how to self-manage their illness, and engaging them in a collaborative treatment process. Third, because clinical reputations and institutional viability are on the line, we need to determine the level of confidence we should have in

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data before such data are made public. Fourth, we need sophisticated cost effectiveness studies that examine the extent to which evidence-based practice saves money, even though there is strong research evidence that it improves outcomes. More than a decade ago, Relman (1988) predicted there would be a new “era of assessment and accountability.” That era has arrived, but only just. Our methods are developing into useful tools, but the process of moving research findings into clinical practice has been slow. Changes will occur but only as we retool medical education, develop strategies that reduce clinician resistance, and support clinical leaders in quality improvement techniques. Transferring knowledge gained through research is likely to be as expensive as the research itself. Foundations and federal funding agencies must consider how they can best support transfer technology activities that build on the research funding investment. Physicians, other mental health professionals, and managed care organizations also must be willing to rethink how best to deliver the treatments we know work.

REFERENCES Berwick DM, Godfrey AB, Roessner J: Curing Health Care. San Francisco, CA, Jossey-Bass, 1990 Bodenheimer T: Disease management: promises and pitfalls. N Engl J Med 340:1202–1205, 1999 Brook RH, Lohr KN: Efficacy, effectiveness, variations, and quality: boundary crossing research. Med Care 23:710–722, 1985 Cochrane AL: Effectiveness and Efficiency: Random Reflections on Health Services. London, England, Nuffield Provincial Hospitals Trust, 1972 Deming WE: Out of the Crisis. Cambridge, MA, Massachusetts Institute of Technology Press, 1986 Dickey B, Wagenaar H: Evaluating mental health care reform: including the clinician, client and family perspective. J Ment Health Adm 3:313–319, 1994 Donabedian A: The Definition of Quality and Approaches to Its Assessment, Vol 1. Ann Arbor, MI, Health Administration Press, 1980 Druss BG, Rosenheck RA, Stolar M: Patient satisfaction and administrative measures as indicators of the quality of mental health care. Psychiatr Serv 50:1053– 1058, 1999 Jewett JJ, Hibbard JH: Comprehension of quality of care indicators: differences among privately insured, publicly insured and uninsured. Health Care Financing Review 18:75–94, 1996 Juran JM: Managerial Breakthrough. New York, McGraw-Hill, 1964 Lohr K: How do we measure quality? Health Aff 16:22–25, 1997 McGlynn EA, Norquist GS, Wells KB, et al: Quality-of-care research in mental health: responding to the challenge. Inquiry 25:157–170, 1988

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Povar GJ: What does quality mean? in Striving for Quality in Health Care. Edited by Palmer RH, Donabedian A, Povar GJ. Ann Arbor, MI, Health Administration Press, 1991, pp ??–?? Reinhardt UE, Cheng MT: The cost–quality trade off, in Profiting From Quality: Outcomes Strategies for Medical Practice. Edited by Isenberg SF, Gliklich RE. San Francisco, Jossey-Bass, 1999a, pp 37–53 Reinhardt UE, Cheng MT: The quality of health care, in Profiting From Quality: Outcomes Strategies for Medical Practice. Edited by Isenberg SF, Gliklich RE. San Francisco, CA, Jossey-Bass, 1999b, pp 3–36 Relman AS: Assessment and accountability, the third revolution in medical care. N Engl J Med 319:1220–1222, 1988 Welch GH, Miller MK, Welch WP: Physician profiling: an analysis of inpatient practice patterns in Florida and Oregon. N Engl J Med 330:607–612, 1994 Wells KB, Sherbourne CD, Schoenbaum M, et al: Impact of disseminating quality improvement programs for depression in managed primary care. JAMA 283:212–220, 2000

7 Risk Adjustment of Clinical Outcome Data Sebastian Lorenz, M.D., M.P.A Lloyd I. Sederer, M.D.

INTRODUCTION Treatment outcomes are not just the result of the care provided, they are also heavily influenced by conditions beyond the control of the clinicians delivering care. It is difficult to measure the effects of medical treatment because the actual care rendered accounts for only a relatively small amount of the variation in health status. Genetic vulnerabilities, sociodemographic status, lifestyle factors, and environmental stress account for about 75% of the variation in health status, and medical treatment accounts for the other 25% (Eday et al. 1998). In clinical trials, the research design can control for these other factors to isolate the effect of the treatment provided. However, in nonexperimental studies of health plans, the “noise” that other factors add makes it difficult to measure treatment outcomes. Treatment outcomes include symptoms, psychological and social functioning, well-being, and satisfaction with treatment. To compare the performance of one clinical service with another, treatment outcome data must

We would like to acknowledge the valuable comments and suggestions made by Barbara Dickey, Ph.D., and Suki Gill, M.D., and the editorial assistance provided by Ms. Karin Zambelis. This work was supported in part by an unrestricted educational grant (S.L.) by Studienstiftung des deutschen Volkes and Harvard University. Inquiries may be directed to the first author at Inselspital Bern University Hospital, 3010 Bern, Switzerland, or E-mail: [email protected] 89

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be adjusted for severity of illness, patient age and gender, where the patient lives, and other clinical and demographic variables. Risk adjustments for clinical outcomes “account for pertinent patient characteristics before making inferences about effectiveness or quality of care based on patient outcome” (Iezzoni 1997a, p. 3–4).

KEY TERMS To distinguish risk adjustment from other statistical and measurement activities involving the word risk, we discuss here some important terms with particular reference to behavioral health care. Risk adjustment for clinical outcome data aims to mitigate the effects of case mix on treatment outcomes used to compare services and clinicians. If we want to compare health maintenance organization (HMO) A with HMO B on treatment outcomes, we need to establish a level playing field to ensure that we can control personal and genetic factors if they are systematically distributed. Similarly, if we want to make a fair comparison between hospital A and hospital B, we need to take patient differences into account, especially when comparing teaching hospitals (which admit more severely ill patients) with other hospitals. Clinical risk adjustment is needed at all levels at which aggregate outcome data are available: individual facilities, practice networks, integrated delivery systems, behavioral health carve-outs, or individual clinicians. Risk adjustment for clinical outcome data is different from risk adjustment for health economics and insurance regulation. Health economics seeks to develop a means of predicting expenditures by examining health risk characteristics of covered populations; it quantifies the process by identifying which high-risk patients select certain health plans to maximize their care, consequently exposing these plans to extraordinary costs (Newhouse 1996; Newhouse et al. 1997). Risk adjustment for reimbursement aims to prevent overpayment or underpayment for services by recognizing favorable or adverse selection (Cutler and Zeckhauser 1997; Newhouse et al. 1989). Health plans have an incentive to select healthier members (or not enroll high-risk patients) to optimize their clinical performance and minimize financial exposure (Frank et al. 1995; Iglehart 1998). This is called risk selection. Patients who have chronic illnesses or require a high level of services (e.g., a person with schizophrenia or a high-risk pregnant woman) tend to select plans that offer specialty services that meet their needs. Conversely, health plans may try to avoid these potentially expensive enrollees.

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RATIONALES FOR RISK ADJUSTMENT There are three main rationales for risk adjustment of mental health outcome data: clinical, economic, and administrative or policy rationales. Although there is considerable overlap among these principles, we discuss them separately.

Clinical Rationale As HMOs and insurers are increasingly held to measurable, comparable quality and satisfaction standards, they may eschew patient populations for which clinical improvements are costly or unlikely. In addition, clinicians today are concerned that they may be penalized for treating sicker patients unless universally accepted measures exist that adjust for the different odds of obtaining favorable outcomes (Burack et al.1999). Clinicians will continue to be reluctant to publicize patient outcome data or contribute to comparative (benchmarking) databases until they are confident that their patient risk profiles will be adjusted (Iezzoni 1994, 1997b). Furthermore, risk adjustment of clinical outcomes can help consumers evaluate and compare services. Patients may also benefit because using adjusted outcomes to evaluate clinicians and facilities may lead to more informed choice and thus, ultimately, more effective treatment.

Economic Rationale The direct and indirect (e.g., loss of productivity) costs of psychiatric illness are high and continue to rise (Croghan et al. 1998; Yandrick and Oss 1994). Risk adjustment of treatment outcome has important economic utility. First, it can help inform treatment choices by identifying less effective treatment options. Second, risk adjustment enables providers to take on financial risk while reducing their incentive to withhold care (Iezzoni et al. 1998). Third, payors benefit from risk adjustment by being able to demonstrate to consumers the value of the services they cover, thus enabling them to link reimbursement to outcome (i.e., paying for quality outcome). Payors may also be better able to choose providers for their network who have (adjusted) good outcomes.

Administrative and Policy Rationales Rising health care costs compete with other socially desired goods (e.g., education, welfare, and defense) and drain business and government treasuries. President Clinton proclaimed that protecting the functioning of the health care system and maintaining the health, productivity, and standard of living of Americans are desirable but competing ends (White House

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Domestic Policy Council 1993). Risk-adjusted outcomes can serve to direct funds in a responsible way while preserving the quality of the delivery system. Consumers are not likely to tolerate a measurable deterioration in the performance of the world’s most technically advanced health care system, particularly if it affects them and their loved ones (see Chapter 3). We need a method of evaluating quality that everyone supports, including patients. Moreover, unless objective, reliable data reassure patients that care is good and has not been compromised to spare the pocketbooks of plans or providers, patients will continue to distrust doctors, hospitals, and health plans (Codman 1917/1996; Grumbach et al. 1998; Kassirer and Angell 1998).

STUDIES OF RISK-ADJUSTED OUTCOMES Actual attempts in clinical risk adjustment go back to the 1830s, when death rates were adjusted for hospitals in London. Although the foundations for risk adjustment were established so long ago, the quest to use risk adjustment to explain treatment outcomes in clinical practice is relatively recent, especially for behavioral health (Bowen 1995; Iezzoni 1997a). We found only one recently published study that specifically examined risk adjustment for mental health outcome data. Hendryx et al. (1999) tested a set of estimation models using diagnostic and demographic variables, functional status, health-related quality of life, and patient satisfaction outcomes in six public mental health outpatient programs in the state of Washington. The authors empirically identified those variables that consistently predicted satisfaction and outcome. They used categorical health status information such as presence of schizophrenia, but they did not obtain severity information. Their results indicated that not all outcome risk adjusters are useful and that the predictive power of these risk adjusters varies depending on the clinical outcome measure.

RISK ADJUSTMENT IN MENTAL HEALTH Methods Although a large body of risk adjustment literature exists for prospective payment systems, the work on clinical risk adjustment to date is limited, especially for psychiatric services. Currently, clinical risk adjustment models used in surgery or general medicine rely on mortality and quality of life outcomes adjusted for demographic and cost factors (Hornbrook and Goodman 1996) and are not very useful for assessing mental health treatment. Moreover, risk adjustment in psychiatry, which commonly includes


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age, gender, diagnosis, and comorbidity variables, has shown a limited capacity to explain variation in outcome scores (Ettner et al. 1998). Risk adjustment can be carried out in several ways. The most simple is to report a particular mean outcome score by category of patient type. Thus, we might report the mean outcome score for females or males under the age of 35 with depression. We could compare this mean score with the mean score from a similar patient group in another setting. In more complex approaches (e.g., multivariate modeling), outcome data are adjusted to reflect the likelihood of treatment success for a population under care. For example, data on outcomes are mathematically modified when comparing a tertiary care hospital that treats indigent patients with poor preadmission health status, advanced disease, and low outpatient treatment compliance with a private, for-profit hospital in an affluent suburb serving a select patient population. This mathematical adjustment corrects for case-mix variation to yield an outcome difference that can be compared fairly (Pincus et al. 1996; Shwartz et al. 1997). Three different classes of risk adjustment variables have been identified for mental health outcome data: sociodemographics, health status, and previous health care expenditures. The greatest variance in outcomes is likely to be explained by health status (e.g., the RAND Medical Outcome Study’s Short Form, SF-36 [Ware and Sherbourne 1992] or the Agency for Health Care Policy and Research’s Acute Physiology and Chronic Health Evaluation, APACHE-III [Knaus et al. 1991]). These appear to be promising risk adjusters for mental health and substance abuse (Ellis et al. 1996; Iezzoni et al. 1998).

Sample Size Any method of risk adjustment requires a large sample size. To return to the example provided earlier, the simplest method of reporting outcomes is grouping them by specific patient characteristics. We must be sure to have enough patients in each category of characteristics to give us confidence that the mean outcome score is not based on just a handful of individuals. As a rule of thumb, we want at least 10 patients in each of the categories. If we compared outcomes from two different facilities of depressed women and men under age 35, we would need at least 40 patients, 20 from each site. If we wanted, in addition, to add a comparison of the outcomes of men and women with depression over age 35, we would then need another 40 patients in the sample, 20 from each site.

Availability of Data A great shortcoming of mental health risk adjustment is the difficulty in gaining easy access to comorbidity and severity information (Charlson et

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al. 1987; Greenfield et al. 1993; Newhouse 1998). It is often impractical in busy clinical settings to obtain severity measures, although many efforts are currently under way that use patient self-report data to estimate severity on admission to a treatment program.

Gaming It is important that outcomes adjusted for differences in patient risk be free of distortion. Any measure that has the potential for raising questions about clinical competence is vulnerable to intentional distortion, or gaming. Gaming is different from fabricating data because it involves selecting measures and processes to optimize performance scores. It can take the form of “upcoding” diagnostic or severity information or reporting only certain comorbidities. Because there is an advantage to ratings that show greater improvement or better outcomes, clinician-rated instruments are also subject to gaming and thus are distrusted among advocacy and payor groups. Self-report questionnaires of symptoms and functioning are less subject to gaming; however, they may systematically lack data from the most severely ill patients.

Reporting Outcome Data Provider (individual, facility, or health plan) profiling can be an important source of public information; however, this information also must be risk adjusted to ensure that it is does not unfairly favor certain professionals or their organizations. Chassin et al. (1996) documented the benefits and hazards of reporting medical outcomes using examples from cardiovascular surgery. They demonstrated how differences in facility type (e.g., hospitals versus hospices) profoundly skew death rates and potentially damage the public reputation of hospitals. Others have found that consumers tend to value patient satisfaction and low rates of adverse events (e.g., death and surgical complications) over favorable health outcomes and low cost (Edgman-Levitan and Cleary 1996; Hibbard and Jewett 1997; Jewett and Hibbard 1996; Tumlinson et al. 1997). A similar study of mental health care providers has yet to be undertaken.

Cost Despite the economic utility of risk adjustment, payors and providers also need to consider its cost; data collection for risk adjustment is anything but cheap. It is estimated that it will cost the public and private health care sectors hundreds of millions of dollars to develop effective risk adjusters and to implement the adjusting systems (Swartz 1995). The current health care


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environment of increasing costs and shrinking revenues deters many health care organizations from investing in the technology necessary to acquire risk adjustment data. Moreover, the slow development of software that interfaces with existing data sources is an almost universal problem in the provider community.

Data Reliability The lack of standardized instruments for collecting information means that data may vary solely as a function of variation in their source. Age, for example, appears to be a simple variable to collect and report, but it may be recorded incorrectly or documented in different formats (e.g., as a birthdate or as the age when a patient first enters treatment). Without specific rules defining how age should be recorded, the data may be unreliable. Furthermore, if the data come from clinical records, it is necessary to ascertain whether medical record abstractors meet an acceptable standard for reliability. Table 7–1 summarizes the availability of risk adjustment variables in typical clinical settings and their potential for gaming. The column at the far right assigns value to the risk adjustment variable by taking into account these two properties. The Catch-22 of risk adjustment, at least to date, is that the more easily measurable variables explain less of the variance and are more prone to gaming; on the other hand, the more robust variables are more difficult to obtain but may be less likely to be gamed. Aside from the Global Assessment of Functioning (GAF) scale, clinician-rated instruments were omitted from the table because the staff and cost burdens make them less likely to be widely used for risk adjustment.

FUTURE DIRECTIONS: RISK ADJUSTMENT OF PSYCHIATRIC OUTCOMES Physician involvement in developing, evaluating, implementing, and responding to the results of clinical risk adjustment is essential (Iezzoni et al. 1996). The development of clinical risk adjusters must be informed by sophisticated clinical input. Implementing risk adjustment in clinical performance measurement will not be achieved without clinical leadership at the highest levels of the organization. Finally, as Iezzoni et al. emphasized, it is the physicians (and other clinicians) who will have to stand by their results and use them to improve care. We recognize and applaud the broad call for resources and leadership to support mental health researchers in their search for new clinical risk

Table 7–1.

Risk variables

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Type of variable

Possible mental health risk adjusters

Availability Most common source

Suitable risk Likelihood of gaming adjustment?

+++ ++

Medical record/administrative Medical record/administrative

++ ++

(Y) Y

+

Medical record/administrative

+

(Y)

Health status

Disability (Medicare recipient under age 65) Any form of current substance abuse MOS SF-36 score on admission BASIS-32 score on admission GAF (DSM-IV Axis IV) on admission

+++ ++ + + ++

Administrative Clinician rating Self-report instrument Self-report instrument Clinician rating instrument

o + o o ++

Y (Y) Y Y (Y)

Demographic

Age Gender Income (by ZIP code) Ethnicity (self-reported)

+++ +++ +++ ++

Administrative Administrative Administrative Self-report instrument

o o o o

Y Y Y N

Note. o = none or minimal; + = low; ++ = moderate; +++ = high; Y = yes; (Y) = perhaps yes; N = No. BASIS-32 = 32-item Behavior and Symptom Identification Scale; GAF = Global Assessment of Functioning; ICD-10 = International Classification of Diseases, 10th Edition; MOS SF = Medical Outcome Study Short Form questionnaire.

I MP ROV ING M ENT AL H E ALTH CARE

Diagnosis related Diagnosis (ICD-10, section F; DSM-IV Axis I) Any psychiatric comorbidity (ICD-10, section F; DSM-IV Axes I and II) Any somatic comorbidity (ICD-10, all other sections; DSM-IV Axis III)


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adjusters. A major challenge for researchers will be to establish a core set of risk factors that can be shown to systematically influence health outcomes in each of the major diagnostic groups. Diagnosis-specific risk factors have particular importance because so much of health care is diagnostically driven: no one can be admitted to a hospital without a diagnosis, treatment planning is principally diagnostically focused, prognosis is typically keyed to diagnosis, and payment will not be made without a diagnosis. The mental health services research community must demystify methods of risk adjustment. Clinicians respect and respond to well-formulated, data-driven, patient-oriented analyses. Research groups should link their research to clinical practice by involving practicing clinicians and clinical practice sites in all stages of their work whenever possible. Consumers must also be involved in this process; their participation in research advisory groups is already a common practice, but greater effort must be made to encourage them to participate in research on treatment outcomes. Clinical and research leaders together need to pursue research and administrative funding (Altman 1995; Eisenberg 1998; Luft 1993; U.S. Government 1994). The often unspoken issue of gaming must be made a continuous subject of discussion and review. Diagnoses, diagnosis-related groups, and risk categories are prone to “risk creep” that make weak outcomes look better, enhance reputation, and increase reimbursement. Optimal patient care, ethical propriety, and a fairer global system of reimbursement will all depend on identifying risk adjusters that make gaming difficult and unprofitable.

REFERENCES Altman SH: Testimony July 19, 1995, by Stuart H. Altman, Chairman, Prospective Payment Assessment Commission Senate Finance Medicare Payments. Washington, DC, Federal Document Clearing House, 1995 Bowen B: The practice of risk adjustment. Inquiry 32:33–40, 1995 Burack JH, Impellizzeri P, Homel P, et al: The effect of public reporting of surgical mortality on the practice of cardiothoracic surgery: a survey of surgeons’ attitudes in New York state. Presented at the Annual Meeting of Thoracic Surgeons, San Antonio, TX, January 1999 Charlson ME, Pompei P, Ales KL, et al: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis 40:373–383, 1987 Chassin MR, Hannan EL, DeBuono BA: Benefits and hazards of reporting medical outcomes publicly. N Engl J Med 334:394–398, 1996 Codman E: A study in hospital efficacy as demonstrated by the case report of the first five years of a private hospital (1917). Reprint. Oakbrook Terrace, IL, Joint Commission on Accreditation of Health Care Organizations, 1996

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Croghan TW, Obenchain RL, Crown WE: What does the treatment of depression really cost? Health Aff 17:198–208, 1998 Cutler DM and Zeckhauser RJ: Adverse Selection in Health Insurance (Working Paper 6107). Cambridge, MA, National Bureau of Economic Research, 1997 Eday LA, Begley CE, Lairson DR, et al: Evaluating the health care system: effectiveness, efficiency, and equity. Chicago, IL, Health Administration Press, 1998 Edgman-Levitan S, Cleary P: What information do consumers want and need? Health Aff 15:42–56, 1996 Eisenberg J: Health services research in a market-oriented health care system. Health Aff 17:98–108, 1998 Ellis RP, Pope GC, Iezzoni LI, et al: Diagnosis-based risk adjustment for medicare capitation payments. Healthcare Financing Review 17:101–128, 1996 Ettner SL, Frank RG, McGuire TG, et al: Risk adjustment of mental health and substance abuse payments. Inquiry 35:223–239, 1998 Frank RG, McGuire TG, Newhouse JP: Risk contracts in managed mental health. Health Aff 14:50–64, 1995 Greenfield S, Apolone G, McNeil BJ, et al: The importance of co-existent disease in the occurrence of postoperative complications and one-year recovery in patients undergoing total hip replacement. Co-morbidity and outcomes after hip replacement. Med Care 31:141–154, 1993 Grumbach K, Osmond D, Vranizan K, et al: Primary care physicians’ experience on financial incentives in managed-care systems. N Engl J Med 339:1516–1521, 1998 Hendryx MS, Dyck DG, Srebnik D: Risk-adjusting outcome models for public mental health outpatient programs. Health Serv Res 34:171–195, 1999 Hibbard JH, Jewett JJ: Will quality report cards help consumers? Health Aff 16:218–228, 1997 Hornbrook MC, Goodman MJ: Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. Health Serv Res 31:283–302, 1996 Iezzoni LI: Using risk-adjusted outcomes to assess clinical practice: an overview of issues pertaining to risk adjustment. Ann Thorac Surg 58:1822–1826, 1994 Iezzoni LI (ed): Risk Adjustment for Measuring Healthcare Outcomes, 2nd Edition. Chicago, IL, Health Administration Press, 1997a Iezzoni LI: The risks of risk adjustment. JAMA 278:1600–1607, 1997b Iezzoni LI, Shwartz M, Ash AS: Using severity measures to predict the likelihood of death for pneumonia inpatients. J Gen Intern Med 11:23–31, 1996 Iezzoni LI, Ayanian JZ, Bates DW, et al: Paying more fairly for Medicare capitated care. N Engl J Med 339:1933–1938, 1998 Iglehart JK: Physicians as agents of social control: the thoughts of Victor Fuchs. Health Aff 17:90–96, 1998 Jewett JJ, Hibbard JH: Comprehension of quality of care indicators. Healthcare Financing Review 18:75–94, 1996 Kassirer JP, Angell M: Risk adjustment or risk avoidance? (editorial). N Engl J Med 339:1925–1926, 1998


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Knaus WA, Wagner DP, Draper EA, et al: The APACHE III prognostic system: prediction of hospital mortality for critically ill hospitalized adults. Chest 100:1619–1636, 1991 Luft HS: Risk adjustments for differences in enrollee mix. Testimony November 9, 1993, by Harold S. Luft, Ph.D., House Ways and Means Committee on Health Care Reform. Washington, DC, Federal Document Clearing House, 1993 Newhouse JP: Reimbursing health plans and health providers: efficiency in production versus selection. Journal of Economic Literature 24:1236–1263, 1996 Newhouse JP: Risk adjustment: where are we now? Inquiry 35:122–131, 1998 Newhouse JP, Manning WG, Keeler EB, et al: Adjusting capitation rates using objective health measures and prior utilization. Healthcare Financing Review 10:41–54, 1989 Newhouse JP, Beeuwkes MB, Chapman JD: Risk adjustment and Medicare: taking a closer look. Health Aff 16:26–43, 1997 Pincus HA, Zarin DA, West JC: Peering into the “black box”: measuring outcomes of managed care. Arch Gen Psychiatry 53:870–877, 1996 Shwartz M, Ash AS, Iezzoni LI: Comparing outcomes across providers, in Risk Adjustment for Measuring Healthcare Outcomes, 2nd Edition. Edited by Iezzoni LI. Chicago, IL, Health Administration Press, 1997, pp 471–516 Swartz K: Reducing risk selection requires more than risk adjustments. Inquiry 32:6–10, 1995 Tumlinson A, Bottigheimer H, Mahoney P, et al: Choosing a health plan: what information will consumers use? Health Aff 16:229–238, 1997 U.S. Government: Health Care Reform: Considerations for Risk Adjustment Under Community Rating (Report GAO/HEHS-94–173). Washington, DC, U.S. Government Printing Office, 1994 Ware JE, Sherbourne CD: The MOS 36-item short-form health status survey (SF-36), I: conceptual framework and item selection. Med Care 30:MS253– MS265, 1992 White House Domestic Policy Council: Health Security: The President’s Report to the American People. New York, Touchstone, 1993 Yandrick R, Oss ME: Industry statistic: affective disorders afflict 12 million people, costing $43.7 billion annually. Open Minds 8:6, 1994


8 Building Partnerships in Accountability Consumer Satisfaction Jean Campbell, Ph.D. Kimberlee Einspahr

INTRODUCTION Medicine is experiencing a scientific revolution, and professional uncertainty is emerging as one dominant reality. The rapid growth of managed health care organizations and the displacement of public-sector health care through privatization has created widespread concern about the quality of patient care. As Marques et al. (1994) observed, “[P]urchasers, consumers and managed behavioral health care organizations have come together in their realization of the need to move beyond simple measures of utilization and cost containment when assessing the quality and effectiveness of care” (p. 28). In both public and private mental health settings, administrators have been urged to link treatment and service variables with client status. The importance of patient satisfaction has a long history of debate, beginning over two millennia ago in Rome. Plato suggested in Statesman that because every doctor “can hurt…by knife or cautery or by demanding fees” as though he were exacting tribute on his patient, he should be held accountable (Skemp 1952, p. 204). Accountability could be achieved by calling an assembly of the people and inviting them to offer opinions about disease and how “drugs and surgical instruments [are] appropriate to the treatment of the sick” (Skemp 1952, p. 205). We may infer from Plato’s comments that he understood the importance of involving individuals in the treatment process and recognized the validity of consumer perspectives. 101

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Avedis Donabedian (1966), a seminal writer on the measurement of quality of care, argued that the ultimate validator of the quality of care is the achievement of health and satisfaction. Based on industrial quality control methods, sectors of the health care industry have embraced the marketplace belief that, for goods and services, the customer “knows best.” This has led to an increasing reliance on a system of customer feedback through evaluation and client-based outcome studies. Measures of satisfaction can easily be collected in a wide variety of settings and are inexpensive and understandable to the lay population. Consumer satisfaction with health care services is generally defined as the consumer’s view of services received and treatment results. Evaluators use the results of satisfaction assessments to inform providers about consumer needs in the hope that these providers will thus be more able to render appropriate services.

PROBLEMS WITH INTERPRETING SATISFACTION DATA Along with the rise of satisfaction assessment as an evaluation tool, problems of interpreting data have developed because concepts of satisfaction are unclear or too broad to be useful. Satisfaction research has been criticized as not having the theoretical basis to guide the variables chosen for study or the hypotheses being tested. Ambiguity has led to serious theoretical and methodological reservations about the validity of consumer satisfaction studies. There appears to be an underlying belief that consumer satisfaction already exists in the population and is simply awaiting measurement. Locker and Dunt (1978), in an early review of the literature, stated that “it is rare to find the concept of patient satisfaction defined and there has been little clarification of what the term means either to the researchers who employ it or respondents who respond to it” (p. 283). In one focus group composed of mental health consumers, participants considered the definition and validity of the concept of satisfaction; most agreed that the idea of satisfaction itself is too vague to generate a clear and useful definition. They attributed this vagueness to the misuse of satisfaction assessments by providers in mental health services. According to one participant, consumer satisfaction surveys may actually be a vehicle by which providers may falsely claim that they are rendering satisfactory service. Another participant maintained that the concept of satisfaction and the usefulness of services are not seen as being correlated: “If you ask people if they are satisfied, by and large people say ‘yes.’ But if you ask them ‘How has this program been helpful to you?,’ most will say that it hasn’t been very helpful.” In this respect, satisfaction assessments often may not

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reflect the true feelings of consumers. The group concluded that people who receive mental health services have been disempowered, making few decisions if any about the services they receive and having little involvement in their treatment and recovery plans. This focus group also determined that satisfaction assessments are often guided by political agendas that serve to support programs and funding for providers rather than accurately assess the needs and feelings of consumers. One participant noted a distinct difference in the way mental health service providers and consumers approach assessments; for providers, assessments generally support existing services, whereas service recipients use assessments to challenge the delivery of services and treatments. Focus group members identified the need to eliminate the “professional” definition of satisfaction created by providers and to institute consumer-oriented measures of quality. Furthermore, focus group participants believed that qualitative data are more useful than quantitative data. They reported that quantitative information consistently produced high satisfaction rates even though the personal experiences documented by consumer writings and testimonies call into question the validity of such findings. In particular, the group felt that qualitative information revealed more negative information about mental health services and successfully identified appropriate concerns of mental health service recipients. Participants strongly agreed that the most successful satisfaction assessments were those using face-to-face interviews and open-ended questions. This focus group highlighted one of the problems with current satisfaction instruments; they are sensitive to satisfaction but insensitive to dissatisfaction. High satisfaction rates reported in mental health studies are often contradicted by published interviews, patient narratives, first-person accounts, and biographies, all of which may stand as robust critiques of mental health service delivery systems and treatments. However, little attention focuses on studying the nature and extent of dissatisfaction. Instead, satisfaction measures tend to focus on issues professionals feel are important to consumers and not on the major areas of consumer discontent. Only recently has research begun to examine how the negative effects of coercion in service delivery are related to consumer dissatisfaction.

THEORIES OF SATISFACTION Study of consumer satisfaction is necessary to establish a general theory of provider–recipient relations that can serve as a basis for practical action and for future research. Little is known about how consumers arrive at opinions of the services they receive or what criteria and standards they use to inform

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these opinions. As a result, it is difficult to interpret satisfaction assessments. Williams (1994) suggested that health consumerism relies on three fundamental factors: 1) the existence of consumer opinion; 2) the consumer’s belief in the legitimacy of that opinion; and 3) a willingness to engage in an expression of that opinion. He further noted that “a number of assumptions are often made concerning what patients actually mean when they say they are ‘satisfied.’ Such assumptions in interpretation are themselves the result of assumptions made about the way patients evaluate. Consequently, inferences made from survey results may not reflect the true beliefs of service users” (p. 509). Even so, it is widely assumed that there is a strong link between the expression of satisfaction and the fulfillment of consumer expectations. Most satisfaction studies view satisfaction as the difference between desired rewards and those actually received, the difference between what occurred and what was expected. Although the values and expectations of service recipients are important to most theories of satisfaction, there can be situations in which expectations might not even exist, for example, when an individual is encountering a program or institution for the first time. Service recipients may not believe in the legitimacy of their own expectations because of the technical nature of many treatments. Although medical technology can be an important determinant of clinical outcomes, studies have found that a person’s evaluation of the technical aspects of his or her own health care explains little of the variance in global satisfaction. On the other hand, satisfaction could be a product of the recipient’s confidence in the medical procedures received. Linder-Pelz (1982) asserted that “beliefs about doctor conduct prior to an encounter play a significant role in determining subsequent evaluations of the doctor’s conduct, irrespective of what (s)he actually did or was perceived to have done. It suggests that patients are likely to express satisfaction no matter what care the doctor gives” (p. 588). Another possible conclusion is that satisfaction is really an expression of the recipient’s gratitude for being treated. Some investigators have discovered that consumers’ reports of satisfaction are positively associated with adherence to treatment plans. A review of research (Becker and Maiman 1980) concluded that satisfaction was one of 10 factors that subsequently affected patient compliance. However, patient acceptance of physician instructions is not necessarily a sign of satisfaction. People who appear to understand and accept medical instructions may merely be adapting to a social situation that they feel unable to control and unwilling to resist. “Good patients” may actually feel helpless because they cannot personally influence the situation. In such cases, satisfaction with a provider would be a reflection of passive acquiescence to the treatment plan rather than approval of the quality of care or outcomes.


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A conceptual model of satisfaction must recognize that people who experience illness and those who try to help them see the world from very different perspectives. Consumer researchers have pointed out that mental health professionals’ priorities are more often to control symptoms and behaviors or maintain the effectiveness of the service system, whereas recipients of services ask, “What does this do for my life?” (Scott 1993, p. 4). It is essential to determine whether satisfaction as a concept is meaningful to consumers themselves: Is satisfaction related to people’s everyday lives? In interviews with medical patients, Fitzpatrick and Hopkins (1983) “were struck by a lack of fit, in many respects, between patients’ own accounts of their experiences and the assumptions about patients contained in the satisfaction research” (p. 297). The study of satisfaction in relation to process and outcome variables can address substantive theoretical questions because the convergent and discriminate validity of satisfaction measures can be tested. Perceptions of satisfaction are the result of both individual patient characteristics and the medical care they receive. Although only a weak association has been established between satisfaction and service recipient characteristics (e.g., age, race, or gender), surveys of large health consumer groups have consistently found that consumer satisfaction is significantly correlated with professional competence and personal communication styles of physicians. Lochman (1983) found that factors with the clearest relationship to satisfaction include the accessibility of medical care, the organizational structure of clinics, treatment length, perceived competence of physicians, affiliative behavior, clarity and retention of physicians’ communication to patients, and controlling behavior of physicians. Medical professionals do not often see the consumer–provider relationship as an integral part of their clinical role. However, patients’ perceptions of the interpersonal aspect of their care is important. For example, patients who believe their physician is friendly, understands their concerns, and possesses good communication skills have consistently had higher levels of satisfaction and compliance. It has also been established that physicians who listen to their patients and answer patients’ questions have more satisfied patients; highly controlling doctors have less favorable evaluations. The amount and clarity of information provided to consumers and the number of nonmedical conversations are strongly correlated with satisfaction in physicians. Communication problems in the consumer–provider relationship result partly from differences in medical knowledge and language as well as in goals or interests. Some medical professionals are reluctant to communicate information to patients because they assume that lay persons are unable to comprehend or cope emotionally with medical information.

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Others fear that informed consumers could threaten their professional status in therapeutic situations. By withholding information, physicians believe they might avoid confrontations, reduce the likelihood of mistakes being uncovered, protect their patients from sadness, and assume greater control. The exercise of professional authority by providers tends to reinforce consumer resistance to actively work with them. To mental health professionals, this consequently justifies their decision to withhold information. However, there is evidence that people who participate with their physicians in making decisions about managing illness are more prepared to engage in health-promoting behaviors. Conversely, Seeman and Seeman (1983) found that “a low sense of control is…significantly associated with 1) less self-initiated preventive care; 2) less optimism concerning the efficacy of early treatment; 3) poorer self-rated health; and 4) more illness episodes, more bed confinement and greater dependence upon the physician” (p. 144). Encouraging people to take a more active role in managing their health problems may be the best way to change unsatisfying consumer–provider relationships into effective ones. Physicians need to “set aside their image of themselves as making life-and-death decisions alone” (Kassirer 1983, p. 901) and spend more time collaborating with the patient.

RISING CONSUMER INFLUENCE Unfortunately, satisfaction research rests on published literature that fails to reflect the experience and insight of consumers. Most studies by consumers are found in unpublished reports, proceedings, and newsletters because the work of consumer researchers has seldom conformed to the standards of experimental research design. Still, with the advent of managed care and the recognition of the need for quality indicators, there is a growing willingness to reexamine assessment protocols. In the field of mental health, consumer focus groups and public forums have articulated a core set of indicators that could be included in quality management systems. The Accreditation Council on Services for People with Disabilities (1993) developed value-based performance measures from the input of persons with physical and mental disabilities. Desired outcomes included achieving personal goals, choice of treatments, social inclusion, satisfying relationships, consumer rights, being treated with dignity and respect, good health, satisfactory environment, security, and satisfaction with care. In 1993, the Center for Mental Health Services (CMHS) also supported a series of focus groups to explore consumer values and outcomes in mental


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health care. According to participants, traditional mental health systems pathologize problems in living, hold low expectations of consumer achievement, are paternalistic, offer a limited range of options, and define anger as a symptom of illness. Some key consumer concerns included the threat of involuntary treatments, subtle forms of coercion, lack of respect, and the debilitating side effects of medications. It was also observed that researchers failed to ask questions that would capture unanticipated detrimental effects of treatment and care (e.g., about the presence of personal freedom and decision-making power). Recovery, personhood, well-being, and liberty were identified as relevant outcomes that are seldom measured in traditional mental health research or program evaluation (Campbell 1999). In the past few years, the federal government has continued to investigate service priorities and quality indicators to help service recipients select providers and monitor the quality of their own care. Two major consumer projects were funded by the CMHS to identify valued mental health outcomes and provide input into consumer-oriented mental health report cards and the consumer component of the State Mental Health Agency Profiling System (Campbell 1998). Consumers brainstormed descriptions of specific individual and system indicators and measures. Using conceptmapping technology, domains of concern were identified and ranked. These included damaging effects of the system, consumer impact on service delivery and system development, oppression due to race and gender, healing and recovery, coercion/control and autonomy, personhood, alternatives to traditional services, threats to citizenship, quality of life, and employment (Dumont and Campbell 1994). Many behavioral health report cards have been devised to assess the benefits and impact of services for a broad range of system stakeholders, including consumers, practitioners, managed care administrators, and hospitals. Among the national organizations engaged in producing reports cards are the Mental Health Statistics Improvement Program (MHSIP), the National Alliance for the Mentally Ill, the National Committee for Quality Assurance, the American Managed Behavioral Healthcare Association, the Institute for Behavioral Healthcare, and the Joint Commission on Accreditation of Healthcare Organizations. Because customers of health care services were not necessarily included when these report cards were developed or implemented, the report cards focus on data that only partially addresses the information needs of consumers. Moreover, in a survey of 366 federally funded community mental health centers, Sorensen et al. (1979) examined the methodology used to assess consumer satisfaction. Only 48% of these mental health centers had gathered satisfaction data from consumers. Most reports of satisfaction data typically went to agency administrators, department directors, agency

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boards, and service providers. Only 16% of the centers reported results to other mental health agencies in the community, 14% reported results to the public, and only 9% reported the information back to consumers. More recently, Campbell (1998) found that state departments of mental health were not involving mental health consumers in meaningful roles in the design and implementation of survey instruments and services research projects, except as members of advisory boards (50%) and focus groups (71.7%). Only 13% of states funded consumer participation in research activities, and less than 40% of states funded consumer initiatives to monitor satisfaction. Recipients of mental health services have begun to encourage the research community to engage consumers (Campbell 1997). Key activities of consumers have included the formulations of satisfaction teams in Georgia, Pennsylvania, Florida, Louisiana, and South Carolina. The consumer perspective has been articulated through dialogues with mental health administrators and psychiatrists; the facilitation of focus groups and concept-mapping pilots; and consumer research on self-help, well-being and empowerment, and recovery. The central characteristic of a mental health consumer perspective is that it is grounded in the values of the consumer movement and proceeds from a shared vision of what quality is in mental health services and how it should be measured (Campbell 1999).

CONSUMERS AS EVALUATORS A 1998 survey of satisfaction assessments by the authors indicated that consumer evaluations of services are becoming more widespread and sophisticated. The authors also identified the trend toward involving consumers in significant research roles. Through detailed interviews, this study gathered direct input from 19 consumers and nonconsumers who had been extensively involved in consumer satisfaction assessments. Respondents were asked to identify and describe the best practices for conducting consumer satisfaction surveys. They determined that the involvement of consumers in all aspects of satisfaction measurement was the fundamental best practice in conducting consumer satisfaction surveys. Respondents reported that previous experience with the mental health system and appreciation and understanding of the emotions and experiences of fellow consumers allowed them to contribute to the survey process in ways that nonconsumers could not. Consumers understood the importance of improving the lives of fellow consumers. In addition, consumers brought a more diverse understanding of the mental health system to the assessment process because they were exposed to services from the “inside,” whereas providers and nonconsumers were not.


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According to respondents, the use of consumers as surveyors to conduct face-to-face interviews has been highly successful. Consumers personalize interviews by conveying genuine interest in the opinions of those surveyed. This tends to increase response rates. However, respondents also noted that not all consumers are receptive to face-to-face interviews because such interviews are time consuming and difficult to schedule. Confidentiality was a serious concern for some interviewees as well, making it difficult for interviewers to receive honest responses. Therefore, respondents recommended that telephone interviews and mail surveys be used to reach people who may need to voice their opinions anonymously or may experience other barriers to face-to-face interviews. It was suggested that evaluators, before beginning surveys, assess the needs and restrictions of the target population to determine the most promising methods for reaching them. Most important, results of this survey showed that information from consumer-led evaluations was distributed broadly to mental health stakeholder groups and used to improve services. Consumers were given access to all of the information obtained in the project in more than half of the projects covered by this survey, and 47% of respondents reported that the information from their survey results had been used to initiate system reforms. By defining the consumer perspective and helping mental health professionals better understand service recipient needs, survey respondents demonstrated a new evaluation model based on the involvement of consumers.

EVOLVING MODELS OF CONSUMER SATISFACTION One example of a mental health service quality improvement process that has extensive consumer participation is the Consumer Assessment Team Specialists (CATS). This Florida project is staffed almost entirely by consumers and their family members who conduct face-to-face interviews to determine satisfaction with services and clinical environment. The CATS interviewers were trained to conduct interviews without using paper surveys or taking notes. During interviews they ask about every aspect of individual care, including what respondents liked or did not like about the services, what they would like to see changed, their perceptions of caregiving staff and clinical activities, and whether they were given choices in their treatment plans. Recently, the CATS efforts centered on the issue of managed care, discovering that many consumers do not understand the terms of their health organization. As a result, the CATS program recommended that behavioral health organizations educate consumers further. The Georgia Evaluation and Satisfaction Team (GEST) uses teams of consumers and family members to collect information for continuous qual-

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ity improvement at the facilities where interviews take place. Initially piloted in 1993, GEST is an ongoing project dedicated to the training, support, and certification of the GEST approach. Teams specialize in tailoring their assessments to specific populations by combining standardized tools with questions developed by consumer panels and focus groups. Similarly, C’EST Bon, the Consumer Evaluation Survey Team from Louisiana, aims to promote continuous quality improvement for both services and facilities by conducting extensive interviews with consumers, family members, and staff. Composed of specially trained mental health service consumers and family members, teams gather information about quality of mental health services, satisfaction with services, and quality of life using a consumer-developed assessment tool. In addition, the teams interview staff about service delivery and the quality of their relationships with service recipients. Results are analyzed to identify necessary changes and opportunities for improvement.

BEST PRACTICES IN MEASURING SATISFACTION In consumer evaluation, four basic assumptions guide the planning and implementation of satisfaction assessments. 1. Satisfaction assessments should be inexpensive in terms of time and money. It is important to carefully consider the length of the questionnaire and the anticipated sample size. As a general rule, surveys should be only long enough to capture the most important information. The surveys should be interesting and worthwhile, and the time spent should be consistent with compensation for the respondent. 2. Assessments should be standardized, both in administration and in interpretation, to permit comparisons between different programs: What works best for whom and at what cost? 3. It is essential to practice value-added research in plans for using results that are established a priori to improve the quality of services at the individual and system level. 4. All persons involved in conducting satisfaction assessments should aim to serve consumers by endeavoring to understand their experiences of services.

CONCLUSION More than 20 years ago, Prager and Tanaka (1979) stated, “Representing the consumer’s perspective on the meaning of mental illness and the correlates of getting better, the process of client involvement in evaluation


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design and implementation is not only realistic and feasible; it is, we feel, a professional necessity whose time is overdue” (p. 51). Without developing ways for constructive dialogue and shared decision making to occur, the evaluation of services can reinforce a “kind of turf war over controlling human beings in a landscape that includes an entire array of service options and widely divergent goals and definitions of mental health and quality of life” (Scott 1993, p. 5). The final report of the MHSIP Task Force on the Design of Performance Indicators Derived from the MHSIP Content (1993) suggested that the ideal environment for the development of a system of performance indicators is one in which 1) the intentions of all stakeholders are articulated and shared, 2) there is a culture of respect for and constructive use of data, 3) changes are accomplished through participatory development, and 4) resistance is reduced through disclosure of fears and implementation of safeguards that address those fears. Within a collaborative framework it cannot be assumed that mental health care will be optimized, even if data elements are refined and collected through statistically valid methods. Most research literature on evaluation methodologies, as well as discussions of quality assurance information systems, focuses primarily on defining the appropriate indicators and data collection instruments to capture needed information. There appears to be a certain naïve faith that if the “right” data collection tools could be developed, the road to quality and cost effectiveness would be clearly defined. However, this faith may be misplaced. People must recognize and be critical of the political and social relations of measurement and the structural components of information systems for research and evaluation to realize the promise of measuring satisfaction for improving services and reducing costs. There are profound issues of power and knowledge in how evaluations are conducted. If information on satisfaction is important to consumers making health care decisions and to providers designing systems of care, access to this information is of critical importance. As public mental health systems become privatized and enter a marketplace environment, mental health services consumers are demanding a more prominent voice in how research and evaluation shapes the content, character, and influence of mental health services. Therefore, the processing and presentation of information as it flows through the health care system and the distribution and feedback loops that allow for shared decision making must support quality improvement in policy, management, and clinical practice. If the voices of service recipients are welcomed as partners, the knowledge gained from satisfaction assessments can become a major value-added force in mental health service delivery.

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REFERENCES Accreditation Council on Services for People with Disabilities: Outcome Based Performance Measures. Landover, MD, Accreditation Council on Services for People with Disabilities, 1993 Becker MH, Maiman LA: Strategies for enhancing patient compliance. J Community Health 6:113–135, 1980 Campbell J: How consumers/survivors are evaluating the quality of psychiatric care. Evaluation Review 21:357–363, 1997 Campbell J: The technical assistance needs of consumer/survivor and family stakeholder groups within state mental health agencies. Alexandria, VA, National Technical Assistance Center for State Mental Health Planning, 1998 Campbell J: Consumerism, outcomes, and satisfaction: a review of the literature, in Mental Health, United States, 1998. Edited by Manderscheid R, Henderson M. Washington, DC, U.S. Government Printing Office, 1999 Donabedian A: Evaluating the quality of medical care. Milbank Memorial Fund Quarterly 44:166–203, 1966 Dumont J, Campbell J: A preliminary report for the mental health reform report card task force of the MHSIP Ad Hoc Advisory Group. Rockville, MD, Center for Mental Health Services, 1994 Fitzpatrick R, Hopkins A: Problems in the conceptual framework of patient satisfaction research: an empirical exploration. Sociology of Health and Illness 5:298–311, 1983 Kassirer JP: Adding insult to injury: usurping patients’ prerogatives. N Engl J Med 308:898–901, 1983 Linder-Pelz S: Social psychological determinants of patient satisfaction: a test of five hypotheses. Soc Sci Med 16:583–589, 1982 Lochman JE: Factors related to patients’ satisfaction with their medical care. J Community Health 9:91–109, 1983 Locker D, Dunt D: Theoretical and methodological issues in sociological studies of consumer satisfaction with medical care. Soc Sci Med 12:283–292, 1978 Marques C, Geraty R, Harbin H, et al: Quality and access in the managed behavioral health care industry. Behav Healthc Tomorrow 3:22–29, 1994 MHSIP Task Force on the Design of Performance Indicators Derived from the MHSIP Content: Performance Indicators for Mental Health Services: Values, Accountability, Evaluation, and Decision Support. Rockville, MD, Center for Mental Health Services, 1993 Prager E, Tanaka H: A client-developed measure (CDM) of self-assessment and change for outpatient mental health services, in New Research in Mental Health. Columbus, OH, Ohio Department of Mental Health, 1979, pp 48– 541 Scott A: Consumers/survivors reform the system, bringing a “human face” to research. Resources 5:3–6, 1993 Seeman M, Seeman TE: Health behavior and personal autonomy: a longitudinal study of the sense of control in illness. J Health Soc Behav 24:144–160, 1983


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Skemp JB: Plato’s Statesman. New Haven, CT, Yale University Press, 1952 Sorensen J, Kantor L, Margolis R, et al: The extent, nature, and utility of evaluating consumer satisfaction in community mental health centers. Am J Community Psychol 7:329–337, 1979 Williams B: Patient satisfaction: a valid concept? Soc Sci Med 38:509–516, 1994


9 Toward a National Report Card Measuring Consumer Experiences Susan V. Eisen, Ph.D. Brian Clarridge, Ph.D. Vickie Stringfellow James A. Shaul, M.H.A. Paul D. Cleary, Ph.D.

INTRODUCTION Assessment of consumer satisfaction with mental health services was reported to be commonplace as early as 1982 (Lebow 1982). At that time, assessment efforts were attributed to program evaluation efforts at federally funded community mental health centers, increasing consumer orientation throughout the medical field, and an increasing demand for accountability among private and government-financed insurance programs. Kalman (1983) highlighted the potential for consumer satisfaction to improve patient compliance with treatment, attain optimal use of appropriate services, improve the design and delivery of mental health services, and ultimately improve clinical outcomes. During the 1990s, assessment of consumer experiences increased, fueled by the rising costs of services and demands for accountability by payors and regulatory organizations (Eisen et al. 1991). Demands for accountability have focused not just on cost control but also on enhancing the quality of care. Consequently, assessment of consumer experiences with care is now an integral part of continuous quality improvement (CQI) efforts in most mental health institutions and programs. 115

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Early efforts to assess consumer experiences relied on qualitative methods (e.g., personal interviews, letters from patients, focus groups) as well as comprehensive data from the broad application of survey methodology. Many facilities developed customized instruments to ask about consumer experiences with services. However, failure to use standardized methods, difficulties in obtaining random samples, and the lack of normative data were among the challenges faced by program administrators and policymakers. Although customized site-specific instruments may be useful for internal quality monitoring efforts as well as for marketing or public relations, they are not useful for comparing consumer perceptions between facilities. As a result, an interest in developing standardized report cards to rate mental health care has emerged from national efforts to broaden the scope of quality assessment (Dickey 1996). Spurred by criticism about quality in managed care plans, health maintenance organizations and others offering managed behavioral health services have become increasingly interested in assessing consumer experiences. Standardized quality measures are under development that can be used nationally to compare different service settings and types of health care delivery systems. To develop these measures, all stakeholders need to achieve consensus on identifying the best indicators of quality, indicators must be incorporated into a standardized instrument, systematic surveys must be implemented within systems of care, and the comparative quality of the organizations assessed must be reported (Eisen et al. 1999). Several national organizations have become involved in such efforts. The National Committee for Quality Assurance (NCQA) developed the Health Plan Employer Data and Information Set (HEDIS), the most widely used health care report card system to compare quality of care across health plans (National Committee for Quality Assurance 1997). However, the HEDIS currently includes only a few performance indicators that pertain to behavioral health, all of which are utilization measures rather than consumer evaluations (e.g., follow-up visit within 7 days of discharge from inpatient hospitalization). The Foundation for Accountability (FAcct), a not-for-profit organization, has also supported the development of quality measures and dissemination of performance information to consumers (Skolnick 1997). FAcct endorsed quality assessment tools for specific medical conditions, including depression. The American Managed Behavioral Healthcare Association (AMBHA) developed Performance Measures for Managed Behavioral Healthcare Programs (PERMS), a set of 16 indicators measuring access to care, consumer satisfaction, and quality of care (Ross 1997).

Toward a National Report Card

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One of the most prominent efforts to develop a set of performance indicators for the medical care industry is the Consumer Assessment of Health Plans Study (CAHPS, Crofton et al. 1999). Funded by the Agency for Health Care Policy and Research (AHCPR), CAHPS is a collaborative effort involving diverse representatives of the health care industry and consumers working with Harvard University, RAND Corporation, and the Research Triangle Institute. The goal of the CAHPS project was to develop a set of standardized survey questionnaires and report formats that can be used to collect and report meaningful and reliable information from health plan enrollees about their experiences with medical services and insurance plans (Hays et al. 1999). In 1999, the CAHPS consumer survey was incorporated into the HEDIS reporting requirements. The Health Care Financing Administration (HCFA) is also currently using the CAHPS survey to assess health services to Medicare beneficiaries.

CHALLENGES IN ASSESSING THE QUALITY OF A BEHAVIORAL HEALTH PLAN The development of a behavioral health version of the CAHPS survey represented an attempt to extend the CAHPS effort to the behavioral health arena. The design principles that guided development of the Consumer Assessment of Behavior Health Survey (CABHS), the conceptual domains included in the survey, the process of survey development, and findings from the first field test of the instrument are reported elsewhere (Eisen et al. 1999). The CABHS instrument was developed following extensive review of existing consumer surveys for behavioral health; performance indicators identified by the NCQA, AMBHA, and the Mental Health Statistics Improvement Program (MHSIP); practice guidelines published by the American Psychiatric Association for psychiatric evaluation and treatment of major depressive disorders, schizophrenia, bipolar disorders, substance use disorders, and eating disorders; input from experts in the fields of mental health and survey development; and input from a wide range of behavioral health and substance abuse service consumers. Guidelines and design principles adopted by the CAHPS were used to guide development of the CABHS instrument (Crofton et al. 1999). Eight specific issues were identified during the development of the behavioral health survey. Many of these issues were identified by consumers who participated in focus groups conducted during the survey development process. The eight issues are discussed individually in the following sections.

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Identifying Aspects of Service and Benefits That Are the Responsibility of the Plan If a goal is to evaluate the performance of insurance plans that provide behavioral health services, then only the aspects of service and benefits that are the responsibility of the plan should be included. A major consumer complaint is that coverage for behavioral health services is usually much more restricted than it is for general medical services. Many plans limit enrollees to 8 or 10 outpatient visits. The purchasers of the policy (i.e., employer or government), not the plan, often determine the limitations on behavioral health coverage. Although coverage is often not determined by the plan, consumers still tend to hold plans responsible for the level of benefits. Discussions in three different focus groups all identified benefit coverage as an important area for a behavioral health survey to address. Consequently, we included a question on the survey asking enrollees whether all, some, or none of their treatment was paid for by their health plan.

Identifying Services Received Through the Plan Versus Outside the Plan Community-run mental health programs and support groups play a major role in the mental health service system. These services might be provided by the respondents’ health plan, not provided by but reimbursed by the plan, or neither provided nor reimbursed by the plan. The services may be organized by mental health facilities, general health organizations, government-funded mental health agencies, other social service organizations, or consumer-run organizations. Focus group discussions made it apparent that many consumers do not know which services are provided or reimbursed by their insurance plans. Because it seemed likely that respondents would be unable to distinguish services received within versus outside the plan, we made the decision to include all outpatient treatment or counseling received by the enrollees.

Deciding What Types and Levels of Service Should Be Included in the Survey Instrument Mental health treatment comes in many forms and different levels of intensity. The survey instrument we developed asks about outpatient treatment or counseling received in the previous 12 months for a mental illness, a personal or family problem, or a substance use disorder. Substance abuse services were included for three reasons: first, because most insurance plans combine coverage for mental health services with coverage for substance


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abuse services; second, many mental health treatment facilities include programs for substance abuse within the facility; and third, substance use disorders frequently co-occur with psychiatric diagnoses. We decided not to include inpatient hospital care because hospitalizations are relatively infrequent; most episodes of psychiatric illness are treated in outpatient settings. In the first field test of the CABHS only 10% of users of mental health services reported an inpatient hospitalization during the 12-month time frame. Consequently, we thought that a separate survey to assess quality of the inpatient episode, delivered only to those who received inpatient care, would provide a more appropriate mechanism for assessing quality of the inpatient segment of treatment. Of respondents to the field test, 17% indicated that they had attended a self-help group at least once in the past 12 months. Because self-help programs are considered valuable components of treatment plans and a consensus of stakeholders agreed that health plans should refer consumers to these groups, we decided to include a question about referral to self-help programs.

Deciding How to Include the Multiple Providers and Treatment Settings in Which People Receive Care Behavioral health services frequently involve a team of clinicians or a treatment program that may include a psychopharmacologist, therapist, social worker, case manager, or other clinician. The multiplicity of providers raised questions as to whether the survey should focus on one main provider or, alternatively, on all of the caregivers providing services within the specified time frame. We included a question asking respondents whether they thought one person served as their main clinician for mental health or substance abuse services. If the consumer said “yes,” the survey asked for an overall rating of their main clinician on a scale ranging from 0 to 10, where 0 was the worst rating and 10 was the best. For all of the remaining evaluation questions, instructions asked respondents to include all treatment or counseling they received in the previous 12 months, excluding any care received during an overnight hospital stay or from a self-help program. Accounting for multiple encounters with multiple providers over a 12-month time period presented another dilemma for respondents, particularly if some encounters were perceived favorably and others were not. Cognitive testing procedures used in developing the CAHPS survey suggested that a reasonable solution to this problem was to use frequency of occurrence response options (i.e., never, sometimes, usually, always), allowing respondents to indicate how often a particular event occurred (Harris-Kojetin et al. 1999). The behavioral health survey used the same response options for most of the questions.

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Maintaining Consumer Confidentiality Maintaining consumer confidentiality is important when surveying individuals who receive mental health services. The CAHPS Survey and Reporting Kit recommends that health plans hire a vendor to collect survey data and report results (Agency for Health Care Policy and Research 1998); this increases the likelihood of obtaining unbiased, credible results. Hiring an outside vendor, however, also means releasing names, addresses, and phone numbers to an external organization. Many providers are reluctant to release identifying information for individuals who have used mental health services or who have a psychiatric or substance abuse diagnosis. Some common techniques can be used to mitigate this problem. First, any organization that receives names and addresses should sign a confidentiality agreement indicating that the relevant information will be used only for contacting potential respondents. All individuals surveyed should be given the opportunity to decline to participate in the survey without jeopardizing their benefits or services. Each individual should be assigned an arbitrary identification number to appear on any completed survey. After data collection is complete, the list of names and addresses should be destroyed so that survey responses cannot be linked to individual names and addresses. If a health plan also covers general medical care, it is possible to create a mixed sample composed of enrollees with either a medical or psychiatric diagnosis so that an outside vendor would have no way of identifying which of the individuals received mental health services. The CABHS instrument uses “screening” questions that ask respondents to indicate whether they received treatment or counseling for a mental illness, personal or family problem, alcohol use, or drug abuse. There is no assumption that the individual received such services.

Obtaining Adequate Response Rates The NCQA recommends a response rate of at least 60% for reporting health plan survey results. The CAHPS Survey and Reporting Kit recommends that a minimum of 300 completed surveys per plan product be obtained for meaningful plan comparisons to be made. With these criteria as guidelines, a sample of at least 500 cases needs to be selected to achieve 300 completed surveys and a 60% response rate, assuming that all selected cases report having received services and have completed the remainder of the survey. However, experience with mental health service recipients suggests that response rates may be lower than those achieved by users of other medical services. There are several possible reasons for lower response rates from mental health consumers. First, because of the stigma associated with mental illness, respondents may be less likely to report having


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received services. A field test of the CABHS instrument indicated that although all respondents were identified by the plan as having received services during the year, 14% reported on the survey that they had not received any services. Second, clinical impairment may reduce respondents’ capacity to complete a survey. Third, mental health consumers may be more difficult to locate and reach by telephone than those who receive general medical care. The CABHS field test found that individuals on public assistance were more difficult to reach by mail or telephone than those with commercial insurance, lowering the response rate for Medicaid beneficiaries. To maximize response rates, we recommend a “mixed-mode” methodology using mailed surveys first and a reminder postcard; if these attempts to contact potential respondents do not succeed, providing an opportunity to complete the survey over the telephone may increase response rates.

Reporting Results Results of consumer evaluations can be reported to the clinical and administrative leadership of provider organizations, purchasers of care, and oversight organizations such as accrediting bodies or state mental health departments. Reports can summarize results using text, tables, graphs, and charts and are often incorporated into report cards that are used internally for quality improvement purposes. However, standardization and widespread reporting of identified performance indicators allows for much broader dissemination of results. The CAHPS project developed report formats for presenting information to help consumers choose health plans (McGee et al. 1999). The project used previous research on consumers’ interests and needs for information as well as feedback from experts, focus groups, and cognitive testing to create a variety of report formats, including bar charts, stars (one, two, or three stars indicating below average, average, or above average performance), and arrows (down, up, or horizontal for below average, above average, or average). It is expected that parallel report formats will be used to report behavioral health care quality and plan performance.

Disseminating Results to Stakeholders Several organizations have disseminated health plan report cards to consumers. In 1998, the Washington State Health Care Authority, in collaboration with the CAHPS project and The Picker Institute, distributed a report showing how 16 health plans were rated by state employees who used each plan (Veroff et al. 1998). In 1998, in both Michigan and Massachusetts, statewide reports were made available to consumers comparing

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consumer evaluations of medical, surgical, and maternity care provided by specific hospitals within each state. Videotapes presenting information about health plan performance have also been developed and disseminated by the CAHPS team. Performance reporting is more fully developed for medical and surgical care than it is for behavioral health care. However, we expect that widespread use of a standardized behavioral health survey such as CABHS along with additional behavioral health quality indicators will come of age for large organizations in the near future.

CONSUMER ASSESSMENT OF BEHAVIORAL HEALTH SURVEY Domains The following domains of quality were included in the CABHS instrument: 1) access to care, including timeliness of appointments and availability of help by telephone; 2) interpersonal aspects of care, including communication with providers, information about treatment options given by providers to consumers, information about consumer preferences given to providers, continuity and coordination of care, and global evaluation of care; and 3) quality of the health plan, including access to information and services provided by the plan, administrative burden, and global evaluation of the plan. In addition, we identified four areas with special importance to behavioral health services that were not included in CAHPS instruments: 1) respect for consumer/patient rights, 2) consumer/patient participation in the treatment process, 3) information about benefits and risks of psychotropic medication, and 4) health plan payment for services received.

Structure of the CABHS Instrument The basic structure of the CAHPS survey was used for the CABHS behavioral health survey. For example, yes/no screening questions were used to identify use of particular services and occurrence of events that might not apply to everyone. Many questions used frequency response options (never, sometimes, usually, always) to indicate how often events occurred during treatment. Global evaluations also were requested; these used a 10-point rating scale for which 0 indicated the worst possible care and 10 indicated the best.

Refinement of the Instrument Several stages of survey development over a 9-month time period resulted in modifications and refinements to the instrument. Input from experts and


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consumers obtained through focus groups and individual cognitive interviews with recipients of mental health services were especially helpful in the process. The focus groups included men and women, a range of ages (between 18 and 65), racial/ethnic minorities, individuals with a range of psychiatric diagnoses (depression, anxiety, schizophrenia, bipolar disorders, and substance abuse), and individuals insured by private and public health plans. Cognitive interviews were used to evaluate questionnaire items to ensure that respondents were able to answer the questions, that questions were consistently understood by respondents, and that respondents interpreted the content of the questions in the way intended by the instrument developers (DeMaio and Rothgeb 1996; Forsyth and Lessler 1991).

Field Test The CABHS survey was first field tested with two groups of mental health consumers: commercially insured individuals (n=200) and Medicaid enrollees (n=300) (Eisen et al. 1999). Both groups were members of a health maintenance organization for which the behavioral health component was managed by an external managed behavioral health care organization. The survey was conducted by an independent survey research center. Survey response rate was 63% for commercially insured consumers who were reachable by telephone. The response rates to mailed surveys alone was about 32%, which highlights the importance of telephone follow-up. A large percentage of consumers, particularly Medicaid enrollees, were not reachable by phone (55%). Of those reached by telephone, 57% participated in the survey.

Respondent Characteristics Most respondents were female (80%), were between the ages of 25 and 44 (61%), and were high school graduates (82%). Forty-nine percent were commercially insured; 51% were Medicaid beneficiaries. Almost two-thirds (64%) were Caucasian, 19% were black, and 17% were members of other racial groups. Overall health was reported to be “fair” or “poor” by 27% of the sample. Mental health was reported to be “fair” or “poor” by 34% of the sample. Respondents did not differ significantly from nonrespondents in terms of age or gender.

Service Utilization Questions regarding behavioral health and substance abuse service use indicated that 86% reported receiving treatment for mental illness or personal or family problems and that 76% of these respondents reported tak-

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ing prescription medications as part of their treatment. Nine percent of the sample reported receiving services for alcohol abuse and 7% reported receiving services for drug abuse. Ten percent of respondents reported having received inpatient care.

Evaluation of Care Received Overall, responses to survey questions indicated relatively positive experiences. Clinician–consumer interaction was rated more favorably than access to care. The highest-rated aspects of care were respect for consumers shown by the clinician and office staff, frequency with which consumers felt clinicians listened to them, and clinicians’ explanation of things in ways that consumers could understand. Least favorable ratings concerned accessibility to help in the evenings and on weekends.

Evaluation of Insurance Plan for Behavioral Health and Substance Abuse Among questions about administrative burden and global evaluation of the insurance plan, responses were most favorable to the question about paperwork; this result was expected because all participants in this field test were members of a health maintenance organization for which no paperwork was required. Respondents gave the least favorable responses to a question about handling of phone calls to the plan without a long wait. Overall evaluation of the plan (8.33 on a 0–10 scale on which 10 was the most positive rating) was almost identical to the overall evaluation of services (8.24).

Differences Between Plans One of the goals of the CABHS survey was to compare consumer experiences with different health plans. The field test was able to identify perceived differences between the two plans assessed (i.e., commercial plan and Medicaid). Consumers enrolled in the commercial plan rated timeliness of help on weekdays, evenings, and weekends more highly than did those in the Medicaid plan. In addition, commercial plan members more often reported that they were told they could refuse treatment they did not want (87%) compared with Medicaid plan members (67%). On the other hand, Medicaid enrollees rated their health plan higher overall than consumers in the commercial plan (mean global rating of plan was 8.80 for the Medicaid plan and 7.84 for the commercial plan, where 10 is the highest rating). Reported differences between the health plans were not associated with differences between the groups in demographic variables or health status of enrollees (Eisen et al. 1999).


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FUTURE DIRECTIONS A revised instrument based on the field test was sent to consumers representing five different behavioral health plans. This was done as part of a comparative evaluation of the CABHS and the MHSIP consumer survey (Center for Mental Health Services 1996). The goal of this effort was to develop a behavioral health survey instrument that combined the best features of both surveys. This process parallels a similar collaborative effort with the NCQA that resulted in a “converged” instrument (CAHPS 2.0). That survey is now required by the NCQA for accreditation of managed care organizations across the United States. Similarly, our study was designed to guide development of a national consumer survey for behavioral health that will help purchasers and consumers choose insurance plans and will provide information needed to improve the quality of behavioral health care. The CABHS-MHSIP field test results were reviewed by a task force composed of representatives from the CABHS-MHSIP research team, mental health service consumers, the MHSIP Report Card Task Force, Human Services Research Institute, CAHPS instrument development team, Center for Mental Health Services, NCQA, Washington Circle Group (on substance abuse services), and National Alliance for the Mentally Ill. The task force recommended that a new instrument be developed based on results of the field test. Two conference calls and two day-long meetings were held to synthesize the recommendations of all groups involved in the development process. The draft instrument for managed care organizations or health plans, called ECHO (Experience of Care and Health Outcomes survey), is appended to this chapter. Additional cognitive testing of the ECHO survey was done in the spring of 2000 and a field test of the new survey began in August 2000. Future work will focus on final standardization of the instrument, psychometric testing, development of implementation strategies that optimize response rates while protecting consumer privacy, and development of scoring and reporting formats that will be useful to consumers and purchasers in choosing behavioral health services and plans.

REFERENCES Agency for Health Care Policy and Research: CAHPS 1.0 Survey and Reporting Kit. Rockville, MD, Agency for Health Care Policy and Research, 1998 Center for Mental Health Services: The MHSIP Consumer-Oriented Mental Health Report Card: The Final Report of the Mental Health Statistics Improvement Program (MHSIP) Task Force on a Consumer-Oriented Mental Health Report Card. Washington, DC, Center for Mental Health Services, 1996

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Crofton C, Lubalin JS, Darby C: Foreword. Med Care 37(suppl):MS1–MS9, 1999 DeMaio TJ, Rothgeb JM: Cognitive interviewing techniques: in the lab and in the field, in Answering Questions: Methodology for Determining Cognitive and Communicative Processes in Survey Research. Edited by Schwarz N, Sudman S. San Francisco, CA, Jossey-Bass, 1996 Dickey B: The development of report cards for mental health care, in Outcomes Assessment in Clinical Practice. Edited by Sederer LI, Dickey B. Baltimore, MD, Williams & Wilkins, 1996, pp 156–160 Eisen SV, Grob MC, Dill DL: Outcome measurement: tapping the patient’s perspective, in Psychiatric Treatment: Advances in Outcome Research. Edited by Mirin SM, Gossett J, Grob MC. Washington, DC, American Psychiatric Press, 1991, pp 213–235 Eisen SV, Shaul JA, Clarridge BR, et al: Development of a consumer survey for behavioral health services. Psychiatr Serv 50:793–798, 1999 Forsyth BH, Lessler JT: Cognitive laboratory methods: a taxonomy, in Measurement Errors in Surveys. Edited by Biemer PP, Groves RM, Lyberg LE, et al. New York, Wiley, 1991 Harris-Kojetin LD, Fowler FJ, Brown JA, et al: The use of cognitive testing to develop and evaluate CAHPS 1.0 core survey items. Med Care 37(suppl):MS10– MS21, 1999 Hays RD, Shaul JA, Williams VSL, et al: Psychometric properties of the CAHPS 1.0 survey measures. Med Care 37(suppl):MS22–MS31, 1999 Kalman TP: An overview of patient satisfaction with psychiatric treatment. Hospital and Community Psychiatry 34:48–54, 1983 Lebow J: Consumer satisfaction with mental health treatment. Psychol Bull 91:244–259, 1982 McGee J, Kanouse DE, Sofaer S, et al: Making survey results easy to report to consumers: how reporting needs guided survey design in CAHPS. Med Care 37(suppl):MS32–MS40, 1999 National Committee for Quality Assurance: HEDIS 3.0, Vol 2: Technical Specifications. Washington, DC, National Committee for Quality Assurance, 1997 Ross EC: Managed behavioral healthcare premises, accountable systems of care, and AMBHA’s PERMS. Evaluation Review 21:318–321, 1997 Skolnick AA: A FAcct-filled agenda for public information. JAMA 278:1558, 1997 Veroff DR, Gallagher PM, Wilson V, et al: Effective reports for health care quality data: lessons from a CAHPS demonstration in Washington state. Int J Qual Health Care 10:555–560, 1998

Appendix EXPERIENCE OF CARE AND HEALTH OUTCOMES (ECHO) SURVEY Sponsored by [Sponsor Name] All information that would let someone identify you or your family will be kept private. [Sponsor Name] will not share your personal information with anyone without your OK. You may choose to answer this survey or not. If you choose not to, this will not affect the benefits you get. If you want to know more about this study, please call 1–800-xxx-xxxx.

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Experience of Care and Health Outcomes (ECHO) Survey Survey Instructions Answer all the questions by checking the box to the left of your answer. You are sometimes told to skip over some questions in this survey. When this happens, you will see an arrow with a note that tells you what question to answer next, like this: 1❏Yes

2❏No

➙ If Yes, Go to Question 1.

Personal or family counseling People can get counseling, treatment, or medication for many different reasons, such as: • Feeling depressed, anxious, or “stressed out” • Personal problems (like when a loved one dies or when there are problems at work) • Family problems (like marriage problems or when parents and children have trouble getting along) • Needing help with drug or alcohol use • For mental or emotional illness 1. In the last 12 months, did you get counseling or treatment for any of these reasons? Yes ➙ If Yes, Go to Question 2. 1❏ No ➙ If No, Go to Question 56. 2❏ Your counseling and treatment in the last 12 months

Your counseling and treatment in the last 12 months 3. In the last 12 months, when you needed to get counseling or treatment right away, how often did you see someone as soon as you wanted? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏ 4. In the last 12 months, did you make any appointments for counseling or treatment? Yes 1❏ No ➙ If No, Go to Question 6. 2❏ 5. In the last 12 months, how often did you get an appointment for counseling or treatment as soon as you wanted? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

For these questions, please include all counseling or treatment you got in the last 12 months except what you got during an overnight stay or from a self-help group.

6. In the last 12 months, did you call someone to get professional help or advice for yourself over the phone? Yes 1❏ No ➙ If No, Go to Question 8. 2❏

2. At any time in the last 12 months, did you need to get counseling or treatment right away? Yes 1❏ No ➙ If No, Go to Question 4. 2❏

7. In the last 12 months, how often did you get the professional help or advice you needed over the phone? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

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Experience of Care and Health Outcomes (ECHO) Survey 8. In the last 12 months, how many times did you go to an emergency room or crisis center to get counseling or treatment for yourself? None 0❏ 1 time 1❏ 2 times 2❏ 3 times or more 3❏

12. In the last 12 months, how often did the people you went to for counseling or treatment explain things in a way you could understand? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

9. In the last 12 months (not counting emergency rooms or crisis centers), how many times did you go to an office, clinic, or other treatment program to get counseling, treatment, or medication for yourself ? None ➙ If None, Go to 0❏ Question 29. 1 to 10 times 1❏ 11 to 20 times 2❏ 21 times or more 3❏

13. In the last 12 months, how often did the people you went to for counseling or treatment show respect for what you had to say? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

10. In the last 12 months, how often did you see the person you went to see for counseling or treatment within 15 minutes of your appointment time? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

14. In the last 12 months, how often did the people you went to for counseling or treatment spend enough time with you? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

The next questions are about all the counseling or treatment you got in the last 12 months during office, clinic, and emergency room visits as well as over the phone. Please do the best you can to include all the different people you went to for counseling or treatment in your answers.

15. In the last 12 months, how often did you feel safe when you were with the people you went to for counseling or treatment? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

11. In the last 12 months, how often did the people you went to for counseling or treatment listen carefully to you? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

16. In the last 12 months, have you taken any prescription medications as part of your treatment? Yes 1❏ No ➙ If No, Go to Question 18. 2❏

17. In the last 12 months, were you told what side effects of those medications to watch for? Yes 1❏ No 2❏

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Experience of Care and Health Outcomes (ECHO) Survey 18. In the last 12 months, how often were you involved as much as you wanted in your counseling or treatment? Never 1❏ Sometimes 2❏ Usually 3❏ Always 4❏

23. In the last 12 months, were you given information about your rights as a patient? Yes 1❏ No 2❏

19. In the last 12 months, did anyone talk to you about whether to include your family or friends in your counseling or treatment? Yes 1❏ No 2❏

24. In the last 12 months, did you feel you could refuse a specific type of medication or treatment? Yes 1❏ No 2❏

20. In the last 12 months, were you told about self-help or support groups, such as consumer-run groups or 12-step programs? Yes 1❏ No 2❏

25. In the last 12 months, as far as you know, did anyone you went to for counseling or treatment reveal information to others that should have been kept private? Yes 1❏ No 2❏

21. In the last 12 months, were you given information about different kinds of counseling or treatment that are available? Yes 1❏ No 2❏

26. Does your language, race, religion, ethnic background, or culture make any difference in the kind of counseling or treatment you need? Yes 1❏ No ➙ If No, Go to Question 28. 2❏

22. In the last 12 months, were you given as much information as you wanted about what you could do to manage your condition? Yes 1❏ No 2❏

27. In the last 12 months was the care you received responsive to those needs? Yes 1❏ No 2❏

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Experience of Care and Health Outcomes (ECHO) Survey 28. Using any number from 0 to 10, where 0 is the worst counseling or treatment possible and 10 is the best counseling or treatment possible, what number would you use to rate the counseling or treatment you received in the last 12 months? ❏ 0 Worst counseling or treatment possible ❏ 1 ❏ 2 ❏ 3 ❏ 4 ❏ 5 ❏ 6 ❏ 7 ❏ 8 ❏ 9 ❏ 10 Best counseling or treatment possible Optional: Insert additional questions about counseling or treatment services here. 29. In the last 12 months, how much were you helped by the counseling or treatment you got? Not at all 1❏ A little 2❏ Somewhat 3❏ A lot 4❏ 30. In general, how would you rate your overall mental health now? Excellent 1❏ Very good 2❏ Good 3❏ Fair 4❏ 5❏ Poor 31. Compared to 12 months ago, how would you rate your ability to deal with daily problems now? Much better 1❏ A little better 2❏ About the same 3❏ A little worse 4❏ Much worse 5❏

32. Compared to 12 months ago, how would you rate your ability to deal with social situations now? Much better 1❏ A little better 2❏ About the same 3❏ A little worse 4❏ Much worse 5❏ 33. Compared to 12 months ago, how would you rate your ability to accomplish the things you want to do now? Much better 1❏ A little better 2❏ About the same 3❏ A little worse 4❏ Much worse 5❏

34. Compared to 12 months ago, how would you rate your problems or symptoms now? Much better 1❏ A little better 2❏ About the same 3❏ A little worse 4❏ Much worse 5❏

Your health plan for counseling and treatment The next questions ask about your experience with your health plan for counseling or treatment in the last 12 months. 35. Are you now in [Plan Name]? Yes ➙ If Yes, Go to Question 1❏ 37. No 2❏ 36. What is the name of your health plan now? (Please print) None ➙ If None, Go to 1❏ Question 54. Plan Name: _______________________

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Experience of Care and Health Outcomes (ECHO) Survey 37. Is this the health plan you use for all or most of your counseling or treatment? Yes 1❏ No 2❏

44. Since you joined your health plan, how much of a problem, if any, was it to get someone you are happy with? A big problem 1❏ A small problem 2❏ Not a problem 3❏

38. How many months or years in a row have you been in this health plan? Less than 12 months 1❏ 12 up to 24 months 2❏ 2 up to 5 years 3❏ 5 up to 10 years 4❏ 10 or more years 5❏

45. In the last 12 months, how much of a problem, if any, were delays in counseling or treatment while you waited for approval from your health plan? A big problem 1❏ A small problem 2❏ Not a problem 3❏

39. How much of the counseling or treatment you got in the last 12 months was paid for by your health plan? All of it 1❏ Most of it 2❏ Some of it 3❏ 4❏ None of it was paid for

46. In the last 12 months, how much of a problem, if any, was it to get the counseling or treatment you thought you needed? A big problem 1❏ A small problem 2❏ Not a problem 3❏

47. In the last 12 months, did you look for 40. In the last 12 months, did your health plan stop paying for counseling or any information in a booklet or on the treatment because you had used all your Web about counseling or treatment benefits? from your health plan? Yes Yes 1❏ 1❏ No ➙ If No, Go to Question 43. No ➙ If No, Go to Question 49. 2❏ 2❏ 48. In the last 12 months, how much of a 41. At the time your health plan stopped problem, if any, was it to find or paying, did you think you still needed counseling or treatment? understand the information you looked Yes for? 1❏ No ➙ If No, Go to Question 43. A big problem 2❏ 1❏ A small problem 2❏ Not a problem 3❏ 42. Were you told about other ways to get counseling, treatment, or medication? Yes 1❏ No 2❏

49. In the last 12 months, did you call your health plan’s customer service to get information or help about counseling or treatment? Yes 1❏ No ➙ If No, Go to Question 51. 2❏

43. When you joined your health plan or at 50. In the last 12 months, how much of a problem, if any, was it to get the help any time since then, did you get someone you needed when you called your health new for counseling or treatment? Yes plan’s customer service? 1❏ No ➙ If No, Go to Question 45. A big problem 2❏ 1❏ A small problem 2❏ Not a problem 3❏

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Experience of Care and Health Outcomes (ECHO) Survey 51. In the last 12 months, did you have to fill 55. In the last 12 months, was any of your out any paperwork about counseling or counseling or treatment for help with treatment for your health plan? alcohol use or drug use? Yes Yes 1❏ 1❏ No ➙ If No, Go to Question 53. No 2❏ 2❏ About you 52. In the last 12 months, how much of a problem, if any, did you have with paperwork for your health plan? A big problem 1❏ A small problem 2❏ Not a problem 3❏

56. In general, how would you rate your overall health now? Excellent 1❏ Very good 2❏ Good 3❏ Fair 4❏ Poor 5❏

53. Using any number from 0 to 10, where 0 is the worst health plan possible and 10 is the best health plan possible, what number would you use to rate your health plan for counseling or treatment in the last 12 months? ❏ 0 Worst health plan possible ❏ 1 ❏ 2 ❏ 3 ❏ 4 ❏ 5 ❏ 6 ❏ 7 ❏ 8 ❏ 9 ❏ 10 Best health plan possible

57. What is your age now? 18 to 24 1❏ 25 to 34 2❏ 35 to 44 3❏ 45 to 54 4❏ 55 to 64 5❏ 65 to 74 6❏ 75 or older 7❏

Optional: Insert additional questions about health plan for counseling or treatment services here. 54. In the last 12 months, was any of your counseling or treatment for personal or family problems or mental or emotional illness? Yes 1❏ No 2❏

58. Are you male or female? Male 1❏ Female 2❏ 59. What is the highest grade or level of school that you have completed? 8th grade or less 1❏ Some high school, but did not 2❏ graduate High school graduate or GED 3❏ Some college or 2-year degree 4❏ 4-year college graduate 5❏ More than 4-year college degree 6❏ 60. Are you of Hispanic or Latino origin or descent? Hispanic or Latino 1❏ Not Hispanic or Latino 2❏

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Experience of Care and Health Outcomes (ECHO) Survey 61. What is your race? Please mark one or more. White 1❏ Black or African-American 2❏ Asian 3❏ Native Hawaiian or other Pacific 4❏ Islander American Indian or Alaska Native 5❏ Other (please print): 8❏ _________________________________

63. How did that person help you? Check all that apply. Read the questions to me 1❏ Wrote down the answers I gave 2❏ Answered the questions for me 3❏ Translated the questions into my 4❏ language Helped in some other way (please 8❏ print) _________________________________

62. Did someone help you complete this Optional: Insert additional demographic survey? questions here. Yes ➙ If Yes, Go to Question 63. 1❏ No ➙ If No, Please return the THANK YOU! 2❏ survey in the postage-paid Please return this survey in the envelope. postage-paid envelope to: [Vendor Name and Address]

10 Practical Steps Toward Quality Improvement A Public-Sector Example Astrid Beigel, Ph.D. Christine Torre

INTRODUCTION Over the past decade, quality improvement efforts have emphasized the importance of treatment outcome and client satisfaction. Although there is widespread agreement that a strong quality improvement system should be the foundation of ongoing quality management efforts, such a foundation does not, in fact, exist in all health care institutions. Behavioral health programs are still in the early stages of testing measures and selecting indicators of quality. However, we have not yet developed a systematic process for using data on outcome and client satisfaction to actually improve programs and services. Most agree that client perspectives are essential in assessing satisfaction levels, and that client perception of outcome is useful in identifying areas needing improvement. After quality improvement plans have been implemented, client views are again important to determine the success achieved in improving particular programs (Coughlin 1997, 1998, 1999). Many of today’s most commonly used measures of client satisfaction and outcome provide little concrete information that can be used to pinpoint areas needing improvement at the program and system levels. One key to successfully implementing a quality improvement system is the careful selection of measures, indicators, or instruments that ask the right questions about the aspects of care that need improvement (Beigel and Torre 135

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1998). In addition, the process of collecting, reporting, and using the information gathered must be cost effective; easily conducted; perceived as useful and meaningful to clients, service providers, managers, administrators, and others; and ongoing to continue the improvement process. Los Angeles County has been the site of a Department of Mental Health pilot project for testing various approaches to measuring treatment outcome and client satisfaction with services. We used client self-report measures repeatedly at specified intervals with selected adult outpatient populations to measure changes in outcome over time. The goal of the project was to test the feasibility of instrument administration, data compilation, and reporting. Secondarily, the usefulness of the actual data collected for quality management purposes at the client, program, and system levels was reviewed. We found that the data were not useful when aggregated above the individual client level, leading us to rethink the process and selection of measures used so that we could maximize quality improvement at the program and system levels.

BACKGROUND The Los Angeles County Department of Mental Health began working collaboratively with San Francisco Mental Health in the early 1990s to develop a multifaceted approach to the collection of outcome data. The approach was similar to that of other sites across the nation that were relying on the repeated administration of self-report instruments to measure change in outcome over time. Widely used measures (e.g., Behavioral and Symptom Identification Scale, 36-Item Short Form Health Survey, the clinician-rated Global Assessment of Functioning Scale, and the Client Satisfaction Questionnaire) were administered at admission, at 6 months, annually, and at discharge. It was hypothesized that the data gathered would be useful immediately at the client level and, when aggregated, would provide useful data for program- and system-level quality improvement.

Problems Encountered Preliminary findings from the repeated administration of the measures indicated usefulness of the information gathered for assessment, treatment planning, and progress monitoring at the individual client level. However, the data, when aggregated for reporting on program performance and quality improvement, were difficult to apply to quality improvement. This is consistent with other findings across the country (Dewan 1999; Koch 1999). Many methodological problems surrounding the use of repeated

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measures were evident in our project (Beigel et al. 1999). The most serious problem was the high attrition rate, nearly 25% by the second administration and 75% by the third. This made the task of generalizing results from the small group of clients to the larger group impossible (Bernardi 1998; Graham and Donaldson 1993). In addition, although statistical significance was achieved in some cases because of large sample sizes, the magnitude of change in scores from first administration to subsequent administrations was small and not clinically meaningful in most cases. The prospect of using this data for program assessment and quality improvement was poor. Program managers were looking for data that would provide more specific information about areas needing improvement and wanted measures that were more sensitive to change after a quality improvement plan was initiated. In addition, the process of repeatedly administering measures to clients at individually prescribed times was time consuming, costly, and methodologically unmanageable with only a small amount of value added.

FINDING A NEW APPROACH To develop a more useful system of collecting information, a task force led by the Department of Mental Health’s quality management staff reviewed outcome measures and methods for improving data collection. They gave preference to measures that could be used in many settings, were appropriate for seriously mentally ill consumers, assessed satisfaction and outcome with sensitivity, and were easily administered. They also decided that collecting data should not require the repeated administration of surveys to the same clients. The instrument selected for use in the project, the Mental Health Statistics Improvement Program (MHSIP) Consumer Survey (Human Services Research Institute 1998; Task Force on Consumer Oriented Report Card 1996), most closely met the criteria set by the reviewers (see chapter appendix). The version used was the 21-item measure developed by the MHSIP Report Card Task Force under the auspices of the Substance Abuse and Mental Health Services Administration (SAMHSA) and included input from consumers, providers, and others. Changes in client outcomes can be determined without administering the survey to the same clients at different points in time. Items cluster into four subscales: satisfaction, access, appropriateness, and outcome. Clients are asked to answer questions with responses on a five-point scale ranging from strongly agree to strongly disagree. The survey is formatted on a computer-scannable card. Demographic information, gender, ethnicity, and age are included as well as a client record number and site identifier. Two open-ended questions were

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also included in the Los Angeles survey: the first asked clients to provide one suggestion for improving “the services being provided,” and the second asked for other ideas for improving the program, if any. The rationale was that no matter how good the services being provided already were, they could always be improved.

DESCRIPTION OF THE PROJECT: DATA-BASED QUALITY IMPROVEMENT Design and Participants The survey was conducted at the 11 Institute for Mental Disease (IMD) facilities in Los Angeles County. The IMD facilities are residential programs that house adult clients with severe mental illness and are staffed 24 hours a day. Many clients are admitted to the programs after being discharged from the state hospital or another inpatient facility. One of the IMD facilities is open, whereas the others are locked. All have similar staffing and program requirements and are overseen by the county mental health system. One goal of the project was to test the feasibility of using the MHSIP survey with clients in these programs. Implementation of the project was discussed with IMD administration and site/facility managers. Prior to the start of the project, surveys were delivered to each participating site by staff from the quality management division. A form was supplied for each client who had been in residence at the IMD for at least 2 months at the time of the survey. Staff at the sites were trained to ask each client to complete the survey and to answer the open-ended questions. Clients who needed assistance were helped as much as possible. If clients refused to participate or were unable to complete the survey, their forms were marked with the reason for noncompletion. Facilities were given approximately 3 weeks to complete the surveys. Completed surveys were then returned to quality management staff for data entry, analysis, and reporting.

Data Analysis Methodology To ensure that enough items and subscales were completed to provide meaningful data, separate criteria were established for the exclusion of individual clients and individual sites. Clients were excluded who failed to complete at least 50% of the total number of items in the survey or 50% of the items in any one subscale. Failures were defined as nonresponses or a response of “not applicable.” It should be noted that there was a relatively small number of such exclusions.


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Sites were excluded that did not have a sufficient number of surveys completed (at least 60% of the clients who had been in the program for 2 months or longer were required for inclusion). Reasons for noncompletion included refusal, inability to respond, or failure to meet the criteria noted above. Eight of the 11 sites submitted completed MHSIP surveys from at least 60% of eligible clients. Survey completion ranged from 61% to 89%.

Results Description of Sample The total sample consisted of 301 clients who completed the survey or 68% of the eligible clients at all sites, that is, those who had spent 2 or more months in one of the IMD programs. The number of clients participating at each of the eight sites ranged from 25 to 69. Most of the clients were between the ages of 30 and 59 years; the remainder were between 18 and 29. Only one site had respondents over the age of 60, and the number was minimal. Two-thirds of the respondents were male (64%), with the distribution relatively consistent from site to site. There was some variation in race and ethnicity among sites; whites made up the largest group (39%) compared with African-Americans (26%), Hispanics (18%), Asian/Pacific Islanders (9%), American Indians (2%), and others (6%). Mental Health Statistics Improvement Program Survey We found that most clients were able to complete the MHSIP survey. Most exclusions were due to the clients’ inability to complete the survey because of their mental state at the time, as judged by the staff. A very small number refused, and an even smaller number were unable to complete the survey because of a language barrier. Data on the number and percentage of completed surveys by site are presented in Table 10–1. The greatest variation was on the satisfaction subscale. When aggregated by the IMD program, satisfaction levels in the agree to strongly agree categories ranged from as high as 88% to as low as 41%. With this sample size, a difference of 10% or more in subscale scores should exist to be meaningful. We found variations of this magnitude and more between sites. Furthermore, differences in mean scores ranged from 2.0 to 2.9 on a five-point scale. This variation in satisfaction provided an important quality improvement indicator. The variation in client-rated access, appropriateness, and outcome subscales was somewhat more restricted than was observed for the satisfaction subscale. However, the rank order and clustering among sites was very similar to that noted above. The ratings on the outcome subscale were particularly interesting to compare with the satisfaction subscale. Clients at all

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Table 10–1.

Surveys completed by site

Sites included in study, n (%)

Eligible clients*

MHSIP surveys completed

All sites

443

301 (68)

1

28

25 (89)

2

79

48 (61)

3

50

31 (67)

4

98

69 (70)

5

42

31 (74)

6

40

29 (73)

7

55

34 (62)

8

51

34 (67)

Note. MHSIP= Mental Health Statistics Improvement Program. *Eligible clients are all clients in program 2 months or longer at time of survey

sites except one (Site 1) rated their perceived outcomes considerably higher than their ratings of satisfaction. Finally, the findings by site were fairly consistent for all subscales and the total MHSIP survey (see Figures 10–1 through 10–5).

Figure 10–1. Satisfaction subscale. Graph shows the percentage of positive responses per site as rated on a five-point scale ranging from strongly agree to strongly disagree. Ratings of 2.5 or less indicate a positive response. *Total for all sites combined.


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Figure 10–2. Access subscale. Graph shows the percentage of positive responses per site as rated on a five-point scale ranging from strongly agree to strongly disagree. Ratings of 2.5 or less indicate a positive response. *Total for all sites combined.

Figure 10–3. Appropriateness subscale. Graph shows the percentage of positive responses per site as rated on a five-point scale ranging from strongly agree to strongly disagree. Ratings of 2.5 or less indicate a positive response. *Total for all sites combined.

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Figure 10–4. Outcome subscale. Graph shows the percentage of positive responses per site as rated on a five-point scale ranging from strongly agree to strongly disagree. Ratings of 2.5 or less indicate a positive response. *Total for all sites combined.

Figure 10–5. Total scores. Graph shows the percentage of positive responses per site as rated on a five-point scale ranging from strongly agree to strongly disagree. Ratings of 2.5 or less indicate a positive response. *Total for all sites combined.

Quality Improvement Questions Of the 301 clients across all sites who completed the MHSIP survey, 141 or almost half (47%) made at least one suggestion as to how to improve the


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program. In addition, 40 clients made two or more suggestions. Because the IMD programs were residential, responses relating to personal comfort were common. The other two categories of responses were associated with the program and the staff; most of the 181 suggestions made pertained to program (44%) or personal comfort (40%), whereas the remaining 16% were related to staff issues. In the program category, the most common suggestions were from clients wanting both more and more varied activities as well as more information about medication and treatment modalities. Personal comfort suggestions related to better food, improved facilities, more privacy, and either more or less smoking. The staff category elicited responses pertaining to desires to be treated with respect and to be given more attention by staff. These suggestions were not covered by individual items on the MHSIP survey and raised additional areas needing improvement.

USING THE DATA FOR QUALITY IMPROVEMENT No Matter How Good It Is, It Can Always Be Better The underlying philosophy guiding the mental health system in Los Angeles County and therefore the programs in this study is that of continuous quality improvement. The major challenge is to convince managers, service providers, and others in the various organizations to truly accept this philosophy. Although the idea of quality improvement may be threatening to these individuals, they acknowledge its importance. Quality improvement is still confused with the old notion of a “pass/ fail” program evaluation concept that contributes to resistance, misinterpretation, and lack of support and consequently undermines efforts to improve care. Successful data collection and information reporting were accomplished through the collaborative work of the quality management staff (the Department of Mental Health’s experts in this area); the manager at the county level (IMD Administration Division), who oversees the privately contracted agencies that operate the facilities where services are provided; and the site administrators and line staff. We modeled this collaborative process when presenting the data and findings to the staff. Furthermore, we presented the information in an accurate, clear, and nonthreatening manner, realizing that the manner of presentation was critical to the success of the project. Figure 10–6 depicts the organizational structure and relationship of the sites to the larger mental health system.

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Figure 10–6. Los Angeles County Department of Mental Health organizational chart. IMD=Institute for Mental Disease.

Reporting to Providers After the completion of the first phase of the project, a brief, two-page narrative summary report that included Table 10–1 was prepared for the participating providers by the Los Angeles County Department of Mental Health Quality Management Division. This report intentionally did not identify the sites by name. Identifying information for each site was made available to the department’s IMD Administration Division and each site was provided a copy with only its own information identified. In addition, each site was given a summary of its own categorized responses to the quality improvement questions. The purpose was to provide comparative information as a baseline but not to foster competition among sites. Each site was encouraged to assess its own strengths and areas in which improvement was desirable. It was also recommended that the report formats be modified in order to be useful at other levels within the site. Line supervisors, service providers, and the clients themselves were all vital groups in the quality improvement process, and each had different areas of interests and responsibility in the quality management and improvement process that needed to be reflected in the reports.

Developing Quality Improvement Strategies at Sites: A Case Study in Process One of the keys to quality improvement is collaboration between different levels of provider administration. The distribution and dissemination of


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findings must also be handled judiciously and tactfully. Most important, the focus must be on the positive—that is, finding ways to improve each program. This positive approach sets the stage for beginning discussions and ultimately arriving at improved ways to provide cost-effective, appropriate, quality services. An initial meeting convened the managers of all 11 sites and the two Department of Mental Health divisions. When the reports were presented by the quality management staff to the management of the IMD Administration Division, the data were presented objectively and assistance in interpretation was offered. The responsibility of the Quality Management Division was to provide consultation and technical assistance regarding all aspects of quality improvement but not to evaluate or make programrelated decisions. At the request of IMD Administration management, quality management staff participated in initial meetings with the various sites to explain the findings. Following these meetings, strategies for quality improvement were developed by the site. Prior to presenting the findings, a tone of collaboration was set to allay fears that punitive action would be taken. It was clearly stated that it would be the responsibility of each IMD facility manager to carry this spirit forward in order to engage staff in the process. Based on these findings, areas for quality improvement were then to be defined. Technical assistance and training were offered. The major focus at the session was the use of the MHSIP survey findings to 1. Review common implementation problems, such as survey completion rates and survey administration problems 2. Discuss findings common to all facilities as well as site-specific differences 3. Consider quality improvement approaches that were effective at various facilities 4. Provide suggestions and approaches to formulating improvement strategies The next step in the process was that each of the 11 sites (including the three that did not have sufficient data for analysis) held their own meetings, involving all levels of staff and clients. The purpose of the meeting was to assess their findings, pinpoint site-specific areas needing improvement, and select areas on which to focus and develop specific plans. A relatively short amount of time was allowed for this process (6 weeks following the initial session). The reason for this was the importance of engaging in the process while the information was new and thus maintain momentum.

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Defining Realistic Goals Emphasis was placed on having facilities define realistic goals that could be addressed immediately, be assessed again 6 months after the quality improvement effort was implemented, and, it was hoped, demonstrate measured progress toward the goals. Examples of goals included establishing smoking areas, setting up groups to explain medication side effects, and convening client focus groups to allow for further articulation of the clients’ desire to be treated with greater respect. Consultation with staff from the Quality Management Division was available to assist in reviewing plans. The IMD Administration group was also available to provide program assistance. Training Additional training was offered by the Quality Management Division regarding ways to engage more clients in the process and increase the response rate and the quality of responses to the survey questions. Training was needed not only to improve the administration of the instrument but also to encourage and foster the continuous quality improvement process (Torre and Beigel 1999). Future Measurement We plan to repeat the survey 6 months after the implementation of the quality improvement program. We anticipate that changes in the participation rates of eligible clients will be noticeable at all facilities and that other areas targeted for improvement will at least be partially achieved. The entire process is scheduled to be repeated annually and the results considered in combination with other program data.

SUMMARY AND FUTURE PLANS This project represented the necessary first steps toward an effective, ongoing quality improvement program using client self-report as a key tool. This phase demonstrated that it is possible to obtain perceptions about aspects of care as well as satisfaction from a residential population of severely mentally ill persons. We found that this information is useful for quality improvement at the program and system levels using the MHSIP survey, which met our instrument selection criteria. The findings of this study were encouraging. The methodological problems commonly associated with the measurement of change, including attrition due to repeated administrations of surveys to the same persons, were overcome. Information about client perceptions of satisfaction, access, appropriateness, and outcome were obtained and distinguished


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between sites. The addition of two specific narrative questions focusing on continuous quality improvement yielded information that augmented the data derived from the individual items and subscales on the MHSIP survey. The Department of Mental Health’s administration was receptive to the findings and eager to work with the IMD facilities’ staff to implement the quality improvement process. The limitations of this project are likely to exist elsewhere and so should be noted. Although a substantial number of clients was surveyed, the number at any one of the facilities was relatively small. The MHSIP survey was administered only in English, raising questions about the potential differences in perceptions of clients unable to respond because of language barriers. As in any field study, regardless of the diligence in training staff, there were questions about the uniformity of administration from site to site and between individual staff. The narrative questions added information and validity; however, the analysis of the responses (e.g., reading, coding) was extremely time consuming and may not be feasible as part of a routine assessment. Such open-ended information gathering should be considered only for special projects. In the future, we may also consider analyzing the data to examine differences in age, gender, ethnicity, diagnosis, and other factors known to influence client perception. At this stage of the project, we decided simple comparisons using the percentage difference on two indices—positive ratings and mean scores—were most appropriate. This study focused exclusively on the clients’ perceptions of the efficacy of the services at the facility where they were receiving residential care. Clients’ perceptions are critical; however, they are only one aspect of the total quality assessment and management process. Providers’ perceptions of progress, as well as objective indicators such as measurable changes in functioning and quality of life (e.g., housing, employment), are also important. In addition, routinely collected factors such as cost and other administrative variables must be considered in combination with client perceptions. Finally, what remains to be determined as part of this project are the changes that take place following the establishment and implementation of the quality improvement plans at the various sites. This project demonstrated the usefulness of our approach, measurement, and methods for this population. Actually improving quality of care may prove to be an even greater challenge.

REFERENCES Beigel A, Torre C: Asking the right questions: a rational approach to measuring client satisfaction, in 1999 Medicaid Managed Behavioral Care Sourcebook. Edited by Coughlin K. New York, Faulkner & Gray, 1998, pp 359–367

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Beigel A, Nagel N, Torre C: Methodological issues in the measurement of change: implications for consumers, providers, and policy makers. Paper presented at 9th Annual Conference on State Mental Health Agency Services Research, Program Evaluation, and Policy, Alexandria, VA, February 1999 Bernardi RA: Sample attrition and Cronbach’s alpha: a five-year longitudinal study. Psychol Rep 82:1223–1231, 1998 Coughlin K (ed): 1998 Behavioral Outcomes and Guidelines Sourcebook. New York, Faulkner & Gray, 1997 Coughlin K (ed): 1999 Medicaid Managed Behavioral Care Sourcebook. New York, Faulkner & Gray, 1998 Coughlin K (ed): 1999 Behavioral Outcomes and Guidelines Sourcebook. New York, Faulkner & Gray, 1999 Dewan NA: Behavioral benchmarks: results from the National Outcomes Management Project, Center for Quality Innovations and Research, Department of Psychiatry, University of Cincinnati, in 1999 Behavioral Outcomes and Guidelines Sourcebook. Edited by Coughlin K. New York, Faulkner & Gray, 1999, pp 687–699 Graham JW, Donaldson SI: Evaluating interventions with differential attrition: the importance of nonresponse mechanisms and use of follow-up data. J Appl Psychol 78:119–128, 1993 Human Services Research Institute: Toolkit on Performance Measurement Using the MHSIP Consumer-Oriented Report Card, Version 1.0. Cambridge, MA, Human Services Research Institute Evaluation Center, 1998 Koch R: Methodological challenges in the implementation of performance and outcome measurement systems. Paper presented at 9th Annual Conference on State Mental Health Agency Services Research, Program Evaluation, and Policy, Alexandria, VA, February 1999 Torre C, Beigel A: New perspectives in quality management training, in 1999 Behavioral Outcomes and Guidelines Sourcebook. Edited by Couglin K. New York, Faulkner & Gray, 1999, pp 39–44 Task Force on Consumer Oriented Report Card: The MHSIP Consumer Oriented Mental Health Report Card. Rockville, MD, Center for Mental Health Services, 1996

Appendix MHSIP Consumer Survey (Short Version)

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MHSIP Consumer Survey (Short Version) Please indicate your agreement/disagreement with each statement by circling what best represents your opinion. I liked the services that I received. If I had other choices, I would still choose to get services from this health plan. I would recommend this health plan to a friend or family member. The location of services was convenient (parking, public transportation, distance, etc.) My caregivers were willing to help as often as I felt it was necessary. My calls were returned within 24 hours. Services were available at times that were good for me. I was able to get all the services I thought I needed. My caregivers believed that I could grow, change and recover. I felt free to complain. I was told what side effects to watch for. My wishes about who is and is not to be given information about my treatment were respected. My caregivers were sensitive to my cultural/ethnic background. My caregivers helped me obtain the information needed so I could take charge of managing my illness. As a direct result of the services I received: I deal more effectively with daily problems I am better able to control my life. I am better able to deal with crisis. I am getting along better with my family. I do better in social situations. I do better in school and/or work. My symptoms are not bothering me as much.

SA SA

A N D SD NA A N D SD NA

SA

A N D SD NA

SA

A

N D SD NA

SA

A

N D SD NA

SA SA

A A

N D SD NA N D SD NA

SA SA

A A

N D SD NA N D SD NA

SA SA SA

A A S

N D SD NA N D SD NA N D SD NA

SA

S

N D SD NA

SA

A

N D SD NA

SA A SA A SA A SA A SA A SA A SA A

N N N N N N N

D D D D D D D

SD SD SD SD SD SD SD

NA NA NA NA NA NA NA

Key. SA=strongly agree; A=agree; N=neutral; D=disagree; SD=strongly disagree; NA=not applicable.

11 The American Psychiatric Association’s Agenda for Evidence-Based Quality Deborah A. Zarin, M.D. Joyce C. West, Ph.D., M.P.P. Claudia Hart

INTRODUCTION One of the primary goals of the American Psychiatric Association (APA) Office of Quality Improvement and Psychiatric Services is to promote evidence-based psychiatry and quality improvement by disseminating scientifically valid data and accumulated clinical experience to guide clinical and policy decision making. Evidence-based medicine has been defined as “decision making based on data regarding the likely impact of different treatments on specific outcomes for specific populations” (Sackett et al. 1996, p. 71). Practicing evidence-based medicine requires implementation of the following cycle: 1) the extraction and synthesis of data specific to a particular treatment or procedure; 2) individualized clinical experience using the particular treatment or procedure; 3) the evaluation of clinical outcomes; and 4) research on the safety and efficacy of treatment, which then contributes to the extraction and synthesis of data (Zarin et al. 1997a). Figure 11–1 provides a conceptual model to highlight the mechanisms through which these activities are implemented and the roles of external organizations (e.g., policy/regulatory and quality oversight organizations), health plans, clinicians, and patients in influencing quality of treatment provided. 151

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Factors affecting evidence-based psychiatry and quality improvement.


Figure 11–1.

APA Agenda for Evidence-Based Quality

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This chapter outlines the APA Office of Quality Improvement and Psychiatric Services’s current plans to promote evidence-based psychiatry to improve the quality of care provided to individuals with mental disorders. The key components of the APA’s quality improvement program include 1) developing evidence-based practice guidelines, easy-to-use practice guideline formats, and quality improvement tools; 2) developing and disseminating key guideline treatment recommendations and quality indicators that are clinically important, have appropriate breadth, and are scientifically valid; 3) conducting health services research to inform the development of evidence-based practice guidelines and quality improvement efforts; and 4) evaluating the effectiveness of quality improvement interventions.

DEVELOPING EVIDENCE-BASED PRACTICE GUIDELINES AND OTHER PRODUCTS The APA has published 11 evidence-based practice guidelines, including guidelines for psychiatric evaluation and for the treatment of eating disorders, major depressive disorder, schizophrenia, bipolar disorder, substance use disorders, nicotine dependence, Alzheimer’s disease and related dementias, panic disorder, delirium, and HIV/AIDS (see chapter appendix). To ensure these guidelines met accepted standards, they were developed in accordance with the principles for practice guideline development published by the American Medical Association (1996) and the Institute of Medicine (1992). These guidelines provide treatment strategies to assist psychiatrists in their clinical decision making (Zarin et al. 1993). The guidelines consist of the following: 1) a critical review and synthesis of the treatment literature, 2) a framework for clinical decision making, 3) treatment recommendations for a typical patient, and 4) implications of comorbidities and other complicating factors for treatment. As part of the guideline development process, specifications for computerized literature searches were developed and rules for reviewing specific articles were prescribed; in addition, explicit rules and evidence tables were designed to guide the extraction of data from articles. These and other standardized processes are described in depth elsewhere (Pincus and Zarin 1998; Zarin et al. 1997a, 1999). Conformance with evidence-based practice guidelines is shown to be related to improved outcomes of treatment for specific mental disorders (Katon et al. 1995; Lehman and Steinwachs 1998; Schulberg et al. 1996; Wells and Sturm 1996) and to well-accepted measures of quality of care (Lohr 1990). However, substantial research also indicates that dissemination of practice guidelines in the usual printed format does not increase

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conformance with key guideline recommendations (Col et al. 1996; Kanouse et al. 1995; McLaughlin et al. 1996). The APA seeks to make the guidelines more user friendly to achieve wider use. Quick reference guides, which graphically summarize key guideline recommendations for phases of treatment, are being developed. In addition, educational brochures are being designed for patients and families as companions to each practice guideline. To facilitate use of its guidelines, the APA is developing several other new products. Examples include residency training materials, a residency education guideline implementation demonstration initiative, slide presentation materials for use by clinicians, and practice guideline medical record inserts. The APA has already developed, for research purposes, a structured medical record guide containing a checklist to help remind psychiatrists of key guideline assessment and treatment recommendations. This checklist will serve as a model for revising medical record documentation so it can be used with the guidelines. In addition, the APA is considering how disorder-specific medical record forms, treatment planning guides, and medical informatics systems that contain guideline algorithms and treatment recommendations might encourage conformance to the guidelines. Thus far, collaboration with the American Board of Psychiatry and Neurology has led to the incorporation of guideline-related questions in board certification and recertification exams. The APA is also planning to use an Internet Web site that highlights major treatment recommendations from the practice guidelines to educate clinicians and patients.

DEVELOPING AND DISSEMINATING GUIDELINE-BASED QUALITY INDICATORS Major emphasis is being placed on quality of care and clinical performance monitoring by public and private payors, quality oversight organizations (e.g., the Health Care Financing Administration, the National Committee for Quality Assurance, and the Joint Commission on Accreditation of Health Care Organizations), health plans, and consumer groups. This environment is conducive to the adoption and dissemination of evidencebased treatment recommendations. To inform these quality improvement activities, the APA is systematically developing quality of care guideline recommendations and key indicators. The two primary sources for the development of quality recommendations and indicators are empirical research-based data and expert consensus. Quality indicators can be applied to individual patients or clinicians, group practices or facilities, health plans, or payors. Some examples of the major quality assessment


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domains include access to care, clinical processes, outcome/effectiveness, and satisfaction/perceptions of care. The APA’s evidence-based practice guidelines are the foundation of process-of-care quality measures, whereas other sources of information are used to develop quality indicators across a broader range of domains. The quality-of-care recommendations derived from practice guidelines systematically address aspects of treatment, including patient assessment, psychiatric management, psychosocial and psychopharmacologic treatment, treatment of comorbid mental and general medical conditions, and ongoing monitoring and revision of the treatment plan. The strength of evidence of these underlying recommendations, the importance of adherence to the recommendations for overall quality of care, and the proportion of patients for whom the recommendations are likely to be relevant are all criteria for the quality indicators being developed. Lack of conformance to guidelines is an arbitrary indication of poorquality care. However, it is essential to recognize that clinically appropriate reasons for “nonconformance” are to be expected. For example, Eddy (1990) pointed out that guidelines apply approximately 75%–95% of the time. Consequently, any system of quality improvement must be able to distinguish “clinically appropriate” from “clinically inappropriate” guideline nonconformance. Data from the APA’s Practice Research Network (Pincus et al. 1999; Zarin et al. 1997b) have already been used to assess the overall levels of nonconformance with treatment guideline recommendations for patients with major depression, schizophrenia, and bipolar disorder. Additional research is needed to explore reasons for nonconformance; assess the reliability, validity, and appropriateness of the quality-of-care indicators; and inform/target quality improvement initiatives. Table 11–1 illustrates dimensions by which quality can be measured according to a variety of respondents (e.g., patient, clinician, facility). Table 11–1.

General or disorder-specific dimensions for measuring quality Group/ Patient Clinician Facility

Health system Payor

Access to care Clinical processes Outcome/effectiveness Satisfaction/perception of care ❒Using evidence-based practice guidelines or other empirically derived basis for assessing quality. ❒Using expert consensus as the basis for assessing quality.

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Another important goal in the development and implementation of disorder-specific quality indicators is to ensure that the indicators provide sufficient breadth in terms of the measurement domains. A treatment system with an excessive focus on one subset of clinical processes (e.g., psychopharmacologic treatment for patients with major depression) and less attention to other aspects of care (e.g., evaluation, psychiatric management, or provision of psychotherapy for depressed patients) can lead to suboptimal overall treatment. Although some practice guideline recommendations and quality-of-care indicators can be assessed cross-sectionally (e.g., the recommended use of a mood stabilizer for acutely ill patients with bipolar disorder), many guidelines are more complicated (e.g., nonlinear treatment algorithms that require historical information on the treatment process). An example of this would be the monitoring and iterative adjustment of psychopharmacologic and psychosocial treatments for patients with bipolar disorder who do not respond to first-line treatments. To assess the quality of this care, it is necessary to have detailed information regarding the patient’s phase of illness, current clinical status, prior treatment history, treatment preferences, and past response to treatments as well as the dates of each specific intervention for the disorder in question. Similarly, a seemingly simple measure such as medication dose cannot always be adequately evaluated cross-sectionally. One needs to know what phase of treatment the patient is in to determine whether the dose is appropriate; for example, a low antipsychotic dose for a patient with schizophrenia may indicate either poor-quality care or that the patient is in the initial phase of treatment and has not yet reached the recommended target dose. Applying guidelines and treatment algorithms to patients who have comorbid mental and general medical conditions is also difficult.

CONDUCTING HEALTH SERVICES RESEARCH Research must inform future practice guidelines and target quality improvement efforts. In a published study, detailed clinical data from the Practice Research Network revealed that specific patient, health plan, setting, and psychiatrist factors are associated with guideline nonconformance for the treatment of patients with major depression. Regression analyses determined which factors were most strongly associated with nonconformance (West et al. 1999). We found that nonconformance with guidelines was associated with lack of financial incentives, caseloads with low proportion of public patients, patients in health plans that were not managed, and treatment provided by older psychiatrists (over age 62).


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Future Practice Research Network follow-up studies will assess the extent to which factors such as physician knowledge, attitudes, training, and practices; patient response to treatment (including side effects and lack of response to treatments); patient preferences; and health care coverage may affect conformance with evidence-based practice guideline recommendations. Findings from past and current research will be incorporated into future practice guidelines. For example, treatment recommendations for which conformance appears to be particularly low may be strengthened or highlighted to increase conformance. Or, alternatively, specific clinical scenarios under which there may be clinically appropriate reasons for not conforming with the guidelines will be more explicitly outlined (e.g., when the use of higher or lower doses of antipsychotic medications may be appropriate for the treatment of patients with schizophrenia). Health services researchers studying quality of care using insurance claims data and other existing databases (e.g., National Ambulatory Medical Care Survey conducted by the National Center for Health Statistics) have been urged by the APA to focus on characteristics of health plans and managed care organizations that may affect quality of care (Pincus et al. 1996).

EVALUATING THE EFFECTIVENESS OF QUALITY IMPROVEMENT INTERVENTIONS The APA collaborated with the RAND Health Sciences Program, the New York State Psychiatric Association, New York chapters of the National Alliance for the Mentally Ill, and the National Depressive and Manic Depressive Association to evaluate a quality improvement intervention for adults with major depressive disorder. This randomized intervention study assessed psychiatrists’ adherence to key practice guideline treatment recommendations before and after the guideline implementation initiative. The intervention was designed to increase physician adherence to the guideline recommendations by 1) disseminating guideline-related materials to physicians and their patients to improve physician knowledge and awareness of the recommendations, 2) using an opinion leader/peer influence design to encourage physician acceptance and use of the guideline recommendations, and 3) providing a medical record reminder tool designed to help physicians make treatment decisions. The overall impact of this intervention appears to be minimal. Because of the limited and conflicting research about the effectiveness of quality improvement initiatives and guideline adherence, the APA will combine strategies already known to be effective in its future collaborative efforts (Chassin 1997; Col et al. 1996; Davis 1998; Goldberg et al. 1998;

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Kanouse et al. 1995; McLaughlin et al. 1996). In addition, a needs assessment will be conducted to assess reasons for nonconformance with practice guideline treatment recommendations, physician readiness for change, and barriers to changing practices prior to designing and implementing interventions (Davis 1998; Oxman 1998; Soumerai 1998). To expedite this type of resource-intensive longitudinal research, the APA will also seek to collaborate with research groups carrying out clinical effectiveness studies incorporating guideline-based quality indicator measures. Guideline conformance analyses can be “piggy-backed” onto clinical effectiveness studies to assess the extent to which conformance is associated with improved outcomes of care.

SUMMARY Quality of care is influenced by 1) the availability of relevant research to guide clinical decision making, 2) clinicians’ and patients’ ability to make appropriate use of available data, 3) the impact of external oversight groups and health care delivery systems, and 4) the capacity to conduct continuous quality improvement. The APA Office of Quality Improvement and Psychiatric Services seeks to improve the quality of care by promoting evidence-based psychiatry. The development and effective dissemination of the APA’s evidence-based practice guidelines and their derivative products, including guideline-based quality-of-care indicators, is central to the APA’s ongoing quality improvement initiatives.

REFERENCES American Medical Association: Attributes to Grade the Development of Practice Parameters. Chicago, IL, American Medical Association, Office of Quality Assurance, 1996 Chassin MR: Assessing strategies for quality improvement. Health Aff 16:151–161, 1997 Col NF, McLaughlin TJ, Soumerai SB, et al: The impact of clinical trials on the use of medications for acute myocardial infarction: results of a community based study. Arch Intern Med 156:54–60, 1996 Davis D: Does CME work? An analysis of the effect of educational activities on physician performance or health care outcomes. Int J Psychiatry Med 28:21– 39, 1998 Eddy D: Practice policies: what are they? JAMA 263:877–880, 1990 Goldberg HI, Wagner EH, Fihn SD, et al: A randomized controlled trial for CQI teams and academic detailing: can they alter compliance with guidelines? Jt Comm J Qual Improv 24:13–42, 1998


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Institute of Medicine: Guidelines for Clinical Practice From Development to Use. Edited by Field MJ, Lohr KN. Washington, DC, National Academy Press, 1992 Kanouse DE, Kallich JD, Kahan JP: Dissemination of effectiveness and outcomes research. Health Policy 34:167–92, 1995 Katon W, Von-Korff M, Lin E, et al: Collaborative management to achieve treatment guidelines: impact on depression in primary care. JAMA 273:1026–1031, 1995 Lehman AF, Steinwachs DM: Translating research into practice: the Schizophrenia Patient Outcomes Research Team (PORT) treatment recommendations. Schizophr Bull 24:1–10, 1998 Lohr KN: Medicare: A Strategy for Quality Assurance, Vol 1. Washington, DC, National Academy Press, 1990 McLaughlin TJ, Soumerai SB, Willison DJ, et al: Adherence to national guidelines for drug treatment of suspected acute myocardial infarction: evidence for undertreatment in women and the elderly. Arch Intern Med 156:799–805, 1996 Oxman TE: Effective educational techniques for primary care providers: application to the management of psychiatric disorders. Int J Psychiatry Med 28:3–9, 1998 Pincus HA, Zarin DA: Evidence-based psychiatry in the United States. Australasian Psychiatry 6:119–122, 1998 Pincus HA, Zarin DA, West JC: Peering into the ‘black box.’ Measuring outcomes of managed care. Arch Gen Psychiatry 53:870–877, 1996 Pincus HA, Zarin DA, Tanielian TL, et al: Psychiatric patients and treatments in 1997: findings from the American Psychiatric Practice Research Network. Arch Gen Psychiatry 56:441–449, 1999 Sackett DL, Rosenberg WMC, Haynes RB, et al: Evidence based medicine: what it is and what it isn’t. BMJ 312:71–72, 1996 Schulberg HC, Block MR, Madonia MJ, et al: Treating major depression in primary care practice: eight month clinical outcomes. Arch Gen Psychiatry 53:913– 919, 1996 Soumerai SB: Principles and uses of academic detailing to improve the management of psychiatric disorders. Int J Psychiatry Med 28:81–96, 1998 Wells KB, Sturm R: Informing the policy process: from efficacy to effectiveness data on pharmacotherapy. J Consult Clin Psychol 64:638–645, 1996 West JC, Leat P, Zarin DA: Health plan characteristics and conformance with practice guidelines: psychopharmocologic treatment recommendations for major depression. Presentation at Association for Health Services Research scientific meeting, Chicago, IL, June 1999 Zarin DA, Pincus HA, McIntyre JS: Practice guidelines (editorial). Am J Psychiatry 150:175–177, 1993 Zarin DA, Seigle L, Pincus HA, et al: Evidence-based practice guidelines. Psychopharm Bull 33:641–646, 1997a Zarin DA, Pincus HA, West JC, et al: Practice-based research in psychiatry. Am J Psychiatry 154:1199–1208, 1997b

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Zarin DA, McIntyre JS, Pincus HA, et al: Practice guidelines in psychiatry and a psychiatric practice research network, in Textbook of Psychiatry. Edited by Hales RE, Yudofsky SC, Talbott JA. Washington, DC, American Psychiatric Press, 1999, pp 1655–1667

Appendix

The APA’s guidelines have been published, after peer review, in the American Journal of Psychiatry and are available on the Internet at http://www.psych.org under clinical resources. They can also be ordered individually or in book form from American Psychiatric Publishing, Inc. (APPI) at 800-368-5777. Educational brochures and quick reference guides can be ordered directly from APPI.

APA PRACTICE GUIDELINES American Psychiatric Association: Practice guideline for major depressive disorder in adults. Am J Psychiatry 150(suppl):1–26, 1993 American Psychiatric Association: Practice guideline for the treatment of patients with bipolar disorder. Am J Psychiatry 151(suppl):1–36, 1994 American Psychiatric Association: Practice guideline for the psychiatric evaluation of adults. Am J Psychiatry 152(suppl):67–80, 1995 American Psychiatric Association: Practice guideline for treatment of patients with substance use disorders: alcohol, cocaine, opioids. Am J Psychiatry 152(suppl 11):1–59, 1995 American Psychiatric Association: Practice Guidelines. Washington, DC, American Psychiatric Association, 1996 American Psychiatric Association: Practice guideline for the treatment of patients with nicotine dependence. Am J Psychiatry 153(suppl 10):1–31, 1996 American Psychiatric Association: Practice guideline for the treatment of patients with Alzheimer’s disease and other dementias of late life. Am J Psychiatry 154(suppl):1–39, 1997 American Psychiatric Association: Practice guideline for eating disorders. Am J Psychiatry 150:212–228, 1999 American Psychiatric Association: Practice guideline for the treatment of patients with delirium. Am J Psychiatry 156(suppl):1–20, 1999 American Psychiatric Association: Practice guideline for the treatment of patients with panic disorder. Am J Psychiatry 155(suppl):1–34, 1998 American Psychiatric Association: Practice guideline for the treatment of patients with schizophrenia. Am J Psychiatry 154 (suppl):1–63, 1997 161


12 Status of National Efforts to Improve Accountability for Quality Ronald W. Manderscheid, Ph.D. Marilyn J. Henderson, M.P.A. David Y. Brown

INTRODUCTION The trend toward accountability in mental health services is relatively recent. This trend was accelerated by the demise of national health care reform, the growth of managed behavioral health care in both public and private sectors, and the continued erosion of resources for behavioral health care. The Health Security Act proposed by President Clinton in 1993 included specific provisions for the development of health care report cards. Such report cards were seen as a vehicle both for facilitating consumer choice among health plans and for promoting competition and accountability. Since then, the use of report cards to evaluate health services has gained broad acceptance in corporate and governmental circles as competition and expectations about accountability have grown. The advent of managed behavioral health care fostered new demands that providers respond to the wishes of customers. In the private sector, this took the form of satisfaction surveys about plan performance; in the public sector, it took the form of surveys about consumer expectations and problems with plans. These efforts were part of broad-based consumerism throughout American society and especially the consumer movement in public sector mental health. Since the national health care reform debate, financial resources for mental health have declined dramatically. The transformation of health 163

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care services into a commodity subject to market forces has led to dramatically lower payments for mental health services. In the case of health care, the commodity is a person (i.e., a life) covered by a health plan, and the price of that life is the amount that a payor is willing to pay for health care coverage. This shift in how health care is paid for has dramatic implications for negotiations between payors and providers. Where once providers held the upper hand in how payment rates were set, today’s payors benefit when providers are unable to demonstrate accountability for either outcomes or quality of their services. According to the Hay Group (1999), the value of behavioral health benefits decreased 54% between 1988 and 1998, whereas the value of general medical benefits decreased only 11.5% during this period. This decline is in large part due to the lack of accountability tools in the mental health field—that is, practice guidelines, outcome assessments, report cards, and performance indicators (Manderscheid 1998). Without such tools, those negotiating managed care contracts with payors cannot compete on quality and instead must compete solely on cost.

What Is Accountability? Accountability generally refers to financial responsibility and less frequently to quality responsibility. Although there is clear understanding about what constitutes financial accountability, quality accountability means different things to different people; where you sit determines what you see. There are four different types of accountability: Accountability for practices. This is the degree to which providers follow generally agreed-upon procedures (i.e., practice guidelines) for delivering care. It can also refer to the extent to which practitioners adhere to accepted management practices (e.g., the amount of time it takes a clinician to return a phone call from a plan member). Accountability for outcomes. This is the assessment of outcomes from the point of view of the client or the provider. The assessment is intended to answer the question, “What changes occurred for the client as a result of the intervention?” In the past, outcomes tended to reflect only the provider’s point of view. More recently, managed behavioral health care has embraced the concept of outcomes from the point of view of the consumer. Needless to say, the domains of outcome emphasized by the provider and by the consumer may be different. Accountability for plan performance. Report cards provide information about how health plans are operating. Generally, these report cards are from the point of view of the payor, the consumer, and the family. Different features of performance are emphasized by different audiences.

Status of National Efforts to Improve Accountability for Quality

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Accountability for system performance. This is how well largescale systems are operating, as measured by performance indicators. By large-scale systems we mean an entire state, a corporate entity, or a nation (see Chapter 14). The audience can be a state legislature, corporate stockholders, the United States Congress, or a particular mental health or substance abuse community. Indicators used will depend on the point of view of the group making the report. These different levels of accountability are interrelated. Practice and outcome measures can be aggregated and included in report cards. Report cards can be aggregated and included in broader system performance measures. It is important to be aware of what different levels of aggregation are reported and the perspective from which the report comes in order to judge potential bias.

Can Accountability Be Made Uniform? Quality measurement tools promoting accountability seem to be most useful when applied uniformly. Currently there are many efforts to promote unity of measurement; however, this frequently conflicts with the trend toward health care as a commodity. In the marketplace environment, products are sold based on uniqueness, not on similarity to other products. In the commodity market, unique, not commonly shared, quality tools would be promoted. Results from a survey conducted for the Center for Mental Health Services by the Public Sector Outcome Measurement Interest Group illustrate another problem in applying quality measurement uniformly (unpublished report, 1998). A preponderance (85%) of ambulatory facilities surveyed said they were currently using outcome measures. However, only a small minority were using outcome measures recognized at the national level. The remaining facilities were using home-grown instruments “especially designed to meet local needs” (see Table 12–1).

What Selection Criteria Will Promote More Uniform Quality Measurement? Widely accepted criteria by which to judge quality measurement tools need to be developed to foster accountability. These criteria should reflect the essential characteristics that quality measurement tools need to possess to be considered appropriate for use. Three criteria are proposed (Manderscheid 1999): 1. Simplicity. Accountability reports should be understandable and credible to the intended audience.

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Table 12–1.


Type of instruments/measures used to collect client-level outcome data

Instrument/Measure Behavior and Symptom Identification Scale–32 (BASIS-32) 36-Item/12-Item Short-Form Health Survey (SF 36/12) Quality of Life Interview (QOLI) Symptom Checklist–90 (SCL90) Global Assessment of Functioning (GAF) Scale Axis V subscales Client Satisfaction Questionnaire–8 (CSQ-8) In-house development Conduct focus groups Other

Number

Percent

77

14.5

21

4.0

12 15 303

2.3 2.8 57.1

95 51

17.9 9.6

338 53 158

63.7 10.0 29.8

Note. Total equals more than 100% due to multiple responses. Source. Center for Mental Health Services Public Sector Outcome Measurement Interest Group (unpublished report, 1998).

2. Communality. A small set of common quality measures should be used for all programs, supplemented by additional program-specific measures to address unique concerns. Such common measures would strengthen the credibility of the behavioral health field and would also establish historical and cross-system benchmarks for making comparisons. 3. Appropriateness. Measures selected should help balance the discussion of cost and quality. Cost per outcome is important in a commodity market, but additional measures would also be required; for example, need—the number of people in a given population who require treatment—can be compared with access, which is the number of people who receive treatment. Measurement of quality for accountability can also be a quality improvement strategy. In such an environment, the definition of goals is arrived at through a consideration of key values and principles of the plan or agency. At the clinical level, this can take the form of a discussion between a consumer and a clinician about the goals of the treatment intervention. At the plan level, this can be a discussion among key participants—consumers, family members, providers, payors, and managed care representatives— about plan goals.


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Once goals are defined, practices can be implemented to reach those goals. Outcome measures at the individual level, report card measures at the plan level, and performance indicator measures at the broader system level are each intended to measure the degree to which these goals have been attained. Feedback loops promote better goal attainment through the modification of practices as original goals are accomplished. Figure 12–1 denotes these relationships.

Practice Guidelines What Are Practice Guidelines? Practice guidelines should help rather than hinder the provision of care. Used appropriately, they represent signposts that can help measure the level of quality of care (Practice Guidelines Coalition 1998). At least two types of practice guidelines can be distinguished: clinical practice guidelines, which are guides for clinicians delivering specific treatment interventions, and system practice guidelines, which are guides for program and plan managers operating service delivery systems. Much confusion has surrounded the development and implementation of practice guidelines (Noonan et al. 1998). Most mental health professional disciplines are developing clinical practice guidelines for their own members. When viewed across disciplines, these guidelines are sometimes inconsistent; to the outside observer, the field appears uncoordinated and disorganized. The same can be said about system guidelines but for different reasons. Very few system guidelines have actually been developed for the mental health field, and little has been done to define or elaborate this topic. Two notable exceptions are practice guidelines for the Program of Assertive Community Treatment, developed by the National Alliance for the Mentally Ill (NAMI) (Allness and Knoedler 1998) and the practice guidelines for case management developed by the National Association of Case Management (Giesler and Hodge 1999). What Work Is Currently Under Way? The federal Center for Mental Health Services (CMHS) has provided partial support for the Practice Guidelines Coalition, an association of more than 60 national organizations and professional societies representing behavioral health care. This coalition aims to develop simple clinical practice guidelines for use by all types of mental health providers. It has defined a process for developing such guidelines based on the best research findings and on consensus in the field. The products developed through this process are brief and consist of minimum key recommendations for clinicians.

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Quality process. Figure 12–1.


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Thus far, the first guideline was developed for panic disorder and another is under development for chronic pain. In a related set of activities, the CMHS is developing summary papers on the current status of clinical and system practice guidelines under a contract with Abt Associates, Inc. The same contract is being used to develop “Decision Support 2000+,” which will include measures for both clinical and system practice guidelines. This information prototype is based on the public health model. It includes three general clusters of information— population, services, and effects—for three different aggregations: small geographic areas (e.g., states/counties), service programs and health plans, and persons served. This information prototype is outlined in Figure 12–2. The Agency for Healthcare Research and Quality (AHRQ), formerly known as the Agency for Health Care Policy and Research (AHCPR), has funded a number of academic centers to develop and test practice guidelines. After such guidelines are developed, the AHRQ places them on their guidelines Web site (http://www.ahrq.gov/clinic/cpgarchv.htm). The U.S. Departments of Defense and Veterans Affairs were seeking to implement practice guidelines as part of their quality improvement initiatives. A major priority of early efforts has been implementation of practice guidelines on depression. Accrediting entities, such as the National Committee for Quality Assurance (NCQA), are just beginning to include use of practice guidelines as a criterion for accreditation of health maintenance organizations. What Does the Future Hold? Most clinicians currently practicing in the mental health field have little understanding of practice guidelines and little or no motivation to adopt them. At present, incentives to use guidelines are increasing, and it seems likely that information technology (i.e., the Internet) will be used increasingly to train mental health clinicians in the use of practice guidelines. Another underdeveloped area concerns practice guidelines for selfmanagement of disorders and guidelines for family management of a family member with a mental disorder. This type of guideline is fully consistent with the rapidly developing consumer and family movements in mental health and the evolving literature on disease management in the general health field.

Outcome Measurement What Are Outcomes? Outcomes reflect the effects of care on the consumer. They can refer to changes in symptoms, functioning, or quality of life. Outcome measurement is very important at the present time because it can help identify more

170

Structure and use of the information system.


Figure 12–2.


171

or less effective practices. Outcome data also have implications for financial efficiency. Clinicians in the future are likely to be reimbursed on the basis of outcomes achieved rather than services provided. What Work Is Currently Under Way? A prodigious effort is currently under way to develop outcome measurement systems. First, the CMHS has a document on methodological standards for outcome measures available (Outcome Measurement Standards Committee 1996) that provides a set of minimum standards that should be met by all outcome measurement systems. Standards are specified for system design, sampling, data collection, data edits, and analysis. Second, the CMHS supports the Outcome Roundtable for Children and Families. This group includes mental health and child welfare representatives as well as consumer, family, and academic participants. The Outcome Roundtable has subdivided outcomes into key measurement domains: safety, health, functioning, life satisfaction and fulfillment, and satisfaction with services. The framework used to examine children’s outcomes is derived in part from the same principles that guide the adult outcome systems developed earlier by the NAMI Outcome Roundtable (1995). Currently, the Outcome Roundtable for Children and Families is translating measurement domains into specific measures, designing a pilot test, and considering how information gathered should be reported. The CMHS also supports the work of the NAMI Outcome Roundtable for Adults, which has been considering what the role of Web-based technology should be in collecting outcomes information from consumers and family members. Third, the CMHS is also preparing a paper on person-centered outcomes for adults: how they are defined, how they might be assessed, and what the implications are for outcomes measurement activities. Personcentered outcomes proceed from the consumer’s point of view and from what consumers say they desire from care. This approach has been successfully used in the developmental disability field (Council on Quality and Leadership in Supports for People with Disabilities 1997). Fourth, the U.S. Department of Health and Human Services’s Administration on Children, Youth, and Families (1999) has developed the Adoption and Foster Care Analysis and Reporting System (AFCARS). An effort is being made to coordinate CMHS activities with the AFCARS. This system, administered by states, is intended to provide a minimum national data set for all instances of adoption and foster care. As CMHS modifies its Mental Health Statistics Improvement Program (MHSIP) minimum data set, an effort will be made to provide common data elements with AFCARS whenever possible.

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Finally, performance indicators were developed by all federal agencies in response to the 1993 Government Performance and Results Act. The Substance Abuse and Mental Health Services Administration has developed four key outcome measures, each of which is oriented to community functioning. For adults, these four measures are employment levels, housing status, criminal justice involvement, and use of alcohol and drugs. For children, the key outcome measures are participation in school, living in a home-like environment, involvement with the juvenile justice system, and use of alcohol and drugs. The importance of outcome measurement systems for the future cannot be overestimated. As public-sector payors and private-sector benefits managers write contracts that tie reimbursement to outcomes, the pragmatic need for comparable outcome measurements is obvious, but the field is not likely to arrive at a unitary outcome measurement system soon. Work will be needed to calibrate results across different outcome measurement systems; some such work has already begun. We have seen harbingers of this in at-risk contracting for behavioral health care and warranted care for substance abuse (which allows for a defined number of free subsequent visits when initial care does not meet consumer expectations). Without the use of appropriate outcome measures, behavioral health care resources will be moved to primary care with increasing rapidity.

Report Cards What Are Report Cards? Like their academic counterparts, report cards for mental health and substance abuse services are intended to provide feedback on achievements and problems. At least three dimensions must be considered when discussing report cards: content, point of view, and intended audience. 1. Content. Content refers to the topics included. In school, this would be the courses rated. Generally, report cards for behavioral health care services cover one or more of the following: access, appropriateness, cost, and outcome. The two most common aspects of care covered are access and cost. 2. Point of view. Point of view refers to the perspective taken. In school, the perspective is that of the teacher. In a behavioral service setting, the perspective might be that of the payor, the managed care company, the provider, the consumer, or a family member. Most frequently, the point of view is that of the managed care entity. 3. Intended audience. The intended audience can be both explicit and implicit. In schools, the explicit audience of a report card is the parent;


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an implicit audience might be a future employer. In behavioral service settings, the explicit audience could be the payor, the managed care entity, the provider, the consumer, or a family member. Most often, the explicit audience of a behavioral service report card is the payor; the implicit audience is frequently the media. In most discussions about behavioral health report cards, content domains are discussed, whereas point of view and intended audience are ignored. What Work is Currently Under Way? In 1994, the CMHS convened key mental health policy leaders at the Carter Center in Atlanta, Georgia, to ask them whether the CMHS and the MHSIP should undertake the development of a report card for the mental health field and, if so, from what perspective. The group attending responded with strong affirmation that development effort should be undertaken and that the point of view should be that of the consumer. Subsequently, a task force of technical persons and consumers met on several occasions over a 1-year period to develop the prototype, which was released to the field at a public meeting in April 1996. Later in 1996, the CMHS awarded 20 states grants to begin a pilot test of the report card. In 1997 and 1998, the number of state grants was increased to 40. The MHSIP Report Card (1996) covers access, appropriateness, prevention, and outcome (see Chapter 10). The point of view is that of the consumer; the explicit audience is the health care plan, and the implicit audiences are consumers and family members. The report card consists of two components: administrative health plan data and a consumer survey. The logic is that the administrative data are drawn from the health plans’ information systems, whereas the consumer survey information is collected during care and after the conclusion of an episode of care. Because the report card is consumer oriented, a major goal in implementation is to have consumers collect and process report card data. Such report card data could also be supplemented by focus groups of consumers who could aid in interpreting the results. The MHSIP program has undertaken a revision of the report card to incorporate data emerging from the field through the state grants and from other tests. The second generation of the report card is intended to be more user friendly. What Does the Future Hold? Report cards will be a vehicle not only for summarizing performance but also for showing the results of prevention activities designed to improve the

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health of the population (i.e., all plan members). Hence, future report cards will not only need to address service delivery questions but will also need to include questions about the mental health status of the plan population. The CMHS has begun work on the development of a population-based report card.

PERFORMANCE INDICATORS What Are Performance Indicators? Performance indicators are rates of the occurrence of particular events that describe the performance of large-scale systems and operations. The audience can be a legislature, corporate stockholders, the United States Congress, or a particular mental health or substance abuse community. The indicators selected will depend on the point of view. Efforts to make government agencies accountable, such as the Government Performance and Results Act, have influenced the development of performance indicators. This federal legislation required that each federal program have performance indicators in place by fiscal year 1999. Some states, such as Texas, have similar systems. Other factors related to the development of behavioral health care performance indicators include declining financial resources, the transformation of federal block grants into performance partnerships, and the call for more quality accountability from all parts of the field but principally from consumers and family members.

What Work Is Under Way? Much of the work on performance indicators in behavioral health care can trace its intellectual lineage to the MHSIP Consumer-Oriented Mental Health Report Card described earlier. The same measurement domains of access, appropriateness, prevention, and outcome used in the report card were used by the National Association of State Mental Health Program Directors’s (NASMHPD) Framework for Performance Indicators (NASMHPD Research Institute 1998a, 1998b). Likewise, the MHSIP has also provided the framework for the indicators developed by the American College of Mental Health Administration (1997) as well as the work of the National Association of Psychiatric Health Systems (Dewan et al. 1999) and the Association of Behavioral Group Practices. In each of the latter three instances, additional domains have been added. For example, the NASMHPD President’s Task Force on Performance Indicators added a Structure and Management Domain.


175

The CMHS’s work on performance indicators began with a five-state feasibility study that tested 28 performance indicators over a 9-month period (see Table 12–2). After it was demonstrated that it was feasible to collect data on these performance indicators, the CMHS undertook a larger project late in 1998 that involved 16 states. The pilot project tested 32 indicators and is slated for completion at the end of fiscal year 2001. In 1998, the CMHS funded a survey to identify performance measures being widely used in behavioral health care settings and to determine their feasibility for national implementation. The study consisted of a series of meetings, a literature review, and a mail survey of members of the National Association of Psychiatric Health Systems. The domains of measurement included health status, client perception of care, coordination of care, clinical performance, family involvement in child and adolescent treatment planning, and peer review. The results of the survey (Dewan et al. 1999) indicated that facilities and group practices measure performance in multidimensional categories; that measures of clinical performance and perception of care are most commonly used; and that for most measures, definitions were consistent across respondents.

WHAT DOES THE FUTURE HOLD? Both the public and private sector work to develop common performance indicators has great potential. The prognosis is good for the future because of the positive collaborative relationship that has developed in these endeavors. In the future, we expect that such systems will be operated through Web-based technology with both plan- and geographic-based reports available.

CONCLUSION Practice guidelines, outcome measures, report cards, and performance indicators will all be an increasingly important part of our quality measurement landscape for the foreseeable future. The challenge will be incorporating them into day-to-day clinical and management decisions so that both efficiency and effectiveness of care are actually improved over time. Continuous quality improvement is the theme that will tie together practice guidelines, outcomes measurement, report cards, and performance indicators.

176

Table 12–2.


Results of the five-state feasibility study of mental health performance indicators

Performance indicator Outcomes Percent of consumers reporting improved outcomes from services Improvement of functioning, closed cases Improvement of functioning, open cases maintained or improved Reduction in symptoms, closed cases Reduction of symptoms, open cases maintained or improved Improvement in school behavior Percent employed Percent living independently Percent homeless Criminal justice involvement Appropriateness/Quality Percent of consumers agreeing services were appropriate Percent contacted within 7 days of discharge Percent receiving assertive community treatment Percent receiving supported housing Percent receiving supported employment Percent receiving case management Use of restraints: episodes per 1,000 patient days Use of seclusion: episodes per 1,000 patient days Percent readmitted within 30 days Percent involuntarily admitted—civil commitment Percent involuntarily admitted—criminal commitment Percent receiving atypical medications in state hospitals Percent receiving atypical medications in community

States used States Median similar able, n score definitions 3

65%

Yes

2 3

30% 84%

No No

2 2

42% 87%

No No

2 4 5 5 3

32% 15% 79% 3% 10%

No Yes No Yes No

3

74%

Yes

3 3 5 5 4 5 5 5 5

54% 5% 3% 2% 51% 9.2% 6.2% 9% 75%

Yes Yes No No Yes Yes Yes Yes Yes

5

7%

Yes

4

53%

Yes

1

26%

Yes


Table 12–2.

Results of the five-state feasibility study of mental health performance indicators (continued)

Performance indicator Access Percent of consumers reporting satisfaction with access to services Rate of mental health service utilization per 100,000 population Percent of clients using community services Structure/Plan management Percent of budget spent on community mental health services Per capita expenditures for community mental health Source. 1998a.

177

States used States Median similar able, n score definitions 3

77%

Yes

5

1,580

Yes

5

96%

Yes

5

59%

Yes

5

$30.47

Yes

National Association of State Mental Health Program Directors Research Institute

REFERENCES Administration for Children and Families: The AFCARS Report: Current Estimates as of January 1999. Washington, DC, Department of Health and Human Services, 1999 Allness D, Knoedler W: The PACT Model of Community-Based Treatment for Persons With Severe and Persistent Mental Illnesses: A Manual for PACT Start-Up. Arlington, VA, National Alliance for the Mentally Ill, 1998 American College of Mental Health Administration: Final Report of the Santa Fe summit on behavioral health: preserving quality and value in the managed care equation. Pittsburgh, PA, American College of Mental Health Administration, 1997 Council on Quality and Leadership in Support of People with Disabilities: Personal Outcomes Measures. Towson, MD, Council on Quality and Leadership in Support of People with Disabilities, 1997 Dewan N, Bramlage R, Behle M, et al: Benchmarking Indicators Survey Report. Washington, DC, National Association of Psychiatric Health Systems, 1999 Giesler L, Hodge M: Case management in behavioral health care. Int J Ment Health 27:26–40, 1999 Hay Group: Health care plan design and cost trends: 1988 through 1999. Prepared for the National Association of Psychiatric Health Systems and Association of Behavioral Group Practices, Arlington, VA, April 1999

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Manderscheid R: From many into one: addressing the crisis of quality in managed behavioral health care at the millennium. J Behav Health Serv Res 25:233–236, 1998 Manderscheid R: Untangling the accountability maze: developing outcome measures, report cards and performance indicators. Managed Behavioral Health News, April 15, 1999, pp 6–7 Mental Health Statistics Improvement Program Report Card Phase II Task Force: The MHSIP Consumer-Oriented Mental Health Report Card. Rockville, MD, Center for Mental Health Services, 1996 National Alliance for the Mentally Ill Outcomes Roundtable: Principles of consumer outcomes assessment. Dialogue on Outcomes for Mental and Addictive Disorders Winter:1–3, 1995 National Association of State Mental Health Program Directors Research Institute: Five-State Feasibility Study on State Mental Health Agency Performance Measures. Draft of final report prepared for U.S. Center for Mental Health Services. Alexandria, VA, National Association of State Mental Health Program Directors Research Institute, 1998a National Association of State Mental Health Program Directors Research Institute: Recommended Operational Definitions and Measures to Implement NASMHPD’s Framework of Mental Health Performance Indicators. Report submitted to the NASMHPD President’s Task Force on Performance Indicators. Alexandria, VA, National Association of State Mental Health Program Directors Research Institute, 1998b Noonan D, Coursey R, Edwards J, et al: Clinical practice guidelines. Journal of the Washington Academy of Sciences 85:114–124, 1998 Outcome Measurement Standards Committee: Methodological Standards for Outcome Measurement. Draft, prepared for U.S. Center for Mental Health Services. Rockville, MD, Outcome Measurement Standards Committee, 1996 Practice Guidelines Coalition: Seeking a Solution for the Quality Crisis in Behavioral Healthcare. Reno, NV, Practice Guidelines Coalition, 1998

PART III Case Reports


Introduction

If we can agree that there is always room for improvement, how then might we go about making changes? We asked colleagues at all levels of clinical administration to contribute examples of projects they have undertaken that empirically show how quality of care improved. They provide us with information about how they identified the problem, what steps they took to determine the best intervention, and the data collected after the intervention that suggested that it had a positive effect on quality of care. Although most of these case studies are not research, they represent efforts to use institutional or patient data to identify problems, develop interventions, and measure success in the turbulent environment of health care. These activities may not have the rigor or precision of research, but they are useful nonetheless; they have the advantage of being timely for administrators and staff, being specific to their setting, and involving clinicians in matters that affect them as well as their patients. We selected a range of settings and types of program interventions to illustrate some of the approaches to quality improvement. To make the work more transparent and to encourage replication elsewhere, we asked authors to specify in detail what they did. Many of the chapters have appendixes that include measures used or protocols that may be useful to readers. We also included contact information for some of the authors should a reader wish to contact them for more information. Replication in other clinical settings is not the only purpose for including case reports. They are also intended to inspire readers, in a more general way, to think about what plan each might consider that would lead to improved quality of care. We urge readers to take action. We can improve the quality of the care we provide, and the authors in this section show us some of the ways. The case reports can be grouped into several quality-of-care themes: the importance of continuity of care, treatment in the least restrictive setting, and the systematic application of appropriate treatment. These themes are 181

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not new, nor are they likely to give rise to controversy. Nevertheless, moving from conceptual themes to active practice is never easy to accomplish. Every treatment setting brings obstacles that must be overcome before practice can incorporate new ways of carrying out the imperatives these themes suggest. The case reports cover top-down initiatives and bottom-up improvements. The settings span national efforts to small inpatient unit projects. The perspectives presented range from public health population-based examples to individual patient care. Chapters cover different diagnoses, different age groups, and different problems in quality. The resources that supported these case reports were often slim, but some were more than $30,000. However, they all have one thing in common: the chapter authors were actively involved in bringing about the changes they describe, and they have data to support their contention that their interventions were successful.

13 Intensive Psychiatric Community Care in the Department of Veterans Affairs Robert A. Rosenheck, M.D. Michael S. Neale, Ph.D.

DEVELOPMENT OF COMMUNITY PROGRAMS FOR PEOPLE WITH SEVERE MENTAL ILLNESS In the early 1970s, the community mental health movement generated inspiration and hope with an idealistic plan in which the federal government would blanket the nation with more than 2,000 community mental health centers (CMHCs), decreasing the need for large, restrictive institutions. Although the movement enjoyed widespread public support, it served people with severe mental illness poorly because the movement’s capabilities and the needs of this population were mismatched. This was difficult to document because CMHC performance objectives were not operationally defined or monitored. It eventually became apparent that new professional models of community support needed to be developed and tested (Stein and Test 1980; Turner and TenHoor 1978).

A VETERANS AFFAIRS DEMONSTRATION OF INTENSIVE PSYCHIATRIC COMMUNITY CARE In 1987, the director of the northeast region of the Department of Veterans Affairs (VA) initiated a clinical demonstration project to determine whether 183

184


assertive community treatment (ACT) teams modeled after the program developed by Stein and Test (1980) could provide cost-effective care for a limited number of the most severely mentally ill VA patients. Both budgetary pressures and a desire to provide state-of-the-art treatment stimulated the development of this new empirically based initiative. Although VA followed the pattern of other public mental health systems and closed 40,000 mental health beds in the decades from the 1950s to the 1980s, it still relied heavily on inpatient treatment for severely mentally ill veterans, spending over 70% of its mental health budget on hospital care as recently as fiscal year 1996 (Rosenheck and Dilella 1998). In an experimental evaluation study, 873 veterans with the highest levels of hospital use in the preceding year were randomly assigned to one of the Intensive Psychiatric Community Care (IPCC) programs (as the ACT teams were called in VA) or standard VA care, stratified by study setting. These settings included six general medical and surgical hospitals characterized by short lengths of stay and located in urban settings and four neuropsychiatric hospitals characterized by longer lengths of stay and more often located in rural settings (Rosenheck and Neale 1998). The 2-year study findings confirmed the results of previous experimental research. Results included significant reductions in hospital use and costs at neuropsychiatric sites as well as improvements in psychiatric status and social functioning for veterans receiving IPCC services at general medical and surgical sites. Although it demonstrated the potential for effective implementation of ACT in VA, the study also illustrated the importance of carefully monitoring implementation that addresses facility and client characteristics, administrative mission and support, and model fidelity, all of which substantially influence program development and impact (Rosenheck and Neale 1998).

NATIONAL DISSEMINATION AND PERFORMANCE MONITORING In response to congressional hearings that were critical of VA care for severely mentally ill veterans, $11.5 million was allocated to establish additional IPCC programs in 1993. To ensure fidelity to the IPCC model, implementation was overseen by VA’s Northeast Program Evaluation Center, which had conducted the original IPCC demonstration. Implementation included formal education and training in IPCC followed by qualitative performance monitoring through site visits, conference calls and personal contacts, and quantitative performance monitoring using structured data-gathering instruments to evaluate client- and site-level outcomes.

Psychiatric Care in the Department of Veterans Affairs

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Qualitative Performance Monitoring: Mentor-Monitors To help the new IPCC teams, experienced teams from the initial demonstration project were designated as mentor-monitors. As mentors, members of established teams offered the new teams support, guidance, and constructive criticism. As monitors, they observed and assessed new team functioning and progress and conferred regularly with other mentormonitors about positive and negative developments. Over a 2-year period, mentor-monitor teams participated in various planning and training activities including a 2-day training meeting, weekly conference calls, orientation and training sessions with clusters of teams, site visits, and ongoing formal and informal communications via mail, E-mail, fax, and telephone conference calls.

Quantitative Monitoring Quantitative monitoring of the IPCC program implementation involved systematic gathering and analysis of data on program structure and patient experience. Sources of data included 1) a monthly full-time employee equivalent (FTEE)/caseload form that monitored program productivity, workload, staff turnover, and admissions to the program; 2) a readmission review form, completed by an IPCC clinician at the time of each readmission of an IPCC patient, documenting the reasons for readmission and describing the involvement of IPCC staff in the decision making; 3) structured clinical interviews with each veteran at entry and semiannually thereafter to elicit client characteristics, clinical status, symptom assessments, community functioning, and service use; 4) structured clinical progress reports on IPCC service delivery and outcomes completed by a veteran’s primary case manager semiannually following entry; 5) VA automated records of inpatient and outpatient service use; 6) ACT fidelity assessments of program conformity with IPCC and ACT program guidelines, completed by mentor-monitors during the first year of operation using a standard measure (Teague et al. 1998); and 7) the Typical Week Inventory of Staff Time (TWIST) completed by each staff person to document the allocation of time across categories of clinical and administrative activities. IPCC program evaluation targeted four domains to monitor that were consistent with the classic formulation of quality by Avedis Donabedian (1980): 1) program structure: organizational characteristics and caseload levels; 2) patient characteristics: sociodemographics, disability level, past hospital use, and clinical status at entry; 3) program process: pattern of service delivery, therapeutic activities and alliance, and adherence to the ACT model; and 4) outcomes: client use of hospital services including readmissions, service expenditures, symptoms, functioning, quality of life, and satisfaction

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with services. Table 13–1 lists 45 IPCC program monitors and summarizes mean values, their standard deviation, and the range of site values.

MONITORING THE PERFORMANCE OF INDIVIDUAL SITES Through this monitoring system, we generate data that provide guidance and orientation for program developers and front-line clinicians, allowing comparison of each team’s performance with that of its peers (Kasprow et al. 1998; Rosenheck and Cicchetti 1998; Seibyl et al. 1998). Although absolute practice standards have not been established for IPCC, statistical norms can be computed for selected critical monitors. The distinction between practice standards and statistical norms is important; whereas practice standards are established by a consensus of professionals and represent directive guidelines for how clinical care should be delivered, statistical norms reflect how health care is practiced on average, without specifying exactly what is and what is not acceptable. Using statistical norms, we can measure practice variation within the IPCC program and identify statistical outliers.

Critical Monitors Nineteen of the 45 IPCC monitoring measures identified in Table 13–1 were selected as critical monitors because they assess aspects of the program’s performance that are of special importance to fulfilling its mission and also have clear directionality (i.e., extremely high or low values suggesting a departure from program goals).

NATIONWIDE DISSEMINATION PERFORMANCE DATA Program Structure Altogether, 246 FTEEs were allocated to 40 teams at an average of 6.15 per team. However, at 12 sites at least one position (about 10% of all positions) had remained unfilled for 6 months. Almost all sites had appropriate designated medical and nursing support. Case loads averaged 12.3 veterans per case manager.

Patient Characteristics During the initial dissemination period between October 1, 1996, and March 31, 1997, the IPCC teams treated 2,099 veterans. These veterans had spent an average of 135 days in the hospital in the year prior to IPCC enrollment and three-quarters of them had a diagnosis of psychotic illness.

Veterans Affairs Intensive Psychiatric Community Care (IPCC) program monitors, fiscal year 1998

Monitoring domain Program monitor

Overall values

I. Structure

II. Patient

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

Total allocated positions (initial FTE) Total filled positions (FTE) Percent of FTE used Total allocated funds (initial allocation) Total expended funds (fiscal year 1996) Medical support (0.2 M.D.; % Yes)* Nursing support (1.0 R.N.; % Yes)* Total clinical case managers (FTEE) FTEE unfilled for 6 months or more (% Yes)* FTEE assigned to non-IPCC activities (% Yes) Total veterans enrolled (3/31/97) Clients per clinical case manager*

Range

6.15 5.51 90.5% $304,728 $317,420 97.5% 92.5% 4.12 30.0% 12.5% 50.5 12.3

1.92 1.84 13.5 $70,424 $99,516

3.50 > 11.00 2.83 > 11.00 61.5% > 100.00% $30,671 > $413,076 $170,747 > $573,189

Percent of clients with 30+ hospital days in the prior year* Percent of clients with psychotic diagnosis at entry* Percent of clients with 2+ years of lifetime hospital use Percent of clients receiving any public support Percent of clients receiving Veterans Affairs compensation Global functioning at entry (Global Assessment of Functioning)* Low Instrumental Activities of Daily Living skills (percent of domains rarely/ never) 20. Severity of illness (18-item Brief Psychiatric Rating Scale total)

91.3% 77.1% 57.9% 90.6% 56.4% 45.7 19.6%

16.8% 13.2% 20.3% 8.0% 16.1% 5.3 0.1%

32.0% > 100.0% 41.6% > 100.0% 23.6% > 90.3% 71.3% > 100.0% 14.3% > 100.0% 32.5 > 55.5 8.1% > 38.5%

7.7

30.6 > 62.5

40.6

1.53

25.8 3.4

1.75 > 8.50

20 > 157 7.6 > 26.0

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Standard deviation

Mean


Table 13–1.

Veterans Affairs Intensive Psychiatric Community Care (IPCC) program monitors, fiscal year 1998 (continued)


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Table 13–1.

Overall values Mean

21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31.

New veterans added Percent of clients terminated (continuity)* Percent of clients seen at least weekly (frequency) Percent of clients seen 61+ minutes/week (intensity)* Percent of clients seen 51%+ community (location)* Face-to-face contacts per week per client* Percent of clients seen for rehabilitation Percent of clients seen for substance abuse Percent of change in therapeutic alliance (6 months) Assertive community treatment fidelity score (total) Percent of IPCC preinvolvement in readmissions*

34 15.9% 85.0% 52.7% 77.8% 1.64 55.3% 32.7% +10.4% 4.02 65.1%

IV. Outcome

32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42.

Percent of clients readmitted within 6 months 35.0% Change in Veterans Affairs hospital days (6 months pre/post) –50.1 Percent of change in Veterans Affairs hospital days (6 months)* –64.0% Estimated change in Veterans Affairs health care cost –$698,032 Percent of change in client symptoms on Brief Psychiatric Rating Scale* –6.5% Percent of change in client symptoms on Brief Symptom Inventory* –5.9% Percent of change in client functioning on Global Assessment of Functioning* +3.5% Percent of change in client functioning on Instrumental Activities of Daily Living* +1.2% Percent of change in client quality of life on Quality of Life Interview* +8.4% Percent of change in client satisfaction with Veterans Affairs mental health care +9.7% Percent of change in client satisfaction with IPCC versus Veterans Affairs* +12.6%

Range

15.1 9.6% 15.7% 20.6% 18.7% 0.6 20.2% 15.0% 0.12% 0.37 19.9%

3 > 65 0.0% > 34.0% 36.3% > 100.0% 9.0% > 100.0% 14.3% > 100.0% 0.7 > 3.9 13.6% > 88.7% 7.7% > 70.4% –19.5% > +40.4% 3.2 > 4.6 22.2% > 100.0%

14.8% 36.0 21.3% $611,521 10.5% 8.9% 9.3% 6.4% 6.0% 10.2% 11.6%

4.0% > 65.0% +4.7 > –157.5 +14.7% > –97.4% +$6,314 > –$2,295,881 +10.7% > 42.4% +9.9% > 33.0% –19.3% > +25.6% –13.9% > +15.9% –6.9% > +24.0% –8.0% > +38.8% –10.0% > +43.0%

I MP R OV IN G M EN T A L H E A L TH CAR E

III. Process

Standard deviation

Veterans Affairs Intensive Psychiatric Community Care (IPCC) program monitors, fiscal year 1998 (continued)


Overall values Mean

V. Cost

43. IPCC cost per veteran 44. IPCC cost per FTEE 45. IPCC cost per visit

$6,049 $47,641 $59

Standard deviation $2,152 $10,155 $28

Range $2,718 > $2,475 $28,178 > $90,787 $31 > $147

Note. 0.2 M.D. = percentage of teams with at least 0.2 full-time medical doctors; 1.0 R.N. = percentage of teams with at least 1 full-time registered nurse; FTE= full-time employee; FTEE=full-time employee equivalent. *Critical IPCC monitor.


Table 13–1.

189

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Over half had hospitalizations that totaled more than 2 years for psychiatric or substance abuse treatment, and they reported more than two decades of illness since their first hospitalization. More than half of IPCC clients reported a service-connected disability, and 9 in 10 reported receiving either VA compensation, Social Security funds, or both. Almost half indicated that their funds were handled by a designated representative payee. This was a severely disabled group of veterans with long-term illnesses.

Program Process On average, IPCC clients had 1.64 face-to-face visits with IPCC staff per week, per veteran. Nearly 80% received most of their care in the community, a major accomplishment. As expected, few IPCC veterans (16%) left the program, with most (84.1%) treated for more than 1 year. Therapeutic alliance scores increased about 10% over baseline ratings of their previous clinicians. On the overall measure of fidelity to the ACT model, the 40 teams had an average score of 4.02 on a 1–5 scale (5 equaling highest fidelity). These scores are similar to those generated from a series of model ACT programs and were substantially higher than scores from the original IPCC demonstration (Teague et al. 1998). However, two teams with lowerthan-expected scores had the implementation of their programs reviewed and strengthened.

Outcomes: Service Use and Cost Veterans treated by IPCC teams showed average reductions of 64% in psychiatric hospital days during the first 6 months of program participation. Data from the readmission process form showed that 35% of veterans had been readmitted. Examination of the IPCC team reports suggest that about 15% of these readmissions would have been avoidable if the IPCC team had been involved earlier or more effectively. A crude estimate of program cost impact for the 1,373 veterans who were treated for 6 months suggests overall inpatient cost reductions of $26.3 million (726 of the 2,099 admissions had not yet received 6 months of treatment); for every program dollar spent, two dollars were saved. In the absence of a control group, these reductions cannot be completely attributed to the impact of IPCC, but they represent rapid, substantial, and encouraging declines in resource use.

Outcomes: Symptoms, Functioning, and Satisfaction Analyses of symptom measures show significant symptom improvement of about 6% on both Brief Symptom Inventory (BSI) and Brief Psychiatric


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Rating Scale (BPRS) symptom severity scores after 6 months. Significant improvement in general functioning and quality of life following IPCC treatment was observed using the Global Assessment of Function (GAF) and Quality of Life Interview (QOLI) scales. Comparison of satisfaction with IPCC relative to standard VA services shows that program participants favored IPCC services by almost 13% over general services after 6 months.

CONCLUSIONS The development of IPCC in VA represents a quality improvement model that sequences problem identification, program development, evaluation, and systematic dissemination of evaluation results. A national implementation and monitoring system has been developed to guide the dissemination of the program, to ensure its continued fidelity to the program model, and to monitor service delivery and outcome on an ongoing basis. The value of detailed data on the performance of each site relative to national program norms was demonstrated when flaws in performance at certain sites were recognized and technical assistance was provided to improve the programs. Without this Northeast Program Evaluation Center support program, performance might have declined and valuable resources would have been used ineffectively. National program performance monitoring data continue to be presented in an annual report distributed to each site (Rosenheck et al. 1997).

REFERENCES Donabedian A: The Definition of Quality and Approaches to Its Assessment. Ann Arbor, MI, Hospital Administration Press, 1980 Kasprow WJ, Rosenheck RA, Chapdelaine J: Health Care for Homeless Veterans Programs: Tenth Progress Report. West Haven, CT, Northeast Program Evaluation Center, 1998 Rosenheck RA, Cicchetti D: A mental health program report card: a multidimensional approach to performance monitoring in public sector programs. Community Ment Health J 34:85–106, 1998 Rosenheck RA, Dilella D: National Mental Health Program Performance Monitoring System: Fiscal Year 1997 Report. West Haven, CT, Northeast Program Evaluation Center, 1998 Rosenheck RA, Neale MS: Cost-effectiveness of intensive psychiatric community care for high users of inpatient services. Arch Gen Psychiatry 55:459–466, 1998 Rosenheck RA, Neale M, Baldino R, et al: Intensive Psychiatric Community Care (IPCC): A New Approach to Care for Veterans With Serious Mental Illness in the Department of Veterans Affairs. West Haven, CT, Northeast Program Evaluation Center, 1997

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Seibyl CL, Rosenheck RA, Medak S, et al: The Ninth Progress Report on the Domiciliary Care for Homeless Veterans Program. West Haven, CT, Northeast Program Evaluation Center, 1998 Stein LI, Test MA: Alternative to mental hospital treatment, I: conceptual model, treatment program, and clinical evaluation. Arch Gen Psychiatry 37:392–397, 1980 Teague GB, Bond GR, Drake RE: Program fidelity in assertive community treatment: development and use of a measure. Am J Orthopsychiatry 68:216–232, 1998 Turner JC, TenHoor WJ: The NIMH Community Support Program: pilot approaches to a needed social reform. Schizophr Bull 4:319–349, 1978

14 A National Strategy to Reduce Depression and Suicide in England Rachel Jenkins, M.A., M.B., Bchir, M.D.(Cantab), F.R.C.Psych.

INTRODUCTION: THE BURDEN OF DEPRESSION Mood disorders are a major public health burden because of their high prevalence, associated disability, and mortality, which lead in turn to economic loss, social exclusion, and negative influences on subsequent generations. It is estimated that 10% of the world’s disability is due to unipolar depression (Murray and Lopez 1996). The high prevalence (Üstün and Sartorius 1995), severity, duration (Mann et al. 1981), and accompanying disability (Jenkins et al. 1998) of mood disorders means that this illness cannot be treated exclusively by specialists. Even in the richest of nations, specialist care can usually reach only those who have the greatest needs— about 10% of those with mood disorders. Yet the cost of failing to treat all individuals in need is immense; the consequences include repeated general practitioner consultation (Lloyd et al. 1996); absenteeism due to sickness (Jenkins 1985a); labor turnover (Jenkins 1985b); reduced productivity; negative impact on families; intellectual, psychological, and physical damage to children; adverse effects on physical health; high-risk behaviors; substance abuse; suicide attempts; and suicide. In addition to the devastating clinical effects, mental disorders and especially mood disorders have major implications for primary care, specialist care, nursing education, and social care policy. 193

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OVERALL STRATEGY Our strategy for reducing the societal cost of mood disorders included gaining national and local commitment to action; establishing working partnerships with primary and specialist care providers as well as other agencies such as social services, the police, and the prisons; training and education of primary care teams; creating mental health information systems; research and development; and supporting community action in the schools and workplaces.

GAINING NATIONAL COMMITMENT AND CREATING PARTNERSHIPS The process of gaining national commitment and creating partnerships between different sectors was stimulated by the World Health Organization’s (WHO; 1981) Health for All by the Year 2000 initiative. In England, this effort was led by the Health of the Nation strategy (Department of Health 1992), launched in 1992, which set specific targets for reducing suicide rates and for improving the health and social functioning of people with mental illness. It laid out a three-part plan of action: improving information and understanding about mental illness, developing comprehensive mental health services, and developing model practice in prevention and treatment. To assist the implementation of the Health of the Nation strategy, the mental illness Key Area Handbook (Department of Health 1994) was produced; it emphasized the role of primary care in the prevention and treatment of depression and also suggested activities for schools and workplaces. The government also recently produced “Saving Lives: Our Healthier Nation” (Department of Health 1999), which maintained the suicide reduction target and put further emphasis on promotion and prevention as well as on treatment and rehabilitation.

IMPLICATIONS OF THE GREAT BRITAIN NATIONAL PSYCHIATRIC MORBIDITY SURVEY The Great Britain National Psychiatric Morbidity Survey was the first national survey in any country to collect data on prevalence, risk factors, and associated disability simultaneously in household, institutional, and homeless samples through the use of standardized assessment techniques (Jenkins et al. 1997b). The survey covered depression, psychosis, alcohol and drug misuse, and other mental illnesses (Jenkins et al. 1997a). The household survey found that one in six adults ages 16–64 who live in private households had suffered from some type of neurotic disorder in

Strategy to Reduce Depression and Suicide in England

195

the week before the survey interview; half of these disorders were mixed anxiety depression. All types of common mental disorders, especially depression and mixed anxiety depression, were more frequent in women than men. Other key risk factors included marital status, for which rates of illness were substantially higher among separated, divorced, and widowed individuals of both genders and among cohabiting women. Unemployment was the strongest risk factor, with unemployed people twice as likely to suffer from depression as those who were employed. About half of those with depression also suffered from longstanding physical complaints. Depression was also associated with increased rates of general practitioner visits. Subjects with significant depressive symptoms were also asked about suicidal ideation. Just under 1% of the total sample in the household survey reported suicidal thoughts in the previous week, two-thirds of this group were women. The levels of social disability were high for depression as well as for other disorders such as psychosis (Jenkins et al. 1998).

PRIMARY CARE EDUCATION The high prevalence of depression meant that it was imperative to ensure appropriate education and training of primary care teams. There was also a need to develop good practice guidelines with locally agreed-upon criteria for referral and shared care and to initiate measures for prevention and early detection. There have been a number of initiatives to pursue these goals. The Department of Health, in collaboration with the Mental Health Foundation and the Gatsby Trusts, funded a senior mental health fellow in general practice for 4 years to develop knowledge and skills about depression through tutors and organizers of courses for general practitioners. This scheme evolved into a Royal College of General Practitioners Unit for Mental Health Education in Primary Care, largely focused on depression. The unit developed a “train the trainers” course for trainers (usually a general practitioner and a nurse working together) that offered flexible, multidisciplinary practice-based training in their localities. During the last decade, attempts have been made to involve nurses more fully in the treatment of depression. Several studies have demonstrated the feasibility of nurses extending their role and using their training to • Provide follow-up support to people with depression. Adherence to antidepressant medication has been found to be similar in patients receiving nurse or general practitioner follow-up care (Mann et al. 1998; Wilkinson et al. 1993)

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• Screen postnatal mothers and provide nondirective counseling, which is expected to lead to significant reduction in postnatal depression in mothers (Holden and Schuman 1995) In addition, nurse facilitators, nurses whose principal responsibility is to help primary care teams improve their clinical practice, became involved in the educational process. The Department of Health funded a primary care nurse facilitator to lead the national effort in mental health education for primary care nurses and other facilitators. This evolved into the development of a national education center for primary care nurses that works in close partnership with the general practice education center affiliated with the Royal College of General Practitioners.

FACILITATING DETECTION AND TREATMENT OF DEPRESSION IN GENERAL PRACTICE The Department of Health funded a number of studies in primary care. In one, an external nurse (health visitor) facilitator worked with six inner-city general practices to help general practitioners and their staffs improve the early detection and prompt treatment of depression. In another study, a practice nurse who previously had no mental health training took the active mental health lead and supported her colleagues within the general practice; a third initiative developed a depression audit package for general practitioners to use in primary care (Jenkins 1992; Jenkins and Üstün 1998). The experience gained in these pilot programs was then made widely available through a primary care mental health toolkit containing tools designed by the project teams for the screening, assessment, and management of common mental disorders (Armstrong 1997). Another important approach has been encouraging primary care practitioners to share their ideas and projects with one another. The Department of Health funded conferences about innovations in primary care mental health leading to the development of a continuously updated database now sited at the Sainsbury Centre for Mental Health.

USE OF GOOD PRACTICE GUIDELINES Good practice guidelines are intended to be a useful way of improving practice. A wide variety are currently available, including the general practitioner toolkit described above and the WHO International Classification of Diseases–10 guidelines, which are being adapted for the United Kingdom and have been piloted in several sites (World Health Organization 1996).


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DEFEAT DEPRESSION CAMPAIGN The Defeat Depression campaign was initiated by the Royal College of Psychiatrists in collaboration with the Royal College of General Practitioners and was supported by the United Kingdom Department of Health. It promoted the production of educational materials for general practitioners and leaflets for patients and also sponsored media events. The evaluation of the 5-year campaign (Rix et al. 1999) found that two-thirds of the general practitioners surveyed were aware of the campaign, and 11% indicated that they had made changes in their management of depression as a result of the campaign. Another 30% indicated that they had possibly made changes, and 56.4% had attended a teaching session on depression in the past 3 years. The authors concluded that a national campaign of this kind could have a useful impact but needed to be supplemented by local and practice-based teaching activities.

SUICIDE PREVENTION Mortality figures are routinely collected by most countries and provide helpful data on trends over time as well as sociodemographic variables. Although efforts to reduce mortality from road traffic accidents, homicides, heart disease, and cancer have been made for many decades, there has been a surprising lack of effort devoted to suicide reduction until recently, due in part to the stigma and therapeutic nihilism surrounding mental illness. Suicide is the tenth leading cause of death in the world, roughly equivalent to deaths from malaria and closely following deaths from road traffic accidents. Psychopathology is present in most suicides. Clinical research indicates that 90% or more of suicide victims have been diagnosed with or retrospectively met criteria for a psychiatric disorder, usually substance abuse, depression, or psychosis. A large proportion have a formal history of prior contact with mental health services. High-risk groups for suicide include people with severe mental illness; 15% of people with bipolar mood disorder, 10% of people with schizophrenia, and 15% of people with severe alcohol abuse will kill themselves. People who attempt suicide have a 100-fold increase in risk in the 12 months after the attempt. Suicide risk reduction in these groups is affected by the quality of coordination of care and the quality of the assessment and management of suicidal risk they receive. In addition to high-risk mental disorders, a number of high-risk occupational groups such as doctors, nurses, pharmacists, dentists, veterinarians, and farmers have stressful occupations and knowledge of and access to methods of suicide. Suicide in these groups is mediated by primary care

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doctors, who assess and manage depression and suicidal risk. Most people who kill themselves have consulted their doctor in the preceding few weeks. Suicide rates are higher among the unemployed, prisoners, and people suffering social exclusion and poverty, all factors that can precipitate and prolong mental disorder. The Great Britain suicide prevention strategy included (see Table 14–1) • Education of health and social care professionals about suicidal risk • Support of high-risk groups (people with severe mental illness, especially depression and comorbid depression; those committing deliberate self-harm; and certain occupational groups) • Reducing access to the means of suicide • Development of primary and secondary care services • Audit of all suicides to learn lessons that can be used for future prevention • Working with the media to encourage responsible reporting that does not glamorize nor report the method The National Confidential Inquiry into Suicides and Homicides by People with Mental Illness began collecting information on suicide in 1993 (Appleby et al. 1997). It has provided valuable insights into suicide, including the need for clarity of professional responsibilities, multidisciplinary cooperation, communication, treatment compliance, risk assessment skills, adequate staff numbers, continuing education programs to maintain clinical skills, and integration between the voluntary and statutory services. In addition, the information gathered illuminated the relationship between the quality of patients’ living environment and the risk associated with change in care. Table 14–1 summarizes the strategies used.

EFFECTIVENESS OF THE INTERVENTION Following publication of the Health of the Nation strategy in 1992, the overall suicide rate fell by 11.7% in 5 years (Department of Health 1992). If early trends continue, the rate will be expected to fall by another 7% by 2002. This represents several hundred lives saved each year. Unfortunately, without an experimental design, it is not possible to establish cause and effect with any certainty. However, evidence from the United Kingdom and other countries where national suicide strategies have been put into place, and where governments have made explicit commitments to suicide reduction, suggests that this type of national program is indeed effective and does in fact save many lives.


Table 14–1.

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Strategies for prevention of suicide

Steps in the pathway to suicide

Actions to prevent suicide

Factors causing depression

Policy on employment, education, social welfare, housing, child abuse, children in care and leaving care, substance abuse School mental health promotion (coping strategies, social support, and anti-bullying strategies), workplace mental health promotion action on alcohol and drugs, action on physical illness and disability

Depressive illness and other illnesses with depressive thoughts

Support of high-risk groups Professional training about prompt detection, assessment, diagnosis, and treatment

Suicidal ideation

Good risk management in primary care

Suicidal plans

Taboo enhancement, good practice guidelines on looking after suicidal people in primary and secondary care

Access to means

Controlling access to means of suicide

Suicide attempt

Prompt intervention, good assessment, and follow-up of those who deliberately selfharm and/or attempt suicide

Aftermath

Audit and learn lessons for prevention Encourage code of practice for media

REFERENCES Appleby L, Shaw L, Amos T: National Confidential Inquiry into Suicide and Homicide by People with Mental Illness. Br J Psychiatry 170:181–182, 1997 Armstrong E: The Primary Health Mental Health Care Toolkit. London, England, Royal College of General Practitioners, 1997 Department of Health: The Health of the Nation: A Strategy for Health in England, Cm 1986. London, England, Her Majesty’s Stationery Office, 1992 Department of Health: Health of the Nation: Key Area Handbook, 2nd Edition. London, England, Her Majesty’s Stationery Office, 1994 Department of Health: Saving Lives: Our Healthier Nation, Cm 4386. London, England, Her Majesty’s Stationery Office, 1999 Holden EW, Schuman WB: The detection and management of mental health disorders in pediatric primary care. Journal of Clinical Psychology in Medical Settings 2:71–87, 1995 Jenkins R: Minor psychiatric morbidity in civil servants and its contribution to sickness absence. British Journal of Industrial Medicine 42:147–154, 1985a

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Jenkins R: Minor psychiatric morbidity and labor turnover. British Journal of Industrial Medicine 42:534–539, 1985b Jenkins R: Developments in the primary care of mental illness: a forward look. Int Rev Psychiatry 4:237–242, 1992 Jenkins R, Üstün TB (eds): Preventing Mental Illness: Mental Health Promotion in Primary Care. Chichester, United Kingdom, Wiley, 1998 Jenkins R, Bebbington P, Brugha TS, et al: The national psychiatric morbidity surveys of Great Britain: initial findings from the household survey. Psychol Med 27:775–790, 1997a Jenkins R, Bebbington P, Brugha TS, et al: The national psychiatric morbidity surveys of Great Britain: strategy and methods. Psychol Med 27:765–774, 1997b Jenkins R, Bebbington P, Brugha TS, et al: British psychiatric morbidity survey. Br J Psychiatry 173:4–7, 1998 Lloyd K, Jenkins R, Mann AH: The long-term outcome of patients with neurotic illness in general practice. BMJ 313:26–28, 1996 Mann AH, Jenkins R, Belsey E: The twelve-month outcome of patients with neurotic illness in general practice. Psychol Med 11:535–550, 1981 Mann AH, Blizard R, Murray J, et al: An evaluation of practice nurses working with general practitioners to treat people with depression. Br J Gen Pract 48:875– 879, 1998 Murray CJL, Lopez AD: The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability From Diseases, Injuries, and Risk Factors in 1990 and Projected. Cambridge, MA, Harvard University Press, 1996 Rix S, Paykel ES, Lelliot P, et al: Impact of a national campaign on GP education; and evaluation of the Defeat Depression campaign. Br J Gen Pract 49:99–102, 1999 Üstün TB, Sartorius N (eds): Mental Illness in General Health Care: An International Study. Chichester, United Kingdom, Wiley, 1995 Wilkinson G, Allen P, Marshall E, et al: The role of the practice nurse in the management of depression in general practice. Psychol Med 23:229–237, 1993 World Health Organization: Development Indicators for Monitoring Progress Toward Health for All by the Year 2000. Geneva, Switzerland, World Health Organization, 1981 World Health Organization: Diagnostic and Management Guidelines for Mental Disorders in Primary Care: ICD-10. Gottingen, Germany, World Health Organization, 1996

15 The Texas Medication Algorithm Project William V. Rago, Ph.D., M.B.A. Steven P. Shon, M.D.

THE PROBLEM AND HOW IT WAS IDENTIFIED It is not unusual to hear consumers and family members raise the same concerns about treatment, which sound something like this: The symptoms of my mental illness have remained pretty much the same for over 15 years. I went to see a psychiatrist who started me on medication that was helpful. Then I moved across town and went to a different clinic and saw a different doctor who switched my medication to something else. Then, he left and his replacement changed it again. When I moved to another city, the doctor there changed my medication again. I stopped taking it for a while, ended up in the hospital, and they put me on a different medication. After discharge the outpatient clinic changed my medication again. This pattern has gone on for years; as a result, I have been on every medication for my illness two or three times without any long-term improvement.

In an environment of treatment variability and uncertainty, clinical practice guidelines can offer significant benefit. Several organizations have developed such guidelines. One set of guidelines, developed by the Agency for Health Care Policy Research (1993), is designed to help the primary care practitioner treat depressed patients encountered in general practice. The American Psychiatric Association’s (1996) guidelines use literature reviews to organize medication and psychosocial treatment options according to the levels of scientific certainty. Yet most of this work focuses on acute care rather than on seriously and chronically mentally ill patients who receive much of their treatment in the public sector. 201

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In the only effort of its kind to our knowledge, the Texas Department of Mental Health and Mental Retardation (TDMHMR) entered into a collaborative relationship with Texas’s medical schools and universities, led by the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, and the University of Texas at Austin College of Pharmacy. This collaboration, known as the Texas Medication Algorithm Project (TMAP), developed, applied, and evaluated medication treatment algorithms for schizophrenia, major depressive disorders, and bipolar disorders. An algorithm is a step-by-step procedure for making a clinical decision, often set in the form of a chart in which the answer to each question determines the next step to be taken. The intent of algorithms is to improve the quality of treatment by producing a more rapid and significant positive response (symptom reduction, functional restoration) with better tolerability than treatment-as-usual.

HOW IS THIS PROBLEM RELATED TO POOR QUALITY OF CARE? It is not that medication treatment uninformed by algorithms (treatmentas-usual) leads to poor care, but rather that algorithm-informed treatment represents a scientific advance that can improve outcomes. Table 15–1 identifies the major clinical and administrative benefits of using algorithms. The most important clinical benefit is the facilitation of appropriate clinical decision making to sustain a preferred level of quality care. In the absence of such guidance, there is a greater likelihood of variation in treatment with a resulting loss of patient and family confidence in the treatment regimen. Effective algorithms inform treatment while being flexible enough to take into account prior treatment history, individual patient factors (such as comorbid conditions and concomitant medications), and, whenever possible, patient preference. Another clinical reason for using algorithms is for consistency. As the duration of hospital stays decreases, patients may not remain under the care of the initial prescriber long enough after beginning a new medication for the doctor to determine whether the medication produces optimal results. Therefore, it is critical that treatment plans are consistent from one site or physician to the next so that treatment trials (which may last 4–8 weeks) are conducted consistently and individual response can be accurately gauged. There are also several administrative reasons suggesting that algorithms may be beneficial. For example, they can improve the cost efficiency of treatment. In addition, costs are more predictable because the treatment is more standardized. Furthermore, once a baseline practice is established with a particular algorithm, the clinician can then insert new

The Texas Medication Algorithm Project

Table 15–1.

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Reasons for using algorithms

Clinical Facilitates clinical decision making Improves quality of care Lists options for tailoring treatment to individuals Makes treatment plans consistent across sites and physicians Provides adequate clinical documentation Administrative Improves cost efficiency of treatment Makes costs more predictable Defines where new medications fit for optimal clinical outcomes Provides a basis for defining when new medications are cost effective Defines costs related to specific treatments or outcomes Provides adequate clinical documentation

medications into the recommended sequence of treatment steps at various points to provide empirical data regarding the most cost-efficient use of the medication. Finally, algorithms provide—at least in theory—a mechanism by which to relate costs to specific treatments or particular outcomes. Because public funding for mental health services is rarely sufficient to meet the need, the system must limit access either to particular services or to the system itself. Perhaps the most enlightened strategy for managing access is preventing the need for more costly services earlier on by providing the most effective treatment at the outpatient level. The funds “saved” can then be used to serve more people even when the overall budget remains the same. Figure 15–1 illustrates the potential benefit of moving from minimal to preferred care from a system perspective.

INTERVENTION DESIGNED TO ADDRESS THE PROBLEM Two of three planned phases of the TMAP project have been completed, and Phase 3 is currently under way. Phase 1 focused on the development of empirical, scientifically valid medication algorithms for major depression, schizophrenia, and bipolar disorder. Phase 2—a feasibility trial completed in August 1997—involved nearly 40 physicians at more than a dozen sites who enrolled patients with one of the three target disorders in order to determine whether the algorithms’ recommendations were acceptable. The purpose of Phase 2 was to measure response rates to each step, to revise the strategic template used to map the flow of specific algorithms when appropriate, and to evaluate which of several potential symptom out-

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Figure 15–1.


Consequences of preferred versus minimal care.

come measures were most useful in assessing changes in patient outcomes. Phase 3 research has been completed and data analysis is concurrently under way. Phase 3 was a prospective controlled study of 1,400 patients who were followed up for 1 year to evaluate the clinical and economic impact of medication algorithms, clinical supports, and patient and family education. Phase 3 included two control groups: one consisting of patients who received treatment as usual at a site that did not use algorithms, and one consisting of patients who received treatment as usual at a site that used algorithms, but for different illnesses. The purpose of the second control group was to see if the algorithm culture influenced the manner in which physicians treated patients whose care was not being guided by an algorithm. Patients were compensated for undergoing independent outcomes assessments at baseline and every 3 months thereafter. In addition to evaluating changes in symptoms, quality of life, and patient and staff satisfaction, the researchers collected economic data on use of resources both inside and outside the state mental health system. Specifically, the costs of psychiatric hospitalizations, emergency department visits, use of other psychiatric resources, and contacts within the criminal/legal, welfare, and general medical systems were measured.


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GENERAL DESCRIPTION OF THE ALGORITHMS A detailed discussion of the content of the three algorithms is presented elsewhere (Crismon et al. 1999; Gilbert et al. 1998; Miller et al. 1999). The most recent versions of the algorithms are presented in the appendix to this chapter. Each algorithm has multiple stages. There are two algorithms for major depressive disorder: one for psychotic depression, which has five stages, and one for nonpsychotic depression, which has seven. The schizophrenia algorithm has six stages and includes a side effect algorithm and a coexisting symptoms algorithm. For bipolar disorder there is a seven-stage algorithm for mania and a six-stage algorithm for the depressed phase. Each algorithm includes the particular medication(s) or medication class(es) recommended; the preferred oral doses or serum concentration ranges; the time to remain at the dose; common side effects and how to evaluate them; and key decision points regarding whether to continue the medication unchanged, modify the dose, discontinue the medication, begin a new medication, or augment the first medication with a second medication. The algorithms also recommend frequency of visits. Figure 15–2 illustrates the exemplar algorithm (our template) for making strategic treatment recommendations, and Figure 15–3 shows an exemplar algorithm informing tactical recommendations. During the development of the algorithms, the TMAP identified treatment strategies that are equivalent in their expected efficacy and safety as determined by the scientific literature and/or expert clinical judgment. In such cases, one alternative may be as effective as another, and therefore either may be chosen. When clinically equivalent, these strategies are presented to patients (and clinicians) with choice points as to which medication to use. For patients to make informed choices based on the algorithms, they need to be knowledgeable about the course of their illness, its symptoms, and any medication side effects. In this regard, the TMAP has also developed extensive patient and family educational materials with the assistance of advocacy groups. It is hoped that education will enable patients to make decisions that will increase their adherence to treatment.

GETTING PHYSICIAN ACCEPTANCE Regardless of how useful the algorithms are, physician acceptance is crucial to their implementation. The TMAP developed four interrelated strategies to facilitate physician acceptance: 1) design credible algorithms, 2) support physicians in their implementation, 3) provide training and ongoing consultation, and 4) minimize the paperwork involved.

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Figure 15–2.


Exemplar algorithm showing strategic recommendations.

Algorithm credibility was established in several ways. Physicians implementing the algorithms participated in their development, which helped to ensure that the algorithms were as practical and simple as possible while providing a range of options. Thus practitioners could tailor their use of the algorithms to individual patients and circumstances. Physicians also were encouraged to use their own clinical judgment and were not required to follow the algorithms blindly. Thus the TMAP’s algorithms were viewed as informing clinical judgment, not replacing it. Compared with treatment-as-usual in the TDMHMR system, algorithm-informed treatment specified visit frequency and time between visits. It also required that the physician spend more time with each patient, especially at the initial visit. Because the algorithms were tied to patient outcomes, a minimum amount of data had to be collected at each visit. This


Figure 15–3.

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Exemplar algorithm showing tactical recommendations.

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meant that busy physicians needed to find more time if they were to use the algorithm. The TMAP helped by providing clinical coordinators who assisted physicians with assessing patients and completing paperwork. Physicians and clinical coordinators were trained in their respective roles in administering the algorithms (TMAP developed comprehensive manuals for each algorithm that detailed functional responsibilities). However, it quickly became apparent that ongoing physician consultation was also needed. This was provided through weekly teleconferences during which the module director responded to questions asked by physicians participating in the feasibility study. This significantly increased the confidence of practicing physicians in using the algorithms and also helped us make necessary modifications. Paperwork management involved modifying existing forms in the medical record to facilitate the collection of information needed for the algorithm. This also helped physicians record their treatment decisions in the medical record.

EFFECTIVENESS OF THE INTERVENTION In Phase 1, the algorithms and patient/family education materials were developed. Phase 2 tested their feasibility. Data from Phase 2 indicated that the algorithms positively affected patient symptoms and functioning, were used by physicians working in the public sector, and had strong support from all participants regarding their overall improvement on the quality of treatment in the TDMHMR system. In the Phase 2 feasibility trial, 222 TDMHMR patients received the algorithm-based treatment. Of these, 62 patients enrolled in the major depressive disorders (MDD) module (34 outpatients, 28 inpatients); 91 enrolled in the schizophrenia (SCZ) module (60 outpatients, 31 inpatients); and 69 enrolled in the bipolar (BPD) algorithm (44 outpatients, 25 inpatients). To determine whether treating physicians followed the algorithms, we looked at the average time a patient spent in a particular algorithm step and whether the time between outpatient visits approximated the algorithm’s recommendations. Physician adherence to the algorithm was analyzed by looking at the percent of enrolled patients who used one or more steps in the algorithm. For the purpose of this analysis, a step indicated the algorithm stage the patient was actually in, independent of the actual stage number; for example, if the algorithm had six stages and the patient entered the algorithm at stage 2, this was identified as step 1. Because the patient could enter the algorithm at any stage and because stages of each algorithm moved from less to more complex treatments, we examined the first step, rather than the first stage, to gauge use of the algorithm.


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Most patients received only one step of treatment: 65% of MDD patients, 71% of SCZ patients, and 59% of BPD patients. Those receiving two steps on average spent less time on the first step than those receiving only one step. This information suggested an internal validity of the data and an appropriate use of the algorithms. The algorithms appeared to be properly implemented. The time between visits for outpatients for all three disorders exceeded the algorithm guidelines, which recommended that patients requiring initiation or change of medication be seen every 1–2 weeks. For the MDD group, the time between visits one and two was 21.5 days, whereas for the SCZ group, the average time between visits for the first six patient visits ranged from 21 to 28 days. For those patients requiring seven or more visits, the average time between visits ranged from 10 to 19 days. The average time between visits for BPD patients ranged from 15 to 23 days. We then asked whether patients who received multiple steps needed them. The results of this analysis indicated that patients from all three modules who received only one step did better clinically at the first stage than patients who required multiple steps. This conclusion was based on the fact that symptom reduction as determined by the Brief Psychiatric Rating Scale (BPRS) measure (Lukoff et al. 1986) was significantly greater for those treated in one step than for patients with more than one step. The symptoms and level of functioning for algorithm patients improved measurably. Symptom ratings (BPRS for all three groups and Inventory for Depressive Symptomatology–Clinician Rated [IDS-C] for the MDD algorithm patients) showed statistically and clinically significant reductions (Rush et al. 1996). A preliminary analysis of the speed of symptom reduction was analyzed using survival curves. For the MDD group, we used a criterion of 50% reduction from IDS-C baseline; after 90 days of treatment, 38% of patients met or exceeded this criterion. For the SCZ and BPD groups, the criterion was a 30% reduction from the BPRS baseline scores. After 90 days, 55% of the SCZ group and 50% of the BPD patients met or exceeded this criterion. Patient functioning as measured by the Multnomah Community Ability Scale total score also showed significant functional improvement for the MDD and BPD groups (Zani et al. 1999). This was not true for the SCZ group. The last analysis measured physician satisfaction with the algorithms. Clinicians were asked to rate three specific statements at the end of treatment for each patient in each algorithm: 1) following the algorithm was difficult with this patient, 2) using the algorithms assisted me in making treatment decisions, and 3) the patient’s symptoms have improved since starting the algorithm. Analysis of all three questions revealed that both inpatient and outpatient physicians endorsed using treatment algorithms.

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The TMAP represents a substantive shift in medical practice, from caseby-case decision making to systematic application of evidence-based guidelines. It is the hope of the TDMHMR that consumers of public services will receive a uniformly higher standard of care when the TMAP is fully implemented.

REFERENCES Agency for Health Care Policy and Research: Clinical Practice Guideline Number 5: Depression in Primary Care, Vol 1: Detection and Diagnosis (AHCPR Publ. No. 93–0550). Rockville, MD, U.S. Department of Health and Human Services, Agency for Health Care Policy Research, 1993 American Psychiatric Association: APA Practice Guidelines. Washington, DC, American Psychiatric Association, 1996 Crismon ML, Trivedi MH, Pigott TA, et al: The Texas Medication Algorithm Project: report of the Texas consensus conference panel on medication treatment of major depressive disorder. J Clin Psychiatry 60:142–156, 1999 Gilbert DA, Altshuler KZ, Rago WV, et al: Texas Medication Algorithm Project: definitions, rationale, and methods to develop medication algorithms. J Clin Psychiatry 59:345–351, 1998 Lukoff D, Nuechterlein KH, Ventura J: Manual for the expanded BPRS. Schizophr Bull 1:594–602, 1986 Miller AL, Chiles JA, Chiles JK, et al: The Texas Medication Algorithm Project (TMAP) schizophrenia algorithms. J Clin Psychiatry 60:649–657, 1999 Rush AJ, Guillion CM, Basco MR, et al: The inventory of depressive symptomatology (IDS): psychometric properties. Psychol Med 26:477–486, 1996 Zani B, McFarland B, Wachal M, et al: Statewide replication of predictive validation for the Multnomah Community Ability Scale. Community Ment Health J 35:223–229, 1999

Appendix SCHIZOPHRENIA ALGORITHMS • Antipsychotic • Side effects and coexisting symptoms

DEPRESSION ALGORITHMS • Strategies for the treatment of major depression (nonpsychotic) • Strategies for the treatment of major depression (psychotic) • Tactics for the treatment of major depression (nonpsychotic)

BIPOLAR ALGORITHMS • Treatment of bipolar major depressive episodes • Treatment of hypomanic/manic episodes

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Antipsychotic algorithm for schizophrenia.

Appendix

IM=intramuscularly; PO=orally; PRN=as needed.

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Side effects (top) and coexisting symptoms (bottom) algorithms for schizophrenia.

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Depression algorithm: strategies for the treatment of major depression (nonpsychotic). BUP = bupropion; MAOI = monoamine oxidase inhibitor; MRT = mirtazapine; NEF = nefazodone; SSRI = selective serotonin reuptake inhibitor; TCA = tricyclic antidepressant; VLF = venlafaxine.

Appendix

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Depression algorithm: strategies for the treatment of major depression (psychotic). ECT=electroconvulsive therapy; SSRI=selective serotonin reuptake inhibitor; TCA=tricyclic antidepressant; VLF= venlafaxine.

216 I MP R OV IN G M EN T A L H E A L TH CAR E

Depression algorithm: tactics for the treatment of major depression (nonpsychotic).

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Treatment algorithm for bipolar major depressive episodes (MDEs). AD = antidepressant; AD-1 = bupropion SR or SSRI; AD-2 = venlafaxine or nefazodone; CBZ = carbamazepine; ECT = electroconvulsive therapy; MAOI = monoamine oxidase inhibitor; RC = rapid cycling; SSRI = selective serotonin reuptake inhibitor.

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Treatment algorithm for hypomanic/manic episodes. CBZ=carbamazepine; DVP=divalproex sodium; ECT =electroconvulsive therapy.

16 Improving the Quality of Community-Based Services for Children Mary E. Evans, R.N., Ph.D., F.A.A.N.

THE PROBLEM AND HOW IT WAS IDENTIFIED During the early 1990s, the New York State Office of Mental Health (NYSOMH) reported that the length of stay in state-supported psychiatric centers for children and adolescents was considerably longer than in other psychiatric inpatient settings and was also an expensive means of treatment. A considerable body of literature was also accumulating around this time that focused on inappropriate use of inpatient units and suggested that care in community settings was preferable for many children and preferred by their families (Burns 1991; Kutash et al. 1994; Stroul and Friedman 1986). Medical records reviews indicated that one-third of the children 8 years old and younger admitted to public inpatient settings in New York had not received mental health services prior to their admission (Bureau of Evaluation and Services Research 1990). The NYSOMH was concerned that hospitalization was often the first point of contact for these children. In response to this concern, the NYSOMH took advantage of the funding provided by the National Institute of Mental Health’s Child and Adolescent Service System Program (CASSP) for the development of community-based care. The agency hoped that CASSP funding would alleviate two problems: 1) long hospital stays for children and 2) hospital admissions that might have been diverted if other alternatives had been available. In response to these problems, the NYSOMH developed, implemented, and then evaluated a system of community-based services for children with severe emotional disturbances. 219

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HOW IS THIS PROBLEM RELATED TO POOR QUALITY OF CARE? Lengthy hospitalizations do not represent state-of-the-art treatment for children and adolescents with serious emotional disturbance (SED). Currently, the objectives of hospitalization are likely to be stabilizing the crisis and maintaining the child’s safety while connections with communitybased services can be made. Because many children with SED need intensive, prolonged services, a variety of community supports is necessary to maintain children in their natural settings. Coordination of these services is essential. Furthermore, there are only five children’s centers within New York’s public inpatient hospitals. Before the NYSOMH began its review, many hospitalized children were placed in facilities located several hours away from their homes and families, which made it difficult to maintain family relations. It also made it difficult to plan post-hospital services for these children, because the treatment team often had no prior contact with gatekeepers in the child’s community or any personal knowledge of the local resources. Although there are still only five children’s centers, communitybased services and hospitalization in non-state facilities closer to home are beginning to have an affect on state-operated facilities (which is likely to continue in the next decade). Finally, despite the frequency of hospital use, the NYSOMH had not previously studied the outcomes associated with hospitalization or the factors associated with varying lengths of stay in its children’s psychiatric centers. This did not distinguish the NYSOMH from other public mental health authorities, because the outcomes of hospitalization for children and the relationship between length of stay and outcome were virtually unknown (Burns 1991; Pottick et al. 1997).

SYSTEMIC FACTORS CONTRIBUTING TO THE IDENTIFIED PROBLEM A number of system-wide factors contributed to the problem of long hospital stays in state psychiatric centers. First, little was known about the characteristics, precipitating events, lengths of stay, and outcomes experienced by children who were hospitalized. In addition, no client tracking system existed to determine what services children received in the community and what outcomes they experienced. Second, there was the problem of how community-based alternatives could be financed. As is the case in many states, inpatient stays in New York were often paid by Medicaid. To establish community-based services, such

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as case management and intensive in-home programs, the NYSOMH worked with its control agencies (e.g., the Division of the Budget and Civil Service) to determine who should be providing the services, for whom they should be designed, and how services should be reimbursed. Third, virtually no information was available at the time regarding effective models of case management for children, and no single systematic study had been reported in the literature regarding the outcomes for children with SED receiving case management services. This created some uncertainty among decision makers about the effectiveness of the proposed intervention, intensive case management.

THE INTERVENTION In late 1988, the NYSOMH decided to implement an intensive case management program for children and youth on a limited basis in some counties of the state. The case management model developed was a linkage and advocacy model (Armstrong and Evans 1992). The program’s key characteristics were • The target population was identified as children with SED who had a history of or were at risk for out-of-home placement. • Each intensive case manager would be assigned a caseload of 10 children. • The program goal was to ensure that the needed supports and services were available to keep children in the community, which included brokering existing services as well as identifying and assisting in the development of additional services for the target population. • The program focused on the child with SED within the context of his/ her family experience. • Intake was administered by an interagency committee whose membership varied by county but generally included representatives from childserving agencies such as juvenile justice, social welfare, mental health, and education. • Services were available 24 hours a day, 7 days a week. • A designated amount of flexible service dollars was available for each child each year. • Advocacy for enrolled children was provided, and services they needed were improved. • The length of the intervention was determined by the needs of the child and the intensive case manager’s assessment regarding whether the goals of the intervention had been met.

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The interagency intake committee reviewed the records of all children who were hospitalized, recently discharged from inpatient care, or referred because of the possibility of out-of-home placement. These children were then ordered by rank in terms of need for case management services and were assigned to case managers, by priority ranking, as slots became available. Most of the new intensive case managers had master’s degrees; these were primarily in social work but there were a few in nursing (Evans et al. 1993). If the case manager had a minimum of four face-to-face contacts with the child during the month, intensive case management services became eligible for Medicaid reimbursement. A reporting system using two brief forms was established to elicit data about the program. The Client Description Form and the Program Termination Form were completed at intake to and termination from case management services (Hallfors et al. 1996). Completed forms were mailed to the NYSOMH’s Bureau of Evaluation and Services Research, where they were entered into the database. This information was used to describe the characteristics of the children receiving community-based services, their referral patterns, their length of stay in a service, and their referral to other services. It was also used to compare their symptoms and functioning on admission to and discharge from a service or program. Eventually this database included information on eight types of community-based services for children receiving public sector services, including intensive case management, treatment foster care, crisis services, teaching family homes, and residential services such as group homes.

EFFECTIVENESS OF THE INTERVENTION Three different methods were used to determine the effectiveness of intensive case management: monitoring and accountability, program evaluation, and comparative analysis.

Monitoring and Accountability The Bureau of Evaluation and Services Research staff reported the demographic characteristics, treatment history, symptoms, and functioning for children admitted to a program and reported the proximal outcomes for children discharged. This information was shared with staff members in the Bureau of Children and Families at the NYSOMH who had responsibility for the development and implementation of community-based programs. The evaluation staff and the program staff discussed whether the expected target population was being admitted to the program, whether the length of stay was as anticipated, and whether the proximal outcomes matched the


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desired expectations. These data were also shared with staff at the Division of the Budget who controlled important aspects of the program such as recommending the numbers of case managers to be hired and trained. The discussions with program staff about the descriptive data indicated that the target population of children was being admitted to intensive case management. The average age of the children on admission was 12 years, and most were non-Hispanic and white (61%) and male (67%). Most lived in the custody of a biological parent (77%), and many (49%) lived in a single-parent household. More than half were in a special education placement (57%). On enrollment, these children had approximately six problem behaviors and symptoms and were functionally impaired in an average of 2.5 of 5 areas of functioning. The typical child had been placed out-ofhome twice, in either a hospital or residential setting, because of psychiatric problems. Children who were enrolled continued to receive services for an average of 18 months and generally showed improvement in functioning and a decrease in the number of their problem behaviors and symptoms before discharge from the program (Evans et al. 1994). Over the course of the case management intervention, 70% of the children were maintained in their natural home.

Program Evaluation The NYSOMH evaluated in greater depth the child and family outcomes for a sample of 199 children systematically selected from among the early admissions to the program. Case managers of the selected children were asked to assess the children’s unmet service needs as well as their functioning and symptoms at admission and every 6 months for 18 months or at discharge, whichever came first. They were also asked to assess family strengths and family functioning. This evaluation showed that at discharge, the enrolled children experienced significant decreases in aggression, suicidal behavior, sexual acting out, cruelty to animals, fire setting, and psychotic behavior. They also had experienced a significant decrease in their need for education and housing services, while they continued to need mental health, dental, and recreational services. It also suggested that the informal social support network of the families needed strengthening. Analysis indicated hospital admissions decreased during the period from 2 years prior to enrollment in intensive case management to 2 years after enrollment (Evans et al. 1994). No significant changes in family functioning were demonstrated during the program evaluation period. Shortly after admission, case managers were asked to assess the family conditions that interfered with the child’s treatment. They identified these conditions as poverty (in 53% of families),

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unstable relationships among adult family members (60%), an adult family member who had been abused as a child (41%), mental illness in an adult family member (56%), chronic unemployment (33%), domestic violence (27%), and alcohol or substance abuse by an adult family member (30%). Many of these conditions were of prolonged duration, and the case management intervention had virtually no effect.

Comparative Analysis To validate our positive program evaluation findings, we compared the psychiatric hospitalization use levels of children enrolled in intensive case management with those of a comparison group of children eligible for enrollment but not enrolled. The pool of potential control subjects was obtained by extracting all inpatient admission records for children under 18 years of age for a 6-year period. After children who had received intensive case management were excluded from this pool, 9,365 children were available as a potential comparison group. Because of the phase-in of intensive case management, most of these children were located in counties where this intervention was not yet available. A computerized algorithm matched intensive case management enrollees with potential control subjects according to age at first admission to a psychiatric center, gender, race/ethnicity, region of residence within the state, diagnosis, total number of inpatient admissions, and total number of inpatient days. This identified the control child who most closely matched a member of the intensive case management cohort on these variables. This procedure resulted in a final pool of 392 pairs, representing 784 individuals. The treatment histories of both groups of children were compared over twenty-four 90-day intervals representing a 6-year period. The intensive case management group began the period at a significantly higher level of use of hospitals than the comparison group, but use declined at a steeper rate over the 3-year period. Although both groups showed a reduction in use, this decrease was statistically significant only for children enrolled in intensive case management (Evans et al. 1994, 1996). When patterns of decreased use were annualized, children in intensive case management showed a decline of 83% in inpatient use (from about 28 days during the year just prior to enrollment to about 6 days in the period 2–3 years after enrollment) compared with a decline of 34% for children in the comparison groups for the same time period (Evans et al. 1994). We also used the matched control analysis to estimate cost savings that could be realized from decreased inpatient use by children enrolled in the program. These children used 22,109 inpatient bed days or 60.57 beds during the year prior to their enrollment (Time 1) compared with 3,759 days


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or 10.3 beds in the year beginning 2 years after enrollment (Time 2), a difference of 18,350 bed days or 50.3 beds. During the comparable time periods, the matched control group used 15,994 days or 43.8 beds during Time 1 compared with 10,556 days or 28.9 beds in Time 2. The difference accounted for by intensive case management was 12,912 bed days annually (18,350–5,438) or 35.4 beds (50.3–14.9).

CONCLUSIONS Based on these three evaluation strategies, we concluded that intensive case management was a cost-effective intervention that enrolled the target group of children. Positive gains in functioning and symptom reduction occurred as well as decreased need for some services. At the same time, hospital use by the enrolled children dropped significantly compared with that by a comparison group of eligible but not enrolled children. Of lingering concern was the failure to demonstrate an impact on family functioning. We were aware that without strengthening and empowering families, our intervention may have had only short-term rather than longterm positive outcomes. This led us to redesign the case management intervention to be family focused rather than child focused. We evaluated this family-centered intensive case management model and demonstrated its superiority to the foster care model in child and family outcomes and cost savings. The methodology and outcomes of this experiment are detailed elsewhere (Evans et al. 1998). One outcome that resulted from this evaluative research was the development and implementation of a policy regarding admissions of children 8 years of age and younger to state-supported psychiatric centers. Because of the existence and effectiveness of community-based alternatives to hospitalization, the admission of young children to state-supported psychiatric centers was discouraged by the NYSOMH.

REFERENCES Armstrong MI, Evans ME: Three intensive community-based programs for children and youth with serious emotional disturbance and their families. Journal of Child and Family Studies 1:61–74, 1992 Bureau of Evaluation and Services Research: The Eight and Younger Case Studies. Albany, NY, New York State Office of Mental Health, 1990 Burns BJ: Mental health service use by adolescents in the 1970s and 1980s. J Am Acad Child Adolesc Psychiatry 30:87–98, 1991 Evans ME, Huz S, McNulty TL: Intensive case management for children and youth with serious emotional disturbance: an emergent role for nursing? The Journal of the New York State Nurses Association 24:4–8, 1993

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Evans ME, Banks SM, Huz S, et al: Initial hospitalization and community tenure outcomes of intensive case management for children and youth with serious emotional disturbance. Journal of Child and Family Studies 3:225–234, 1994 Evans ME, Huz S, McNulty TL, et al: Child, family, and system outcomes of intensive case management in New York state. Psychiatr Q 67:273–283, 1996 Evans ME, Armstrong MI, Kuppinger AD, et al: Preliminary outcomes of an experimental study comparing treatment foster care and family centered intensive case management, in Outcomes for Children and Youth With Behavioral and Emotional Problems and Their Families: Program and Evaluation Best Practices. Edited by Epstein MH, Kutash K, Duchnowski A. Austin, TX, ProEd, 1998, pp 543–580 Hallfors D, McQuide P, Brach C, et al: First Steps: A Guide to Integrating Information for Systems Evaluation of Children’s Mental Health Services, 2nd Edition. Boston, MA, The Technical Assistance Center for the Evaluation of Children’s Mental Health Services, Judge Baker Children’s Center, 1996 Kutash K, Duchnowski AJ, Sondheimer DL: Building the research base for children’s mental health services. Journal of Emotional and Behavioral Disorders 2:194–197, 1994 Pottick K, Coyne L, Barber CC, et al: Factors associated with inpatient length of stay for children and adolescents, in The 10th Annual Research Conference Proceedings: A System of Care for Children’s Mental Health: Expanding the Research Base, February 23–26, 1997. Edited by Liberton C, Kutash K, Friedman R. Tampa, FL, University of South Florida, The Louis de la Parte Florida Mental Health Institute, Research and Training Center for Children’s Mental Health, 1997, pp 401–407 Stroul B, Friedman R: A System of Care for Children and Youth With Severe Emotional Disturbances, Revised Edition. Washington, DC, Georgetown University, CASSP Technical Assistance Center, 1986

17 Improved Emergency Access for Medicaid Clients A Colorado Case Study Paula M. Dahl, M.B.A., C.P.H.Q. Sandra L. Forquer, Ph.D.

INTRODUCTION Timely access to mental health services for Medicaid clients has been an area targeted for measurement by most states with Medicaid managed care initiatives. States have addressed their concerns about access by including performance standards in their requests for proposals. In some states, failure to meet minimum access standards is tied to financial penalties. In other states, financial incentives are awarded if the minimum standard is met with additional incentive dollars for significantly exceeding that minimum standard. A common concern expressed by advocates, consumers of services, policymakers, and academicians is that access to care will be reduced as more states adopt managed care for Medicaid mental health benefits. In Colorado, which initiated its Medicaid capitation project for mental health services in late 1995, historical data indicate long waiting times for initial appointments, outpatient appointments following an inpatient episode, and emergency response. These delays often result in escalated problem severity between the time of initial telephone contact and first appointment or reported hospitalization in the absence of a timely follow-up after inpatient discharge. This lack of timely emergency response is also associated

Inquiries may be directed to the first author at: [email protected] 227

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with increased family burden, intervention of law enforcement officials, and increased involuntary hospitalizations.

IDENTIFICATION OF THE PROBLEM Before September 1995, emergency access to community mental health centers for Medicaid clients was deemed inadequate because most centers failed to meet the state standard of clinician response: telephoning the client within 15 minutes of the initial emergency call. Colorado previously measured this standard by conducting an annual emergency response test that entailed simulating an emergency call to the health centers and awaiting the clinician’s response. Testing results indicated poor response times to emergency calls in most areas, particularly in the rural areas. In many cases, this delay in emergency response was due to geographic barriers that posed challenges for efficient coordination of local community agencies.

RELATION TO QUALITY OF CARE The speed with which emergency calls are responded to can provide a mental health patient with immediate intervention and possible prevention of hospitalization or other adverse outcomes (e.g., suicide attempt). According to the National Alliance for the Mentally Ill (NAMI), many managed care companies fail to ensure immediate intervention for mental health consumers. Specifically, very few companies define a suicide attempt as an emergency requiring specific policies and procedures that ensure immediate intervention (Hall et al. 1997). There is a general concern that managed behavioral health care rations care dollars by limiting access to services. National advocacy organizations such as NAMI have conducted surveys of their members to ascertain whether managed care is providing more appropriate and coordinated care or whether care is withheld. According to the Consumer Bill of Rights and Responsibilities (Advisory Commission on Consumer Protection and Quality in the Health Care Industry 1997), all consumers have the right to emergency health care services when the need arises. Health plans, including managed behavioral health care organizations, must educate their members about the availability, location, and appropriate use of emergency services. They must design services to provide emergency screening and stabilization and promote continuity of care. It is evident that consumers are getting more involved in evaluating their mental health care needs and that managed behavioral health is under the scrutiny of organizations such as NAMI. Consumers are influencing public policy and demanding more appropriate and timely intervention of care. They are demanding that state

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agencies hold managed care organizations accountable for the quality of care provided.

SYSTEMIC FACTORS Several systemic factors traditionally played a role in slow response time. Survey findings revealed some programs did not have staffing back-up systems. Most community mental health centers had an on-call emergency clinician available 24 hours a day. However, when the identified clinician was involved in an emergency case, the next emergency had to wait until the clinician completed the first evaluation and became available to tend to subsequent calls. Another systemic factor identified during the survey was the lack of solid working relationships with other community agencies, including the local sheriff and police departments, child welfare agencies, and local answering services. Although a few community mental health centers were large and located in urban locations, many were located in rural areas. Those located in rural areas had limited back-up resources in their own agencies and had to rely on extended community resources. When community agencies attempted to triage a mental health emergency call, they delayed the transfer of information to the community mental health centers because they lacked training in identifying mental health intervention needs. Training was another factor influencing response times. During a particular emergency test, a call was routed to the local police department located between two county lines. The police department personnel were unsure of the jurisdiction rules and delayed involving the community mental health center until clear lines of jurisdiction were established. This further caused a delay in response and was identified as a systemic barrier. In addition, emergency response teams in community mental health centers and community agency staff were trained in mental health emergencies when first hired, but they were rarely given updated policies and procedures. Staff turnover and schedule changes were problems in all community agencies.

INTERVENTIONS In July 1995, three limited liability corporations were formed in Colorado between eight community mental health centers and Options Healthcare, Inc. (now ValueOptions, Inc.). These new entities, collectively referred to as Colorado Health Networks (CHN), were awarded contracts to provide managed mental health services to the Medicaid population under a capitation arrangement. These contracts stipulated emergency response time and intervention. CHN was responsible for providing services to over 90,000 members in the west slope of Colorado, which extends from the

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New Mexico/Arizona border to the Utah border; the southern tier, extending to the Kansas border; and the Front Range south of Denver. The governance structure for each entity was a 50/50 partnership with a Board of Managers composed of equal representation from the community mental health centers and ValueOptions. Each board also had equal representation on the working committees, which were responsible for meeting contractual obligations and focusing on quality improvement. CHN adopted a philosophy of immediate access to care and initiated the Plan-Do-Check-Act quality model (Scholtes 1992). The CHN Quality Improvement Steering Committee designed an emergency response study and began conducting monthly emergency tests in April 1996, lasting through October 1996. Results indicated a problem in the rural locations. CHN became more committed and creative in developing corrective actions for identified issues. By focusing on outcomes and integration of care and adopting a proactive approach, CHN believed it could ensure immediate access. Monitoring access and requiring greater accountability from the community mental health centers and other local agencies was the proposed strategy for success (Table 17–1). Through its Quality Improvement Steering Committee, CHN developed adequate clinician back-up systems for all eight community mental health centers. In centers where only one clinician was on call, a protocol was designed that arranged for a second clinician to be available when the first clinician was preoccupied with an emergency situation. Community mental health center professionals met with answering service agencies to develop policies and procedures that ensured quick response from the primary and secondary clinicians. This effort also involved the training of the mental health professionals and the nonclinical answering service personnel. When the development and training was complete, retesting was conducted. Additional efforts from the urban community mental health centers resulted in the development of a 24-hour emergency walk-in center. One community health center with existing 24-hour residential services developed a program for after-hours emergency calls. After-hours calls from a local answering service were rerouted to the 24-hour program where a clinician provided immediate assessment of the emergency situation. This increased clinician availability ensured all emergency calls were given priority and immediate clinical attention. The next intervention involved developing stronger working relationships between the community mental health center and other community agencies to extend and strengthen services and continuity of care. Rural community mental health centers using the 911 call service had previously relied on local sheriff and police departments to contact the appropriate mental health professionals. During the initial survey period, we found that


Table 17–1.

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Continuous quality improvement principles and Colorado Health Networks improvement plan

CQI principles

CHN improvement plan

1. Develop a strong customer focus 2. Continually improve all processes 3. Involve employees

Adopted the “Immediate Access” philosophy

Identified emergency response time as one of many processes for improvement Quality Improvement Steering Committee composed of community mental health center staff and Options Health Care, Inc. staff; Board of Managers composed of executive staff from the community mental health center and Options Health Care, Inc. 4. Mobilize both data and team Shared all Quality Improvement Steering knowledge to improve Committee data with staff and state and decision making community agencies to identify and develop interventions

some agencies waited too long to involve mental health professionals. We consequently identified the need to continuously train nonclinical community personnel in quickly assessing mental health emergencies and contacting the appropriate personnel. This intervention involved the development of training materials for nonclinical professionals, including quick tips for identifying mental health conditions. Questions to assess suicidal and homicidal tendencies were part of the training material and were emphasized during each session. In areas of overlapping jurisdiction, the community mental health centers also developed systems that ensured there were no barriers to access, and they provided updated call lists to community agencies. This resulted in collaborative working agreements between agencies across county and city boundaries, providing quick, immediate response time and decreasing the geographic barriers. Training, routine retraining, and periodic testing occurred regularly between the community agencies and CHN.

EFFECTIVENESS The corrective actions and interventions resulted in positive outcomes. Retesting occurred after implementation of each intervention and results were compared with initial testing. Although the initial average response time (in 1996) was shorter than the 15-minute standard (see Figure 17–1), the community mental health centers that previously did not maintain the standard were in compliance immediately after intervention. By 1997, all response times were less than 6 minutes.

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Figure 17–1. 1997.


Average emergency response time in minutes, 1996 versus

Testing continued through 1996 and 1997 until an acceptable response time was achieved. Results demonstrated statistically significant changes in three areas, but more revealing was the change in collaborative effort between community agencies throughout the 43 counties. The Quality Improvement Steering Committee reviewed results and interventions continuously and shared outcomes with management and boards of managers. Other proximal measures were used to identify the impact of this quality improvement effort, including monitoring the number of suicide attempts and suicide deaths (see Table 17–2). Although there is no direct evidence that the emergency response test interventions had a direct impact on suicide attempts, the number of attempts was slightly lower in 1997 than in the initial testing time period in the covered counties. Emergency access, routine access, and other proximal measures (e.g., suicide rates, hospital admissions) are currently monitored to ensure maintenance of gains and continuous collaborative effort to serve the Medicaid mental health clients of Colorado (see Table 17–3). Table 17–2.

Suicide attempts Suicide deaths

Monitoring number of suicide attempts and deaths 1996

1997

1998

60 6

59 9

57 4


Table 17–3.

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Hospital admissions from 9/1/95 to 6/30/98 9/1/95–6/30/96 7/1/96–6/30/97 7/1/97–6/30/98

Pikes Peak-Options SyCare-Options West Slope Casa-Options

315 541 297

377 675 285

216 312 236

Recently, the West Slope-Options Board of Managers endorsed efforts to reexamine its crisis response strategy in rural counties. In the interest of continuous quality improvement, CHN focused its efforts during fiscal year 1999–2000 on reexamining crisis intervention services and expanding the continuum to include in-home crisis intervention services for rural areas. CHN continues to monitor all quality improvement interventions and reinforce its organizational philosophy of seeking opportunities to improve care.

REFERENCES Advisory Commission on Consumer Protection and Quality in the Health Care Industry: Consumer Bill of Rights and Responsibilities: Report to the President of the United States. Washington, DC, Advisory Commission on Consumer Protection and Quality in the Health Care Industry, 1997 Hall LL, Edgar ER, Flynn LM: Stand and Deliver: Action Call to a Failing Industry. The NAMI Managed Care Report Card. Arlington, VA, National Alliance for the Mentally Ill, 1997 Scholtes P: The Team Handbook. Madison, WI, Joiner Associates, 1992


18 Improving Employment Outcomes for People With Severe Psychiatric Disabilities Deborah R. Becker, M.Ed. Robert E. Drake, M.D., Ph.D.

THE PROBLEM Prior to the late 1980s, most community mental health centers in the United States provided primarily facilities-based services, including clinical and day treatment programs, to people with severe psychiatric disorders. Clients with severe or long-term impairments were often maintained in these programs for several hours per day, several days per week, over a period of months or years. Medicaid funding regulations encouraged intensive, long-term involvement of clients. Rehabilitative day treatment programs reinforced clients’ roles as mental patients. However, it became clear that people with severe psychiatric disorders were not interested in spending their days in mental health settings or in learning to be good patients. They aspired to lead satisfying lives and participate in meaningful activities such as jobs and friendships, and they wanted to have enough income to live safely and independently. In response to the changing needs of clients and their families, the New Hampshire Division of Mental Health began to promote the reintegration of clients with severe psychiatric disorders into the lives of their communities. In 1989, the division encouraged West Central Services, a community mental health center, to discontinue operating one of its two rehabilitative day treatment programs. The day treatment programs were part of a community support service for people with severe mental disorders in two New England towns, each with a population of approximately 30,000, and their 235

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surrounding rural areas. The Division of Mental Health saw this as an opportunity to begin promoting community reintegration.

HOW IS THIS PROBLEM RELATED TO POOR QUALITY OF CARE? The Rehabilitation Act Amendments of 1986 gave authority to states to provide supported employment services to people with severe psychiatric disorders to help them gain competitive employment. Many professionals working in the mental health field have long known that one concrete and feasible way to promote rehabilitation is through work. In addition, most clients with severe psychiatric disorders express a desire for competitive employment (Rogers et al. 1991). However, Tashjian et al. (1989) reported that only 3% of people with severe psychiatric disorders had received supported employment services nationally. Rates of competitive employment for people with serious mental illness receiving vocational rehabilitation services were low, and people in psychosocial programs and sheltered settings usually did not move on to community employment (Bond and Boyer 1988). In the New Hampshire community mental health centers, the mean rate of competitive employment for people with severe psychiatric disorders was 7% in 1990.

SYSTEMIC FACTORS CONTRIBUTING TO THE IDENTIFIED PROBLEM Five factors appeared to contribute to the low rate of competitive employment: 1) many staff believed that clients could not work in competitive jobs; 2) the administrative and financial separation of the mental health and vocational rehabilitation systems made it difficult to coordinate services effectively for individual clients; 3) lengthy prevocational assessment and training discouraged many clients and rarely led to competitive employment; 4) vocational counselors were often office based and did not actually get out into the community to develop jobs and provide direct support to help clients secure and maintain jobs; and 5) job searches were rarely individualized to meet clients’ needs and skills. In addition, financial support for vocational programs was limited. Funding sources dictated the types of services being provided, and rehabilitative day treatment services were handsomely reimbursed by Medicaid, leading to incentives to maintain clients in day activity centers. Until recently, there had been no leadership at the state or local level to champion competitive employment for people with severe mental illness.

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INTERVENTION DESIGNED TO ADDRESS THE PROBLEM Organizational Structure In 1990, West Central Services staff converted one of its two day treatment programs to a supported employment program, Individual Placement and Support (IPS), which helped people with severe mental illness obtain competitive employment. The approach emphasized rapid searches for competitive jobs with support and continuous work-based assessment by IPS employment specialists. In addition, treatment was provided through team-oriented community-based services (Becker and Drake 1994). In the IPS program, clients continued to receive psychiatric services, case management, substance abuse treatment, and housing support. All staff received training in the new approach to vocational services and learned to understand their special role in supporting clients’ efforts to perform regular jobs that paid at least the minimum wage. Some of the day treatment staff positions were converted to employment specialist positions, and other staff were transferred elsewhere within West Central Services. The employment specialists joined the multidisciplinary treatment teams in order to integrate vocational services with mental health treatment. The specialists assumed only work-related responsibilities for their clients; they were generalists, each providing the entire employment service including engagement of the client, assessment, individualized job finding, and onthe-job support. Employment specialists were supervised by an IPS coordinator who made client assignments and provided weekly group vocational supervision. The IPS workers provided backup and support for each other. The rest of the treatment team provided information that was helpful in identifying a work situation consistent with a client’s coping skills, symptoms, interests, strengths, preferences, and skills. The team met regularly (at least weekly), and team members communicated information to each other, often on a daily basis. Through this team approach, clients received clear, consistent messages.

Eligibility Criteria and Engagement All adult clients with severe mental illness who received community mental health center services and expressed a desire for competitive employment were eligible for IPS services, including individuals who traditionally were often screened out of work-related services (i.e., those with co-occurring substance use disorder or recent histories of violence). There was no work readiness criteria for enrollment in the program. Clients were paired with an employment specialist with the immediate goal of seeking competitive employment without lengthy prevocational assessment and training.

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Assessment and Job Acquisition Vocational assessment was work based, relying on work samples and work adjustment activities rather than on pencil and paper tests. The employment specialist also gathered information from the client, the clinical record, treatment team members, and, with the client’s permission, family members and previous employers. The job search process attended to both the client’s interests and the ability of the employment specialist to create a good job match. Generic job searches were discouraged because they were not aimed at filling the needs of the individual client. Employment specialists used a networking approach to identify job leads. All team members, including the medical staff, canvassed their personal and professional contacts for settings and jobs that matched the preferences and needs of consumers. With permission from the client, employment specialists secured job opportunities directly with employers and, if necessary, advocated for accommodation to the client’s disability. The jobs filled by these clients were usually entrylevel service industry positions, paying at or just above the minimum wage. Clients on disability income worked up to the maximum hours allowed without jeopardizing their entitlements.

Job Support All members of the team provided individualized follow-along support. Psychiatrists monitored medications and made adjustments as necessary, often based on reports of how each client was doing on the job. Case managers and other clinical staff helped clients with coping strategies and improving interpersonal skills at work. Employment specialists met regularly with clients to discuss work issues. Most meetings took place away from the job site because clients found it stigmatizing to discuss problems in the presence of their employer and fellow workers. However, some clients requested that the employment specialist help them get started at a job site as a way of relieving some initial anxiety, particularly those clients who had never held a regular job or had been unemployed for a long time. The employment specialists helped clients terminate jobs as needed. Some specialists also had regular contact with employers to increase the likelihood of a successful employment outcome. All work experiences were viewed positively. Even if someone was fired from a job after only a few days of work, information was garnered from that experience and used in planning the next job. All staff reinforced the message that they believed people with severe mental illness could work in community jobs.

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EFFECTIVENESS OF THE INTERVENTION The Division of Mental Health asked the New Hampshire Dartmouth Psychiatric Research Center to evaluate the effectiveness of this program. The rehabilitative day treatment program that did not convert to the vocational support program was identified as the comparison program. Vocational outcomes were collected for the baseline year and 1-year follow up (Drake et al. 1994). In the program that converted to supported employment, rates of competitive employment increased significantly for the baseline year from 25.4% to 39.4% at 1-year follow up for all clients (n=71); for clients who had been high users of day treatment (n=27), rates of employment increased from 33.3% to 55.6%. In the comparison day treatment program, rates of competitive employment were statistically unchanged at 12.5% from 13.4% for all clients (n=112), whereas competitive employment rates for the high users in the program (n=35) decreased from 14.3% to 8.6%. In the IPS program, no evidence of negative outcomes, such as significantly higher rates of hospitalization, incarceration, homelessness, or dropouts, was found (Torrey et al. 1995). After learning of these outcomes, staff and consumers of the second day program also decided to convert to IPS. One year after conversion, rates of competitive employment increased significantly from 12.5% to 23.2% at 1-year follow-up for all clients (n=112) and from 8.6% to 40% for the high service users (n=35) (Drake et al. 1996). Figure 18–1 shows that the rates of competitive employment increased similarly as the two programs converted to IPS.

Figure 18–1. Rates of competitive employment for regular day treatment attendees (n=62).

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Ten years after the initial shift away from day treatment, competitive employment is one of the primary outcomes at West Central Services. Whereas work had been a peripheral aspect of the community support program, it is now a central feature in the program’s vision of recovery and community reintegration. The treatment teams view all clients in the community support program as potential jobholders. IPS has dramatically increased the number of people with severe psychiatric disorders who receive vocational services and subsequently work in community jobs.

REFERENCES Becker DR, Drake RE: Individual Placement and Support: a community mental health center approach to vocational rehabilitation. Community Ment Health J 45:487–489, 1994 Bond GR, Boyer SL: Rehabilitation programs and outcomes, in Vocational Rehabilitation of Persons With Prolonged Mental Illness. Edited by Ciardiello JA, Bell MD. Baltimore, MD, Johns Hopkins University Press, 1988, pp 231–263 Drake RE, Becker DR, Biesanz JC, et al: Rehabilitative day treatment vs. supported employment, I: vocational outcomes. Community Ment Health J 30:519–532, 1994 Drake RE, Becker DR, Biesanz JC, et al: Day treatment versus supported employment for persons with severe mental illness: a replication study. Psychiatr Serv 47:1125–1127, 1996 Rogers ES, Walsh D, Masotta L, et al: Massachusetts Survey of Client Preferences for Community Support Services: Final Report. Boston, MA, Center for Psychiatric Rehabilitation, 1991 Tashjian M, Hayward B, Stoddard S, et al: Best Practice Study of Vocational Rehabilitation Services to Severely Mentally Ill Persons. Washington, DC, Policy Study Associates, 1989 Torrey WC, Becker DR, Drake RE: Rehabilitative day treatment vs. supported employment, II: consumer, family and staff reactions to a program change. Psychosoc Rehab J 18:67–75, 1995

19 Matching Substance Abuse Patients to Services Grant R. Grissom, Ph.D.

A long-standing problem that has impeded quality improvement efforts in substance abuse treatment is how to apply research to treatment programs in such a way that outcomes improve. This case example illustrates how streamlined referral procedures and customized matching of substance abusers to individualized treatment protocols can effectively reduce “no show” rates and improve outcomes. Integra, Inc., an organization providing managed behavioral health care services, developed the instrument and procedures described in this chapter (Byrne et al. 1996), and the staff of its Employee Assistance Program (EAP) implemented the program. However, the program can be used in any substance abuse treatment setting. Outcomes management systems based on the matching procedures described in this chapter were implemented in Minnesota, California, New York, Florida, and Pennsylvania.

THE PROBLEM AND HOW IT WAS IDENTIFIED An effective EAP must assess individual need for treatment, make a referral, motivate the patient to follow through with the treatment recommendation, and later ensure that the patient actually received the prescribed care. Integra conducted an informal internal evaluation of its own performance, asking • What percentage of persons who contacted Integra to discuss treatment for substance abuse showed up for a scheduled evaluation interview with a counselor? 241

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• Of those who were referred for treatment following the evaluation, what percentage showed up at the treatment program? • What services were provided to those who went for treatment? • What percentage of attendees completed treatment? • Of those who completed treatment, what percentage maintained sobriety for at least 6 months? The findings were troubling. Of those who sought help for a substance abuse problem, only about 50% showed up for treatment. The others either failed to appear for evaluation or did not get to treatment. Despite the wide variation among patients in their clinical presentation (many had comorbid depression and/or serious marital, medical, legal, or employment problems), treatment programs tended to provide the same set of services for all patients. Of those who began treatment, 25% dropped out prior to completion. Relapse rates were also high. Precise figures were not available because of high rates of nonresponse to follow-up calls, but the 6-month relapse rates appeared to be around 50% based on a criterion of “any use.”

RELATIONSHIP TO POOR-QUALITY CARE Program managers identified two core problems. First, the time interval between the initial call and the intake appointment at the treatment program was too long. Substance abusers have nearly universal ambivalence toward treatment. Thus, it is vital to engage them before the willingness to enter treatment gives way to renewed denial. The previous Integra procedure requiring a face-to-face clinical evaluation before referral to treatment created a delay in engagement of patients in treatment. During this period, many patients’ initial motivation flagged. Furthermore, the necessity of a full evaluation prior to the referral was questionable: Integra discovered that well over 90% of those who were evaluated were referred to treatment. Once at the treatment program, staff performed a second evaluation. Second, programs failed to match services to patients’ individual needs. Patients rarely present for treatment with a drug or alcohol problem that has not affected other areas of their lives. Indeed, it is very unusual for someone to seek treatment without some precipitating personal crisis. This crisis most often reflects ongoing difficulties rather than a one-time incident. When these difficulties are severe, it is important to supplement standard treatment with additional targeted services such as marital/family counseling or mental health services. Failure to provide services appropriate to a patient’s individual needs makes it more likely that he or she will drop out of a treatment program. Patients may not experience the full benefit of services they do receive if stress affecting their personal lives interferes with treatment compliance.

Matching Substance Abuse Patients to Services

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Moreover, those who complete treatment are more likely to relapse if the stresses caused by other problems continue to overwhelm them.

UNTESTED ASSUMPTIONS CONTRIBUTING TO THE PROBLEM OF POOR OUTCOMES Substance abuse clinicians believe that • Problems in other life areas are the result of the substance abuse and will clear up once the abuse is ended. The first part of this assumption may be true in some cases; other life problems usually are exacerbated by abuse. However, there is no evidence to support the second part of the assumption. When a person stops abusing substances, problems in other areas do not simply disappear. Indeed, the presence of untreated marital, psychiatric, or employment problems are major risk factors for relapse following successful treatment. • Identical treatment (“one size fits all”) is appropriate for all patients entering a particular program. Some clinicians believe that depression or family problems will dissipate once the substance abuse has stopped; this suggests that it is unnecessary to address these problems individually except as they relate to the substance abuse. Some chemical dependency counselors who are former abusers whose recovery has been helped by a specific approach may be untrained in other models. Naturally, such individuals tend to favor the methods they found personally helpful. Providers familiar with matching research know that there is little empirical support for patient–program matching; thus, some erroneously conclude that patient–services matching also is ineffective. The most rigorous, large-scale study of patient–program matching, Project MATCH, tested which types of patients were most suitable for 12-step facilitation therapy, motivational enhancement therapy, and cognitive-behavioral coping skills therapy. The Project MATCH Research Group (1996) concluded that all three models were effective but found very little evidence that even these distinct treatment approaches were differentially effective for different types of patients. Although the findings of Project MATCH did not support the patient–program matching, they left unanswered the question of the usefulness of patient-services matching.

INTERVENTION An effective procedure to match patients to needed services requires three components. The first is an efficient, standardized method for identifying

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a patient’s special service needs, if any. Second, the treating program must commit to providing the special services. Finally, service delivery must be monitored to ensure that the indicated services are indeed being provided. Integra considered these criteria when developing its plan for intake counselors to match patients to appropriate services. The counselors would 1. Use the instrument developed to assess the severity of medical, psychiatric, family, and employment problems 2. Identify problem areas that were severe enough to warrant supplemental services 3. Make a treatment referral contingent on the program’s commitment to make the supplemental services available 4. Contact the patient 2 weeks later to ensure that the services were provided Integra constructed an instrument that could be administered by intake counselors over the phone when an individual seeking treatment for substance abuse contacted Integra. Integra modeled its instrument on the Addiction Severity Index (ASI), the most widely used outcome measure in substance abuse. It is an excellent tool for determining a patient’s need for services in seven areas: medical, legal, family/social, psychiatric, employment, drugs, and alcohol (McLellan et al. 1992). Because the ASI interview takes about 45 minutes, the telephone intake interview was reduced to basic information about the person calling for referral, the problems he or she reported, and the items from the ASI required to derive severity (composite) scores for family, psychiatric, and employment problems. The drug and alcohol areas from the ASI were omitted because Integra rarely decided against referral for a substance abuser seeking care. Items assessing financial and legal problems were dropped because no area programs offered financial or legal counseling, so that information would not be useful in making a referral. Thresholds were established for the composite scores in each of the three target areas to identify the most severe cases. Because routine treatment is sometimes adequate to address problems that are not too severe, not all patients require supplemental services. The Integra intake counselor entered responses to the ASI questions into a computer during the telephone interview. Software developed by Integra staff derived composite scores in each of the three areas and compared the scores with the appropriate threshold values. The software then displayed the results, alerting the counselor to any area in which supplemental services were recommended. Prior to making the referral, the counselor asked the program to provide at least three supplemental sessions in each such area (e.g., “I want to send Joe to your program for drug treat-

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ment. Joe has a very severe family problem. In addition to your regular program, will you provide at least three sessions for Joe with a qualified family therapist?”). Finally, the patient was referred to a program that committed to the services and was told that the Integra intake counselor would make contact after 2 weeks in the program to ensure that the services were in fact being provided. Here is a summary of the steps required to implement this matching procedure: 1. Construct the interview instrument. Obtain a copy of the ASI; identify the composite score items for employment status, psychiatric status, and family/social status; and add any other items desired. 2. Set the thresholds for each area. This can be accomplished by obtaining normative data for the ASI composite scores from the article “The fifth edition of the Addiction Severity Index,” which appeared in the Journal of Substance Abuse Treatment in 1992 (McLellan et al. 1992). The objective is to avoid setting the threshold so high that patients who would benefit from the supplemental services are not identified or so low that the cost of providing supplemental services to large numbers of patients is prohibitive. The Integra thresholds used in this project identified 20%–30% of patients as requiring supplemental services in one or more areas. 3. Develop, test, and install software. Approximately 1–2 weeks are required to write, debug, test, document, and install software to enter, score, and check the ASI responses against thresholds to identify supplemental service needs. 4. Train intake counselors. Training guidelines for the ASI are available from the Treatment Research Institute in Philadelphia, Pennsylvania. Training on how to conduct the interview and use the software requires two sessions totaling about 1 day’s time. The core ASI items for the instrument are provided in the appendix to this chapter together with contact information for obtaining software and threshold scores.

EFFECTIVENESS Effectiveness of the matching procedure was evaluated through analysis of data for 90 (“first cohort”) substance-abusing patients who contacted Integra prior to implementation and 57 substance-abusing patients (“second cohort”) using the procedure described to individualize the treatment program. The findings showed clear benefits to Integra and to patients.

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Patients generally appreciated the instant referral. Ten (18%) seemed uncomfortable entering treatment without a face-to-face assessment by an Integra counselor. These patients were scheduled for interviews. Under the new procedures 72% of the second cohort callers presented for treatment compared with 55% of the first cohort patients. This represents a dramatic 31% improvement in the show rate. In addition to the patient benefit, the reduced use of counselor time for conducting face-toface evaluations was a significant saving to Integra, even after allowing for the additional time required of the intake counselors. Of the second cohort patients who showed for treatment, 86% completed the program. This represents a 15% improvement in completion when compared with the 75% completion rate of the first cohort. Finally, of the second cohort patients who completed treatment, 85% did not re-present for treatment within 6 months. This represents a 31% improvement on this outcomes measure when compared with the 65% success rate for the first cohort. From a systemwide perspective, implementation of the procedures described in this chapter increased costs associated with the supplemental services, reduced patient attrition, and required additional time to conduct the telephone intake interview. Cost savings resulted from elimination of the face-to-face evaluation by the EAP prior to treatment and reduced EAP and treatment costs associated with lower relapse rates. Although the Integra project did not include cost data, it is likely that the number of averted relapses would provide a net cost saving to a capitated system.

REFERENCES Byrne ME, Fox S, McLellan AT: Innovative evaluation and referral of substance abuse clients. EAP Digest 16:24–26, 1996 McLellan AT, Cacciola J, Kushner H, et al: The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment 9:199–213, 1992 Project MATCH Research Group: Matching alcoholism treatments to client heterogeneity: Project MATCH post-treatment drinking outcomes. Rockville, MD, National Institute on Alcoholism and Alcohol Abuse, 1996

Appendix

The composite score items of the ASI that were used by Integra staff are presented in the following tables. The threshold values and software for automating data entry and scoring are proprietary. The author has directed the development of a National Institute on Drug Abuse–sponsored outcomes management system, Substance Abuse Relapse Reduction System (SARRS), that incorporates the matching procedure described in this chapter. Information about these proprietary products can be obtained through PsyberMetrics: Dr. Grant Grissom PsyberMetrics, Inc. 825 Town Center Drive, Suite 110 Langhorne, PA 19047 Tel: 215–741–4959, ext. 12 E-mail: [email protected]

ASI COMPOSITE SCORE ITEMS AND CALCULATIONS Guidelines for clarifying individual items (e.g., “What constitutes ‘serious’ depression?”) and for coding patient responses is provided in the ASI Administration Manual, available from the Treatment Research Institute in Philadelphia, PA. They can be reached at (215) 665–2880.

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Composite score for employment status Item

Response

A. Do you have a valid driver’s license?

Y=1, N=0

B. Do you have an automobile available for your use?

Y=1, N=0

C. How many days were you paid for working in the past 30 days?

0–30

D. How much income did you receive from employment in the past $0–$8,500 30 days? Calculation of employment composite score (ECS): ECS = 1.0–(A/4+B/4+C/120=log D/36) Note.

log D refers to the natural log of D.

Appendix

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Composite score for psychiatric status Item

Response

During the past 30 days have you… A. experienced serious depression?

Y=1, N=0

B. experienced serious anxiety or tension?

Y=1, N=0

C. experienced hallucinations?

Y=1, N=0

D. experienced trouble understanding, concentrating, or remembering?

Y=1, N=0

E. experienced trouble controlling violent behavior?

Y=1, N=0

F. experienced serious thoughts of suicide?

Y=1, N=0

G. attempted suicide?

Y=1, N=0

H. taken prescribed medication for any psychological/ emotional problem?

Y=1, N=0

I.

How many days in the past 30 have you experienced these psychological or emotional problems?

J.

How much have you been troubled or bothered by these psychological or emotional problems in the past 30 days?

K. How important to you now is treatment for these psychological problems?

0–30 0=Not at all 1=Slightly 2=Moderately 3=Considerably 4=Extremely 0=Not at all 1=Slightly 2=Moderately 3=Considerably 4=Extremely

Calculation of psychiatric composite score (PCS): PCS=A/11+B/11+ C/11+D/11+E/11+F/11+G/11+H/11+I/330+J/44+K/44

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Composite score for family/social status Item

Response

A. Are you satisfied with your current marital situation?

0=Yes 1=Indifferent 2=No

B. How many days in the past 30 have you had serious conflicts with your family?

0–30

C. How troubled or bothered have you been in the past 30 days by family problems?

0=Not at all 1=Slightly 2=Moderately 3=Considerably 4=Extremely

D. How important to you now is treatment or counseling for family problems?

0=Not at all 1=Slightly 2=Moderately 3=Considerably 4=Extremely

E. Have you had significant periods in which you have experienced serious problems in the past 30 days with:

0=No 1=Yes 3=Not applicable/ no response

Mother_____ Father_____ Brothers/Sisters_____ Sexual partner/Spouse_____ Children_____ Other significant family_____ Close friends_____ Neighbors_____ Coworkers_____

The list above (Item E) includes nine types of persons. The steps to calculate E are as follows: Step #1: Calculate Y=the number of “Yes” (i.e., code=1) responses. Step #2: Calculate N=the number of “No” (i.e., code=2) responses. Step #3: Calculate E=Y/(Y+N). Because both Y and N can take on values between 0 and 9, the value of E will be between 0 and 1. Calculation of the family/social composite score (FCS): FCS=A/10+ B/150+C/20+D/20+E/5

20 Adverse Drug Reactions An Interdisciplinary Team Approach Carlos A. Zarate Jr., M.D. Arthur Siegel, M.D. Lloyd I. Sederer, M.D.

THE PROBLEM AND HOW IT WAS IDENTIFIED Since 1991, all patients admitted to McLean Hospital, a private not-for-profit psychiatric hospital in Belmont, Massachusetts, have been monitored for adverse drug reactions (ADRs). A continuous quality improvement (CQI) committee was created for the specific purpose of monitoring ADRs associated with the introduction of new drugs into the hospital formulary. One drug monitored by this committee is the atypical antipsychotic drug risperidone, which was introduced into the hospital formulary in April 1994 (Zarate et al. 1997). An ADR surveillance form (see chapter appendix) was completed for each patient and the data were summarized for the ADR CQI surveillance team and the Pharmacy and Therapeutic Committee. With the information collected, the team was able to examine a series of factors that were predictive of an ADR. The systematic collection of data by the CQI committee on 122 consecutively hospitalized psychiatric patients aged 65 years and older who were newly treated with risperidone indicated that many patients exposed to this drug were experiencing hypotension, orthostatic hypotension, and falls. Initial review did not find problems in the care these patients received.

Inquiries may be directed to the first author at [email protected] 251

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Most clinicians were generally following the dosing recommendations for risperidone suggested in the Physician’s Desk Reference (which were developed through efficacy studies on a selected, generally younger and healthier population). The data gathered also permitted us to identify risk factors for severe ADRs associated with risperidone.

HOW THIS PROBLEM IS RELATED TO POOR QUALITY OF CARE ADRs have been associated with serious complications, including prolonged hospitalization in 17% of cases (Carrell 1993); increased costs (Bates et al. 1997); and patient injury, disability, and even death (Miller 1973). In recent years, there has been renewed interest in ADR surveillance by the U.S. Food and Drug Administration and the pharmaceutical industry (Faich et al.1987). In fact, based on negative outcomes obtained by postmarketing clinical experience, several psychotropic drugs (i.e., nomifensine, felbamate, remoxipride, sertindole) either have been withdrawn from the market or have had their use severely restricted (Hayes and Kristoff 1986; Kaufman et al. 1997; “Nomifensine Withdrawn By Manufacturer” 1986; Owens 1996). Adverse event monitoring by hospitals is mandatory for accreditation by the Joint Commission on Accreditation of Healthcare Organizations (1995). This organization suggests that the ADRs be analyzed by method of reporting (retrospective versus concurrent medical record abstraction), type of ADR, and time of occurrence (prior to or during hospitalization). Physicians and pharmacists are also urged to report severe reactions to pharmaceutical companies, to the Federal Drug Administration MEDWatch system (Kessler 1993), and to the Pharmacy and Therapeutic Committee (or its equivalent) at their own institution. A recent study estimated that up to 80% of inpatient ADRs are predictable and thus potentially preventable (Carrell 1993). A program capable of identifying ADRs could lead to decreased frequency, improved patient care, reduced morbidity and mortality, and reduced medical costs.

Definitions of Adverse Drug Reaction and Adverse Drug Event An ADR is defined by the World Health Organization as an effect that is “noxious and unintended, and which occurs at doses used in man [sic] for prophylaxis, diagnosis, or therapy” (Bates et al. 1995; Leape et al. 1991). This definition excludes therapeutic failures, intentional and accidental poisoning, and drug abuse and pertains only to the appropriate use of drugs.

Adverse Drug Reactions

253

However, most preventable drug-related injuries occur as a result of errors in their use (Classen et al. 1991). The term adverse drug event (ADE) is defined as an injury resulting from a medical intervention related to a drug that results in an increased length of hospitalization or severe adverse outcome, such as permanent injury or death. For example, oversedation and aspiration pneumonia resulting from an overdose of a drug would be an ADE rather than an ADR according to these definitions (Bates et al. 1995).

Classification of Adverse Drug Reactions ADRs may be classified by the probability that the reaction is due to the medication and by severity. Probability That the Adverse Drug Reaction Is Related to Drug Therapy The probability that an ADR is related to drug therapy has been classified as definite, probable, possible, or doubtful (Karch and Lasagna 1975; Naranjo et al. 1981). A “definite” reaction 1) follows in a reasonable temporal sequence after a drug is administered or a toxic drug level has been established in body fluids or tissues, 2) conforms to a recognized side effect profile of the suspected drug, and 3) is confirmed by improvement when the patient is taken off of the drug and reappears when the patient is reexposed to the drug. A “probable” reaction 1) follows in a reasonable temporal sequence after a drug is administered, 2) conforms to a recognized side effect profile of the suspected drug, 3) is confirmed by withdrawal but not reexposure to the drug, and 4) cannot be reasonably explained by the known characteristics of the patient’s clinical state. A “possible” reaction 1) follows in a reasonable temporal sequence after a drug is administered, 2) possibly conforms to a recognized side effect profile of the suspected drug, and 3) can be explained by characteristics of the patient’s disease. A reaction is defined as “doubtful” if it seems likely to be related to factors other than the drug. Severity of an Adverse Drug Reaction The severity of an ADR is based on a scale from the American Journal of Health System Pharmacists. The scale ranges from 0 to 6, where severity level is classified as mild (score of 0 or 1), moderate (score of 2, 3, or 4), or severe (score of 5 or 6). The definition of each severity level is as follows: 0 = No medication-related problems occurred 1 = Medication-related problem occurred without harm to patient and without necessitating change in medical treatment

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2 = Increased monitoring or change in treatment required 3 = Change in vital signs or additional laboratory tests required 4 = Additional treatment required, increased length of stay, or medication-related problem related to reason for hospitalization 5 = Transfer to intensive care unit or permanent harm resulted 6 = Death

Incidence of Adverse Drug Reactions in Psychiatric Patients Most studies examining the incidence of ADRs were conducted in nonpsychiatric settings in which patients typically were from medical, surgical, geriatric, pediatric, internal medicine, intensive care, or obstetric settings. As a result, most ADRs reported concern patients exposed to nonpsychiatric drugs. Studies on ADRs in psychiatric patients are few and can generally be grouped into three classes: 1. Studies that examine the rate of ADRs associated with a specific drug. For example, Enghusen-Poulsen et al. (1992) examined the risk of ADRs to disulfiram. In a 23-year period, 154 ADRs to disulfiram were reported. 2. Studies that examine the incidence of ADRs leading to hospital admission. Stewart et al. (1980) and Hermesh et al. (1985) concluded from prospective studies that 5.0%–7.5% of admissions to psychiatric units were iatrogenic; extrapyramidal side effects constituted about half the cases. In contrast to these two studies, Schmidt et al. (1984) found a much lower frequency of ADRs leading to psychiatric hospitalization. In their report, which was confined to neuroleptic-induced toxicity, only 0.6% of admissions were related to the use of this class of drugs. In an updated version of the original Schmidt study, Wolf et al. (1989) reported on a 6-year study of hospitalizations due to ADRs. Of 15,800 consecutive psychiatric admissions to two university hospitals, 112 (0.7%) were caused by ADRs. In 67% of cases these admissions were the result of extrapyramidal symptoms (e.g., parkinsonism and/or akathisia) and neuroleptic-related depression; 25% of cases were due to drug-induced delirium or psychosis. These authors identified older age, polydrug therapy, and parenteral administration of neuroleptics at high dosages as important risk factors for severe ADRs leading to hospitalization. Differences in rates of ADRs leading to admission may have been due to differences in the ADR definition used and the adequacy of the reporting system, population studied (e.g., general, psychiatric, inpatients, tertiary referral centers), methods of assessments (retrospective versus prospective), study size, and duration. 3. Studies that examine the incidence of ADRs leading to transfer from a psychiatric to a general hospital. Popli et al. (1997) reported that transfers of


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psychiatric inpatients to a general hospital for ADRs were relatively rare: 29 medical transfers in a total of 10,994 psychiatric inpatient admissions over a 30-month period (less than 0.3% of psychiatric admissions). The ADRs were neurological syndromes (76%), especially delirium (31%). Low-potency antipsychotic drugs were substantially represented (31%). Most of the ADRs reported in this study were of moderate severity; only 8 cases (incidence 0.07%) required medical hospitalization.

Surveillance Systems for Adverse Drug Reactions Although ADR reporting is mandated by the Joint Commission on Accreditation of Healthcare Organizations, most hospitals still use incident reports (clinician self-reports) that typically identify only about 5% of events (Classen et al. 1991; Keith et al. 1989). Many types of ADR reporting systems have been described in the medical literature. In one institution, a video was developed to educate and encourage nurses to report ADRs (Moonen and Puckett 1992). In other systems, quality assurance departments participate in ADR surveillance (Murphy and Frigo 1993).

FACTORS CONTRIBUTING TO THE IDENTIFIED PROBLEM The lack of a systematic method for collecting data on ADRs made it difficult to identify problems, establish how common they were, and determine why they occurred. Other factors included lack of experience in using a new drug in a patient population characterized by multiple comorbidities, coadministration of different classes of drugs, and a population of older patients. By the systemic intervention of ADR surveillance, we were able to obtain information on all patients exposed to risperidone and identify risk factors for severe ADRs. The risk factors included old age, high initial doses, rapid titration, and high final maximal doses.

INTERVENTION DESIGNED TO ADDRESS THE PROBLEM Table 20–1 provides a chronological summary of the implementation of different aspects of the intervention, which spanned 2 years. One part of our intervention consisted of educational activities through the publication and distribution of internal memos and the McLean Weekly Bulletin to all staff; presentation of our findings to other hospital committees (e.g., internal medicine, nursing, psychopharmacology); and risk management lectures on ADRs. The second part of our intervention was making formulary guideline changes for the prescribing of risperidone (Owens 1996). The

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Table 20–1.

Continuous quality improvement (CQI) educational efforts for prediction and prevention of severe adverse drug reactions involving risperidone

February 1995

Preliminary data regarding cautions with risperidone disseminated to the medical staff

May 1995

Efforts of CQI team published in McLean Weekly Bulletin; risperidone guidelines developed and published in The McLean Pharmacy Legend

June 1995

CQI team report presented to executive and other committees

August 1995

Clinical pharmacist position established to contribute to prediction and prevention of ADRs; recent medical alerts with risperidone/selective serotonin reuptake inhibitors appear in The McLean Pharmacy Legend

October 1995

Lucian Leape, M.D., speaks at Academic Conference Risk Management Seminar: Systems Analysis/Adverse Drug Events

March 1996

Drug utilization evaluation (DUE) complete for risperidone use in the elderly

June 1996

“Risperidone in the Elderly: Updated Risperidone Drug Treatment Guidelines” appears in The McLean Pharmacy Legend

January 1997

DUE completed for risperidone/SSRI versus haloperidol/SSRI combination use

March 1997

Presentation of CQI intervention results to medical staff

guidelines specifically recommended initial doses, titration schedules, maximal doses, avoidance of as-needed risperidone, and recommendations for its use in special populations (e.g., the elderly). The guideline changes were based on 1) the work with the multidisciplinary team, 2) the systematic ADR reporting process, and 3) the review of the ADRs by the CQI team. The CQI team consisted of two psychiatrists, an internist, one nurse, the director of the pharmacy, and a pharmacist. One member of the team routinely visited each inpatient psychiatric unit three times a week to solicit voluntary reports from personnel regarding possible ADRs and to review all medical charts. All progress notes, nursing notes, vital signs, laboratory tests, and orders were reviewed twice a week from admission until discharge. ADRs were also identified by 1) consultations made by internal medicine or psychopharmacology, 2) reviewing the ADR hotline (a direct internal phone extension available to all staff for the reporting of an ADR, 3) pharmacy staff monitoring of the order book (e.g., changes in dose, stat orders, as-neededs), and 4) spontaneous reporting by staff.


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The information gathered through our surveillance form (see chapter appendix) included demographic information, organ system affected, reaction type, medications suspected (by drug category), concurrent medications, times the ADR occurred since admission, number of concomitant medications, presence or lack of a history of an ADR, and whether the patient was rechallenged or not with the presumed drug(s) responsible for the ADR. We collected information to determine the probability of the ADR (doubtful, possible, probable, or definite); severity of the ADR (mild, moderate, severe-alive, or severe-deceased); type of consult ordered (internal medicine, neurology, psychopharmacology); type of follow-up (patient care committee; internal medicine consult; contact with the psychiatrist in charge, nursing staff, Food and Drug Administration, pharmaceutical company, or other); and action taken (none, drug held, dose decreased, medication or antidote added, medication discontinued, emergency room visit, or transfer to general hospital). The clinical pharmacist presented a summary of this information to the Pharmacy and Therapeutic Committee for a final determination as to the presence of an ADR and to classify it by its characteristics. We considered a patient as having had an ADR only if the patient either had not had evidence of it prior to the initiation of the drug or had previous side effects that clearly worsened with the initiation of a drug. The pharmacist-completed surveillance forms were reviewed by the CQI team and entered into a computerized database. These data were analyzed to identify any trends that may have resulted from the introduction of a new drug. We reported ADRs to manufacturers and to the Food and Drug Administration through MEDWatch. The information gathered permitted us to classify whether the risperidone dosing of the patient was “slow,” “intermediate,” or “fast” based on whether risperidone was titrated at a slower, equal, or faster rate than the manufacturer’s recommendations. We found that when patients were titrated at medium or fast rates, they were more likely to experience an ADR (e.g., hypotension) than when the dose had been titrated at a slower rate. Based on our findings, we concluded that the manufacturer’s dosing was excessive for most geriatric patients.

EFFECTIVENESS OF THE INTERVENTION The educational program was successful in reducing the rate of severe ADRs. After we implemented the new formulary guideline changes and initiated efforts to alert and educate staff, the rate of severe ADRs decreased from a high of 6.3% in the first quarter of 1995 to 0% by the second quarter of 1996. The rate remained steady at 0% through the first quarter of 1997. Figure 20–1 demonstrates the effectiveness of our intervention.

258 I MP ROV ING M ENT AL H E ALTH CARE

Figure 20–1. Rates of severe adverse drug reactions to risperidone, 1994–1997. Continuous quality improvement intervention began in February 1995.


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We believe that an ADR surveillance effort (devising a reporting system, collecting information on ADRs, and educating staff) can achieve safer drug usage, reduction of medical morbidity, and improved treatment outcomes. ADR programs can contribute to positive outcomes of care and may reduce costs associated with longer hospital stays. Our data were also instrumental in changing the dosing recommendations of the manufacturer.

REFERENCES Bates DW, Cullen DJ, Laird N, et al: Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA 274:29–34, 1995 Bates DW, Spell N, Cullen DJ, et al: The costs of adverse drug events in hospitalized patients. JAMA 277:307–311, 1997 Carrell S: New software helps MDs pick right drugs at low cost. Hosp Pharm Report 7:40, 1993 Classen DC, Pestotnik SL, Evans RS, et al: Computerized surveillance of adverse drug events in hospital patients. JAMA 266:2847–2851, 1991 Drug information and pharmacy news. The McLean Pharmacy Legend, May 1995 Drug information and pharmacy news. The McLean Pharmacy Legend, August 1995 Enghusen-Poulsen H, Loft S, Andersen JR, et al: Disulfiram therapy adverse drug reactions and interactions. Acta Psychiatrica Scand Suppl 369:59–65, 1992 Faich GA, Knapp D, Dreis M, et al: National adverse drug reaction surveillance: 1985. JAMA 257:2068–2070, 1987 Hayes PE, Kristoff CA: Adverse reactions to five new antidepressants. Clin Pharmacy 5:471–480, 1986 Hermesh H, Shalev A, Munitz H: Contribution of adverse drug reaction to admission rates in an acute psychiatric ward. Acta Psychiatr Scand 72:104–110, 1985 Joint Commission on Accreditation of Healthcare Organizations: 1996 Comprehensive Accreditation Manual for Hospitals. Oakbrook Terrace, IL, Joint Commission on Accreditation of Healthcare Organizations, 1995 Karch FE, Lasagna L: Adverse drug reactions: a critical review. JAMA 234:1236– 1241, 1975 Kaufman DW, Kelly JP, Anderson T, et al: Evaluation of case reports of aplastic anemia among patients treated with felbamate. Epilepsia 38:1265–1269, 1997 Keith MR, Bellanger-McCleery RA, Fuchs JE: Multidisciplinary program for detecting and evaluating adverse drug reactions. Am J Hosp Pharm 46:1809– 1812, 1989 Kessler DA: MedWatch: the new FDA medical products reporting program. Clin Pharmacy 12:529–530, 1993 Leape LL, Brennan TA, Laird NM, et al: The nature of adverse drug events in hospitalized patients. N Engl J Med 324:377–384, 1991 Miller RR: Drug surveillance utilizing epidemiological methods: a report from Boston Collaborative Drug Surveillance Program. Am J Hosp Pharm 30:584–592, 1973

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Moonen SL, Puckett WH: Cost-effective communication: in-house production of an ADR videotape for nursing. Paper presented at the annual meeting of the American Society of Hospital Pharmacists, Washington, DC, June 1992 Murphy BM, Frigo LC: Development, implementation, and results of a successful multidisciplinary adverse drug reaction reporting program in a university teaching hospital. Hosp Pharm 28:1199–1204, 1993 Naranjo CA, Busto U, Sellers EM, et al: A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 30:239–245, 1981 Nomifensine withdrawn by manufacturer. FDA Drug Bull 16:7–8, 1986 Owens DG: Adverse effects of antipsychotic agents: do newer agents offer advantages? Drugs 51:895–930, 1996 Popli AP, Hegarty JD, Siegel AJ, et al: Transfer of psychiatric inpatients to a general hospital due to adverse drug reactions. Psychosomatics 38:35–37, 1997 Risperidone in the Elderly: Updated Risperidone Drug Treatment Guidelines. The McLean Pharmacy Legend, June 1996 Schmidt LG, Grohmann R, Helmchen H, et al: Adverse drug reactions: an epidemiological study at psychiatric hospitals. Acta Psychiatrica Scand 70:77–89, 1984 Stewart RB, Springer PK, Adams JE: Drug-related admissions to an inpatient psychiatric units. Am J Psychiatry 137:1093–1095, 1980 Wolf B, Grohmann R, Schmidt LG, et al: Psychiatric admissions due to adverse drug reactions. Compr Psychiatry 30:534–545, 1989 Zarate CA Jr, Baldessarini RJ, Siegel AJ, et al: Risperidone in the elderly: a pharmacoepidemiology study. J Clin Psychiatry 58:311–317, 1997

Appendix Adverse Drug Reaction Surveillance Form

261

McLean Hospital Name____________________________________ MRN________________ DOA_________________ Reaction: RXN DATE_____________ REACTION TYPE________________

262

Adverse Drug Reaction Surveillance Form

AGE________________

SEX__________

Organ system involved: 1. 2. 3. 4.

Nervous system Cardiovascular Respiratory Gastrointestinal

5. 6. 7. 8.

Genitourinary Hematological Endocrine Dermatological

9. 10. 11. 12.

Immunological/Bone Special senses Body as a whole Laboratory

MED CATEGORY: 1.

All antipsychotics

1a. Typical antipsychotic 1b. Atypical antipsychotic

2.

All antidepressants

2a. Tricyclic 2b. SSRI 2c. MAOI 2d. Antidepressant-other

3.

Lithium

4.

Other medicines

4a. Anticholinergic 4b. Anticonvulsant/mood stabilizer 4c. Anxiolytic 4d. Nonpsychotropic

5.

Multiple medicines


MED(S) SUSPECTED_________________________________________________________________________________________________________________

CONCURRENT MEDS: ACTUAL ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________

Appendix

Adverse Drug Reaction Surveillance Form (continued)

CODES FOR ANALYSIS: Date of reaction: DSA: (Day since admission) 1. Day of admission 2. 1–7 3. 8–14 4. 15–21 5. 22–29 6. 30+ 7. Outpt. 8. NA

AGE: 1. 0–11 2. 12–20 3. 21–30 4. 31–40 5. 41–50 6. 51–60 7. 61–65 8. 66–70 9. 71–80

Concomitant medications #CMs: 1. 0–1 2. 2–4 3. 5–7 4. 8–10 5. 11+

10. 81+ ADR HISTORY: RECHALLENGE: PROBABILITY: SEVERITY: CONSULT: FOLLOW-UP:

Yes Yes Doubtful Mild Internal medicine Patient Care Committee U.S. Food and Drug Administration

2. 2. 2. 2. 2. 2. 6.

No No Possible Moderate Neurology Internal medicine Pharmaceutical committee

Comments____________________________ Comments____________________________ 3. Probable 4. Definite 4. Severe, dead 3. Severe, alive 3. Psychopharmacology 4. Nursing 3. Physician in charge 4. Nursing 7. Other

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1. 1. 1. 1. 1. 1. 5.

ACTION TAKEN:

1. 2. 3.

None Drug held Dose decreased

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Adverse Drug Reaction Surveillance Form (continued) 4. 5. 6.

Medication added Medication discontinued Emergency department/transfer general hospital

CONCURRENT MEDS/ MED PROFILE:

NOTES:______________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ Note.

CM=concomitant medications; MAOI=monoamine oxidase inhibitors; MRN= medical record number; SSRI=selective serotonin reuptake inhibitors.

I MP R OV IN G M EN T A L H E A L TH CAR E

MED START/DOSE DOSING STOP ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________ ______________________________________________________________________________________________________________________________________

21 High-Risk Medicaid Enrollees and a Community-Based Support Program Deborah Nelson, Ph.D. Donna Zeh, L.I.C.S.W. Kathleen Bennett, M.D.

UNDERIDENTIFICATION OF HIGH-RISK CONSUMERS MAY LEAD TO POOR QUALITY OF CARE High-risk consumers of behavioral health care include those with psychoses, substance use disorders, or comorbid medical conditions and those served by one or more public human service agencies (e.g., state departments of child welfare or mental health). Public-sector enrollees are disproportionately affected by these high-risk conditions, largely because of their increased exposure to multiple psychosocial stressors. Providers and health maintenance organizations (HMOs) face major challenges in identifying high-risk consumers, delivering the optimal mix of services to support functioning in the community, and coordinating the complex care needs of this group.

Inquiries or comments made be directed to the first author at deborah.nelson@ beaconhs.com, or to Beacon Health Strategies, LLC, 500 Unicorn Park, Woburn, MA 01801. The authors wish to thank several Beacon colleagues for their help in preparation of this chapter: Jill Lack, Ngoc Dinh, Jordan Oshlag, and Catherine Kawachi, who assisted with the program analyses; Chris Degan, for her expertise and experience with community support programs; and Victoria Nixon and Angela Miele for their editing help. 265

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At-risk consumers are frequently not identified early. As a result, their care may be disorganized and inappropriate. Consequences of not identifying at-risk consumers early include multiple (preventable) hospitalizations, poorer outcomes, decreased quality of life, and increased health plan costs. Furthermore, consumers, consumer advocates, and health experts are unanimous in their contention that the more restrictive, intensive, and costly environment of psychiatric hospitals should be employed only as a last resort and that consumers are often better served when able to be safely supported in less restrictive settings. Information about at-risk health plan members comes from a wide variety of sources: screening tools, claims data, chart review data, and provider reports. These disaggregate data sources are not designed to promote proactive, systematic identification of high-risk members. Each source typically captures only some of the information needed; for example, administrative data, such as claims data, are easy to access and include reimbursement information but do not yield adequate information about functional outcome and clinical status.

AN INITIATIVE TO IMPROVE IDENTIFICATION OF HIGH-RISK MEMBERS At our managed behavioral health organization, we work with HMOs who serve public- as well as private-sector enrollees. Depending on the needs and composition of a given HMO, we customize the methods we use to identify consumers who can benefit from early identification and interventions that increase community support and coordination of care. Collaborating with Neighborhood Health Plan (NHP), a Massachusetts-based statewide HMO, we reviewed administrative and survey data and identified the need for a quality management and improvement initiative to improve outcomes for high-risk members of NHP. Approximately 80% of NHP members are enrolled in Medicaid. Many members live in densely populated, impoverished urban areas and experience psychosocial stressors that increase their risk of developing behavioral health problems. More than 40% of the individuals served do not speak English as a first language. A key component of our initiative is improving early identification of high-risk members and subsequent referral of these members to a community-based support program. Our methods for early identification of high-risk enrollees include • Administering health risk assessments (HRAs). All new public-sector enrollees in Massachusetts receive a HRA when entering a health plan. A short questionnaire is completed over the telephone in the member’s

Medicaid Enrollees and a Community-Based Program

•

• •

•

•

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primary language. Positive HRAs that identify at-risk conditions for mental health or substance abuse are entered into our customized information management system as “alerts” so that we can identify and reach out early to at-risk enrollees. Targeting enrollees with medical comorbidities served in the HMO’s intensive care management program. Because research indicates that members who are medically case managed are more likely to be at risk for behavioral health disorders (Kent et al. 1995), we work closely with the medical case managers of the HMOs. We have dedicated our own intensive clinical management clinicians to provide specialty management for members with comorbid medical and behavioral health care disorders. By doing this, we increase the chance that a member’s mental as well as physical health care needs are being met. Using provider referral. Providers often alert us to members who are at risk (e.g., identification of a pregnant woman who abuses substances). Performing random and targeted chart reviews of all services. Chart reviews provide an overall snapshot of quality of care and providers’ success in meeting member needs. Profiling member risk. We use modeling to examine the trajectories of members who use a large number of services and to identify their risk factors. Risk factors include involvement with one or more state agencies, children who have a substance-abusing parent, history of previous hospitalizations, substance abuse, positive health risk screen, and medical comorbidity. Understanding these profiles improves our ability to identify those at high risk on enrollment (Hadley et al. 1992; Kent et al. 1995). Implementing screening initiatives. A number of screening initiatives are used to identify individuals at risk including the Pediatric Screening Checklist (Jellinek et al. 1995; Navon et al. 1999), which has been used to screen over 2,000 children in health centers, and alcohol abuse and dependence screens such as the CAGE or Alcohol Use Disorders Identification Test (AUDIT) (Ewing 1984; Saunders et al. 1993).

SERVICE INTERVENTION FOR IDENTIFIED HIGH-RISK CONSUMERS To improve quality of care and clinical and cost outcomes for at-risk members, we offer community support programs as a key component of our network. These services are available to all members with mental illness or substance abuse disorders whose care is managed through our intensive clinical management program. The primary goals of the community support program are to stabilize at-risk members and to prevent unnecessary

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rehospitalization by providing community-based support services. As a managed behavioral health care organization, we procure these services competitively, seeking high-quality community support providers who can meet our established performance specifications. Community support–type programs have been shown to decrease hospital readmission rates (Degan and Nelson 1995). The program model is based on the principles of community support systems for seriously mentally ill clients as described by Santos et al. (1995) and the principles of case management described by Bachrach (1993) and Kantar (1989). Client identification and outreach, linkages with needed resources, and development of a primary provider network to ensure continuity of care are hallmarks of our community support program. The program worker, who typically has a bachelor’s or master’s degree in a human service field, plays a critical role in coordination of treatment. He or she does not replace the client’s outpatient therapist but supports adherence to treatment by coordinating care with the primary care physician, other providers, state agencies, schools, or other entities and helps the client access medical and dental services. To ensure coordination of aftercare and to facilitate smooth transitions across care settings for clients, community support program workers also attend discharge meetings at hospitals, detoxification facilities, acute residential facilities, or other treatment locations. The amount of support provided through community support programs can be tailored to meet the unique needs of each client, and the intensity of support can be varied as the clients’ needs change over time. Services are provided only with client consent and are authorized in increments. Clients and their families and/or significant others are involved in all phases of the treatment as indicated. By complementing the other services the client is receiving, these programs support treatment in the least restrictive setting possible. Admission criteria are • The consumer is at risk for hospitalization or multiple hospitalizations, as indicated by one or more of the following: 1. The consumer has demonstrated underutilization or noncompliance with recommended community-based services. 2. The consumer has experienced a catastrophic event. 3. The consumer is newly enrolled with one of the HMOs we work with and has a history of multiple hospitalizations. 4. The consumer has been newly diagnosed with a major mental illness. 5. The consumer has special needs related to concurrent medical illness, physical disability, or cultural issues that require multiple-agency coordination of service delivery.


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6. Several members in a single family use intensive services requiring multiple agency coordination. • The consumer has been admitted four or more times to acute 24-hour psychiatric or addiction treatment in the previous 12-month period. • The member has used behavioral health services in excess of a specified dollar amount in any 12-month period or has used inpatient days in excess of a specified number in the previous 12-month period. • The consumer is pregnant or up to 60 days postpartum and has problems with substance abuse. • It is likely that community support program services can improve the consumer’s ability to make use of other services or improve the consumer’s stability in the community. • The member and/or legal guardian are willing to accept community support program services.

EFFECTIVENESS OF THE INTERVENTION Several indicators are tracked to ascertain if the community support program is improving outcomes both for individual clients and for service recipients as a group. The complete set of performance indicators is listed in the chapter appendix. The standardized measurements for these results are depicted in member months, which are calculated according to the number of months a member has been on or off the program in relation to his or her first program intervention within a given time period examined. Use of member months allows normalization of data due to the varying lengths of member enrollment in the HMO or in the community support program intervention. The data are often further stratified to permit examination of effectiveness for categories of members. Results to date for this intervention are summarized in the tables.

Reductions in Inpatient Days Table 21–1 reflects results from an annual program evaluation. It shows highlights of the changes in the number of days per member month for inpatient care, reflecting the proportionate time members spend in acute care as opposed to being in their communities.

Reductions in Discharges Per Member Month Table 21–2 depicts the number of discharges before and after implementation of the community support program and shows fewer admissions to acute care in all categories. Number of discharges is a proxy for number of

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Table 21–1.


Percent changes in inpatient days per member month*

All members Under age 19 Over age 19 Aid to Families with Dependent Children members Disabled members

–51% –48% –53% –48% –36%

*1 year before and 1 year after identification as high risk and implementation of community support program services.

Table 21–2.

Percent changes in discharges per member month*


–50% –100% –46% –80% –40%


readmissions, and the results illustrate not only that inpatient days per member month decrease after the program but also that overall number of admissions per year decreases.

Changes in Cost Table 21–3 shows cost changes before and after implementation of the intervention. For all high-risk members identified, per member/per month costs were cut in half. Costs were actually found to increase following the intervention for two subgroups, youths under age 19 and members with Aid to Families with Dependent Children (AFDC) Medicaid. Table 21–3.

Percent changes in cost per member month*


–51% +72% –69% +40% –67%



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We hypothesize that improving access to needed diversionary and outpatient services may increase costs initially for populations in which historic underidentification has created a barrier to appropriate care. Because prevention and early identification are core philosophies of the program, we believe that increased costs today may help to decrease costs in the future. Many children and adolescents are struggling to maintain a residential placement in the community, and although they do not need to be confined in a hospital, they require more outpatient resources to have a successful outcome. We are continuing to assess cost and use patterns in this group. We are also investigating the extent to which AFDC members decreased inpatient use as a result of community support program intervention and whether those services were replaced with outpatient treatment. We are also analyzing whether overall costs increased when the cost of these programs is added to other treatment costs.

Changes in Functional Status and Satisfaction Inpatient days, discharges, and costs show only part of the picture of the effectiveness of this intervention. We also require our community support program providers to measure members’ satisfaction with services and to measure functional outcomes. Results to date for the functional outcomes indicator must be viewed as tentative, because the sample size is smaller than we would like. Preliminary results with the use of the Behavior and Symptom Identification Scale (BASIS)-32 (Eisen 1996) suggest functional improvements for members served by our largest provider. Additionally, preliminary satisfaction data suggest that most consumers are satisfied with the services received.

CONCLUSIONS AND NEXT STEPS Through annual evaluations, we have learned that the program is effective for most individuals when measured by several core indicators such as inpatient days, number of discharges, and costs. Our next steps include • Continued refinement of models of early identification of at-risk individuals • Continued improvement of our ability to identify those individuals or segments of the population for whom the community support program intervention is less effective and why it is so (includes determining when the intervention is not clinically indicated and determining at what point the intervention, once initiated, is no longer appropriate)

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• Continued refinement of admission criteria to the program with the goal of increasing the number of members served • Continued coordination with providers to obtain member self-report information regarding clinical outcomes and satisfaction • Continued efforts to integrate behavioral health and medical information for at-risk consumers

REFERENCES Bachrach L: Continuity of care: a context for case management, in Case Management for Mentally Ill Patients: Theory and Practice. Edited by Harris M, Bergman HC. Reading, United Kingdom, Hardwood Academic Publishers, 1993, pp 183–198 Degan CD, Nelson DC: Measuring outcomes in a high utilizing, high-risk Medicaid population (poster session). Presented at the annual meeting of the American Psychiatric Association, Boston, MA, October 1995 Eisen SV: Behavior and Symptom Identification Scale (BASIS 32), in Outcomes Assessment in Clinical Practice. Edited by Sederer LI, Dickey B. Baltimore, MD, Williams & Wilkins, 1996, pp 65–69 Ewing JA: Detecting alcoholism: the CAGE questionnaire. JAMA 252:1905–1907, 1984 Hadley TR, Culhane DP, McGurrin MC: Identifying and tracking “heavy users” of acute psychiatric services. Administration and Policy in Mental Health 19:279–290, 1992 Jellinek M, Little M, Murphy JM, et al: The pediatric symptom checklist: support for a role in managed care environment. Arch Pediatr Adolesc Med 149:740– 746, 1995 Kantar J: Clinical case management: definitions, principles, components. Hospital and Community Psychiatry 40:361–368, 1989 Kent S, Fogarty M, Yellowless P: A review of studies of heavy users of psychiatric services. Psychiatr Serv 46:1247–1253, 1995 Navon M, Lack J, Nelson DC, et al: Prevalence of treated and untreated cases of severe emotional disturbance in pediatric primary care settings. Paper presented at the 12th Annual Research Conference, A System of Care for Children’s Mental Health: Expanding the Research Base, Tampa, FL, February 1999 Santos AB, Henggeler SW, Burns BJ, et al: Research on field based services: model for reform in the delivery of mental health care to populations with complex clinical problems. Am J Psychiatry 152:1111–1123, 1995 Saunders JB, Aasland OG, Babor TF, et al: Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption, II. Addiction 88:791– 804, 1993

Appendix PERFORMANCE INDICATORS FOR COMMUNITY SUPPORT PROGRAMS A. Access • Number and percent of cases for which service is initiated within 24 hours of referral B. Case Completion Profile • Number and percent of cases that are closed because consumer refuses further services • Number and percent of cases that are closed because consumer needs more intensive services • Number and percent of cases that are closed because sufficient progress has been made to reduce the intensity of service provision C. Utilization of Emergency Services • Number of crisis intervention contacts during community support enrollment • Admission to 24-hour care • Number of cases requiring readmission to 24-hour levels of care while the case is enrolled with a community service program • Number of cases requiring admission to a hospital inpatient unit, observational bed status, substance abuse level IV, III, or II after termination from community support program services, or acute residential treatment after termination from community support program services within 30 days, 60 days, 90 days, and 6 months D. Behavior Change • Change in level of functioning as measured by Axis V of the DSMIV Global Assessment of Functioning scale or other level of functioning measures 273

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E. Satisfaction F. Primary Care Coordination Communication/Collaboration • Quarterly average of rate of compliance with coordination procedures

22 Increasing Consumer Involvement in Treatment Susan V. Eisen, Ph.D. Barbara Dickey, Ph.D. Lloyd I. Sederer, M.D.

THE PROBLEM AND HOW IT WAS IDENTIFIED As part of the discharge process at McLean Hospital, all patients complete an assessment of the inpatient care they received, using an instrument called the Perceptions of Care (PoC; see chapter appendix). We have set a standard for these ratings: if 20% or more of the patients rate any item as unfavorable, we consider this to be an indicator of poor care. This triggers a continuous quality improvement (CQI) effort to improve that particular aspect of care. For example, in one reporting period, we found that 25% of respondents rated involvement in their treatment planning as occurring “sometimes” or “never,” higher than our threshold of 20%, the point at which a CQI effort is set in motion.

HOW THIS PROBLEM IS RELATED TO QUALITY OF CARE Literature supports the involvement of patients at all levels of mental health care (Anthony 1993; Campbell 1991), leading to positive results with respect to their rehabilitation, psychotherapy outcome, and stability in supportive housing for persons with psychiatric disabilities (Eguster and Wampold 1996; Ridgeway et al. 1998). Patient involvement may serve to enhance the therapeutic alliance between physician and patient, an important factor in clinical outcome (Horvath 1994). In addition to its effect on outcome, therapeutic alliance is associated with adherence to treatment 275

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recommendations, reduction in violent behavior, satisfaction with care, and retention in treatment (Beauford et al. 1997; Olfson et al. 1993; Petry and Bickel 1999). The benefits of a good therapeutic alliance have been demonstrated in inpatient and outpatient settings and among diverse groups of patients across the diagnostic spectrum (Clarkin et al. 1987; Frank and Gunderson 1993). We hypothesized that increasing patient involvement in treatment planning would lead to positive perceptions of care.

SYSTEMIC FACTORS CONTRIBUTING TO THE IDENTIFIED PROBLEM One important factor may have contributed to insufficient patient involvement in the development of the treatment plan. During the clinical evaluation process, physicians and mental health professionals traditionally focused on observing and interviewing the patient, gathering information from family members, and consulting other professionals. Patients’ opinions of their own treatment needs and goals were not systematically assessed. In addition, unless specifically asked for their input, patients may not have felt that their own needs were incorporated into their treatment plans.

INTERVENTION DESIGNED TO ADDRESS THE PROBLEM Our intervention was designed to test the utility of using a standardized patient self-report problem rating scale, the Behavior and Symptom Identification Scale (BASIS)-32, to more fully involve patients in their care (Eisen et al. 1994, 2000). Recognizing and acknowledging patients’ perspectives of their own symptoms and problems may provide a systematic way to include patients in the evaluation and treatment planning process, thereby enhancing the therapeutic alliance. We anticipated that a more effective alliance would result in improved experiences of care received and enhanced clinical outcomes. All patients completed the BASIS-32 as part of their clinical evaluation at admission, and they completed the BASIS-32 and PoC surveys within 24 hours prior to discharge. We told patients that these instruments were used in the hospital’s quality improvement program to evaluate the care received. The patient’s completed BASIS-32 assessment was used as the foundation for discussion between the patient and his or her physician. The discussion centered on problems the patient felt were most in need of treatment during the hospital stay. Within 1 day of admission, a psychiatry resident met individually with each patient assigned to him or her. The resident reviewed the patient’s

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BASIS-32 responses and subscale scores, conveyed his or her understanding of the patient’s self-reported difficulties, and further explored and discussed the patient’s symptoms and concerns. Residents were also given written training materials, and they observed one of the authors (L.I.S.) modeling the patient–doctor discussion prior to the intervention. At rounds, residents reported to the other treatment team members the BASIS-32 problem areas on which to focus treatment efforts. Thus, we introduced a systematic mechanism for the clinical team to target patient self-reported problems as a focus of treatment. To study the effectiveness of this intervention, we used a quasiexperimental design with three groups of inpatients. One group received the intervention, and the other two comparison groups received treatmentas-usual. The intervention was implemented by third-year psychiatry residents who rotated through the program between April and September, 1997. At the initial meeting with the attending physician or resident, diagnostic assessment and formulation were undertaken. However, the physician or resident did not review patients’ BASIS-32 responses or make any systematic effort to use the information for treatment planning. Patients in one of the comparison groups were treated by either of two attending psychiatrists; patients in the other comparison group were treated by third-year psychiatry residents. The same two attending psychiatrists were assigned patients throughout the study period. Attending psychiatrists were board certified and each had more than 10 years of postresidency experience. Patients were sequentially assigned to one of the three groups based on admission date. The research team was not involved in the clinical care of the patients in the study sample, and neither the psychiatry residents nor attending psychiatrists were otherwise involved in the research project. The sample consisted of 109 consecutively admitted inpatients. Most were male (63%), diagnosed with schizophrenia or schizoaffective disorder (56%) or bipolar manic/mixed disorder (31%), and insured by Medicare (disability) or Medicaid (69%). Average age was 37 years. Length of hospitalization ranged from 3 to 118 days with an average of 19 and a median of 16 days. Thirty-nine percent of patients had been hospitalized within 1 year prior to the current hospitalization. Mean Global Assessment of Functioning (GAF, Axis V diagnosis) score at admission was 32, indicating “some impairment in reality testing or communication…or major impairment in several areas” (American Psychiatric Association 1994). There were no significant differences between the three groups on any of these variables or on self-reported symptom or problem difficulty at admission. The data sources were:

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1. Behavior and Symptom Identification Scale (BASIS-32). BASIS-32 is a 32-item self-report symptom and problem rating scale designed to assess treatment outcome across five domains: relation to self/others; depression/anxiety; daily living skills; impulsive/addictive behavior; and psychosis. Patients rate the degree of difficulty they have been having in each area during the past week on a 5-point scale from “no difficulty” to “extreme difficulty.” 2. Perceptions of Care (PoC) survey. The 18-item PoC survey was developed after a review of existing consumer satisfaction surveys for medical and psychiatric care. We added items unique to inpatient psychiatric care and modified others to improve clarity and specificity (Cleary et al. 1991; Eisen et al. 1999; Teague et al. 1997). 3. Medical records. For patients in the study, we added data obtained from medical records on age, gender, payor, previous hospitalizations, DSM-IV diagnoses, GAF rating at admission, and length of stay. We compared mean ratings on the PoC survey regarding degree of involvement in treatment decisions to test the hypothesis that patients who received the intervention would report more involvement in decisions about their treatment than those who did not. One-way analysis of variance was used to assess the overall difference among means for the three groups. Chi-square analyses were performed to assess group differences on the categorical PoC items.

EFFECTIVENESS OF THE INTERVENTION Our analysis (see Table 22–1) showed a difference among the three groups, as we hypothesized, in the direction of more positive perceptions of care. We found that patients in the intervention group rated their care more favorably than patients in the comparison groups. In addition, when asked “How often did the staff treat you with respect and dignity?” and “Would you recommend this facility to others?” patients in the intervention group perceived their care more favorably. To test whether the higher ratings of treatment involvement led to better outcomes, we assessed the effect of the intervention on clinical outcome using multivariate repeated measures analysis of variance with three groups (intervention, no intervention treated by residents, and no intervention treated by attendings) and time point (admission/discharge) as independent variables. We found no effect of the intervention on BASIS-32 outcome scores at discharge, although there was a significant improvement from admission to discharge on each of the BASIS-32 subscales and for the overall mean score. The lack of an effect on clinical outcome may be

Differences among groups in patient perceptions of inpatient care Intervention

Perceptions of Care survey items Were you involved as much as you wanted in decisions about your treatment?a Did the staff treat you with respect and dignity?a Did the staff explain things in a way you could understand?a Did the staff listen carefully to you?a Did the staff who treated you work well together as a team?a Did the staff spend enough time with you?a Did the staff give you reassurance and support?a How much were you helped by the care you received?b

Comparison residents

Comparison attendings

Mean rating 3.67 3.94 3.67 3.72 3.78 3.56 3.67 3.33

2.90 3.66 3.39 3.25 3.54 3.32 3.31 3.22

3.00 3.47 3.37 3.37 3.50 3.39 3.39 3.24

Percent responding yes

P

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