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National Research Council (US) Committee on Engaging the Computer Science Research Community in Health Care Informatics; Stead WW, Lin HS, editors. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington (DC): National Academies Press (US); 2009.

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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions.

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1Health Care in the United States Today

Today’s health care fails to deliver the most cost-effective care and suffers substantially from medical errors and waste. One often-cited data point is the 1998 Institute of Medicine (IOM) estimate that preventable medical errors lead to as many as 98,000 deaths per year in the United States;1 a more recent paper from 2005 suggests that there is still much more work to be done to make significant progress in reducing this figure.2 It has been estimated that, on average, Americans receive about half of the medical care that is recommended for them.3 Conversely, the available evidence suggests that many medical interventions undertaken today are in fact not necessary or are recommended without adequate personalization.4 For example, based on an analysis of regional disparities in Medicare expenditures, Fisher et al. suggest that the United States as a whole could save annually up to 30 percent of Medicare expenditures with no compromise in medical outcomes or patient satisfaction;5 if so, resources might be freed to implement additional coverage for the uninsured and/or additional best practices that are not reflected in today’s health care practices.

For the most part, these persistent problems do not reflect incompetence on the part of health care workers.6 Instead, they are a consequence of the inherent intellectual complexity of health care taken as a whole and a medical care environment that provides insufficient help for clinicians to avoid mistakes or to inform their decision making and practice. Administrative and organizational fragmentation, together with complex, distributed, and unclear authority and responsibility, further complicates the health care environment.

Many of the relevant factors can be classified into three distinct areas: the tasks and workflow of health care, the institution and economics of health care, and the nature of health care IT as it is currently implemented. (In this report, observations from site visits are cross-referenced where appropriate with the notation CxOy. Cx refers to the Category x (1-6) of observation made in Table C.1 (Appendix C), and Oy refers by number (1-25) to a particular observation as listed in Table C.1.


  • Health care decisions that require reasoning in the face of uncertainty. Sources of uncertainty include biological variability,7 uncertainty about the medications that a patient is actually taking because of missing medical records at the point of care,8 uncertainty about the effectiveness of past and future treatments for the particular patient [C1O1], simple randomness arising from inherently stochastic processes, and imperfect models or understanding of causality.
  • Complex and non-transparent workflow [C2O6] that is characterized by many interruptions [C2O7], inadequately defined roles and responsibilities, poorly kept and managed schedules, and little documentation of steps, expectations, and outcomes.9 Poor information flow is particularly apparent at the interfaces of health care (e.g., when a patient transitions from inpatient to outpatient, when nurses change shifts) [C2O5].
  • Increasing complexity of the care provided to patients in a time-pressured environment.10 The aging patient population has a growing number of chronic disease conditions that must be managed.11 According to Yarnall et al.,12 managing in accordance with the preventive guidelines relevant to “average” adult patients would require an average of approximately 40 minutes per patient per year. A typical patient sees his primary care physician only 4 times a year for a 15-minute appointment (for a total of 60 minutes of interaction), which would leave only 20 minutes per year (60 minutes − 40 minutes) for everything other than matters related to the guidelines for preventive care (by 2030, about half of all Americans will have at least one chronic disease).13


  • A large number of payers for health care, each with their own rules for coverage. For example, a large medical center may have to handle the complexity associated with managing thousands of different health insurance plans.14 A typical family physician or internist in the United States wastes 40 to 50 minutes each day on dealing with managed care administrative hassles.15
  • Distorted or perverse incentives for payment. For example, as a general rule, health care providers are compensated more readily and more generously for performing medical procedures than for communication and cognitive work such as diagnosis or preventive care. In many cases, the reimbursement rate is higher when patients develop complications rather than when patients receive quality care—that is, physicians are generally paid to fix the problems their medical care may have caused or did not prevent.16 In addition, the current medical care system offers little recognition or reward for coordinating care and pays primarily for face-to-face (office) visits.17
  • A fragmented and "siloed” environment of health care organizations. Both patients and providers must navigate a confusing landscape of tertiary care centers, community hospitals, clinics, primary and specialist doctors and other providers, payers, health plans, and information sources.18
  • Increasing tightness in the health care labor market for certain specialties, such as nurses,19 primary care physicians,20 health care paraprofessionals, and clinicians with informatics training. (Health/biomedical informatics training is not generally a requirement in most curricula for health care professionals, thus contributing to a scarcity of individuals so trained.)


  • Monolithic and “siloed” information technology. Many health care organizations, especially large ones, do spend considerable money on information technology (IT), but the IT is implemented in ways that make even small improvements hard to introduce [C4O14]. Even across the systems within an organization, interoperability is often awkward and slow [C4O14, C5O21, C5O23]. Information exchange with the information systems of other organizations is rare.21
  • IT applications that appear designed to automate tasks or business processes for administrative efficiency,22 and that provide little support for the cognitive tasks of clinicians [C1O4 and confirmed by IOM23]. IT-based systems for health care are often designed in ways that simply mimic existing paper-based forms and workflow [C1O2, C1O3] and do not take advantage of human-computer interaction principles [C5O20]. One result is poor system design that can increase the chance of error, add to rather than reduce workflow, and compound the frustrations of doing the required tasks. As a result, the computer system frequently increases the workload (for example, lack of trust in a system may force providers to maintain duplicate paper-based data records) and can introduce new forms of error that are difficult to detect. Complex policy and implementation issues relating to protecting privacy also make automation significantly more difficult.


A number of trends will put additional pressure for change on the health care environment. These trends include an aging population and a corresponding increase in the complexity and weight of the disease burden, the emergence of genome-based personalized medicine,24 a larger role for patients in managing their own health care,25 and yet greater emphasis on efficiency and cost control in health care. As a result, health care processes will become more complex and more time-constrained, and the demands placed on care providers will become more intense.


Chapter 2 reviews the IOM vision of 21st century health care and wellness as the appropriate point of departure for the committee’s work. Chapter 3 describes a chasm between current efforts to deploy health care IT and what the committee believes is needed to achieve the IOM vision. Chapter 4 describes the committee’s perspective on principles for developing and deploying successful health care IT, with success defined as progress toward the IOM vision. Chapter 5 describes some illustrative research challenges for the computer science community that emerge from the IOM vision. Chapter 6 presents the committee’s recommendations, based on the results of its study, for government, for the computer science community, and for health care organizations.



Institute of Medicine, To Err Is Human: Building a Safer Health System, National Academy Press, Washington, D.C., 2000, available at http://www​​.php?record_id=9728.


Lucian L. Leape and Donald M. Berwick, “Five Years After To Err Is Human: What Have We Learned?,” Journal of the American Medical Association 293(19):2384-2390, 2005.


Elizabeth A. McGlynn et al., “The Quality of Health Care Delivered to Adults in the United States,” New England Journal of Medicine 348(26):2635-2645, 2003, available at http://content​​/cgi/content/abstract/348/26/2635.


K.A. Kuhn et al., “Informatics and Medicine, from Molecules to Populations,” Methods of Information in Medicine 47(4):296-317, 2008.


Elliott S. Fisher et al., “The Implications of Regional Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care,” Annals of Internal Medicine 138(4):288-298, February 18, 2003.


U.S. Department of Health and Human Services Factsheet: “Improving Patient Safety and Preventing Medical Errors,” HHS Factsheet, March 25, 2002.


Ute Schwarz et al., “Genetic Determinants of Response to Warfarin During Initial Anticoagulation,” New England Journal of Medicine 358(10):999-1008, March 6, 2008.


As much as 30 percent of the information an internist needs is often not accessible during a patient’s visit because of missing clinical information and missing laboratory reports. See D.G. Covell, G.C. Uman, and P.R. Manning, “Information Needs in Office Practice: Are They Being Met?,” Annals of Internal Medicine 103(4):596-599, 1995.


See, for example, S. Panzarasa et al., “Improving Compliance to Guidelines Through Workflow Technology: Implementation and Results in a Stroke Unit,” Studies in Health Technology and Informatics 129(Pt. 2):834-839, 2007.


Center for Studying Health System Change. Physician Survey, available at http://CTSonline​


Brian Raymond and Cynthia Dold, Clinical Information Systems: Achieving the Vision, Kaiser Permanente Institute for Health Policy, Oakland, Calif., February 2002, available at http://www​​/publications/docs/clinical_information​.pdf.


Kimberly S.H. Yarnall, Kathryn I. Pollak, Truls Østbye, Katrina M. Krause, and J. Lloyd Michener, “Primary Care: Is There Enough Time for Prevention?,” American Journal of Public Health 93(4):635-641, April 2003, available at http://www​​/content/full/93/4/635.


Shin-Yi Wu and Anthony Green, Projection of Chronic Illness Prevalence and Cost Inflation, RAND Corporation, October 2000.


Respondents to an informal poll of the ACMI discussion list in June 2008 indicated that their home organizations (medical centers) often had to cope with many dozens of health care payers (usually insurers), each of which had hundreds of different plans with different rules for coverage. (ACMI, the American College of Medical Informatics, consists of elected fellows from the United States and abroad who have made significant and sustained contributions to the field of medical informatics.) The range reported was from a low of 578 plans to a high in excess of 20,000.


L.S. Sommers, T.W. Hacker, D.M. Schneider, P.A. Pugno, and J.B. Garrett, ”A Descriptive Study of Managed Care Hassles in 26 Practices,” Western Journal of Medicine 174(3):175-179, 2001. The term “hassles” was used in the study to refer to issues that interject themselves directly into the doctor-patient visit, including “restricted formularies, limited access to medical specialists, the requirement of prior approvals for procedures, unavailable treatments, lengthy appeals processes, and physician payment delays.”


Vinod K. Sahney, “Engineering and the Health Care Organization,” in National Academy of Engineering and Institute of Medicine, Building a Better Delivery System: A New Engineering/Health Care Partnership, The National Academies Press, Washington, D.C., 2005.


Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, National Academy Press, Washington, D.C., March 2001. As a concrete example, the committee heard of the incentives for an insurance company to do very little for a 62-year-old man developing type 2 diabetes because the costly complications would most likely arise after he turns 65, and would thus be covered by Medicare. Pay-for-performance programs are a notable exception to such perverse incentives, although they have not been widely adopted.


Thomas Bodenheimer, “Coordinating Care—A Perilous Journey Through the Health Care System,” New England Journal of Medicine 358(10):1064-1071, March 6, 2008.


D.E. Hecker, “Occupational Employment Projections to 2014,” Monthly Labor Review 128(11):70-101, 2005.


National Association of Community Health Centers, “Access Transformed: Building a Primary Care Workforce for the 21st Century,” Washington, D.C., 2008, available at http://www​​/client/documents/ACCESS​%20Transformed%20full%20report.PDF.


J. Halamka, J.M. Overhage, L. Ricciardi, W. Rishel, C. Shirky, and C. Diamond, “Exchanging Health Information: Local Distribution, National Coordination,” Health Affairs (Millwood) 24(5):1170-1179, 2005.


William Stead, “Challenges in Informatics,” in National Academy of Engineering and Institute of Medicine, Building a Better Delivery System: A New Engineering/Health Care Partnership, The National Academies Press, Washington, D.C., 2005.


Institute of Medicine, Building a Better Delivery System: A New Engineering/Health Care Partnership, 2005, p. 15. See also Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, National Academy Press, Washington, D.C., 2001, p. 67.


For example, a 2008 study suggests the personalization of drug regimens based on genetic profiles as an important step toward the ultimate goal of providing individualized treatment guided by genetic information. See Amy I. Lynch, Eric Boerwinkle, Barry R. Davis, et al., “Pharmacogenetic Association of the NPPA T2238C Genetic Variant with Cardiovascular Disease Outcomes in Patients with Hypertension,” Journal of the American MedicalAssociation 299(3):296-307, 2008. See also K.A. Kuhn et al., “Informatics and Medicine, from Molecules to Populations,” Methods of Information in Medicine 47(4):296-317, 2008.


Institute of Medicine, Building a Better Delivery System: A New Engineering/Health Care Partnership, 2005, p. 65.

Copyright © 2009, National Academy of Sciences.
Bookshelf ID: NBK20631


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