Click for next page ( 31

The National Academies of Sciences, Engineering, and Medicine
500 Fifth St. N.W. | Washington, D.C. 20001

Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 30
4 Principles for Success Change in the health care system can take two forms—evolutionary change and radical change. In this context, evolutionary change refers to continuous, iterative improvement of existing processes, sustained over long periods of time, that does not depend strongly on new technologi- cal capabilities. The Institute of Medicine (IOM) vision of health care as a “learning system” is one of a system designed to benefit from evolutionary change. By contrast, radical change means new ways of looking at health problems and revolutionary new ways of addressing those problems. Radical change often involves a new capability such as the advent of antibiotics in the 1930s and developments in genomics and proteomics today. Some of the automatic data recording, use of novel sensors, data mining, and visualization techniques recommended in this report fit the radical, revolutionary mode of change. Other committee suggestions fit the evolutionary, incremental change mode. Any approach to health care IT should enable and anticipate both types of change since they work together over time. Abstracting from its site visit observations, the experience of its mem- bers, and the extant literature, the committee identified principles to For a sampling of the relevant literature, see M. Leu et al., “Centers Speak Up: The Clini- cal Context for Health Information Technology in the Ambulatory Care Setting,” Journal of General Internal Medicine: Official Journal of the Society for Research and Education in Primary Care Internal Medicine 23(4):372-378, April 2008; M.R. Jones, “’Computers Can Land People on Mars, Why Can’t They Get Them to Work in a Hospital?’: Implementation of an Electronic Patient Record System in a UK Hospital,” Methods of Information in Medicine 42(4):410-415, 30

OCR for page 30
PRINCIPLES FOR SUCCESS 31 guide successful use of health care IT to support a 21st century vision of health care. In most instances, these principles are not new—but even “old” principles applied properly in a given field or to a given organiza- tion can have the impact and significance of new ones. To place emphasis on the importance of each, the text below categorizes these principles into ones related to evolutionary change and those related to revolutionary change. 4.1  Evolutionary Change 4.1.1  Principle 1: Focus on Improvements in Care— Technology Is Secondary The most important principle for guiding evolutionary change in health care is to focus efforts on achieving the desired improvements in health care rather than on the adoption of health care IT as a goal in itself.  For example, efforts should be structured around clear health care goals (such as those described by the IOM criteria), and with a transparent understanding of the gap between the existing baseline and goal. Only then should there be a focus on process changes needed to close the gap, and an identification of what technology if any is needed to enable the process changes. If early experience shows that the gap is not closing, pro- cess and technology can be adapted until the improvement is achieved. In this approach, health care IT is selected and implemented on an as-needed basis to support iterative improvement, instead of being implemented for its own sake at the outset and then potentially becoming a constraint rather than a facilitator of iterative improvement. 2003; J. Øvretveit et al., “Improving Quality Through Effective Implementation of Informa- tion Technology in Healthcare,” International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care 19(5):259-266, October 2007; Jane Hendy et al., “Challenges to Implementing the National Programme for Information Technology (NPfIT): A Qualitative Study,” British Medical Journal 331:331-336, August 6, 2005; Heather Heathfield, David Pitty, and Rudolph Hanka, “Evaluating Information Technology in Health Care: Barriers and Challenges,” British Medical Journal 316:1959-1961, June 27, 1998; C. Sicotte, J.L. Denis, P. Lehoux, and F. Champagne, “The Computer-Based Patient Record Challenges Towards Timeless and Spaceless Medical Practice,” Journal of Medical Systems 22(4):237-256, August 1998; J.P. Glaser, “Too Far Ahead of the IT Curve?,” Harvard Business Review 85(7- 8):29-33, 190, July-August 2007. A similar perspective can be found in Carol C. Diamond and Clay Shirky, “Health Infor- mation Technology: A Few Years of Magical Thinking?,” Health Affairs 27(5):383-390, August 19, 2008.

OCR for page 30
32 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE 4.1.2  Principle 2: Seek Incremental Gain from Incremental Effort An important corollary is to engage in a portfolio of activities, start- ing with ones that require modest investment and are likely to return perhaps modest, but short-term, visible improvements. If programs can be structured so that small investments yield visible success, stakehold- ers and the relevant decision makers are more likely to be persuaded to continue along such a path. In contrast, programs that require large initial investments of money, effort, and/or time before exhibiting useful results are difficult to sustain and are often politically vulnerable. 4.1.3  Principle 3: Record Available Data So That They Can Be Used for Care, Process Improvement, and Research Systematic improvement of health care is data-driven. Therefore, health care providers should aggregate as much data as feasible about people, processes, and outcomes from all sources, acknowledging the never-ending challenge of maintaining reasonable degrees of patient con- fidentiality in such a data collection effort. Of potential relevance are data about people (e.g., their medical condition and health status, their diet and environmental conditions), processes (e.g., actual health care services received, when, and where with detailed process logs), and outcomes (e.g., clinical and functional status at multiple points in time in multiple different conditions). Even if such collected data cannot immediately be regularized to a common semantic standard necessary for full data interoperability, they are still potentially useful for incremental care or process improvement and for research—future needs cannot be fully foreseen, especially in light of anticipated needs for clinical and envi- ronmental data to correlate with personalized genomic data. Moreover, systematic advances in process improvement and knowledge may require collection of new data types that cannot be anticipated today, suggest- ing the need for a collection infrastructure whose scope can be easily expanded. Automatic recording of actions and interactions at the source will facilitate data capture and is needed to avoid increasing the workload of caregivers and ancillary personnel. 4.1.4  Principle 4: Design for Human and Organization Factors Providers of health care IT can design systems to support people in doing the right thing—by providing incentives for and eliminating bar- riers to doing those things. Entirely apart from technology, barriers and incentives can be sociological, psychological, emotional, cultural, legal, economic, or organizational. Human-centered design pays attention to all of these factors as they relate to technical function and form. Such

OCR for page 30
PRINCIPLES FOR SUCCESS 33 work necessarily involves social scientists who understand real human needs and capabilities, why people err, where workload considerations are essential, and how to develop systems that enhance capabilities, that are understandable with minimal training, and that reduce subsidiary task requirements. The use of health care IT designed in the absence of such input may well lead to greater errors, more stress, and lower productivity. In short, success requires not just technology but also—and perhaps more importantly—social and organizational processes to appro- priately take advantage of technology. 4.1.5  Principle 5: Support the Cognitive Functions of All Caregivers, Including Health Professionals, Patients, and Their Families Organizations investing in health care IT can support the cognitive functions of individuals and organizations as they iteratively adapt roles and work processes. Such support includes analysis of data from practice to identify high-priority improvement opportunities among populations or work processes, analysis of applicable evidence, tools such as order sets for linking evidence into workflow, and aggregation of patient data into decision-centric displays. Importantly, cognitive support needs tend to center on high-level decision making (e.g., diagnosis) for populations, patients, or situations, and tend to span granular transactional tasks such as test ordering or prescribing. Cognitive support is not well served by the task-specific automation systems that make up the majority of today’s health care IT. 4.2  RADICAL CHANGE 4.2.1  Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. It is axiomatic that health care will change dramatically into the future. New knowledge will become available—e.g., genomic medicine. Popula- tion demographics will change—e.g., more people will be elderly, with a correspondingly different emphasis on different kinds of care. Care ven- See, for example, Yong Y. Han et al., “Unexpected Increased Mortality After Implemen- tation of a Commercially Sold Computerized Physician Order Entry System,” Pediatrics 116(6):1506-1512, December 2005; also, Ross Koppel et al., “Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors,” Journal of the American Medical As- sociation 293(10):1197-1203, 2005.

OCR for page 30
34 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE ues will change—e.g., more care will be provided at home, and patients will be required to assume greater responsibilities for care (with the assis- tance of professional care providers). Policy is likely to change—there will be different payment models or reimbursement rates, for example. Thus, any IT-based infrastructure to support today’s health care needs must be designed to accommodate changes in roles and process tomorrow—a point suggesting that architectures based on standard interconnection protocols are much easier to change in comparison to monolithic, tightly integrated all-encompassing systems. Otherwise, even deployment of health care IT successful in solving a problem today could stand in the way of solving tomorrow’s challenges. 4.2.2  Principle 7: Archive Data for Subsequent Re-interpretation Vendors of health care IT should provide the capability of record- ing any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data. Advances in biomedical science and practice will change today’s interpretation of data. In addition, advances in computer science and related disciplines will lead to new ways to extract meaningful and useful knowledge from existing data stores allowing re- analysis of pre-existing data to reveal medically significant relationships and correlations that are currently unknown. Perhaps most importantly, the committee believes that the availability of large amounts of data is itself a driver for progress likely to inspire medically oriented research in machine learning, display technology, data mining, and so on. 4.2.3  Principle 8: Seek and Develop Technologies That Identify and Eliminate Ineffective Work Processes Organizations should seek and develop technologies that allow iden- tification and elimination of ineffective work processes and implementa- tion of new approaches to achieving their purpose. Automation of work processes developed in an era when paper was the medium for commu- nicating and archiving is fraught with cost and unintended consequences. For example, some of the work done within the health care system might be accomplished outside health care by providing support for patients See, for example, Werner Ceusters and Barry Smith, “Strategies for Referent Tracking in Electronic Health Records,” Journal of Biomedical Informatics 39(3):362-378, June 2006. Some of the technology issues involved in archiving are discussed in National Research Council, Building an Electronic Records Archive at the National Archives and Records Administration: Recommendations for Initial Development, The National Academies Press, Washington, D.C., 2003.

OCR for page 30
PRINCIPLES FOR SUCCESS 35 to better understand their medications and treatment plans. Redesign of work to take advantage of ubiquitous information access and communica- tion may be much more effective than automating existing work processes in an attempt to eliminate errors and effort. 4.2.4  Principle 9: Seek and Develop Technologies That Clarify the Context of Data Organizations should seek and develop technologies that present new information in the context of other information available about the patient and relevant biomedical knowledge. The combination of new biomedical technologies, together with increased access to data through health care IT, is increasingly overwhelming health professionals’ ability to make sense of individual findings. “Alert fatigue” is an example. New approaches are needed to present information in context so that patterns and choices stand out.