The Learning Healthcare System Workshop Summary (2007) / Chapter Skim
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Pages 1-36
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From page 1... ...
. Two years later, the Committee released its final report, Crossing the Quality Chasm, underscoring the need for redesigning health care to address the key dimensions on which improvement was most needed: safety, effectiveness, patient centeredness, timeliness, efficiency, and equity (IOM 2001)
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In part, these problems are related to fragmentation of the delivery system, misplaced patient demand, and responsiveness to legal and economic incentives unrelated to health outcomes. However, to a growing extent, they relate to a structural inability of evidence to keep pace with the need for better information to guide clinical decision making.
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The aim was to identify and discuss those issues most central to drawing research closer to clinical practice by building knowledge development and application into each stage of the healthcare delivery process, in a fashion that will not only improve today's care but improve the prospects of addressing the growing demands in the future. Day 1 was devoted to an overview of the methodologic and institutional issues.
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• How much of evidence development and evidence application will ultimately fall outside of even a fully interoperable and universally adopted electronic health record (EHR)
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SUMMARY BOX S-1 Needs for the Learning Healthcare System • Adaptation to the pace of change • Stronger synchrony of efforts • Culture of shared responsibility • New clinical research paradigm • Clinical decision support systems • Universal electronic health records • Tools for database linkage, mining, and use • Notion of clinical data as a public good • Incentives aligned for practice-based evidence • Public engagement • Trusted scientific broker • Leadership match the rate at which new interventions are developed and new insights emerge about individual variation in response to those interventions; • Stronger synchrony of efforts: better consistency and coordina tion of efforts to generate, assess, and advise on the results of new knowledge in a way that does not produce conflict or confusion; • Culture of shared responsibility: to enable the evolution of the learning environment as a common cause of patients, providers, and researchers and better engage all in improved communication about the importance of the nature of evidence and its evolution; • New clinical research paradigm: drawing clinical research closer to the experience of clinical practice, including the development of new study methodologies adapted to the practice environment and a better understanding of when RCTs are most practical and desirable; • Clinical decision support systems: to accommodate the reality that although professional judgment will always be vital to shaping care, the amount of information required for any given decision is moving beyond unassisted human capacity; • Uniersal electronic health records: comprehensive deployment and effective application of the full capabilities available in EHRs as an essential prerequisite for the evolution of the learning healthcare system; • Tools for database linkage, mining, and use: advancing the po tential for structured, large databases as new sources of evidence,
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PRESENTATION SUMMARIES Hints of a Different Way -- Case Studies in Practice-Based Evidence Devising innovative methods to generate and apply evidence for healthcare decision making is central to improving the effectiveness of medical care. This workshop took the analysis further by asking how we might create a healthcare system that "learns" -- one in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.
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implemented by CMS as an opportunity to develop needed evidence on effectiveness. By conditioning coverage on additional evidence development, CED helps clarify policies and can therefore be seen as a regulatory approach to building a learning healthcare system.
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Weissberg also concluded that perhaps the most important requirement for reaping the benefits is that data collection be embedded within a healthcare system that can serve as a "prepared mind" -- a culture that seeks learning. Quasi-Experimental Designs Randomized controlled trials are often referred to as the "gold standard" in trial design, while other trial designs are noted as "alternatives" to RCTs.
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. Practical Clinical Trials Developing valid and useful evidence for decision making requires several steps, including identifying the right questions to ask; selecting the most important questions for study; choosing study designs that are adequate to answer the questions; creating or partnering with organizations that are equipped to implement the studies; and finding sufficient resources to pay for the studies.
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The Evolving Evidence Base -- Methodologic and Policy Challenges An essential component of the learning healthcare system is the capacity for constant improvement: to take advantage of new tools and methods and to improve approaches to gathering and evaluating evidence. As technology advances and the ability to accumulate large quantities of clinical data increases, new opportunities will emerge to develop evidence on the effectiveness of interventions, including on risks, on the effects of complex patterns of comorbidities, on the effect of genetic variation, and on the improved evaluation of rapidly changing interventions such as devices and procedures.
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Robert Califf from the Duke Clinical Research Institute discussed some issues associated with RCTs and the trial methodologies that will increasingly be used to supplement the evidence base. Large RCTs are almost impossible to conduct, and Califf supported use of the term practical clinical trial for those in which the size must be large enough to answer the question posed in terms of health outcomes -- whether patients live longer or feel better.
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Eddy developed a modeling system, Archimedes, that has demonstrated the promise of such systems for developing evidence for clinical decision making. Eddy notes that models will never be able to completely replace clinical trials, which as observations of real events are a fundamental anchor to reality.
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Third, powerful new genetic and phenotypic markers that can predict patients' responsiveness to therapy and vulnerability to adverse effects of treatment are now being discovered. In clinical trials, these markers have the potential for identifying patients' potential for responsiveness to the treatment to be investigated.
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Harlan Weisman of Johnson & Johnson noted that the limited generalizability of the RCTs required for product approval means that post-marketing surveillance is the major opportunity to reveal the true value of healthcare innovations for the general population. Electronic health
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Weisman also discussed how the landscape of information needed changes rapidly and continuously, and called for the development of transparent methods and guidelines to gather, analyze, and integrate evidence -- as well as consideration of how this new form of clinical data will be integrated into policies and treatment paradigms. To ensure that the goals of a learning healthcare system are achieved without jeopardizing patient benefit or medical innovation, Weisman suggested the importance of a road map establishing a common framework for post-marketing surveillance, to include initial evidence evaluation, appropriate and timely reevaluations, and application.
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Some considerations included strengthening feedback loops between research and practice to refine research questions and improve study timeliness and relevance, improving the structure and management of clinical data systems both to support better decisions and to provide quality data at the level of the practitioner, facilitating "builtin" study design, defining appropriate levels of evidence needed for clinical decision making and how they might vary by the nature of the intervention and condition, and changes in clinical research that might help accelerate innovation.
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Because RCTs are not practical, ethical, feasible, or appropriate to all circumstances, these large data systems with built-in study design and feedback loops allow for investigations that have real rigor, utility, and reliability in large populations. Use of Electronic Health Records to Bridge the Inference Gap Clinical decisions are made every day in the context of a certain inference gap -- the gap between what is known at the point of care and what evidence is required to make a clinical decision.
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Standards of Eidence The anchor element in evidence-based medicine is the clinical information on which determinations are based. However the choice of evidence standards used for decision making has fundamental implications for decisions about the use of new interventions, the selection of study designs, safety standards, the treatment of individual patients, and population-level decisions regarding insurance coverage.
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A problem in introducing a more rational approach to these decisions is what Galvin terms the "cycle of unaccountability." Each group in the chain -- manufacturers, clinicians, healthcare delivery systems, patients, government regulators, and payers -- desires system change but has not, to date, taken on specific responsibilities or been held accountable for roles in instituting change. General Electric has initiated a program Access to Innovation as a way to adopt the principles of coverage with evidence development in the private sector.
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In addition, the recent launch of My HealtheVet, a web portal through which veterans will be able to view personal health records and access health information, allows patient-centered care and self-management and the ability to evaluate the effectiveness of these approaches. The result to date has been better control and fewer amputations.
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Initial steps include the development of criteria and performance measures, such as those endorsed by the National Quality Forum, and the design of an approach to aggregate information across the nation through a data-sharing mechanism, directed by an entity such as a national health data stewardship entity that sets standards, rules, and policies for data sharing and aggregation. Enisioning a Rapid Learning Healthcare System The pace of evidence development is simply inadequate to begin to meet the need.
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Developing the Test Bed: Linking Integrated Service Delivery Systems Many extensive research networks have been established to conduct clinical, basic, and health services research and to facilitate communication between the different efforts. The scale of these networks ranges from local, uptake-driven efforts to wide-ranging efforts to connect vast quantities of clinical and research information.
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A second component is the development of integrated clinical research networks through formation of the National Electronic Clinical Trials and Research (NECTAR) network.
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HMORN's advantages include the close ties between care delivery, financing, administration, and patients, which aligns incentives for ongoing improvement of care as well as shared administrative claims and clinical data, including some degree of electronic health record (Larson 2006 [July 20-21]
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. The formation of CAPP was initiated because multispecialty medical groups are well-designed learning systems at the forefront of using health information technology and electronic health records to provide advanced systems of care.
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Employers, health plans, and patient groups are also engaged in various cooperative initiatives to develop a standardized minimum data content description for electronic health records, as well as the processing rules and standards required to ensure data consistency, data portability, and EHR interoperability. Most consumers -- 60 percent according to an eHealth Initiative survey (see Marchibroda, Chapter 6)
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James Weinstein of Dartmouth described what has been learned about discerning patient preferences as a part of shared decision making. Variation in care is a common feature of the healthcare system (Figure S-2)
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Shared decision making of this sort can lead to improved patient satisfaction, improved outcomes, and better evidence. Training the Learning Health Professional In a system that learns from data collected at the point of care and applies the lessons to patient care improvement, healthcare professionals will continue to be the key components at the front lines, assessing the needs, directing the approaches, ensuring the integrity of the tracking and quality of the outcomes, and leading innovation.
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. Future clinical decision making will need not just a personal health record but a personal health knowledge base that is an intelligent integration of information about the individual with evidence related to that individual, presented in a way that lets the provider and the patient make the right decisions.
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. While CME aims to alleviate this burden, the current format is based on a static model of evidence development that will become increasingly inadequate to support the delivery of timely, up-to-date care.
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, and manufacturers have the opportunity to shape policy and practice incentives to accelerate needed changes. Incentives that support and encourage evidence development and application as well as innovation are features of a learning healthcare system.
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Rosenberg also supported the use of claims data analysis by private insurers to support evidence development. For example, the use of pharmacy claims data allows for one form of post-marketing surveillance that can provide valuable insights into both the safety and the effectiveness of drugs when used on a large scale.
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From page 33... ...
One possibility might be an industry-wide approach to evidence-based drug development, in which the creation of effectiveness data, in addition to efficacy and safety data, is a central component of the process of drug development -- rather than an afterthought or part of post-marketing surveillance. Especially with the pending developments in information technology, electronic health records, biomarkers, surrogate markers, and simulations, this may be the time to explore new approaches.
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From page 34... ...
Presentation at the Roundtable on Evidence-Based Medicine Workshop, The Learning Health Care System. Washington, DC: Institute of Medicine, Roundtable on Evidence-Based Medicine.
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. Session : Training the Learning Healthcare Professional, Health Professions Education and Teaching About Eidence.
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From page 36... ...
Presentation at the Roundtable on Evidence-Based Medicine Workshop, The Learning Health Care System. Washington, DC: Institute of Medicine, Roundtable on Evidence-Based Medicine.
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