Redesigning the Clinical Effectiveness Research Paradigm Innovation and Practice-Based Approaches: Workshop Summary (2010) / Chapter Skim
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4 Organizing and Improving Data Utility
4 Organizing and Improving Data Utility
Pages 221-266
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identify the most pressing opportunities to apply these data more effectively to clinical effectiveness research. The papers that follow were derived from the workshop session devoted to organizing and improving data utility.
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Speaking on that topic was Richard Platt, from Harvard Pilgrim Health Care and Harvard Medical School, who reports on several complex efforts to design and implement distributed research models that derive large stores of useful data from a variety of sources for multiple users. THE ELECTRONIC HEALTH RECORD AND CARE REENGINEERING: PERFORMANCE IMPROVEMENT REDEFINED Ronald A
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Despite serious challenges, EHR data may offer an invaluable look into interventions and outcomes in clinical practice and offer promise as a complementary source of evidence directly relevant to everyday practice needs. EHR data also may provide an essential complement to clinical performance improvement initiatives.
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. Efficacy equivalence with existing therapies rather than comparative effectiveness is the dominant focus of most trials, with little or no thought given to economic constraints or consequences.
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Furthermore, decisions based primarily on randomized trial data do not incorporate local values, knowledge, or patient preferences into care decisions. From a distance, EHR data offer promise as a complementary source of evidence to more directly address questions relevant to everyday practice needs.
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In general, healthcare PI relies on "planning" or "experimentation" approaches to improve outcomes. These models employ a diversity of philosophies including a commitment to identifying, meeting, and exceeding stakeholder needs; continuously improving in conjunction with escalating performance standards; applying structured, problem-solving processes using statistical and related tools such as control charts, cause-and-effect diagrams, and benchmarking; and empowering all employees to drive quality improvements.
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Lastly, without a broad strategic framework, PI can be perceived as the "initiative of the month," leading to temporary improvements that are quickly lost due to inadequate hardwiring, support systems, vigilance, or PI integration across an organization. The Geisinger Health System Experience At Geisinger, PI is evolving to become a continuous process involving data generation, performance measurement, and analysis to transform clinical practice, mediated by iterative changes to clinical workflows by elimi
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; several specialty hospitals; a 215,000 member health plan (accounting for approximately one-third of the Geisinger Clinic patient care revenue) ; and numerous other clinical services and programs.
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Data, System, and Analytic Requirements Most performance improvement efforts lack the rich data required to validate outcomes (i.e., test the initial hypothesis) or the integrated data infrastructure required for rapid feedback to refine or modify large-scale interventions.
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For example, one 60-year-old diabetic patient may be prescribed drug A, while a similar diabetic patient may be described drug B because of formulary or practice style differences. When repeated hundreds or thousands of times, routinely collected EHR data offer a unique data mining resource for important clinical and economic insights.
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The Clinical Enterprise represents the "front line" of patient care; its "sources of pain" provide a strong indication of opportunity; its ideas, clinical hunches, and feedback on innovation are essential for success. At Geisinger, research has a multi-year horizon.
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Building a Performance Improvement Architecture The core feature of the PI Architecture (and associated analytics and process methodology) is to support the following key goals: (1)
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Benefits of a Performance Improvement Architecture Several important benefits from our recent experience evolving this approach are noteworthy as potentially generalizable findings.
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2 REDESIGNING THE CLINICAL EFFECTIVENESS RESEARCH PARADIGM BOX 4-2 Performance Improvement Architecture Cycle Step 1: Document Focus -- Document the current state using local data. • Identify settings and circumstances from which the PI is most likely to be generalizable, scalable, and sustainable; choose high-ranking opportunities where stakeholder support is evident or predictable.
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Increased Quality of Hypothesis Generation and Relevance of Initiatives Second, the purview of inquiry moves beyond guidelines, encompassing questions more directly relevant to practice and the related business case, as well as what an organization should stop doing, recognizing that many components of care are embedded by tradition and offer little or no value. Importantly, metrics can be focused on measures that are directly relevant to patient health (e.g., actual low density lipoprotein levels rather than lab orders)
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Summary and Conclusion Many health systems are experimenting with new approaches to quality improvement that leverage EHR capabilities. In addition to practice standardization and decision support, EHR data provide a new source of hypotheses and evidence for both PI and research.
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The many-to-many relations between insurers and providers in the United States, in which an insurer may deal with hundreds of thousands of providers and a provider may deal with tens of insurers, has meant that the only functioning systems are highly standardized and internally consistent. The resulting progress in the development of administrative databases stands in marked contrast to the world of electronic health records, which capture far more complex clinical and laboratory data, and for which there has been the growth of many competing local standards.
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, which circumscribes their permitted activities in order to safeguard individual's medical privacy. Under HIPAA, personally identifying data, termed PHI (protected health information)
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With Privacy Board approval, researchers can approach physicians and institutions holding patients' medical records to verify diagnoses and treatments,
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In part this can come about through routine implementation of algorithms, such as the one described above for venous thromboembolism, into standard units for off-the-shelf programming or routine tabulation. A number of data holders have taken this concept even further, with the concept of "episode groupers," programs that recast a broad range of related claims into single clinical entities, such as for example "community acquired pneumonia."
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Distributed Processing Part of the push for greater sensitivity and speed in drug safety surveillance is taking the form of programs to include large numbers of automated databases in common surveillance mechanisms. At the level of database amalgamation, the large U.S.
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A Note of Caution Observational data, no matter how assembled, require special care in clinical effectiveness research. The likelihood that persons undergoing compared therapies will different with respect to fundamental predictors of outcome is large and needs to be addressed head-on.
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These dynamic patterns of adaptation and evolution underscore the importance of measuring the health and economic outcomes of clinical interventions in everyday practice and drive the renewed interest in developing a robust clinical effectiveness research enterprise. There are various ways of measuring the clinical effectiveness of diagnostic and therapeutic interventions, including so-called pragmatic randomized trials, large administrative dataset analyses, and observational studies using clinical registries (Gliklick and Dreyer, 2007; Tunis et al., 2003)
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In addition to the spreading of a new technology throughout the healthcare system, and its attendant change in outcomes, clinical practice is the locus of much downstream learning and innovation. First, after a new technology is introduced into practice, the medical profession typically expands and shapes the targeted patient population within a particular disease category.
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Whereas LVAD destination therapy was shown to provide a clear survival, functional status, and quality-of-life benefit over medical management, LVADs were plagued by significant serious adverse events, especially bleeding, infections, and thromboembolic events (Rose et al., 2001)
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Administrative datasets, which are less costly in terms of data collection, also lend themselves to this purpose. Using the Medicare dataset, for exam
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2008. National outcomes for the Treatment of ruptured abdominal aortic aneurysm: Comparison of open versus endovascular repairs, pp.
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To expand on our aneurysm case, for example, a clinical registry would have been able to provide important information about the anatomical features of the aneurysm, which are not captured in administrative datasets and yet may have an important influence on outcomes. Moreover, in administrative datasets, it is often hard to distinguish between baseline co-morbidities and adverse events (e.g., myocardial infarction or heart failure)
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. Strengthening Registries Enhancing the value of registries for clinical effectiveness research requires obtaining "trial quality" data at low cost and low burden, and
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Registries can improve data quality by adjudicating adverse events and implementing a monitoring process to ensure data integrity. Functional status and quality of life are critical end-point measures, but difficult to capture and analyze longitudinally, even in randomized trials.
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Data Collection Burden and Cost Improving the efficiency of data collection for registries is crucially dependent on advances in the use of informatics. With the growth and improvement of electronic health records, institutions have the capability of automated transfer of patient, process of care, and outcome data into registries, which may address some of the data collection and cost burden.
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Registries offer the means to do so, and recently, new opportunities for addressing their traditional weaknesses have emerged in the realm of informatics, analytical techniques, and new models of financing. With the expansion and enhancement of electronic health records comes the possibilities of utilizing the clinical encounter to directly populate research registries and decreasing the burden of primary data collection.
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DISTRIBUTED DATA NETWORKS Richard Platt, M.D., M.S. Harvard Medical School, Harvard Pilgrim Health Care The Case for Distributed Networks The information created by the delivery of medical care -- about individuals, their health status, the treatment they receive, and their health outcomes -- also can teach us a great deal about how well treatments work, the risks they entail, and the cost of better health.
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A fully developed distributed data network will be able to address efficiently essentially any question that could be answered by a pooled dataset. Maintaining the information in a distributed network has advantages over a pooled dataset with regard to protection of confidential and proprietary information, local decision making regarding participation in specific activities, and the ongoing involvement of individuals with expertise in interpretation of the data.
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Finally, centralized data systems entail greater risks of catastrophic security breaches. Initiatives to Build Large Distributed Research Networks Several initiatives are currently underway to develop distributed networks that are intended eventually to have access to the health information of a substantial fraction of the U.S.
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they provided detailed, patient-level data, sometimes to a health department, only in the event of a specific need to know more about the individual. The National Bioterrorism Syndromic Surveillance Demonstration Program used a distributed network approach to surveillance for bioterrorism events and clusters of naturally occurring illness, in five HMO Research Network health plans (Lazarus et al., 2006; Yih et al., 2004)
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The health plans operate as a distributed network insofar as they create standard data files and execute shared computer programs that perform the large majority of the analyses, which are shared in tabular form and then pooled. The health plans also obtain detailed clinical information about potential cases of Guillain-Barré syndrome identified through diagnosis codes by obtaining full text medical records.
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The only protected health information that the covered entity shares with the coordinating center is the month and year in which individuals were immunized. These examples of distributed networks illustrate the potential for distributing much of the data processing as well as the data storage.
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. Governance Developing effective governance models for distributed networks to improve population health and healthcare delivery will be a major challenge.
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Specific examples of activities the network might support include the following. The FDA might use relevant parts of the network to support postmarketing surveillance, CDC might use the same or other parts to support prevention initiatives, AHRQ might use it to support comparative effectiveness research, and the NIH might use it to support clinical research.
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Furthermore, it appears feasible to develop distributed networks so that a common infrastructure can support an array of different uses in the public interest. Creation of effective governance mechanisms will be a considerable challenge, as will development of a sustainable mechanism to fund development and maintenance of infrastructure for both technical issues and governance.
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2008. National outcomes for the treatment of ruptured abdominal aortic aneurysm: Comparison of open versus endovascular repairs.
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2006. Hospital costs for left ventricular assist devices for destination therapy: Lower costs for implantation in the post-rematch era.
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2007. Developments in post-marketing comparative effectiveness research.
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2004. National bioterrorism syndromic surveillance demonstration program.
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