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Hints of a Different Way— Case Studies in Practice-Based Evidence

OVERVIEW

The Institute of Medicine Roundtable on Evidence-Based Medicine seeks “the development of a learning healthcare system that is designed to generate and apply the best evidence for the collaborative healthcare choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care” (Roundtable on Evidence-Based Medicine 2006). Generating evidence by driving the process of discovery as a natural outgrowth and product of care is the foundational principle for the learning healthcare system. This has been termed by some “practice-based evidence” (Greene and Geiger 2006). Practice-based evidence focuses on the needs of the decision makers, and narrowing the research-practice divide by identifying questions most relevant to clinical practice and conducting effectiveness research in typical clinical practice environments and unselected populations (Clancy 2006 [July 20-21]).

This chapter highlights several examples of the use of healthcare experience as a practical means of both generating and successfully applying evidence for health care. In the first paper, Peter B. Bach discusses how the Coverage with Evidence Development policy at Centers for Medicare and Medicaid Services (CMS) has aided the development of important evidence on effectiveness for a range of interventions, including lung volume reduction surgery (LVRS), PET (positron emission tomography) scanning for oncology, and implantable cardioverter defibrillators. By identifying information needed for improved understanding of intervention risks and



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The Learning Healthcare System: Workshop Summary 1 Hints of a Different Way— Case Studies in Practice-Based Evidence OVERVIEW The Institute of Medicine Roundtable on Evidence-Based Medicine seeks “the development of a learning healthcare system that is designed to generate and apply the best evidence for the collaborative healthcare choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care” (Roundtable on Evidence-Based Medicine 2006). Generating evidence by driving the process of discovery as a natural outgrowth and product of care is the foundational principle for the learning healthcare system. This has been termed by some “practice-based evidence” (Greene and Geiger 2006). Practice-based evidence focuses on the needs of the decision makers, and narrowing the research-practice divide by identifying questions most relevant to clinical practice and conducting effectiveness research in typical clinical practice environments and unselected populations (Clancy 2006 [July 20-21]). This chapter highlights several examples of the use of healthcare experience as a practical means of both generating and successfully applying evidence for health care. In the first paper, Peter B. Bach discusses how the Coverage with Evidence Development policy at Centers for Medicare and Medicaid Services (CMS) has aided the development of important evidence on effectiveness for a range of interventions, including lung volume reduction surgery (LVRS), PET (positron emission tomography) scanning for oncology, and implantable cardioverter defibrillators. By identifying information needed for improved understanding of intervention risks and

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The Learning Healthcare System: Workshop Summary benefits and designing appropriate trials or mechanisms to accumulate such evidence, CMS has accelerated access to health innovations. Moreover, generation of needed evidence from clinical practice is a means to better inform some of the many difficult clinical decisions inherent to medical practice. In the specific case of LVRS, the work of CMS identified an unproven approach that could have had an adverse impact on many patients before enough evidence was collected. By taking the lead, CMS helped develop timely information useful to other payers, clinicians, and patients. Many risks or benefits of health technologies are not evident when initially introduced into the marketplace, and Jed Weissberg demonstrates the value of collecting, linking, and utilizing data for pharmacovigilance purposes in his paper on Kaiser Permanente’s use of accumulated data for a post-market evaluation of cyclooxygenase-2 (COX-2) inhibitors. In this case, analysis was hypothesis driven and Weissberg notes that substantial work is needed to achieve a system in which such insights are generated customarily as a by-product of care. For such a transformation, we must improve our ability to collect and link data but also make the organizational and priority changes necessary to create an environment that values “learning”—a system that understands and values data and has the resources to act upon such data for the betterment of care. Stephen Soumerai discusses the potential for quasi-experimental study designs to inform the entire process of care. His examples highlight well-designed studies that have been used to analyze health outcomes and demonstrate unintended consequences of policy decisions. He notes that widespread misperception of observational trials belies their strength in generating important information for decision making. Sean Tunis expands this argument by illustrating how practical clinical trials (PCTs) could serve as an effective means to evaluate issues not amenable to analyses by randomized controlled trials (RCTs), using the example of a PCT designed to evaluate the use of PET for diagnosing Alzheimer’s disease. Alan H. Morris’ work with computerized protocols—termed adequately explicit methods—demonstrates the considerable potential for such protocols to enhance a learning healthcare system. In his example, protocols for controlling blood glucose with IV insulin (eProtocol-insulin) provide a replicable and exportable experimental method that enables large-scale complex clinical studies at the holistic clinical investigation scale while reducing bias and contributing to generalizability of trial results. These protocols were also integrated into clinical care electronic health records (EHRs) demonstrating their utility to also improve the translation of research methods into clinical practice. Additionally, they could represent a new way of developing and distributing knowledge both by formalizing experiential learning and by enhancing education for clinicians and clinical researchers.

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The Learning Healthcare System: Workshop Summary COVERAGE WITH EVIDENCE DEVELOPMENT Peter B. Bach, M.D., M.A.P.P.1 Centers for Medicare and Medicaid Services Coverage with Evidence Development (CED) is a form of National Coverage Decision (NCD) implemented by CMS that provides an opportunity to develop evidence on the effectiveness of items or services that have great promise but where there are potentially important gaps between efficacy and effectiveness, the potential for harm without benefit in sub-populations, or an opportunity to greatly enrich knowledge relevant to everyday clinical decision making. Most Medicare coverage determinations are made at a local level through carriers and fiscal intermediaries under contract with CMS. However, a few times each year, an NCD is made at the central level that dictates coverage policy for the entire country. Whether the coverage determination is made locally or through an NCD, these determinations are based on historical data regarding the risks and benefits of items or services. Once coverage decisions are made, Medicare very rarely evaluates utilization, whether or not beneficiaries receiving the services are similar to those studied, or assesses whether outcomes of the covered services match those in the reports used to make the determination. At the extreme, there are many instances in Medicare coverage where determinations are made regarding coverage based on a brief trial of a handful of volunteer research subjects and then the service is provided to hundreds of thousands of patients for a far greater duration, where the patients are also more elderly and have a greater degree of comorbid illness than any of the patients included in the original study. This lack of information collection about real-world utilization and outcomes, the potential for differences between effectiveness and efficacy, and different trade-offs between benefits and risks is viewed by many as an important “forgone opportunity” in health care. CED aims to integrate further evidence development into service delivery. Technically, CED is one form of “coverage with restrictions,” where the restrictions include limiting coverage to specific providers or facilities (e.g., the limitation on which facilities can perform organ transplants), limiting coverage to particular patients, or in the case of CED, limiting coverage to contexts in which additional data are collected. From an implementation standpoint, CED requires that, when care is delivered, data collection occurs. Not a requirement of CED per se, but an expectation of it, is that 1 Dr. Bach is an attending physician at Memorial Sloan-Kettering Cancer Center in New York City. He served as senior adviser to the administrator of the Centers for Medicare and Medicaid Services from February 2005 to November 2006.

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The Learning Healthcare System: Workshop Summary the additional data generated will lead to new knowledge that will be integrated both into the CMS decision-making process to inform coverage reconsideration and into the knowledge base available for clinical decision making. Two case studies illustrate how CED can be used to directly or indirectly develop evidence that augments healthcare decision-making and CMS coverage policy. Specific Examples of CED The National Emphysema Treatment Trial (NETT), funded by CMS, was a multicenter clinical trial designed to determine the role, safety, and effectiveness of bilateral lung volume reduction surgery (LVRS) in the treatment of emphysema. The study had, as a secondary objective, to develop criteria for identifying patients who are likely to benefit from the procedure. While conducted prior to the coinage of the term “coverage with evidence development,” the trial was implemented through a CMS NCD that eliminated coverage of LVRS outside of the trial but supported coverage for the surgery and routine clinical costs for Medicare beneficiaries enrolled in the trial. NETT demonstrates how coverage decisions can be leveraged to directly drive the development of evidence necessary for informed decision making by payers, physicians, and patients. The trial clarified issues of risk and benefit associated with the procedure and defined characteristics to help identify patients who were likely to benefit—information that was incorporated into the revised CMS NCD on lung volume reduction surgery and had significant impact on guidance offered for treatment of emphysema. Emphysema is a major cause of death and disability in the United States. This chronic lung condition leads to the progressive destruction of the fine architecture of the lung that reduces its capacity to expand and collapse normally—leaving patients increasingly unable to breathe. The presence of poorly functioning portions of the lung is also thought to impair the capacity of healthy lung tissue to function. For patients with advanced emphysema, LVRS was hypothesized to confer benefit by removing these poorly functioning lung portions—up to 25-30 percent of the lung—and reducing lung size, thus pulling airways open and allowing breathing muscles to return to normal positioning, increasing the room available for healthy lung function, and improving the ability of patients to breathe. Prior to the trial, evidence for LVRS consisted of several case series that noted high up-front mortality and morbidity associated with the surgery and anecdotes of sizable benefit to some patients. At the time of the NCD, the procedure was a high-cost item with the operation and months of rehabilitation costing more than $50,000 on average. Many health economists predicted that utilization would rise rapidly with tens of thousands of patients eligible for

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The Learning Healthcare System: Workshop Summary the procedure and an estimated cost to Medicare predicted to be as much as $15 billion per year (Kolata 2006). Because of the surgery’s risks and the absence of clear evidence on its efficacy, patient selection criteria, and level of benefit, CMS initiated an interagency project with the National Heart, Lung, and Blood Institute (NHLBI) and the Agency for Healthcare Research and Quality (AHRQ). AHRQ’s Center for Health Care Technology and NHLBI carried out independent assessments of LVRS; they concluded that the current data on the risks and benefits were inconclusive to justify unrestricted Medicare reimbursement for the surgery and suggested a trial to assess the effectiveness of the surgery. NHLBI conducted a scientific study of LVRS to evaluate the safety and efficacy of the current best available medical treatment alone and in conjunction with LVRS by excision. CMS funded the routine and interventional costs. The trial was conducted with the expectation that it would provide answers to important clinical questions about the benefits and risks of the surgery compared with good medical therapy, including the duration of any benefits, and clarification of which subgroups experienced benefit. Some initial barriers included resistance by the public, which considered it unethical to pay for some patients but not others to receive treatment. The trial evaluated four subgroups prespecified by the case series studies and physiological hypotheses. One group was dropped early (homogeneous lung, severe obstruction, very low diffusing capacity) due to severe adverse outcomes including a high up-front mortality. The other three subgroups experienced some level of benefit and patients were followed for two years. On average, patients with severe emphysema who underwent LVRS with medical therapy were more likely to function better and did not face an increased risk of death compared to those who received only medical therapy. However results for individual patients varied widely. The study concluded that overall, LVRS increased the chance of improved exercise capacity but did not confer a survival advantage over medical therapy. The overall mortality was the same for both groups, but the risk of up-front mortality within the first three months was significantly increased for those receiving therapy (Ries et al. 2005). In addition to identifying patients that were poor candidates for the procedure, the trial identified two characteristics that could be used to predict whether an individual participant would benefit from LVRS, allowing clinicians to better evaluate risks and benefits for individual patients. CMS responded by covering the procedure for all three subgroups with any demonstrated benefit. In this case, a well-designed and implemented CED NCD led to the creation of data that clarified the CMS coverage decision, refined questions in need of future research, and provided the types of evidence important to guide treatment evaluation by clinicians (subgroups of patients who might benefit or be at increased risk from LVRS) and patients (symptoms and

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The Learning Healthcare System: Workshop Summary quality-of-life data not previously available). Such evidence development led to informal and formative impressions among patients and providers that caused them to reconsider the intervention’s value. As a result, from January 2004 to September 2005, only 458 Medicare beneficiaries received LVRS at a total cost to the government of less than $10.5 million (Kolata 2006). Alternatively, CED can indirectly provide a basis for evidence development. PET is a diagnostic imaging procedure that has the ability to differentiate cancer from normal tissue in some patients, and thus can help in diagnosing and staging cancer and monitoring a patient’s response to treatment. While the available evidence indicated that PET can provide more reliable guidance than existing imaging methods on whether the patient’s cancer has spread, more data were required to help physicians and patients make better-informed decisions about the effective use of PET scanning. CMS implemented an NCD to cover the costs of PET scanning for diagnosis, staging, re-staging, and monitoring of cancer patients, with the requirement that additional clinical data be collected into a registry. This type of CED allowed CMS to ensure patients would receive treatment benefit and build upon emerging evidence that PET was safe and effective by creating a platform from which other questions of clinical interest could be addressed. The NCD articulated questions that could lead to a reevaluation of the NCD, such as whether and in what specific instances PET scanning altered treatment decisions or other aspects of management of cancer patients. CMS required that information about PET scan be submitted to a registry. The registry then conducted research by following up with physicians to ask why a PET scan was ordered and whether the results of the PET scan altered disease outcomes. Participating patients and physicians were given the opportunity to give consent for their data to be used for research purposes, and other HIPAA (Health Insurance Portability and Accountability Act) issues were avoided by restricting research to the registry. While such research questions are simple and not likely to be independently pursued by agencies engaged in broader investigations such as the National Institutes of Health (NIH), they are typical of the kinds of evidence often needed to ensure the delivery of appropriate and effective health care. Overarching Issues Affecting CED Several overarching issues will affect the long-term viability of CED as a robust policy that spurs the development of a learning healthcare system. Of particular interest are the statutory authorities on which CED is based, the implications for patients who are eligible for services covered under CED, the role that the private-public interface must play for the learning to take place, and the issue of capacity in the healthcare system more broadly

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The Learning Healthcare System: Workshop Summary for such data collection, analysis, interpretation, and dissemination. Each of these issues has been extensively considered in the development of existing CED determinations, so moving forward the implementation of further CED determinations should be somewhat more straightforward. Statutory Authority In describing CED, CMS released a draft guidance followed by a final guidance that articulated the principles underpinning CED and the statutory authorities on which it is based. Both are available on the CMS coverage web site (www.cms.hhs.gov/coverage). In truth, there are two separate authorities, depending on the type of CED determination. When participation in a clinical trial is required as part of coverage, as in the NETT, the authority being used by CMS is based on section 1862(a)(1)(E) of the Social Security Act. CMS terms this “Coverage with Clinical Study Participation (CSP).” This section of the act allows CMS to provide coverage for items or services in the setting of a clinical research trial, and the use of this authority clarifies further that the item or service is not “reasonable and necessary” under section 1862(a)(1)(A) of the Social Security Act—the authority under which virtually all routine services are covered. The CED guidance further articulates that decisions such as NETT, in which coverage is provided only within the context of a clinical study, is meant as a bridge toward a final coverage determination regarding the service being “reasonable and necessary” under section 1862(a)(1)(A). Coverage, such as that provided for the PET registry, is based on the 1862(a)(1)(A) section of the Social Security Act because CMS has made the determination that the service is reasonable and necessary for the group of patients and indications that are covered, but that additional data are required to ensure that the correct service is being provided to the correct patient with the correct indications. As such, the registry is being used to collect additional data elements needed to better clarify the details of the service, patient, and indication. CMS terms this type of CED “Coverage with Appropriateness Determination” (CAD). Implications for Patients Unlike an NCD that provides coverage without restrictions, all NCDs that include restrictions affect how or where or which beneficiaries can receive services. As in coverage for organ transplants being provided only in certain hospitals, CED requires that patients receive services in locales where evidence can be collected. This limitation may be quite significant in terms of its effect on access or not significant at all. For instance, the NETT was conducted at only a handful of centers throughout the United States,

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The Learning Healthcare System: Workshop Summary so Medicare beneficiaries who wanted to receive the service had to travel to one of these centers and be evaluated. The coverage of fluorodeoxyglucose (FDG) PET for cancer is also limited to those PET facilities that have put in place a registry; but in this case, virtually all facilities in the country have been able to do so relatively easily. Not only can CED in some cases limit geographic access, but when CED requires clinical research participation, patients may have to undergo randomization in order to have a chance to receive the service. In the case of the NETT trial, some patients were randomized to best medical care instead of the surgery. In general, it is not unethical to offer services that are unproven only in the context of a clinical trial, when the scientific community is in equipoise regarding the risks and benefits of the service versus usual care and the data are insufficient to support a determination that the service is reasonable and necessary. Sometimes, patients may also be asked to provide “informed consent” to participate in research as part of CED, as in the NETT. However, patients have not been required to allow their data to be used for research when receiving a service such as the FDG-PET scan under CED. Rather, patients have been able to elect to have their data used for research, or not, but their consent has not been required for the service to be covered. (Early reports suggest that about 95 percent of Medicare beneficiaries are consenting to have their data used for research.) Theoretically, under some scenarios in which registries are being used to simply gather supplementary medical information, a requirement for informed consent could be waived due to the minimal risk posed and the impracticability of obtaining it. The Private-Public Interaction Necessitated by CED Because CED leads only to the requirement for data collection, but not to the requirement for other steps needed for evidence development, such as data analysis, scientific hypothesis testing, or publication and dissemination, CED requires a follow-on process to achieve its broader policy goals. To date, these goals have been achieved through partnerships with other federal agencies, providers, professional societies, academic researchers, and manufacturers. For instance, in the NETT, as noted above, the scientific design of data collection and the analysis and publication of study results were orchestrated through NHLBI, which engaged and funded investigators at multiple participating institutions. The involvement of the NHLBI and investigators from around the country ensured that CED would lead to a better understanding of the clinical role of LVRS in the Medicare population. In the case of the FDG-PET registry, the registry was required by CED, but was set up through a collaboration involving researchers at several academic institutions, and professional societies, to form the National Oncologic PET Registry (NOPR). These researchers constructed a research

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The Learning Healthcare System: Workshop Summary study design around the CED requirement, such that there is a high probability that both clinicians and CMS will have a far better understanding of the role of FDG-PET scans in the management of Medicare patients with cancer. Recently, CMS issued another CED decision covering implantable cardiac defibrillators (ICDs), in which all patients in Medicare receiving ICDs for primary prevention are required to submit clinical data to an ICD registry. The baseline registry, which captures patient and disease characteristics for the purpose of gauging appropriateness (i.e., CAD) forms the platform for a 100 percent sample of patients receiving this device for this indication in Medicare. The entity running the registry has since engaged other private payers, cardiologists, researchers, and device manufacturers in order that a follow-on data collection can be put in place to capture the frequency of appropriate ICD “firings” (where the device restores the patient’s heart to an appropriate rhythm). In other words, because CMS requires only the core data elements to be submitted, evidence development is driven only indirectly by CED. However, the establishment of the registry mechanism and baseline data creates the framework for a powerful and important tool that, if utilized, provides the opportunity to conduct and support the kind of research necessary for a learning approach to health care. Capacity The NETT has been completed, and as previously noted, the results of the trial substantially altered clinical practice. The FDG-PET registry and the ICD registry, as well, are still ongoing. These are only two more examples of how needed clinical evidence could be gathered through the CED to ensure that the best available information about utilization, effectiveness, and adverse events is made available to clinicians and policy makers. It is easy to imagine that for these two decisions, it will not be difficult to find qualified researchers to analyze the data or journals interested in publishing the findings. However, these few CED decisions are just a model for what could theoretically become a far more common process in coverage, not only at CMS but more broadly. As the healthcare system moves toward increasing standardization of medical information and toward adoption of EHRs more extensively, better clinical detail should be readily available to satisfy CAD requirements, and longitudinal data should be readily accessible to address study questions. At that point, the current scientific infrastructure, the number of qualified researchers, and the appetite of peer-reviewed journals for such data analyses may constitute obstacles to a learning healthcare system. Aware of these potential system-level limitations, were CED to be implemented more broadly, CMS has cautiously applied the policy in settings where the infrastructure and science were in place or could quickly be

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The Learning Healthcare System: Workshop Summary put into place. Going forward, CMS will likely make relatively few CED determinations, judiciously choosing those areas of medical care in which routine data collection could enhance the data on which coverage determinations are made and improve the quality of clinical care. USE OF LARGE SYSTEM DATABASES Jed Weissberg, M.D. Kaiser Permanente Medical Care Program Integrated delivery systems and health maintenance organizations (HMOs) have a long history of epidemiologic and health services research utilizing linked, longitudinal databases (Graham et al. 2005; East et al. 1999; Friedman et al. 1971; Selby 1997; Platt et al. 2001; Vogt et al. 2004). Research networks currently supported by the government are examining healthcare interventions in diverse populations, representative of the U.S. citizenry. Hypothesis-driven research utilizing existing clinical and administrative databases in large healthcare systems is capable of answering a variety of questions not answered when drugs, devices, and techniques come to market. The following case study illustrates the value of collecting, linking, and utilizing data for pharmacovigilance purposes, outlines key elements necessary to encourage similar efforts, and hints at changes that might develop the potential to discover such insights as a natural outcome of care within a learning healthcare system. A project using a nested, case-control design to look at the cardiovascular effects of the COX-2 inhibitor, rofecoxib, in a large HMO population within Kaiser Permanente (KP) (Graham et al. 2005) demonstrates the potential value of pharmacoepidemiological research and the opportunities offered with the advent of much greater penetration of full EHRs to rapidly increase knowledge about interventions and delivery system design. Much can be learned from this case study on what it will take to move to a learning system capable of utilizing data, so that valid conclusions, strong enough on which to base action, can be identified routinely. While the potential for such a system exists, many barriers including technical data issues, privacy concerns, analytic techniques, cost, and attention of managers and leaders will need to be overcome. Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used to treat chronic pain, but this treatment is often accompanied by upper gastrointestinal toxicity leading to admission to the hospital for ulcer complications in around 1 percent of users annually (Graham et al. 2005). This is due to NSAID inhibition of both isoforms of COX: COX-1, which is associated with gastro protection as well as the COX-2 isoform, which is induced at sites of inflammation. The first COX-2 selective inhibitors,

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The Learning Healthcare System: Workshop Summary rofecoxib and celecoxib, were thus developed with the hope of improving gastric safety. In the five years from the approval and launch of rofecoxib to its withdrawal from the market, there were signs of possible cardiovascular risk associated with rofecoxib use. Using Kaiser Permanente data, Graham et al. examined the potential adverse cardiovascular effects of “coxibs.” The nested, case-control study design was enabled by the availability of a broad set of data on Kaiser Permanente members, as well as the ability to match data from impacted and non-impacted members. As a national integrated managed care organization providing comprehensive health care to more than 6.4 million residents in the State of California, Kaiser Permanente maintains computer files of eligibility for care, outpatient visits, admissions, medical procedures, emergency room visits, laboratory testing, outpatient drug prescriptions, and mortality status for all its members. While the availability of prescription and dispensing data as well as longitudinal patient data (demographics, lab, pathology, radiology, diagnosis, and procedures) was essential to conduct such a study, several other elements related to organizational culture precipitated and enabled action. The organization and culture of KP created an environment that can be described as the “prepared mind” (Bull et al. 2002). The interest of clinicians and pharmacy managers in the efficacy, safety, and affordability of the entire class of COX-2 drugs had resulted in the relatively low market share of COX-2 drugs within KP NSAID use (4 percent vs. 35 percent in the community, Figure 1-1). This 4 percent of patients was selected based on a risk score developed in collaboration with researchers to identify appropriate patients for COX-2 therapy (Trontell 2004). Of additional importance to the investigation was the presence of a small, internally funded group within KP—the Pharmacy Outcomes Research Group (PORG)—with access to KP prescription information and training in epidemiology. These elements were brought together when a clinician expressed concern about the cardiovascular risk associated with COX-2 drugs and suggested that KP could learn more based on its own experiences. A small grant from the Food and Drug Administration (FDA), combined with the operating budget of the PORG, enabled the authors to design and execute a case-control study. The study concluded that rofecoxib usage at a dose greater than 25 mg per day increased the risk of acute myocardial infarction (AMI) and sudden cardiac death (SCD). Additional insights of the study pointed to the differences between other NSAIDs and the inability to assume cardiovascular safety for other COX-2 drugs. The conduct of this study contributed to the FDA’s scrutiny of rofecoxib, which resulted in the manufacturer’s decision to withdraw the drug from the marketplace. In addition, the initial release of the study abstract stimulated similar analyses that clarified the clinical risks associated with this drug class and illustrated the gap between

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The Learning Healthcare System: Workshop Summary because of the extensive literature indicating favorable outcome changes when decision support tools of many kinds are employed to aid clinician decision makers. It has seemed to my colleagues and to me that application of adequately explicit computerized treatment protocols for titration of clinical problems is more easily achieved than application of protocols for diagnosis. The diagnostic challenges frequently seem broader and more encompassing than the treatment challenges once a diagnosis has been made. Furthermore, many clinical decisions should embrace the wishes of patients or their surrogates. Capturing patient or surrogate assessments of outcome utilities and incorporating them in the rules of adequately explicit protocols seems a daunting but surmountable challenge. More systematic work is needed to define the roles of adequately explicit computerized protocols in many diagnostic and therapeutic arenas. The evaluation of a potential target includes assessment of the reliability of available measurements and other replicable data. A measurement-rich and quantified clinical setting increases the likelihood of driving adequately explicit rules with patient-specific data. However, even difficult-to-define constructs such as “restlessness” can be made more replicable by listing the specific observations a clinician might use to identify the construct. We all have only five senses through which we receive information from the world about us. The challenge of knowledge engineering is to specify the few elements received by these senses that drive specific decisions. Our experience during the past two decades indicates that this is manageable. Formalizing Experiential Learning as a Means of Enabling a Learning Healthcare System Adequately explicit computerized protocols could supplement traditional peer-reviewed publication with direct electronic communication between research investigators and thereafter between investigators and clinical care users. This could introduce a new way of developing and distributing knowledge. Evidence-based knowledge for clinical decision making comes from two sources: first, from formal studies that include observational and experimental work (RCTs provide the most compelling results); second, from experiential knowledge. Currently, this experiential knowledge is derived primarily from individual experience and thus is influenced by local factors and bias. This individual experience contributes to strongly held but variable opinions that lead to unnecessary variation in clinical practice (Wennberg 2002). Adequately explicit computerized protocols could formalize this experiential learning in two sequential stages. In the first stage, knowledge could be captured through multiple investigator and center participation in development and refinement of protocol rules. We have used this process successfully in our current NIH Roadmap

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The Learning Healthcare System: Workshop Summary contract work with blood glucose management. Our current computerized protocol for blood glucose management with IV insulin (eProtocol-insulin) was developed and refined by collaborators who include multiple pediatric and adult intensivists in more than 14 U.S. and Canadian clinical sites. This diminishes local factor and bias concerns; they become smaller as the number of different participants and institutions increase. In the second stage, education of practitioners could occur during utilization of a protocol for clinical care. Adequately explicit computerized protocols could take advantage of an electronic infrastructure and translate research experience into clinical practice by adopting a direct electronic education strategy at the point of care or point of decision making. For example, the adequately explicit instructions of eProtocol-insulin could be linked to a new on-demand explanatory educational representation of the protocol logic. A user could question the specific eProtocol-insulin instruction at whatever level of detail the user wishes. The knowledge captured by the protocol developers during the first stage could thus be presented at the time, and within the context, of a specific clinical care question, but only when demanded and without requiring the user to address the published literature. This new educational strategy could complement traditional knowledge transfer through education based on reading and coursework and through published work. For some activities this new educational strategy could become the dominant learning strategy for clinicians. For example, when protocols are modified to incorporate new knowledge, the updated electronic protocol, once validated appropriately, could become the expected and most direct route for transferring this new knowledge to the clinical practitioner. REFERENCES Abramson, N, K Wald, A Grenvik, D Robinson, and J Snyder. 1980. Adverse occurrences in intensive care units. Journal of the American Medical Association 244:1582-1584. Acute Respiratory Distress Syndrome Network. 2000a. Mechanical Ventilation Protocol. Available from www.ardsnet.org or NAPS Document No 05542 (Microfiche Publications, 248 Hempstead Turnpike, West Hempstead, NY). ———. 2000b. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the Acute Respiratory Distress Syndrome. New England Journal of Medicine 342(18):1301-1308. Akhtar, S, J Weaver, D Pierson, and G Rubenfeld. 2003. Practice variation in respiratory therapy documentation during mechanical ventilation. Chest 124(6):2275-2282. American Psychiatric Association (APA). 1998. Practice Guideline for the Treatment of Patients with Bipolar Disorder. Washington, DC: APA. Arkes, H. 1986. Impediments to accurate clinical judgment and possible ways to minimize their impact. In Judgment and Decision Making: An Interdisciplinary Reader, edited by H Arkes and K Hammond. Cambridge, UK: Cambridge University Press.

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