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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary 5 Moving to the Next Generation of Studies INTRODUCTION Scientific information today is expanding much faster than our ability to effectively translate and process knowledge in ways that improve patient care. To expedite the development of information—and to address both existing gaps in the evidence base and newly emerging research challenges—innovation is needed in how we use existing research tools, strategies, and study design methodologies to produce reliable knowledge. Furthermore, new approaches are needed, with special attention to using new tools, techniques, and data resources. Workshop participants discuss the potential of a next generation of studies that complement and possibly supplant those already employed in clinical effectiveness research. In that regard, decisive efforts are need to support the development of new approaches and to nurture their inclusion in research. Papers included in this chapter examine opportunities to take better advantage of emerging resources to plan, develop, and sequence studies that are more timely, relevant, efficient, and generalizable. Also considered are approaches that better account for lifecycle variation of the conditions and interventions in play. Current opportunities and needed advancements also are discussed. A variety of innovations are presented as important components of a redesigned research paradigm as well as immediate opportunities to build toward a next generation of studies. These innovations include new approaches to observational and hybrid studies; tools for collecting and using information captured at the point of care, including those relevant to genetic variation; cooperative research networks; and possible incentives.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary Presenting a vision for new inferential and statistical tools, Sharon-Lise T. Normand from Harvard Medical School discusses opportunities to increase the efficiency with which information is produced through improved use of large data streams from a variety of sources, including clinical registries, billing databases, electronic health records, preclinical research, and trials. New tools are needed to develop and implement data pooling algorithms and inferential tools. In addition, study designs not used to their full potential—including hybrid designs, preference-based designs, and quasiexperimental designs—are well suited to exploit features of the new information sources. Findings of observational studies are intrinsically more prone to uncertainty than those from randomized trials; however, Wayne A. Ray from Vanderbilt University contends that this methodology has great value in its capacity to address the dilemma presented by the logistical difficulties and slow pace of randomized controlled trials (RCTs). Perhaps more importantly, they also enable research on many important clinical questions that RCTs are not appropriate to answer. To exploit the wealth of data becoming available, researchers will need to become more familiar with and adhere to fundamental clinical and epidemiological principles that define state-of-the-art use of observational data. Giving clinicians information on how, for whom, and in what settings specific treatments are best used is essential to improving clinical care. John Rush from the University of Texas Southwestern Medical Center proposes that researchers widen the breadth of study designs that they employ. Rush illustrates how certain clinically important questions can be addressed with observational data obtained when systematic practices are employed, or with new study designs (e.g., hybrid studies and equipoise stratified randomized designs) or posthoc analyses. Additional challenges will be to identify key questions and develop infrastructure to conduct the needed studies. Echoing Rush’s call for a reengineered practice system to better facilitate research, Isaac Kohane from Harvard Medical School discusses opportunities to instrument the health delivery system for research. While speaking specifically to the potential of high-throughput genotyping, phenotyping, and sample acquisition to accelerate genomic research, Kohane emphasizes the additional benefit to quality and performance improvement efforts. Needed for progress are increased investments in information technology (IT), increased transparency in regulation and patient autonomy, continued development of an informatics-savvy healthcare research workforce, and creation of a safe harbor for methodological experimentation. Citing the experience of the Center for Medical Technology Policy (CMTP) in attempting to facilitate private-sector coverage with evidence development, the CMTP’s Wade M. Aubry argues that “coverage with
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary evidence development” should complement, not compete with, traditional research enterprises. Aubry proposes that in order to draw from and expand on the experience of existing models, researchers must formalize ground rules for workgroups and separate evidence gap identification, prioritization, and selection for study design and funding. He discusses coverage with evidence development and outlines concepts for phased introduction and payment for interventions under protocol. Eric B. Larson from the Group Health Cooperative concludes the chapter by suggesting that emerging research networks, such as the development of programs funded by the National Institutes of Health (NIH) under the Clinical and Translational Science Awards, offer opportunities to contribute to a learning healthcare system in ways that produce relevant results that can be generalized. LARGE DATA STREAMS AND THE POWER OF NUMBERS Sharon-Lise T. Normand, Ph.D. Harvard Medical School & Harvard School of Public Health Abstract This paper describes the rationale for integrating information from multiple and diverse data sources in order to efficiently produce information. Key statistical challenges involved in integrating and interpreting information are described. The fundamental issue underpinning the use of large data streams is the poolability of the data sources. New statistical tools are required to integrate the multiple and diverse data streams in order to produce valid scientific findings. Introduction and Background We are witnessing the rapid growth in the quantity, the type, and the quality of health data that are collected. These data derive from many different information sources: preclinical data obtained from the bench, clinical trial data, registries maintained by professional societies such as the American College of Cardiology, electronic health record data, administrative billing data such as those maintained by the Centers for Medicare & Medicaid Services, hospital discharge billing data maintained by state departments of public health, and population-based surveys data such as the Medical Expenditure Panel Survey maintained by the Agency for Healthcare Research and Quality (AHRQ). We also are collecting more information than ever before about outcomes in both the clinical trial and observational settings. This increasingly frequent strategy has been adopted for several reasons: A single outcome
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary may not adequately characterize a complex disease; there may be a lack of consensus of the most important outcome; or there may be a desire to demonstrate clinical effectiveness on multiple outcomes. The consequence of the proliferation of these databases is an unprecedented demand to combine and use diverse data streams. What circumstances have led to the proliferation of databases? First, technology and innovation are evolving rapidly, producing a plethora of new medical devices, biologics, drugs, and combination products. Scientists have made medical devices smaller, smarter, and more convenient for patients. Miniaturization techniques have allowed pacemakers to weigh less than one ounce and are the size of a quarter; biological medical devices, such as microarray-based diagnostic tests for detection of genetic variation to select medication and doses of medications, are promoting personalized medicine; and combination products, such as antimicrobial catheters and drug-eluting stents, have changed the way diseases are diagnosed and treated. Moreover, in the fast-paced device environment, technologies become quickly outdated as designs are rapidly improved. Consequently, at market introduction, the next-generation devices are already under development and under study. Second, information technology has revolutionized medicine. The design, development, and implementation of computer-based information systems have permitted major advances in our understanding of the consequences of medical treatments through access to large data streams. Similarly, the excitement in bioinformatics of discovery of new biological insights has resulted in the development of tools to enable access to and use and management of these computer-based information systems. New initiatives to develop technologies and resources to advance the handling of larger and diverse datasets and to assist interpretation have been established in the fields of proteomics, genomics, and glycomics. Third, rising healthcare costs have prompted stakeholders to assess the value of health care through measurement. Using administrative billing data, early research funded by the AHRQ documented substantial variations in the use of medical therapies across geographic units such as states as well as across patient subgroups such as race/ethnicity and sex. The corresponding lack of geographic variation in patient outcomes prompted research using administrative data enhanced with clinical data to assess the quality of medical care. The number and type of quality measures reported on healthcare providers, such as hospitals, nursing homes, physicians, and health plans, have grown substantially over the past decade (Byar, 1980). A second and related line of research related to rising healthcare costs is the comparative effectiveness of therapeutic options. Information obtained from comparative randomized trials, systematic reviews of randomized trials, decision analyses, or large registries are used to quantitatively assess effectiveness of competing technologies.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary The availability of many large and diverse data sources presents an opportunity and a challenge to the scientific community. Under the current paradigm of assessing evidence, we continue to waste information by adhering to historical analytical and inferential procedures. Data sources relating to the same topic are treated as silos of information rather than as well-integrated information when assessing new technologies; information contained in multiple outcomes and multiple patient subgroups is ignored; and treatment heterogeneity in randomized trials is overlooked. The scientific community is not producing information efficiently. New tools, beyond those that expedite the mechanics of searching and accessing information, are required. Using Diverse Data Streams A fundamental problem of using diverse data sources is that of poolability. Combining data from multiple data sources is not new. At a practical level, for example, zip code level sociodemographic information from census data is often merged with patient-level information in administrative claims data to supplement covariate information. Estimates of treatment effects from diverse studies are commonly combined in the context of meta-analyses in order to learn about adverse events. The next generation of studies need to combine data sources for other reasons, however: to enhance results when the data source from which the information is based is different from the population of interest; to bridge results when transitioning from one definition to another (changing the definition from single to multiple race and ethnicity reporting); and to enhance small area estimation (see Schenker and Raghunathan, 2007, for a summary for combining survey data). Meta-analysis methods for combining information for assessing the relative effectiveness of two treatments when they have not been directly compared in randomized trials but have each been compared to other treatments have recently emerged (Lumley, 2002). When is it sensible to combine data sources? While this is not a new statistical problem, it is increasingly more frequent and more complex. A familiar setting of combining data sources is that of meta-analysis in which the data sources are estimates obtained from multiple studies. In the typical meta-analysis setting, researchers consider whether the study populations are adequately similar, whether the treatments are defined similarly, and whether the clinical outcomes are similar. These decisions are subjective. Once the decision is made to combine data, how should the information be pooled? Even if the patient-level data from each study were available, it would not be sensible to treat the observations from each patient across all of the studies as completely exchangeable. Exchangeability implies that we have no systematic reason to differentiate between the
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary outcomes of patients participating in different studies. There are numerous methodological issues to consider, such as whether data are missing and the reason for missingness, the quality of the data, the completeness of followup, type of measurement error, etc, and are beyond the scope of this paper. In looking forward, however, increasing data pooling should provide more information. Using Observational Data to Enhance Clinical Trial Data The use of observational data to supplement a randomized trial is not a new idea, and there exists a large literature describing advantages and disadvantages. There has been much discussion, for example, on the use of historical controls in clinical trial (Byar, 1980). Viewing data sources as a continuum, at one extreme, we could ignore concurrent observational data but clearly that would be wasteful and inefficient (Figure 5-1). When collecting data from participants in a clinical trial, obtaining parallel information from non-trial participants at study sites will enhance inferences. At the other end of the continuum we could use all available data and treat information obtained from the observational subjects on an equal footing (that is, exchangeable) with the information obtained from the clinical trial participants. This strategy involves a heroic assumption that will typically be unmet in practice. Between these extremes, there are many options available but rarely utilized. Neaton and colleagues summarize strategies for pooling information in the context of designs for circulatory system devices (Neaton et al., 2007). The Mass COMM trial1 is a randomized trial comparing percutaneous coronary intervention (PCI) between Massachusetts hospitals with cardiac surgery-on-site (SOS) and community hospitals without cardiac surgery-on-site. The primary objective of the trial is to compare the acute safety and long-term outcomes between sites with and without cardiac SOS for patients with ischemic heart disease treated by elective PCI. The trial involves a 3:1 (sites without SOS: sites with SOS) randomization scheme that permits community hospitals to keep their volume given the substantial infrastructure investment they have made and the knowledge that volume is important. The recruitment strategy for the randomized study involves only patients presenting to community hospitals2 (it would be very difficult to randomize patients arriving at tertiary hospitals to community hospitals). 1 A randomized trial to compare percutaneous coronary intervention between Massachusetts hospitals with cardiac surgery-on-site and community hospitals without cardiac surgery-on-site (see http://www.mass.gov/Eeohhs2/docs/dph/quality/hcq_circular_letters/hospital_mdph_protocol.pdf). 2 Massachusetts law permits elective angioplasty only at hospitals with cardiac surgery-on-site.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary FIGURE 5-1 Options for pooling data in the context of a randomized trial. SOURCE: Spiegelhalter, D. J., K. R. Abrams, J. P. Myles. 2004. Bayesian approaches to clinical trials and health-care evaluation. West Sussex, England: John Wiley & Sons, Ltd. Reproduced with permission of John Wiley & Sons, Ltd. To bolster inferences and increase efficiency, the Mass COMM investigators adopted a hybrid design that borrows information from patients presenting at tertiary hospitals (concurrent observational controls). Figure 5-2 diagrams the hybrid design of this study, a randomized controlled trial using observational data. How will the data sources (the randomized subjects and the observational subjects) be pooled? From a practical standpoint, it is not sensible to assume the observational patients arriving at tertiary hospitals and the patients randomized from community to tertiary hospitals are completely exchangeable. One strategy is to assume some differences in the outcomes of the observational controls (“additive bias”) compared to the patients randomized to the tertiary hospitals. The Mass COMM investigators assumed that the observational controls either over- or under-estimate the trial end-point by a factor of two. This decision was made prior to the enrollment of any patients. Using Multiple Data Sources to Enhance Inference Drug-eluting stents (DES) are combination products that have largely prevented the problem of restenosis. The critical path for approval of DES, like all first-in-class therapies, included several phases, each of which involved a pass or fail score: basic research, prototype design, preclinical development including bench and animal testing, clinical development, and Food and Drug Administration (FDA) filing. Sharing of knowledge in each of these domains rather than a pass or fail grade should enhance the
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary FIGURE 5-2 Schematic of Mass COMM Trial: One-way randomization with observational arm. FIGURE 5-3 Integrating information: New ontologies (variations to consider in designing processes that link data in the case of drug-eluting stents). SOURCE: Image appears courtesy of the Food and Drug Administration. estimates of effectiveness and safety. A selection of types of data streams for DES includes device, procedure, patient characteristics and outcomes, as displayed in Figure 5-3. It seems sensible to assume that the device characteristics would impact the device, procedural, and patient outcomes and that the procedural characteristics would impact the procedural and patient
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary outcomes, etc. By linking together all of these data streams through pooling, we will make more efficient use of information. How should we pool these data sources? It is clear that there should be some probability model that links together the various silos of information. Statistical models for networks of information like that for DES exist but their practical applications have been limited. Concluding Remarks A key issue in the next generation of studies involves the development and implementation of pooling algorithms. The appropriateness of any pooling algorithm depends on the structure of the data, the data collection tools, and the completeness, maintenance, and documentation of data elements. Expanding our experience with pooling different data sources is the next step. New study designs are needed that exploit features of diverse information sources. There is some experience in pooling observational data with clinical trial data. These designs, such as hybrid designs, preference-based designs, and quasi-experimental designs, while available, have not been exploited to their full potential. Little experience exists for pooling data beyond the historical or concurrent observational control setting. The diverse data streams, such as that illustrated by the DES problem, are increasingly common. More focus on the development of inferential tools that will enable combining data appropriately and assessing the relationships among the streams in large databases is needed. With the increasing number of registries, approaches for building the infrastructure to enable data sharing must be developed. Very little attention and money have been allocated for sufficient data documentation and for quality control. An additional consideration is how to best validate findings. What is the correct strategy for combining preclinical, clinical, and bench data? How do we minimize false discovery rates and determine which hypotheses are true and which are false. Finally, we need to educate researchers, regulators, and policy makers in the interpretation of results from more diverse study designs, and the assumptions made and limitations with these designs. The availability of large data streams does not guarantee valid results—thoughtful use of data sources and innovative analytical strategies will help produce valid information.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary OBSERVATIONAL STUDIES Wayne A. Ray, M.D., M.P.H. Vanderbilt University Observational studies of therapeutic interventions are critical for protecting the public health. However, high-profile, misleading observational studies, such as those of hormone replacement therapy (HRT), have materially undermined confidence in this methodology. While findings of observational studies are intrinsically more prone to uncertainty than those from randomized trials, at present many of these investigations have suboptimal methodology, which can be corrected. Common problems include elementary design errors; failure to identify a clinically meaningful t0, or start of follow-up; exposure and disease misclassification; use of overly broad end-points for safety studies; confounding by the healthy drug user effect; and marginal sample size. If observational studies are to play their needed role in clinical effectiveness studies, better training of epidemiologists to recognize and address these key issues is essential. New technologies and expanding innovations in therapeutic interventions have led to an urgent need for expansion of safety and efficacy studies. The logistical difficulties and slow pace of randomized controlled trials limit its use in many cases; but the RCT is also not appropriate for all research questions. The value of observational studies to address this dilemma and to enable research on many important clinical questions is illustrated by a number of findings regarding safety and efficacy that have been made in the past through observational designs. Prominent examples include the high risk of endometrial cancer associated with unopposed estrogen therapy and the mortality benefit of colonoscopy in colorectal cancer. However, observational studies have been criticized as inadequate for this purpose, having yielded several controversial and misleading findings, such as HRT and vitamin E associated with cardiovascular disease and dementia protection, findings later shown to be inaccurate by randomized controlled trials. The HRT findings led to millions more women using these therapies without the expected benefits. The same pitfalls are present in efficacy and safety studies based on observational data, as illustrated by findings that demonstrated a protective effect of non-steroidal anti-inflammatory drugs (NSAIDs) on dementia.3 The outcome of these well-publicized inaccurate findings is to lead researchers to discount the value of observational studies without exploring the source or analyzing the meth- 3 Thal, L. J., S. H. Ferris, L. Kirby, G. A. Block, C. R. Lines, E. Yuen, C. Assaid, M. L. Nessly, B. A. Norman, C. C. Baranak, S. A. Reines. Rofecoxib Protocol 078 Study Group. 2005. A randomized, double-blind, study of rofecoxib in patients with mild cognitive impairment. Neuropsychopharmacology 30(6):1204-1215.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary FIGURE 5-4 Notation used for observational studies in this paper. odology. A closer look reveals that these errors are really the predictable result of ignoring some basic pharmaco-epidemiologic principles. Figure 5-4 lays out the notation that will be followed throughout this paper. Consider a medication under study. Exposure (E) to a medication is either present (E+) or absent (E–) for various patients. In a clinical trial, individuals are randomized and starting at t0, these individuals are followed forward in time where occurrence and end-points of a disease under-study are recorded for both E+ and E– groups. Observational studies also have E+ and E– groups, follow-up commences at a certain t0, and individuals are followed forward in time to determine end-points; however, there are some important differences. First, the exposure group (E) is determined not by randomization but by measurement, and, secondly, choice by providers and patients in an observational study will lead to differences based on self-selection, some of which may present as confounders of real associations. Other potential problems that frequently surface during pharmacoepidemiology studies include suboptimal t0, immortal person-time with respect to follow-up, misclassification of exposure (both at baseline and time-dependent), misclassification of disease end-points—including overly broad or narrow designations. Potential confounders include the health user effect and variables that are time dependent, unavailable, or misclassified. Finally, the study may be powered inadequately—particularly in situations with infrequent end-points or chronic exposure. The issue of suboptimal t0, or beginning of follow-up, is best illustrated by first considering evaluation of a surgical intervention such as coronary artery bypass graft (CABG). An evaluation that started following patients 90 days after surgery—perhaps to wait for patients to stabilize
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary pioning these ideas and forming ready partnerships with research teams. Similarly, researchers can refine and adapt strategies from the published literature, providing important confirmatory studies or rigorous evaluation of a natural experiment, often in larger and more representative populations. Patients—or, more typically, patient advocacy groups—may point out deficiencies or special needs that suggest research projects. Most importantly, research in a community-based delivery system can yield insights into real-world issues of highest priority to the target population. The recently funded Clinical and Translational Science Award (CTSA) partnership between the University of Washington, Group Health, and the Northwest American Indian/Alaskan Native Network affords an unparalleled opportunity to surface the tribes’ preeminent research priorities and to apply tools and strategies that the Group Health and university researchers devise to address them. We at Group Health were assuming that this network would want us to study accidents, gun safety, and maternal health in their communities. These are all areas in which we have substantial prior experience, including in American Indian and Alaskan Native communities. However, we were astounded to hear, when we spoke with them in person, that the first priority of all of the tribes was methamphetamine abuse, which they told us is destroying the life of their communities. They said, “You can study what you want, as long as you start with meth.” Ultimately, these research examples are not only bidirectional but also adaptive and iterative, as befits a more real-world and less-controlled setting. This does not detract from scientific rigor; rather, it means the protocol is more likely to be calibrated for real-world conditions. Results can be translated more effectively, since the research was conducted in the setting where the findings are applied. What Is Our Vision to Guide the Next Generation of Studies and Exploit the Natural Advantages of Research Networks in Functioning Integrated Care-Delivery Systems? Three general principles underlie our vision: Bi-directionality—with research flowing seamlessly across bench, bedside, and community—will become an accepted aspect of most, if not all, funded health research. The learning healthcare system can be seen as a catalyst, partner, and test bed for research. The infrastructure needed to rapidly ramp up new research studies will evolve to meet the demands of this more complex environment.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary We emphasize that the RCT will still be the cornerstone of bi-directional research. Since Archie Cochrane’s seminal writing (Cochrane, 1971), we have benefited greatly from widespread acceptance that the RCT provides the most reliable evidence for judgment of effect. Effectiveness RCTs should be pragmatic, efficient, and ideally population based for better generalizability. Researchers should do more than simply communicate their results through academic manuscripts. If set in a delivery system, they have a direct route, and indeed a responsibility, to communicate results to providers and usually to participants. Consider how quickly research is translated into practice when a pharmaceutical company promotes a new drug RCT. The goal is to match this speed when we translate into practice any research that involves improving care. Yet we have not consistently done this well. An example is shared decision making for prostate surgery. This was shown in 1995 to improve outcomes and reduce costs, an ideal result (Wagner et al., 1995). However, it was not adopted or used in the delivery system where the research was conducted: Group Health. However, traditional RCTs, which assign single patients randomly to a prespecified treatment or intervention, are expensive and time consuming. They also may be impractical for addressing many important questions. Thus, other types of studies can be valuable and informative if conducted in a well-constructed delivery system. Examples include cluster randomized trials and disease registries. Cohort studies linked to legacy medical records and electronic medical records (EMRs) in stable populations (e.g., Group Health [Smith et al., 2002] and Mayo Clinic) are quite useful for time series analyses, correlations, and quasi-experimental research using observational data generated from clinical practice—especially so-called natural experiments that occur as practice changes are instituted or external environment changes affect medical care and outcomes. We took advantage of a natural experiment when a pilot project deploying the Advanced Medical Home in a single clinic was initiated and we rapidly developed our Advanced Medical Home study. A revival of idealized primary care, the Advanced Medical Home involves a physician and healthcare team committing to serving as the home base for as much of their patients’ medical care as they can provide—and as the coordinators of other care as needed (American College of Physicians, 2006). The rationale behind this model is that this coordination promises to help control costs while improving health outcomes and patient and provider satisfaction. Very preliminary results from our study suggest that the Advanced Medical Home improves the satisfaction of patients and providers without increasing costs. A study called Content of Care is another important example, in which we are using automated data to identify and address high-cost drivers of care across populations—and unwarranted variations in practice between physicians and medical centers.
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary What Are Some Emerging and Uniquely Important Areas Where the Theoretical Advantages of Research Networks Set in Delivery Systems Might Be Especially Valuable? Challenging areas involve detecting drug side effects (Brown et al., 2007), vaccine safety (Hinrichsen et al., 2007), and emergence of antibiotic-resistant infectious agents. These challenges also represent an opportunity: Can research networks in functioning systems improve translation by both producing valid research findings while minimizing false starts and also by detecting side effects or changes in treatment effects more quickly after deployment? Proven examples include the Vaccine Safety Datalink (VSD) project (Centers for Disease Control and Prevention, 2008; Thompson et al., 2007), which is in the process of being emulated for infectious disease biosurveillance, e.g., using HMO Research Network sites to detect changes in antibiotic resistance among sexually transmitted diseases. Directly observing the dissemination of key clinical findings in practice also provides an effective window on translation. The up-to-the-minute, comprehensive data systems of these research networks lend themselves to examining changes in treatment, such as the use of aromatase inhibitors for adjuvant breast cancer therapy following reports of this successful therapeutic approach among referral populations in cancer trials (Aiello et al., 2008). Genomics represents a unique opportunity for research in integrated care-delivery systems to exploit the features that make such research relevant and generalizable. Personalized medicine is an increasingly popular term in the health sector; but realizing its true promise will require working through many operational issues around the data, along with significant transformation in how care is delivered. Privacy issues and ownership considerations abound as large quantities of genomic data are being collected, analyzed, and stored. State-based regulations are likely to play a major role as data stewardship becomes a larger part of this conversation. Housing these data in delivery system-based research networks offers such clear-cut advantages as: Known and diverse population base Avoidance of referral filters Established and typically trusting relationship between patients and their providers in the care-delivery system Empiric study of consent Well-developed EMR to obtain phenotype information EMRs promise a much more efficient way to determine phenotypes for research and also will be uniquely helpful when and if we can “tailor” treatment and especially prevention in a personalized way (i.e., based on known
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary genetic risk or therapeutic responsiveness). But clinical science is hard-pressed to keep up with the pace of marketing forces and natural curiosity driving consumers to seek this information and act on it (Harmon, 2008). Notably, the National Human Genome Research Institute has significant work remaining to develop genomewide array studies based in existing cohorts. Behavioral and sociocultural examinations are accompanying the basic and preclinical research, but much work remains to fully understand the ramifications of collecting and leveraging genomic data, much less tailoring treatment based on these unique characteristics. Is a Culture Change Under Way? The “omics” revolution portends a cultural shift. NIH Director Zerhouni describes medicine that is not only preventive but also preemptive. He enunciates a new vision for translational research in recent publications (Zerhouni, 2005a). One outcome is the NIH Roadmap for Medical Research, a paradigm for re-engineering clinical research, which begat the NIH-funded CTSAs. This program aims to “develop a national system of interconnected clinical research networks capable of more quickly and efficiently mounting large-scale studies.” One consequence of this effort is a nascent culture change and, in places, works in progress—in institutions choosing to “re-engineer” their clinical and translational research programs. Some are realizing the potential of bringing together research networks in integrated healthcare systems with university-based scientists. Reviewers of CTSA grant proposals often highlight these interfaces as particularly strong features of applications. Given the magnitude of the CTSA program and the lofty goals related to national systems of interconnected clinical research networks, the outcomes of this Institute of Medicine (IOM) workshop should aspire to inform the NIH’s CTSA program. Indeed, the IOM’s proposed redesign of the clinical effectiveness research paradigm ideally would address challenges the NIH will face as it aims to re-engineer the massive biomedical research enterprise we currently enjoy in the United States. This reaffirms our second principle: that the learning healthcare system can be viewed as a catalyst, partner, and test bed for clinical research. We believe our third principle is central to any discussion of a new vision or paradigm for research, whether in a learning healthcare system or any other setting. To meet the complex needs of researchers, care providers, and the patients we serve, the operational infrastructure needed to rapidly ramp up will need to evolve to meet the demands of new research studies in this more complex environment. The infrastructure “renovations” should consider the full gamut of opportunities to render research more efficient, including:
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Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches – Workshop Summary Research review by Institutional Review Boards and similar ethics committees: Harmonizing regulations across federal agencies is a pivotal first step; developing stronger federal guidance about avoiding duplicative reviews of multi-institutional studies is another necessary action. Creating repositories of measures, surveys, and other indices, with standardized information about how these measures are used, to avoid reinventing measures de novo. Templates for common research processes such as gaining HIPAA authorization and developing data use agreements and similar data-sharing operations. A knowledge bank of effective participant recruitment strategies, analogous to the Cancer Control PLANET (Plan, Link, Act, Network with Evidence-based Tools) that the National Cancer Institute developed (National Cancer Institute Cancer Control PLANET, 2008). Harmonized manuscript submission procedures adopted by all publishers of medical journals. Continued attention to the architecture of health information—how it is collected, stored, and exchanged. Clinical developments are outpacing our ability to implement these needed innovations. Thoughtful reconsideration of the research process, maintaining the appropriate level of attention to patient privacy, confidentiality, security, and the doctor–patient compact, will help us to close the gap between research advances and their deployment. If the nascent culture change leads to sustainable operational infrastructure, the next generation of research studies can successfully exploit the myriad advantages of emergent research networks in healthcare systems, as long as equal attention is given to the philosophical and practical tenets we have outlined here. Emerging research networks can form a reliable basis for such learning healthcare systems, which have the potential not only to accelerate the translation of research but also to ensure that it confers true benefits to patients and the public health. REFERENCES Aiello, E. J., D. S. Buist, E. H. Wagner, L. Tuzzio, S. M. Greene, L. E. Lamerato, T. S. Field, L. J. Herrinton, R. Haque, G. Hart, K. J. Bischoff, and A. M. Geiger. 2008. Diffusion of aromatase inhibitors for breast cancer therapy between 1996 and 2003 in the cancer research network. Breast Cancer Research Treatment 107(3):397-403. Altshuler, D., J. N. Hirschhorn, et al. 2000. The common PPAR γ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genetics 26(1):76-80.
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