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Despite the benefits of their evidence-based data, RCTs have several disadvantages.
There is general agreement among care providers, payers, regulators, and the public that medical care should be informed by strong evidence, which is ideally produced by high-quality randomized controlled trials (RCTs). Unfortunately, treatment recommendations that are put forth by national guidelines do not rely on the methodically-collected evidence that is gathered by RCTs; rather, recommendations are based on more subjective assessments such as observational data or expert opinion.1
Despite the benefits of their evidence-based data, RCTs have several disadvantages. First, most RCTs conducted today tend to be small and unequipped to definitively answer the clinical questions being asked, nor are they necessarily designed to answer the questions posed by clinicians and patients in the first place. Second, patients enrolled in RCTs are often a highly selective group and not representative of the targeted treatment population. Additionally, most RCTs are sponsored by industry; therefore, there are few head-to-head treatment comparisons or evaluations of non-commercial care strategies, leaving an entire healthcare sector relatively unexplored. Finally and perhaps most challenging is that today’s system of medical evidence development is too expensive, inefficient, and slow. Without a high quality and efficient evidence engine, there is widespread variation in clinical practice and the optimization of patient care and outcomes gets pushed to the wayside.
The solution seems relatively straightforward: we need bigger, better, and more efficient trials, which are otherwise known as pragmatic clinical trials (PCTs). The primary goal of a PCT is to yield more “actionable” information for practitioners and patients at a faster rate and lower cost than conventional RCT designs. Optimally, these trials would be embedded within a healthcare system where clinical care contributes a data-building evidence base and then utilizes this knowledge to rapidly and continually modify care at the patient bedside. This is the so-called “learning healthcare system” envisioned by the Institute of Medicine.
Multiple definitions of what constitute a PCT have been proposed, but most PCTs have a few particular aspects in common: 1) they examine a “real-world” outcome that is important to patients and clinicians; 2) they are conducted in settings and patient populations where the treatment will actually be used; and 3) they are “streamlined” for efficient conduct and data acquisition. The concept of large efficient trials is not entirely novel, but in recent years, there has been a renewed focus on taking trials to the next level of proficiency.
In order to realize the full potential of PCTs, certain conditions have to be in place. Three essential elements of PCTs are interoperable electronic health records (EHRs), distributed data networks, and registries. PCTs have become synonymous with taking advantage of the growing availability of clinical EHRs. Most EHRs contain detailed patient demographics, clinical features and diagnoses, procedural lab and pharmacy data, and longitudinal events and outcomes. Instead of creating from scratch the elaborate data-capture mechanisms typical of a phase 3 trial, a PCT can extract data directly from an EHR or data registry. As a result, PCTs have access to data gathered in real-world treatment settings without needing to invest in the tremendous overhead costs associated with other data-capture systems. EHRs are being rapidly adopted in both ambulatory and hospital settings, thereby provide researchers with the potential to screen, identify, enroll, and follow large numbers of patients.
Pioneering efforts in leveraging real-world data, such as the Food and Drug Administration’s Mini-Sentinel project, have yielded initial examples of EHR mining success. Furthermore, these efforts have provided a foundation for several ongoing investigations that are evaluating the ability of EHR data to facilitate efficient, reliable, and cost-effective clinical research. For example, the Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) trial is assessing the use of low- or high-dose aspirin in patients with heart disease via the Patient-Centered Clinical Research Network (PCORnet) infrastructure. Data collection for ADAPTABLE incorporates the common data model, Medicare claims, and patient-reported outcomes.
Unfortunately, not all EHR-enabled trials are simple, fast, or efficient. In most settings, data elements and their definitions are not standardized across health systems, nor is there consistency among the data being entered into these systems. Consequently, erratic and poor quality data has become a major informatics stumbling block for those who wish to use EHR data for PCTs.
PCTs also rely on a wide array of research designs2 (some of them being relatively new), as well as different approaches to study start-up, oversight, and review. Research designs for PCTs can include cluster-randomized trials where the unit of randomization is not individual patients, but rather, a hospital, health system, city, or region. PCTs may be especially hampered by current regulatory oversight and logistical systems that were created to guide the more “traditional” RCT. For example, difficulties related to institutional review board review (including variability in their criteria and decision-making processes), navigating contracts or other logistical processes, cohort identification, consent, and the appropriate use of data monitoring are all commonly identified as the foremost challenges associated with the deployment and conduct of PCTs.
Much progress still needs to be made in order to speed evidence development and adoption mechanisms. PCTs powered by EHRs offer considerable promise to support a new system of clinical research. By encouraging the application of pragmatic methodologies, we can be the catalysts of change for a clinical research system that is currently in dire need of transformation.
Over a relatively short period of time, thanks to concerted efforts by policy makers and healthcare systems, electronic health records (EHRs) have become ubiquitous. Most if not all Americans now have an EHR, and the data contained in these records represent immense potential for the future of both patient care and clinical research. With appropriate infrastructure and safeguards, EHRs can provide a rich source of data that allows clinical trials to be performed quicker, more efficiently, and at lower cost than would be possible with the cumbersome, expensive, and logistically complex systems used in the past. Furthermore, because the data contained in EHRs are collected from “real-world” patients as part of their normal interactions with healthcare providers, this information may actually provide a clearer picture of how therapeutic interventions will work outside of the research setting.
Ultimately, linked systems of EHR-facilitated research will make a new era of pragmatic clinical trials (PCTs) possible. Efficient patient enrollment and lower costs will allow studies to be done in larger, more diverse, and more representative populations. This, in turn, can potentially increase the validity and generalizability of study findings, while also improving access to research participation for under-served or under-represented groups. In addition, different therapeutics, whether new or already marketed, can be directly compared-even when there is not a compelling commercial incentive to do so. Finally, research into rare diseases can be accelerated, as the ability to rapidly search across thousands or millions of EHRs will greatly enable the identification and recruitment of patients into clinical trials.
Despite the benefits offered by EHRs, a number of serious challenges must be met before they can be widely used in PCTs. First, EHRs are specifically built to support clinical care and reimbursement and are not necessarily designed to accommodate research needs. Before EHRs can be harnessed for PCTs, we must ensure that they are fit for such purposes. Second, despite recent progress in integrating EHR data sources into research efforts, we have yet to establish a mature, comprehensive approach for streamlining clinical trials and leveraging electronic data for secondary uses. Third and finally, the profusion of different EHR platforms and accompanying standards (each with its own unique implementation within a practice or healthcare system) must be harmonized to allow data from myriad disparate sources to be combined in useful and meaningful ways.
“Big data” and “personalized medicine” may have become buzzwords of late, but a thoughtful approach to harnessing EHRs for clinical research could unleash a genuine transformation across the clinical research enterprise-one that will eventually result in improved patient outcomes and better population health. Nevertheless, before this transformation can happen, much work needs to be done to ensure that EHR data are fit for high-quality clinical research analyses. Yet when we consider the stakes, as well as the potential rewards, it seems clear that it will be well worth the effort.
Eric D. Peterson, MD, MPH, FAHA, FACC, is Executive Director of the Duke Clinical Research Institute and the Fred Cobb, MD, Distinguished Professor of Medicine at Duke, Durham, NC. firstname.lastname@example.org
Tricoci P, Allen JM, Kramer JM, Califf RM, Smith SC Jr. Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA. 2009;301(8):831-841.
Anderson ML, Califf RM, Sugarman J; participants in the NIH Health Care Systems Research Collaboratory Cluster Randomized Trial Workshop. Ethical and regulatory issues of pragmatic cluster randomized trials in contemporary health systems. Clin Trials. 2015;12(3):276–286.
Figure. Components of a Pragmatic Clinical Trial
This figure displays the components of the pragmatic clinical trial, including details of the study design, sites, enrollment process, and efficient trial conduct.
Abbreviations: EHR = electronic health record; IRB = institutional review board; MD = medical doctor; SAE = serious adverse event