Across all medical specialties, there is a severe lack of high-quality clinical evidence, in part because the gold standard for evidence is large-scale randomized controlled clinical trials. Such trials are on an unsustainable cost trajectory, as they require expensive, stand-alone data capture infrastructures. Furthermore, they typically enroll highly selected populations that are not necessarily representative of real-world patients. Although the emergence of the electronic health record (EHR) holds great promise for generating much-needed evidence, medical research lags far behind other industries in its ability to use big data to get the answers decision makers need in health care. The ability to harness good quality, usable data from EHRs will likely be as revolutionary to health care as the Internet was to other industries.
The problem is complex, and one facet of the issue is that data from health systems are not interoperable; for example, information such as date of birth, blood pressure, or diagnoses can be recorded in a myriad of ways. Although the Centers for Medicare and Medicaid Services encourages and incents “meaningful use” of EHRs, these systems are customizable to each institution’s needs, and as a result, data from individual health care systems and providers are housed in silos of babel—with limited ability to exchange information between them. Compounding the issue, most organizations erected proprietary systems of digital health data capture before standardized formats were developed and before thoughtful consideration about reuse of these data for research activities gained traction. As a result, it has been infeasible to ask questions as seemingly simple and important as “Which dose of aspirin is associated with better outcomes?”
To counter these problems, the Patient-Centered Outcomes Research Institute (PCORI) funded PCORnet, the National Patient-Centered Clinical Research Network, to support clinical research. PCORnet has built strong partnerships between clinical researchers and patient advocacy networks. In addition, PCORnet has established a Common Data Model to support pragmatic trials and observational research. Use of PCORnet’s Common Data Model will enable large-scale clinical research from data gathered during patient care as well as rapid execution of queries. Data can be collected and harmonized across more than 130 diverse organizations representing more than 122 million individuals who had a medical encounter in the past five years. Additionally, 41 million patients are available for enrollment in clinical trials and other studies.
The model encompasses information from the EHR and administrative data, so it can be used for research and standardizes the data across participating networks. The Food and Drug Administration’s (FDA’s) Sentinel Initiative pioneered this approach to monitor the safety of FDA-regulated medical products. PCORnet’s common data model builds on the Sentinel Initiative, the Health Care Systems Research Network, and the Observational Health Data Sciences and Informatics Initiative Common Data Model.
The Common Data Model: How It works
Participating networks organize data according to the Common Data Model. These include data entered into the EHR during clinical care, data from insurance claims and patient reports, and those collected for administrative purposes. Secure data queries are supported by PopMedNet, a software application that allows investigators to use data from a participating networks’ populations that are generalizable to the real world. These networks can opt into clinical research and review, execute, and return results of queries via the web-based PopMedNet portal. The networks retain ownership of their data by performing analyses of queries behind their firewalls in a data warehouse.
Case Examples
Three major studies are underway, all funded by PCORI, including Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness (ADAPTABLE), the PCORnet Obesity Observational Study, and the PCORNET Bariatric Study. For the ADAPTABLE study, which compares outcomes in patients randomized to two different doses of aspirin, data in the Common Data Model are used to identify prospective patients, who are invited to participate via e-mail. Those who are interested are given an electronic informed consent form and, if they choose to participate, are entered into the trial. In this trial, the Common Data Model supports recruitment, data collection, monitoring, and follow-up. The ADAPTABLE trial is expected to cost a fraction of a traditional cardiovascular disease trial.
The bariatric and childhood obesity observational trials also rely on the power of PCORnet’s Common Data Model. Both are expected to query approximately 600,000 patient records to answer important questions related to obesity. Through the use of the model, researchers expect to conduct these observational studies more quickly and with greater volumes of data than previously thought possible.
The goal of PCORnet’s research platform is to use the Common Data Model to conduct clinical trials and observational studies that are faster, cheaper, and more efficient than is traditional research.
The Future Of PCORnet
The power of PCORnet lies in two areas: the vast amounts of data available in the Common Data Model and in its engaged patient communities. What PCORnet offers is real-world information that serves as a valuable component of evidence generation. But generating real-world information requires more than data collection; it requires patient engagement, smart governance, and trust among collaborators. Work is ongoing to ensure PCORnet’s sustainability. As the needs of the network continue to evolve, the Common Data Model will also evolve to accommodate a variety of research questions. The Common Data Model cracks the foundation of the Tower of Babel in clinical research. Our vision is that PCORnet will be a lasting resource that offers a means to national evidence generation at a transformative scale.
The Tower Of Babel In Clinical Research: PCORnet’s Common Data Model Cracks The Foundation posted first on http://ift.tt/2lsdBiI
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