Lessons learned in developing a national registry for community-led Patient Centered Outcomes Research

Background: The Community Health Applied Research Network (CHARN) is a federally funded research network comprised of 17 community health centers (CHCs) organized into four research nodes (each including an academic partner), and a data coordinating center. This represents over 400,000 patients across the 17 CHCs distributed nationally across 11 states. With the goal of establishing a community-led network for patient-centered outcomes research (PCOR), one key initiative is to develop a robust central CHARN Data Registry. Objective: To establish a centralized data registry mined from electronic health record (EHR) data at CHCs to : 1) better describe our vulnerable, diverse safety-net populations traditionally underrepresented in research, and 2) establish a multi-site, multidisciplinary collaborative infrastructure to advance PCOR. Methods: As a key initial step, we developed multi-level Data Use Agreements (DUA) between all participating CHCs and their representative node, and between the nodes and the data coordinating center. Simultaneously, a multidisciplinary team of community clinicians, researchers, and data programmers defined data elements needed to support future PCOR. Results: Nodes have executed DUA with their CHCs and with the data coordinating center. Each participating CHC has approved this project through its institutional review board or local research committee. Data are currently being submitted to the nodes and subsequently to the data coordinating center to establish this ground-breaking registry. Conclusions: It is feasible to create a centralized data registry with multiple CHC partners, with different types of EHRs, and varying levels of experience and research topics. For this unprecedented research endeavor, it is essential to allow for significant time for approval processes and discussions which ultimately foster collaboration and trust among new partners, and to address technical issues in an era of suboptimal data standardization.