Citations 2
Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations
Patil, Crystal L. , Boyd, Andrew D. , Dailey, Dana L. , Martelly, Victoria de , Ezenwa, Miriam O. , Faurot, Keturah R. , Knisely, Mitch, McLeod, Kaitlyn, O’Brien, Emily C. , Sluka, Kathleen A. , Staman, Karen L. , Zigler, Christina K. , Boyd, Andrew D , Faurot, Keturah R , Cheville, Andrea , Crofford, Leslie J. , Fiol, Guilherme Del , Knisely, Mitchell R. , Morone, Natalia E. , Gonzalez-Guarda, Rosa Maria , Thackeray, Anne , Schlaeger, Judith M.
Applied Clinical Informatics | November 8, 2024 | Volume: 16 | Issue: 02 | Pages: 259-266
Abstract Background The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities. Objectives In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces. Methods To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. Conclusion Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.
Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory
Boyd, Andrew D. , Lawrence, Katharine , Patil, Crystal L. , Ezenwa, Miriam O. , O’Brien, Emily C. , Paek, Hyung , Adeyemi, Oluwaseun , Cuthel, Allison M. , Darby, Juanita E. , Zigler, Christina K. , Faurot, Keturah R, Staman, Karen L. , Dailey, Dana L. , Fiol, Guilherme Del , Knisely, Mitchell R. , Marsolo, Keith , Richesson, Rachel , Faurot, Keturah R. , Lawrence, Katharine , O'Brien, Emily , Cuthel, Allison M , Darby, Juanita E , Del Fiol, Guilherme , Richesson, Rachel L , Boyd, Andrew D , Faurot, Keturah R , Gonzalez-Guarda, Rosa Maria , Braciszewski, Jordan M. , Ho, P. Michael , Leigh, Jonathan W. , Cheville, Andrea , Grudzen, Corita R. , Schlaeger, Judith M.
Journal of the American Medical Informatics Association | June 15, 2023 | Volume: 30 | Issue: 9 | Pages: 1561-1566
Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.