Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory

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...

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Published in:Journal of the American Medical Informatics Association : JAMIA 2023-08, Vol.30 (9), p.1561-1566
Main Authors: Boyd, Andrew D, Gonzalez-Guarda, Rosa, Lawrence, Katharine, Patil, Crystal L, Ezenwa, Miriam O, O’Brien, Emily C, Paek, Hyung, Braciszewski, Jordan M, Adeyemi, Oluwaseun, Cuthel, Allison M, Darby, Juanita E, Zigler, Christina K, Ho, P Michael, Faurot, Keturah R, Staman, Karen L, Leigh, Jonathan W, Dailey, Dana L, Cheville, Andrea, Del Fiol, Guilherme, Knisely, Mitchell R, Grudzen, Corita R, Marsolo, Keith, Richesson, Rachel L, Schlaeger, Judith M
Format: Article
Language:English
Subjects:
Brief Communications
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Summary: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.
ISSN:1067-5027
1527-974X
DOI:10.1093/jamia/ocad115