TY - JOUR
T1 - Potential bias and lack of generalizability in electronic health record data
T2 - Reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory
AU - Boyd, Andrew D.
AU - Gonzalez-Guarda, Rosa
AU - Lawrence, Katharine
AU - Patil, Crystal L.
AU - Ezenwa, Miriam O.
AU - O'Brien, Emily C.
AU - Paek, Hyung
AU - Braciszewski, Jordan M.
AU - Adeyemi, Oluwaseun
AU - Cuthel, Allison M.
AU - Darby, Juanita E.
AU - Zigler, Christina K.
AU - Ho, P. Michael
AU - Faurot, Keturah R.
AU - Staman, Karen L.
AU - Leigh, Jonathan W.
AU - Dailey, Dana L.
AU - Cheville, Andrea
AU - Del Fiol, Guilherme
AU - Knisely, Mitchell R.
AU - Grudzen, Corita R.
AU - Marsolo, Keith
AU - Richesson, Rachel L.
AU - Schlaeger, Judith M.
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - 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.
AB - 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.
KW - community engagement
KW - health equity
KW - health literacy
KW - patient-reported outcomes
KW - social determinants of health
UR - http://www.scopus.com/inward/record.url?scp=85168244755&partnerID=8YFLogxK
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U2 - 10.1093/jamia/ocad115
DO - 10.1093/jamia/ocad115
M3 - Article
C2 - 37364017
AN - SCOPUS:85168244755
SN - 1067-5027
VL - 30
SP - 1561
EP - 1566
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 9
ER -