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

Publication ,  Journal Article
Boyd, AD; Gonzalez-Guarda, R; Lawrence, K; Patil, CL; Ezenwa, MO; O'Brien, EC; Paek, H; Braciszewski, JM; Adeyemi, O; Cuthel, AM; Darby, JE ...
Published in: J Am Med Inform Assoc
August 18, 2023

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.

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Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

August 18, 2023

Volume

30

Issue

9

Start / End Page

1561 / 1566

Location

England

Related Subject Headings

  • United States
  • National Institutes of Health (U.S.)
  • Medical Informatics
  • Humans
  • Health Equity
  • Electronic Health Records
  • Delivery of Health Care
  • Bias
  • 46 Information and computing sciences
  • 42 Health sciences
 

Citation

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Boyd, A. D., Gonzalez-Guarda, R., Lawrence, K., Patil, C. L., Ezenwa, M. O., O’Brien, E. C., … Schlaeger, J. M. (2023). Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. J Am Med Inform Assoc, 30(9), 1561–1566. https://doi.org/10.1093/jamia/ocad115
Boyd, Andrew D., Rosa Gonzalez-Guarda, Katharine Lawrence, Crystal L. Patil, Miriam O. Ezenwa, Emily C. O’Brien, Hyung Paek, et al. “Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.J Am Med Inform Assoc 30, no. 9 (August 18, 2023): 1561–66. https://doi.org/10.1093/jamia/ocad115.
Boyd AD, Gonzalez-Guarda R, Lawrence K, Patil CL, Ezenwa MO, O’Brien EC, et al. Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. J Am Med Inform Assoc. 2023 Aug 18;30(9):1561–6.
Boyd, Andrew D., et al. “Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.J Am Med Inform Assoc, vol. 30, no. 9, Aug. 2023, pp. 1561–66. Pubmed, doi:10.1093/jamia/ocad115.
Boyd AD, Gonzalez-Guarda R, Lawrence K, Patil CL, Ezenwa MO, O’Brien EC, Paek H, Braciszewski JM, Adeyemi O, Cuthel AM, Darby JE, Zigler CK, Ho PM, Faurot KR, Staman KL, Leigh JW, Dailey DL, Cheville A, Del Fiol G, Knisely MR, Grudzen CR, Marsolo K, Richesson RL, Schlaeger JM. Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. J Am Med Inform Assoc. 2023 Aug 18;30(9):1561–1566.
Journal cover image

Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

August 18, 2023

Volume

30

Issue

9

Start / End Page

1561 / 1566

Location

England

Related Subject Headings

  • United States
  • National Institutes of Health (U.S.)
  • Medical Informatics
  • Humans
  • Health Equity
  • Electronic Health Records
  • Delivery of Health Care
  • Bias
  • 46 Information and computing sciences
  • 42 Health sciences