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

Andrew D Boyd; Rosa Gonzalez-Guarda; Katharine Lawrence; Crystal L Patil; Miriam O Ezenwa; Emily C O'Brien; Hyung Paek; Jordan M Braciszewski; Oluwaseun Adeyemi; Allison M Cuthel; Juanita E Darby; Christina K Zigler; P Michael Ho; Keturah R Faurot; Karen L Staman; Jonathan W Leigh; Dana L Dailey; Andrea Cheville; Guilherme Del Fiol; Mitchell R Knisely; Corita R Grudzen; Keith Marsolo; Rachel L Richesson; Judith M Schlaeger

doi:10.1093/jamia/ocad115

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. doi: 10.1093/jamia/ocad115.

Authors

Andrew D Boyd¹, Rosa Gonzalez-Guarda², Katharine Lawrence³, Crystal L Patil⁴, Miriam O Ezenwa⁵, Emily C O'Brien⁶, Hyung Paek⁷, Jordan M Braciszewski⁸, Oluwaseun Adeyemi⁹, Allison M Cuthel⁹, Juanita E Darby⁴, Christina K Zigler¹⁰, P Michael Ho¹¹, Keturah R Faurot¹², Karen L Staman¹⁰, Jonathan W Leigh⁴, Dana L Dailey^{13

14}, Andrea Cheville¹⁵, Guilherme Del Fiol¹⁶, Mitchell R Knisely², Corita R Grudzen¹⁷, Keith Marsolo⁶, Rachel L Richesson¹⁸, Judith M Schlaeger⁴

Affiliations

¹ Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, Illinois, USA.
² Duke University School of Nursing, Durham, North Carolina, USA.
³ Department of Population Health, New York University Grossman School of Medicine, New York City, New York, USA.
⁴ College of Nursing, University of Illinois Chicago, Chicago, Illinois, USA.
⁵ University of Florida College of Nursing, Gainesville, Florida, USA.
⁶ Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
⁷ Biostatistics (Health Informatics), Yale University, New Haven, Connecticut, USA.
⁸ Henry Ford Health, Detroit, Michigan.
⁹ Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine, New York City, New York, USA.
¹⁰ Duke University School of Medicine, Durham, North Carolina, USA.
¹¹ Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA.
¹² Department of Physical Medicine and Rehabilitation, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
¹³ Physical Therapy, St. Ambrose University, Davenport, Iowa, USA.
¹⁴ Department of Physical Therapy and Rehabilitation Science Department, University of Iowa, Iowa City, Iowa, USA.
¹⁵ Mayo Clinic Comprehensive Cancer Center, Rochester, Minnesota, USA.
¹⁶ Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
¹⁷ Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
¹⁸ Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.

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.

Keywords: community engagement; health equity; health literacy; patient-reported outcomes; social determinants of health.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Bias
Delivery of Health Care
Electronic Health Records*
Health Equity*
Humans
National Institutes of Health (U.S.)
United States

Abstract

Publication types

MeSH terms

Grants and funding