Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

Hale M. Thompson, Clair A. Kronk, Ketzel Feasley, Paul Pachwicewicz and Niranjan S. Karnik
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Hale M. Thompson: Department of Psychiatry and Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Clair A. Kronk: Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
Ketzel Feasley: Department of Psychiatry and Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Paul Pachwicewicz: Department of Psychiatry and Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Niranjan S. Karnik: Department of Psychiatry and Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA

IJERPH, 2021, vol. 18, issue 12, 1-12

Abstract: In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center’s EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

Keywords: SO/GI data; nonbinary; population health; clinical informatics; transgender health disparities (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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