ArticleBMC health services research2023
Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center.
Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Stigmatizing Language in Gender-Expansive Patient Records: Corpus Development, Disparity Analysis, and Natural Language Processing-Based Detection Study.Journal of medical Internet research · 2026Article
- Evaluating the impact of discordant and missing demographic information on population health assessments using linked electronic health records and Census Bureau microdata.PLOS digital health · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Collection and Use of Patient Demographic Information at US Dental School Clinics.Journal of dental education · 2026Article
- Including gender data in electronic health records to improve the care provided to transgender and non-binary patients in Quebec: A queer bioethics analysis.International journal of transgender health · 2026Article
- Availability of sexual orientation and gender identity (SOGI) information in a cohort of transgender and gender diverse people: An analysis of electronic health records.Annals of epidemiology · 2025Article
- The Impact of Race and Ethnicity on Cataract Surgery in the Nationwide All of Us Cohort.Ophthalmic epidemiology · 2025Article
- The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization.Journal of medical Internet research · 2025Article
- Measures of Hospital Inclusivity Depend on Definitions of a Hospital's Catchment Area.Hospital pediatrics · 2025Article
- Accuracy of Race and Ethnicity Data in the Pediatric Electronic Health Record: A Concordance and System Adequacy Study.Health equity · 2025Article
- Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.Preventing chronic disease · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCollection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution.
methodsA survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR.
resultsRace was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient's self-identity. Preferred language was 100% concordant with the EHR.
conclusionsAt an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients' self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities.
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