Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center

BMC Health Services Research

Research

Rachel Hopkins¹  Rachael Proumen²  Hannah Connolly³  Nadia Alexandra Debick³ 

[1]Department of Medicine, State University of New York (SUNY) Upstate Medical University, 750 E. Adams St, Syracuse, New York, USA

[2]Department of Medicine, Division of Endocrinology, State University of New York (SUNY) Upstate Medical University, 750 E Adams St., 13210, Syracuse, NY, USA

[3]Department of Medicine, State University of New York (SUNY) Upstate Medical University, 750 E. Adams St, Syracuse, New York, USA

[4]State University of New York (SUNY) Upstate Medical University Norton College of Medicine, Syracuse, New York, USA

[5]State University of New York (SUNY) Upstate Medical University Norton College of Medicine, Syracuse, New York, USA

关键词: Electronic health record;    Race and ethnicity;    Language;    Gender identity;    Data quality;   

DOI  :  10.1186/s12913-023-09825-6

 received in 2023-03-21, accepted in 2023-07-17,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 7