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

ABSTRACT:

Background

Collection 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.

Methods

A 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.

Results

Race wa

SUBMITTER: Proumen R 

PROVIDER: S-EPMC10463428 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature