Department of Medicine, State University of New York (SUNY) Upstate Medical University, 750 E. Adams St, Syracuse, New York, USA.
State University of New York (SUNY) Upstate Medical University Norton College of Medicine, Syracuse, New York, USA.
BMC Health Serv Res. 2023 Aug 22;23(1):884. doi: 10.1186/s12913-023-09825-6.
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.
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.
Race 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.
At 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.
在电子健康记录（EHR）中准确收集患者的种族、民族、首选语言（REaL）和性别认同对于公平和包容的护理至关重要。这些因素的识别错误限制了高危人群健康结果的质量测量。因此，我们的研究目的是评估我们机构的 REaL 和性别认同数据的准确性。
对纽约市中心一所大型学术医疗中心前一天入院的 117 名随机患者进行了调查。患者（或监护人）根据当前 EHR 选项自行报告 REaL 和性别认同数据。对变量进行编码，以确定其与 EHR 记录的数据是否存在差异。
EHR 中患者的种族报告错误占 13%，民族报告错误占 6%。对于大多数白人和黑人患者，种族是一致的。然而，所有多种族患者的自我认同数据与 EHR 不一致。大多数非西班牙裔患者的种族正确记录。一些西班牙裔患者被错误识别。在卡方分析中，报告与 EHR 记录的种族和民族不同的患者比例显著相关（P<0.001）。在报告替代种族的患者中，71.4%的患者也报告了替代民族。大多数患者的性别认同缺失，EHR 中存在的 11%的性别认同条目与患者的自我认同不一致。首选语言与 EHR 完全一致。
在一所学术医疗中心，多种族和西班牙裔患者更有可能在 EHR 中报告其人口统计学数据错误，并且性别认同数据大部分缺失。医疗保健系统需要制定策略，支持准确收集患者的自我报告的 REaL 和性别认同数据，以提高未来识别和解决医疗保健差异的能力。
