Cowden John D, Drake Rachel, Johnson Jessi, Kelty Katiana, Ahmed Mehwish
Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri, USA.
University of Missouri Kansas City-School of Medicine, Kansas City, Missouri, USA.
Health Equity. 2025 May 12;9(1):256-265. doi: 10.1089/heq.2024.0188. eCollection 2025.
Conventional race and ethnicity categories and analysis are reductive and prone to inaccuracy. Because race and ethnicity data validity is essential to health equity efforts, we measured the accuracy of race and ethnicity data in a pediatric electronic health record (EHR) to identify areas for improvement in data collection and use.
Patients and their caregivers reported patient race and ethnicity via in-person survey in four pediatric settings (inpatient, emergency room, urgent care, and primary care). Race and ethnicity data from the EHR were compared with survey data to calculate four measures of EHR data accuracy. The U.S. Census Bureau's novel categorization scheme was used to analyze racial and ethnic identities "alone" and "in combination" with ≥1 other identity.
Caregivers for 561 patients completed the survey; 116 patients aged ≥12 years completed a patient version. For consolidated race and ethnicity fields, overall concordance between survey and EHR was 74.6%. Concordance differed by race and ethnicity category when alone (Black or African American 96.1%, Hispanic 90.6%, and White 92.5%) and in combination with another category (Black or African American 93.9%, Hispanic 88.6%, and White 84.4%). The EHR had low accuracy for patients with multiple racial or ethnic identities (overall sensitivity 35%). Such patients' identities were often oversimplified due to EHR design. Using "alone" and "in combination" analysis for race and ethnicity categories allowed all patient identities to be visible across categories, unlike in conventional race and ethnicity analysis.
Identifying and eliminating health disparities depend on accurate race and ethnicity data, but current EHR design provides an unreliable data foundation for needed analyses. Conventional categorization used in race and ethnicity analysis is problematic, hiding identities in a reductive set of groupings. New approaches to validation, categorization, and analysis, as explored in this study, are urgently needed to advance health equity goals.
传统的种族和族裔类别及分析方法具有简化性且容易不准确。由于种族和族裔数据的有效性对于促进健康公平至关重要，我们对儿科电子健康记录（EHR）中的种族和族裔数据准确性进行了测量，以确定数据收集和使用方面需要改进的领域。
患者及其护理人员通过在四个儿科场所（住院部、急诊室、紧急护理中心和初级护理中心）进行的面对面调查报告患者的种族和族裔。将EHR中的种族和族裔数据与调查数据进行比较，以计算EHR数据准确性的四项指标。美国人口普查局的新型分类方案用于分析单独的以及与≥1种其他身份“组合”的种族和族裔身份。
561名患者的护理人员完成了调查；116名年龄≥12岁的患者完成了患者版调查。对于合并的种族和族裔字段，调查与EHR之间的总体一致性为74.6%。单独分类（黑人或非裔美国人96.1%，西班牙裔90.6%，白人92.5%）以及与另一类别组合时（黑人或非裔美国人93.9%，西班牙裔88.6%，白人84.4%），一致性因种族和族裔类别而异。EHR对于具有多种种族或族裔身份的患者准确性较低（总体敏感性为35%）。由于EHR的设计，此类患者的身份常常被过度简化。与传统的种族和族裔分析不同，对种族和族裔类别使用“单独”和“组合”分析可使所有患者身份在各分类中都可见。
识别和消除健康差距取决于准确的种族和族裔数据，但当前的EHR设计为所需分析提供了不可靠的数据基础。种族和族裔分析中使用的传统分类存在问题，在一组简化的分组中隐藏了身份。本研究中探索的验证、分类和分析新方法对于推进健康公平目标至关重要。
