Michigan Child Health Equity Collaborative, Ann Arbor.
Child Health Evaluation and Research Center, University of Michigan, Ann Arbor.
JAMA Netw Open. 2024 Sep 3;7(9):e2431073. doi: 10.1001/jamanetworkopen.2024.31073.
Without knowledge of the degree of misattribution in racial and ethnic designations in data, studies run the risk of missing existing inequities and disparities and identifying others that do not exist. Further, accuracy of racial and ethnic designations is important to clinical care improvement efforts and health outcomes.
To determine the error rate of racial and ethnic attribution in the electronic medical records (EMRs) across the 3 largest pediatric health systems in Michigan.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study collected race and ethnicity data from parents in outpatient clinics, emergency departments, and inpatient units at the 3 largest pediatric health systems in Michigan. A total of 1594 parents or guardians participated at health system A, 1537 at health system B, and 1202 at health system C from September 1, 2023, to January 31, 2024. Parent or guardian report of race and ethnicity for a child was used as the gold standard for comparison with the designation in the EMR.
Race and ethnicity designations in the EMR. Options for race designation across the health systems ranged from 6 to 49; options for ethnicity, from 2 to 10.
Matching occurred in 3 stages. First, the exact racial and ethnic designations made by parents for their child were compared with what was found in the EMR. Second, for any child whose parent selected more than 1 racial category or for whom more than 1 appeared in the EMR, the designation of a minoritized racial group was used for matching purposes. Third, starting with the product of stage 2, racial designations were combined or collapsed into 6 (health systems A and C) or 5 (health system B) designations.
A total of 4333 survey responses were included in the analysis. The greatest error rate across the health systems occurred with the exact match of parental report of racial designation with the EMR, which ranged from 41% to 78% across the health systems. Improvement in the matching rate for each health system occurred with consolidation of race options provided. Differences between the health systems narrowed at the final consolidation to varying from 79% to 88% matching. Ethnicity matching between the EMR and the parental report ranged from 65% to 95% across the health systems. Missing race or ethnicity data in the EMR was counted as a nonmatch. Rates of missing racial data varied across the health systems from 2% to 10%. The health system with the greatest number of options for race and ethnicity had the highest error rates.
Although there will always be some misattribution of race and ethnicity in the EMR, the results of this cross-sectional study suggest that significant error in these data may undermine strategies to improve care. It is unclear whether those in an organization who determine the number of potential categories are the same persons who use those data to investigate potential disparities and inequities.
如果不知道数据中种族和民族分类的归因程度,研究就有可能错过现有的不平等和差异,并识别出不存在的其他差异。此外,种族和民族分类的准确性对于临床护理改善工作和健康结果至关重要。
确定密歇根州 3 家最大儿科医疗系统的电子病历(EMR)中种族和民族分类的错误率。
设计、地点和参与者:这项横断面研究从密歇根州 3 家最大儿科医疗系统的门诊诊所、急诊部和住院部的家长那里收集种族和民族数据。共有 1594 名家长或监护人参加了医疗系统 A 的研究,1537 名参加了医疗系统 B 的研究,1202 名参加了医疗系统 C 的研究,研究时间为 2023 年 9 月 1 日至 2024 年 1 月 31 日。儿童家长或监护人报告的种族和民族是与 EMR 中指定内容进行比较的金标准。
EMR 中的种族和民族分类。3 个医疗系统的种族分类选项范围从 6 到 49;族裔选项范围从 2 到 10。
匹配发生在 3 个阶段。首先,将父母为孩子指定的确切种族和民族与 EMR 中的内容进行比较。其次,对于任何父母选择了 1 个以上种族类别或 EMR 中出现了 1 个以上种族类别的儿童,将使用少数族裔群体的指定进行匹配。第三,从阶段 2 的结果开始,将种族分类合并或合并为 6(医疗系统 A 和 C)或 5(医疗系统 B)个分类。
共纳入 4333 份调查回复进行分析。整个医疗系统中错误率最高的是父母报告的种族分类与 EMR 的精确匹配,在 3 个医疗系统中的错误率范围从 41%到 78%。每个医疗系统的匹配率都随着提供的种族选项的合并而提高。健康系统之间的差异在最终合并时缩小到 79%至 88%的匹配。EMR 与父母报告的族裔匹配率在整个医疗系统中范围从 65%到 95%。EMR 中缺少种族或民族数据被视为不匹配。3 个医疗系统中种族数据缺失率从 2%到 10%不等。种族和民族选项最多的医疗系统错误率最高。
尽管电子病历中始终会存在一些种族和民族分类的归因错误,但这项横断面研究的结果表明,这些数据中的重大错误可能会破坏改善护理的策略。目前尚不清楚确定潜在类别数量的人是否与使用这些数据来调查潜在差异和不平等的人相同。