Value Institute, NewYork-Presbyterian Hospital, New York, New York, USA.
Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania and Department of Biomedical Informatics, Columbia University, New York, New York.
J Am Med Inform Assoc. 2019 Aug 1;26(8-9):730-736. doi: 10.1093/jamia/ocz113.
We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.
We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information.
Among the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system's EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR.
Race and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health.
Patient self-recording may improve the completeness of race and ethnicity information.
我们旨在评估观察性健康数据库（包括电子健康记录[EHR]）中种族和民族信息的质量，并提出患者自报作为一种改进策略。
我们评估了美国大型观察性健康数据库（医疗保健成本和利用项目和 Optum Labs）以及纽约市一家为不同种族和民族服务的单一医疗系统中种族和民族信息的完整性。我们将通过行政程序收集的种族和民族数据与通过纸质调查（国家健康和营养检查调查和医院消费者评估医疗保健提供者和系统）直接由受访者记录的数据进行了比较。受访者记录的数据被认为是收集种族和民族信息的黄金标准。
在医疗保健成本和利用项目和 Optum Labs 数据集的 1.6 亿患者中，有 25%的患者种族或民族信息未知。在纽约市单一医疗系统的 EHR 中，有 240 万患者中，种族或民族信息未知的比例为 57%。然而，当患者直接记录自己的种族和民族时，86%的患者提供了有临床意义的信息，66%的患者报告的信息与 EHR 不一致。
种族和民族数据对于支持精准医学计划和确定医疗保健差异至关重要；然而，观察性数据库中这些信息的质量令人担忧。通过使用面向患者的工具进行患者自报可以大大提高信息的质量，同时让患者参与自己的健康。
患者自报可能会提高种族和民族信息的完整性。
