Using Patient Race, Ethnicity, and Language Data to Achieve Health Equity
Collecting data on patient race, ethnicity, and language (REL) is an important step in reducing health care disparities, as clinical performance measures can then be stratified by REL to guide quality improvement efforts. In this month’s issue of JGIM, Klinger et al. report important inaccuracies in REL data in electronic health records (EHRs) in 13 primary care clinics.2 For example, 3 % and 6.6 % of patients who self-identified as Hispanic and African American, respectively, were not recorded as such in the EHR, and 20 % of persons documented in the EHRs as English-speaking elected to take a survey in Spanish. Inaccuracies in REL data have clustered around the distinction between Latino and black, American Indians, and multiracial categories, and lack of granularity among Asian American and Latino subgroups.3 Klinger et al.’s study is the first to report inaccuracies in REL data in EHRs. Because meaningful use EHR regulations incentivize the collection of REL data, these data are more likely to be collected and available for electronic use. In this editorial, I will discuss how to accurately collect REL data, where REL data fit within comprehensive efforts to achieve health equity, the powerful incentives that are driving the collection of REL data, and emerging innovative uses of REL data.