Using Patient Race, Ethnicity, and Language Data to Achieve Health Equity

C ollecting 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. 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. 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. The Health Research and Education Trust (HRET) has published best practices and training materials for REL data collection, and many of its recommendations are consistent with those of the Institute of Medicine. Patient self-reporting is the gold standard; thus, clerical staff must directly ask patients about their REL. Patients generally understand the importance of collecting language data for improving communication and to determine the need for interpreter services. The two key questions to ask are BHow would you rate your ability to speak and understand English?^ and BWhat language do you feel most comfortable speaking with your doctor or nurse?^ However, patients sometimes question why race and ethnicity information are collected, and health care organizations must ensure that patients understand why REL data are important for improving the quality of care. For example, HRET recommends saying to patients, BNow I would like you to tell me your race and ethnic background. We use this information to review the treatment patients receive and make sure everyone gets the highest quality of care.^ BDo you consider yourself Hispanic/ Latino?^ BWhich category best describes your race?^ HRET recommends following these two questions with a granular ethnicity screening question such as BI would like you to describe your race or ethnic background. You can use specific terms such as Korean, Mexican, Haitian, Somali.^ Even with this explanation of why REL data are collected, some patients remain suspicious. Thus, organizations must train their staff in how to respond to challenging patient questions, such as: BDo you discriminate against African-American patients like myself?^, BDo you provide worse care for minority patients?^, and BAre you trying to find out if I am a U.S. citizen?^ However, it is not enough to tell the intake clerical staff to ask patients REL questions as recommended above. Multiple departments must take action to collect and use REL data appropriately. The University of Chicago is currently working to improve the process of REL data collection, involving 15 different departments (e.g., information technology, patient care services, center for quality, managed care, population health, ambulatory, finance, etc.). Achieving accuracy in REL data collection is often an uphill battle unless leadership and staff of health care organizations make reducing disparities a priority. The ultimate goal of REL data collection is improving the quality of care and outcomes for all patients and reducing health care disparities. Thus, REL data collection is not an end in itself. REL data alone are not a magic bullet that will end health care disparities, just as universal health insurance or cultural competency training for clinicians in isolation are not complete solutions for achieving equity. REL data are useful for stratifying clinical performance measures to identify disparities and thus increase From the Section of General Internal Medicine, the Department of Medicine, the Chicago Center for Diabetes Translation Research, the MacLean Center for Clinical Medical Ethics, the Robert Wood Johnson Foundation (RWJF) Finding Answers: Disparities Research for Change National Program Office, and the RWJF Reducing Health Care Disparities Through Payment and Delivery System Reform Program Office, University of Chicago, Chicago, Illinois, USA.