RACE/ETHNICITY IN MEDICAL CHARTS AND ADMINISTRATIVE DATABASES OF PATIENTS SERVED BY COMMUNITY HEALTH CENTERS

Objective: The objective of this study was to measure the agreement in classification of patients’ race/ethnicity in the medical charts and the automated practice management systems (PMSs) of seven community health centers. Setting: Community health centers are on the frontlines of providing primary care to the under-served and racial/ethnic minorities. Public and private investments in information technology and the increasing use of automated disease registries hold promise to improve care and reduce ethnic and racial disparities. However, data quality may limit the accuracy of race/ethnicity classification and the ability to measure the effect of population based clinical quality improvements. Design/Participants: In a cross-sectional study, a probability sample of 947 patients with encounters in 2002 was selected from 79,119 patients. Each PMS used a single data field with a pick list that combined ethnicity and race. Race/ethnicity on registration forms completed by patients was abstracted from medical charts. Race/ethnicity classifications were aggregated into seven major categories: Asian/Pacific Islander, Black/African-American, Native American, White, Hispanic/Latino, Other, Missing/Unknown. Outcome Measures: The sensitivity, positive predictive value, and proportion of agreement were outcome measures of agreement between information in the medical chart and PMS. Results: The overall proportion of agreement (PA) between the medical chart (reference) and PMS was 87%. The PA varied significantly by health center (95%–74%). Hispanic/Latino had the highest sensitivity (91%) and positive predictive value (95%) and White the lowest (84% and 80%, respectively). Conclusions: In broad categories, correspondence of race/ethnicity classifications in medical charts and PMS was good, although health centers varied. A careful appraisal of data quality of race/ethnicity is warranted before administrative databases are used in clinical quality improvement programs or research to assess health disparities