Assessing race and ethnicity data quality across XXXX registries and EMRs in two hospitals.

@entity1 @entity5
BACKGROUND: Measurement of @entity1 race/ethnicity in electronic health records is mandated and important for tracking health disparities. OBJECTIVE: Characterize the quality of race/ethnicity data collection efforts. METHODS: For all @entity5 @entity1 diagnosed (2007-2010) at two hospitals, we extracted demographic data from five sources: 1) a university hospital @entity5 registry, 2) a university electronic medical record (EMR), 3) a community hospital @entity5 registry, 4) a community EMR, and 5) a joint clinical research registry. The @entity1 whose data we examined (N   =   17  834) contributed 41   025 entries (range: 2-5 per @entity1 across sources), and the source comparisons generated 1-10 unique pairs per @entity1 . We used generalized estimating equations, chi-squares tests, and kappas estimates to assess data availability and agreement. RESULTS: Compared to sex and insurance status, race/ethnicity information was significantly less likely to be available (  (2   )>   8043, P   <   .001), with variation across sources (  (2   )>   10  589, P   <   .001). The university EMR had a high prevalence of "Unknown" values. Aggregate kappa estimates across the sources was 0.45 (95% confidence interval, 0.45-0.45; N   =   31  276 unique pairs), but improved in sensitivity analyses that excluded the university EMR source (k   =   0.89). Race/ethnicity data were in complete agreement for only 6988 @entity1 (39.2%). Pairs with a "Black" data value in one of the sources had the highest agreement (95.3%), whereas pairs with an "Other" value exhibited the lowest agreement across sources (11.1%). DISCUSSION: Our findings suggest that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data are caused by missing or "Unknown" data values. CONCLUSIONS: To facilitate transparent reporting of healthcare delivery outcomes by race/ethnicity, healthcare systems need to monitor and enforce race/ethnicity data collection standards.
