Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals. (11th December 2015)

• Record Type: Journal Article
• Authors: Lee, Simon J Craddock; Grobe, James E; Tiro, Jasmin A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number 3(2016:May)
• Page Start: 627
• Page End: 634
• Publication Date: 2015-12-11
• Subjects: electronic medical record -- data quality -- race and ethnicity -- cancer registry; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv156
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract : Background Measurement of patient 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 cancer patients diagnosed (2007–2010) at two hospitals, we extracted demographic data from five sources: 1) a university hospital cancer registry, 2) a university electronic medical record (EMR), 3) a community hospital cancer registry, 4) a community EMR, and 5) a joint clinical research registry. The patients whose data we examined ( N = 17 834) contributed 41 025 entries (range: 2–5 per patient across sources), and the source comparisons generated 1–10 unique pairs per patient. 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 (χ 2 > 8043, P < .001), with variation across sources (χ 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 (κ = 0.89). Race/ethnicity data were in complete agreement for only 6988 patients (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.