Simon J Craddock Lee1, James E Grobe2, Jasmin A Tiro3. 1. Department of Clinical Sciences, University of Texas, Southwestern Medical Center, Dallas, TX, USA Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA simoncraddock.lee@utsouthwestern.edu. 2. Department of Clinical Sciences, University of Texas, Southwestern Medical Center, Dallas, TX, USA. 3. Department of Clinical Sciences, University of Texas, Southwestern Medical Center, Dallas, TX, USA Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA.
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.
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 cancerpatients 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.
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