  Healthcare data in the United States often records only a patient's coarse
race group: for example, both Indian and Chinese patients are typically coded
as "Asian." It is unknown, however, whether this coarse coding conceals
meaningful disparities in the performance of clinical risk scores across
granular race groups. Here we show that it does. Using data from 418K emergency
department visits, we assess clinical risk score performance disparities across
26 granular groups for three outcomes, five risk scores, and four performance
metrics. Across outcomes and metrics, we show that the risk scores exhibit
significant granular performance disparities within coarse race groups. In
fact, variation in performance within coarse groups often *exceeds* the
variation between coarse groups. We explore why these disparities arise,
finding that outcome rates, feature distributions, and the relationships
between features and outcomes all vary significantly across granular groups.
Our results suggest that healthcare providers, hospital systems, and machine
learning researchers should strive to collect, release, and use granular race
data in place of coarse race data, and that existing analyses may significantly
underestimate racial disparities in performance.
