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
granular race groups for three outcomes, five risk scores, and four performance
metrics. Across outcomes and metrics, we show that there are significant
granular disparities in performance within coarse race categories. In fact,
variation in performance metrics 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 race
categories. 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.

通过分析美国医疗数据，发现医学风险评分在不同粒度人种群体之间存在显著的性能差异，建议医疗和机器学习研究人员应该收集、发布和使用更精细的种族数据，以更好地认识种族间的表现差距。