Racial treatment disparities after machine learning surgical risk-adjustment

摘要

Black patients are less likely to receive certain surgical interventions. To test whether a health risk disparity and thus differential appropriateness for surgery explains a treatment disparity, researchers must adjust observed rates for patient-level health differences using valid contextual regression controls to increase patient comparability. As an alternative to the standard health adjustment with predetermined diagnosis groups, I propose a machine learning-based method that better captures clinical practices to adjust for the important predictors of invasive surgery applied to the context of acute myocardial infarction (AMI). With data from the Nationwide Inpatient Sample, this method decreases the standard adjusted AMI surgery disparity by 45-55%. Nonetheless, a significant surgery disparity of 5.9 percentage points with hospital fixed effects and 4.5 percentage points with physician fixed effects remains after adjusting for predictive controls. The smaller yet persistent disparity provides evidence of differential AMI treatment beyond that explained by health risk differences.