Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Science. 2019;366:447-53. [CrossRef]

Insurance companies often use commercial algorithms to guide health decisions. The authors found evidence of racial bias in one widely used algorithm. At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care.