Racial Bias in Healthcare Algorithms

Obermeyer and coauthors’ 2019 Science study examined a commercial algorithm used to identify patients with complex health needs for extra care-management support. The tool predicted future healthcare spending as a proxy for health need. The authors found that, at a given risk score, Black patients were considerably sicker than White patients, as measured by uncontrolled illness. The study’s mechanism was structural: because Black patients had historically received less care and generated lower costs at similar levels of illness, a cost-prediction target understated their need. The proxy appeared predictive of spending while encoding unequal access to care. In the study population, replacing the cost target with a measure more closely related to illness would have increased the proportion of Black patients receiving additional help from 17.7% to 46.5%. That figure describes the study’s reallocation result, not a nationwide estimate or a general expected correction rate. The research affected an algorithm used across multiple health systems and drew attention to how proxies can transmit inequity without explicitly including race. It did not show that every use of cost data is biased or that the same pattern appears in every model. The broader lesson is to examine whether a target variable represents the desired construct. Expenditure reflects illness, but also insurance, access, clinician decisions, service availability and historical patterns. A model can be statistically accurate for its proxy and still fail the operational objective of identifying people who need care. Follow-up audits should assess patient-level health outcomes, access to interventions and subgroup effects, while recognizing that the 2019 result is specific to one algorithm, intended use and dataset.