A widely used healthcare risk algorithm sold by Optum used healthcare spending as a proxy for health need. Because structural inequalities mean Black patients historically spent less on healthcare than equally sick white patients, the algorithm systematically underestimated the health needs of Black patients — directing care resources to white patients who were less sick. The study estimated 11.5 million Black patients in the US were affected.
Researchers at UC Berkeley published a study in Science in 2019 demonstrating that a commercial risk-stratification algorithm used by US health systems to identify patients needing complex care management contained systematic racial bias. The algorithm used healthcare cost as a proxy for health need. Because of systemic barriers to healthcare access, Black patients historically spent less on healthcare than equally sick white patients. The algorithm therefore concluded that Black patients were healthier than they actually were, allocating care management resources away from them. The algorithm was found to be in use across health systems covering an estimated 200 million people.
How the Production Safety Framework maps to this failure
A D3 failure at the most foundational level. The training label (healthcare cost) was a biased proxy for the intended outcome (health need). This is a data provenance problem: the label was not fit for purpose, and no bias audit identified the disparity before deployment at scale. D6 compounded the failure — clinical decisions were being made based on algorithm output without structured human review, despite the algorithm operating in a safety-critical domain (healthcare resource allocation).
Specific PSF controls mapped to each failure point
Optum acknowledged the flaw and committed to updating the algorithm after the Science paper was published. The study prompted major scrutiny of algorithmic bias in healthcare and contributed to proposed US federal guidance on AI in clinical settings.
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