UNITED HEALTHCARE

United Healthcare's algorithm relied on historical health costs as a key factor in predicting patient risk. This approach inadvertently resulted in the underestimation of health risks for African American individuals, as their historical costs were lower on average.

This algorithm is a typical examples of a class of commercial risk-prediction tools that, by industry estimates, are applied to roughly 200 million people in the United States each year. Large health systems and payers rely on this algorithm to target patients for “high-risk care management” programs.

A study determined that Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5% -- an estimated 8.29 million Black patients each year.

The case of United Healthcare underscores the importance of regularly auditing algorithms for unintended statistical biases, especially when using historical data.