A recent study by Ziad Obermeyer and colleagues in Science identified a racial bias in a risk stratification algorithm that is used to prioritize patients for care management. Like most algorithms currently in use, it considers past cost to identify individuals most in need of help. Because white people tend to have higher medical expenses, they are prioritized over sicker black patients. The researchers show that if the bias is corrected for, the proportion of black people prioritized for care swings from 17.7 percent to 46.0 percent.
Less than a week later, news of the study appeared in Nature, Business Insider, the Wall Street Journal, and Wired. The State of New York is investigating and threatening suit against UnitedHealthcare and others that employ such approaches.
While this recent discovery is rightfully gaining attention, it is just one of many known biases and shortcomings of the health care system’s current approach to risk stratification. Obermeyer and his colleagues’ study and the concerns it raised offer an opportunity to carefully consider unintended consequences of the prevalent approaches of stratifying risk to find a new way forward.
Flaws And Unintended Consequences
The algorithms in question are decades-old adaptations of actuarial models. They rely mostly on claims (that is, billing) data as input. With the introduction of managed care in the 1980s, health plans needed a healthcare and updates