“Automated tools for allocating care have major problems as well. In a well-publicized 2019 study60, public health scholar Ziad Obermeyer and his collaborators evaluated a prediction system used by hospitals, physician groups (including health maintenance organizations or HMOs), and health insurance groups to identify patients who may have complex health needs and provide more resources for care management. The algorithm they assessed had been applied to about 200 million people in the United States, nearly two-thirds of the population. Obermeyer and his team found that the algorithm dramatically underestimated the care needed for Black individuals, compared to white individuals. The team found that this was largely due to the lack of access to health care for Black people: the algorithm used previous expenditures to predict future expenditures on health care, rather than actual health care needs. Black people in the dataset were, on balance, sicker than white people, but were less likely to seek treatment (likely due to cost, availability, and potential discrimination).”