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October 14, 2020 06:44 pm

From a Small Town in North Carolina To Big-City Hospitals, How Software Infuses Racism Into U.S. Health Care

An anonymous reader shares a report: The railroad tracks cut through Weyling White's boyhood backyard like an invisible fence. He would play there on sweltering afternoons, stacking rocks along the rails under the watch of his grandfather, who established a firm rule: Weyling wasn't to cross the right of way into the white part of town. The other side had nicer homes and parks, all the medical offices, and the town's only hospital. As a consequence, White said, his family mostly got by without regular care, relying on home remedies and the healing hands of the Baptist church. "There were no health care resources whatsoever," said White, 34. "You would see tons of worse health outcomes for people on those streets." The hard lines of segregation have faded in Ahoskie, a town of 5,000 people in the northeastern corner of the state. But in health care, a new force is redrawing those barriers: algorithms that blindly soak up and perpetuate historical imbalances in access to medical resources. A STAT investigation found that a common method of using analytics software to target medical services to patients who need them most is infusing racial bias into decision-making about who should receive stepped-up care. While a study published last year documented bias in the use of an algorithm in one health system, STAT found the problems arise from multiple algorithms used in hospitals across the country. The bias is not intentional, but it reinforces deeply rooted inequities in the American health care system, effectively walling off low-income Black and Hispanic patients from services that less sick white patients routinely receive. These algorithms are running in the background of most Americans' interaction with the health care system. They sift data on patients' medical problems, prior health costs, medication use, lab results, and other information to predict how much their care will cost in the future and inform decisions such as whether they should get extra doctor visits or other support to manage their illnesses at home. The trouble is, these data reflect long-standing racial disparities in access to care, insurance coverage, and use of services, leading the algorithms to systematically overlook the needs of people of color in ways that insurers and providers may fail to recognize.

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