Racial bias is everywhere in medicine, including the calculators doctors commonly use to predict a patient’s risk of disease and inform their treatment. A growing movement is encouraging medical specialties and hospitals to reconsider the use of race in those tools.
But a new study shows that removing bias isn’t as simple as taking race out of the equation.
Using records from thousands of colorectal cancer patients in California, researchers from the University of Washington tested the performance of four algorithms that predicted the likelihood cancer would return after a tumor was removed. The model that included race and ethnicity as a predictive variable, they found, performed more equally across groups than a model with race redacted.
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