Sherri Rose
Associate Professor of Health Policy, Stanford University; Co-Director, Stanford Health Policy Data Science Lab; Faculty Affiliate, Stanford HAI
This brief explores the complexities of accounting for race in clinical algorithms for evaluating kidney disease and the implications for tackling deep-seated health inequities.
It is no secret that the healthcare system is rife with inequities—from geography to race to class. Recent advances in data science and algorithmic fairness modeling are empowering researchers to quantify those inequities at unprecedented scale. These AI-driven opportunities may have a significant impact on the pricing of healthcare. This brief seeks to provide policymakers a new opportunity to realign the healthcare market’s incentives in favor of patients, recommending interventions that shape companies’ incentives around the pricing models they deploy—all of which come with their own trade-offs.