  Over the years, several studies have demonstrated that there exist
significant disparities in health indicators in the United States population
across various groups. Healthcare expense is used as a proxy for health in
algorithms that drive healthcare systems and this exacerbates the existing
bias. In this work, we focus on the presence of racial bias in health
indicators in the publicly available, and nationally representative Medical
Expenditure Panel Survey (MEPS) data. We show that predictive models for care
management trained using this data inherit this bias. Finally, we demonstrate
that this inherited bias can be reduced significantly using simple mitigation
techniques.
