This project will: 1) examine unfairness in the Centers for Medicare and Medicaid Services' Hierarchical Condition Categories (HCC) score by race/ethnicity, urban/rural status, and socioeconomic status; 2) determine whether integrating individual or area-level metrics into predictive models would mitigate algorithmic unfairness in the HCC score; and 3) quantify the impact of a fairer HCC score on risk-adjusted reimbursements to health plans and accountable care organizations that disproportionately serve people of color, rural, or socioeconomically disadvantaged patients. For these analyses, the team would use Medicare claims from 2015–2019 linked to Medicare Current Beneficiary Survey (MCBS) data.
Examining Unfairness in CMS's Hierarchical Condition Categories Score and the Potential Impact on Health Systems and Health Plans Serving Minorities
Grantee Organization
Trustees of the University of Pennsylvania
Principal Investigator
Amol Navathe, M.D., Ph.D.
Term
3/1/22 - 12/1/23
Award Amount
$200,000
Approval Year
Related Program
Health Care Delivery System Reform
Topics
Health Disparities,
Medicare,
State Health Policy and Medicaid
Grantee Organization
Trustees of the University of Pennsylvania
Principal Investigator
Amol Navathe, M.D., Ph.D.
Term
3/1/22 - 12/1/23
Award Amount
$200,000
Approval Year
Related Program
Health Care Delivery System Reform
Topics
Health Disparities,
Medicare,
State Health Policy and Medicaid