Identifying Undercompensated Groups Defined By Multiple Attributes in Risk Adjustment

Abstract: Risk adjustment in health care aims to redistribute payments to insurers based on costs. However, risk adjustment formulas are known to underestimate costs for some groups of patients. This undercompensation makes these groups unprofitable to insurers and creates incentives for insurers to discriminate. We develop a machine learning method for "group importance" to identify unprofitable groups defined by multiple attributes, improving on the arbitrary nature of existing evaluations. This procedure was designed to evaluate the risk adjustment formulas used in the U.S. health insurance Marketplaces as well as Medicare. We find that a number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is larger than with single attributes. No complex groups were found to be consistently under- or overcompensated in the Medicare risk adjustment formula. Our work provides policy makers with new information on potential targets of discrimination in the health care system and a path towards more equitable health coverage.