  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.
