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Risk Adjustment for People with Chronic Conditions in Private Sector Health Plans

The MEDSTAT Group, Inc., Washington, DC

Ronald J. Ozminkowski, PhD

The MEDSTAT Group, Inc., Washington DC, Ann Arbor, MI, ron.ozminkowski{at}medstat.com

Adele Kirk

University of California at Los Angeles

Susan L. Ettner, PhD

University of California at Los Angeles

John Drabek, PhD

Division of Aging and Long Term Care Policy, Office of the Assistant Secretary for Planning and Evaluation, Department of Health and Human Services

Background. Although the problem of adverse selection into more generous health insurance plans has been the focus of previous work, risk adjustment systems have only recently begun to be implemented to blunt its effect. Objectives. This study examines the ability of the leading risk adjustment systems to predict health care expenditures for people with chronic conditions, using claims and enrollment data from 2 large employers. Research design. Predictive errors and total financial losses/gains are compared for different risk adjustment approaches (primarily hierarchical condition categories [HCCs] and adjusted clinical groups) for several chronic conditions. Results. One of the best performing risk adjusteent systems was a regression-based HCC method, which had an average under-prediction error rate of 9% or 6%, depending on the employer. In comparison, more typical actuarial risk adjustments based on just age, gender, and prevailing area wages lead to a prediction error of at least 50%. We did not find evidence that payments for particular chronic conditions would be consistently and significantly under- or overestimated. Conclusion. The leading risk adjustment approaches substantially reduce the incentives for adverse se-lection but do not eliminate them.

Key Words: risk adjustment • chronic conditions • health insurance • payment

Medical Decision Making, Vol. 23, No. 5, 387-405 (2003)
DOI: 10.1177/0272989X03257264


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