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Article
October 23, 1996

Taking Health Status Into Account When Setting Capitation RatesA Comparison of Risk-Adjustment Methods

Author Affiliations

From the Health Research Center, Institute for Research and Education, HealthSystem Minnesota, Minneapolis (Drs Fowles, Fowler, and Ireland and Mr Knutson); and Health Services Research and Development Center, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, Md (Drs Weiner and Tucker). Dr Ireland is currently with the Department of Pediatrics, University of Minnesota, Minneapolis.

JAMA. 1996;276(16):1316-1321. doi:10.1001/jama.1996.03540160038030
Abstract

Objective.  —To compare performance of different health status measures for risk-adjusting capitation rates.

Design.  —Cross-sectional study. Health status measures derived from 1 year were used to predict resources for that year and the next.

Setting.  —Group-network health maintenance organization in Minnesota.

Participants.  —Sample of 18- to 64-year-old (n=3825) and elderly (aged ≥65 years; n=1955) members enrolled in a network-model health maintenance organization in Minnesota.

Main Outcome Measures.  —Total expenditures in the year concurrent with the health status survey (July 1991 through June 1992) and total expenditures in the year following the survey (July 1992 through June 1993).

Results.  —Capitation adjustment based on demographic measures performed least well. Both self-reported health status measures and diagnoses predicted future expenditures twice as well as demographics. When predicting costs for groups of patients rather than individuals, the demographic model worked well for average groups but tended to overpredict healthier groups and underpredict sicker groups. Ambulatory Care Groups based on diagnoses performed better than self-reported health status both in the retrospective models and across healthier and sicker groups.

Conclustion.  —Without risk adjustment, capitation rates are likely to overpay or underpay physicians for certain patient groups. It is possible to improve prediction using health status measures for risk adjustment. When selection bias is suspected and administrative data are available, we recommend a risk-adjustment method based on diagnostic information. If diagnostic data are not available, we recommend a system based on simple self-reported measures, such as chronic conditions, rather than complex functional status measures.

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