Risk Adjustment for High Utilizers of Public Mental Health

Published in: Journal of Mental Health Policy and Economics, v. 3, 2000, p. 129-137

Posted on RAND.org on January 01, 2000

by Kanika Kapur, Alexander Young, Dennis Murata

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BACKGROUND: Publicly funded mental health systems are increasingly implementing managed care systems, such as capitation, to control costs. Capitated contracts may increase the risk for disenrollment or adverse outcomes among high cost clients with severe mental illness. Risk-adjusted payments to providers are likely to reduce providers' incentives to avoid or under-treat these people. However, most research has focused on Medicare and private populations, and risk adjustment for individuals who are publicly funded and severely mentally ill has received far less attention. AIM OF THE STUDY: Risk adjustment models for this population can be used to improve contracting for mental health care. Our objective is to develop risk adjustment models for individuals with severe mental illness and assess their performance in predicting future costs. The authors apply the risk adjustment model to predict costs for the first year of a pilot capitation program for the severely mentally ill that was not risk adjusted. They assess whether risk adjustment could have reduced disenrollment from this program. METHODS: This analysis uses longitudinal administrative data from the County of Los Angeles Department of Mental Health for the fiscal years 1991 to 1994. The sample consists of 1956 clients who have high costs and are severely mentally ill. The authors estimate several modified two parts models of 1993 cost that use 1992 client-based variables such as demographics, living conditions, diagnoses and mental health costs (for 1992 and 1991) to explain the variation in mental health and substance abuse costs. RESULTS: The authors find that the model that incorporates demographic characteristics, diagnostic information and cost data from two previous years explains about 16 percent of the in-sample variation and 10 percent of the out-of-sample variation in costs. A model that excludes prior cost covariates explains only 5 percent of the variation in costs. Despite the relatively low predictive power, the authors find some evidence that the disenrollment from the pilot capitation initiative input have been reduced if risk adjustment had been used to set capitation rates. DISCUSSION: The evidence suggests that even though risk adjustment techniques have room to improve, they are still likely to be useful for reducing risk selection in capitation programs. Blended payment schemes that combine risk adjustment with corridors or partial fee-for-service payment should be explored. IMPLICATIONS FOR HEALTH CARE PROVISION, USE AND POLICY: Our results suggest that risk adjustment method, as developed to data, do not have the requisite predictive power to be used as the sole approach to adjusting capitation rates. Risk adjustment is informative and useful; however, payments to providers should not be fully capitated, and may need to involve some degree of risk sharing between providers and public mental health agencies. A blended contract design may further reduce incentives for risk selection by incorporating a partly risk-adjusted capitation payment, without relying completely on the accuracy of risk adjustment models. IMPLICATIONS FOR FURTHER RESEACH: Risk adjustment models estimated using data sets containing better predictors of rehospitalization and more precise clinical information are likely to have higher predictive power. Further research should also focus on the effect of combination contract designs.

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