OBJECTIVE: To compare the performance of various risk adjustment models in behavioral health applications such as setting mental health and substance abuse (MH/SA) capitation payments or overall capitation payments for populations including MH/SA users. DATA SOURCES/STUDY DESIGN: The 1991-93 administrative data from the Michigan Medicaid program were used. We compared mean absolute prediction error for several risk adjustment models and simulated the profits and losses that behavioral health care carve outs and integrated health plans would experience under risk adjustment if they enrolled beneficiaries with a history of MH/SA problems. Models included basic demographic adjustment, Adjusted Diagnostic Groups, Hierarchical Condition Categories, and specifications designed for behavioral health. PRINCIPAL FINDINGS: Differences in predictive ability among risk adjustment models were small and generally insignificant. Specifications based on relatively few MH/SA diagnostic categories did as well as or better than models controlling for additional variables such as medical diagnoses at predicting MH/SA expenditures among adults. Simulation analyses revealed that among both adults and minors considerable scope remained for behavioral health care carve outs to make profits or losses after risk adjustment based on differential enrollment of severely ill patients. Similarly, integrated health plans have strong financial incentives to avoid MH/SA users even after adjustment. CONCLUSIONS: Current risk adjustment methodologies do not eliminate the financial incentives for integrated health plans and behavioral health care carve-out plans to avoid high-utilizing patients with psychiatric disorders.
OBJECTIVE: To compare the performance of various risk adjustment models in behavioral health applications such as setting mental health and substance abuse (MH/SA) capitation payments or overall capitation payments for populations including MH/SA users. DATA SOURCES/STUDY DESIGN: The 1991-93 administrative data from the Michigan Medicaid program were used. We compared mean absolute prediction error for several risk adjustment models and simulated the profits and losses that behavioral health care carve outs and integrated health plans would experience under risk adjustment if they enrolled beneficiaries with a history of MH/SA problems. Models included basic demographic adjustment, Adjusted Diagnostic Groups, Hierarchical Condition Categories, and specifications designed for behavioral health. PRINCIPAL FINDINGS: Differences in predictive ability among risk adjustment models were small and generally insignificant. Specifications based on relatively few MH/SA diagnostic categories did as well as or better than models controlling for additional variables such as medical diagnoses at predicting MH/SA expenditures among adults. Simulation analyses revealed that among both adults and minors considerable scope remained for behavioral health care carve outs to make profits or losses after risk adjustment based on differential enrollment of severely ill patients. Similarly, integrated health plans have strong financial incentives to avoid MH/SA users even after adjustment. CONCLUSIONS: Current risk adjustment methodologies do not eliminate the financial incentives for integrated health plans and behavioral health care carve-out plans to avoid high-utilizing patients with psychiatric disorders.
Authors: Stephan R Lindner; Miguel Marino; Jean O'Malley; Heather Angier; Steffani R Bailey; Megan Hoopes; Rachel Springer; K John McConnell; Jennifer DeVoe; Nathalie Huguet Journal: Diabetes Care Date: 2019-12-19 Impact factor: 19.112
Authors: Benjamin C Sun; Nicoleta Lupulescu-Mann; Christina J Charlesworth; Hyunjee Kim; Daniel M Hartung; Richard A Deyo; K John McConnell Journal: Ann Emerg Med Date: 2017-11-24 Impact factor: 5.721