Development and Validation of a Psychiatric Case-Mix System

Background: Although difficulties in applying risk-adjustment measures to mental health populations are increasingly evident, a model designed specifically for patients with psychiatric disorders has never been developed. Objective: Our objective was to develop and validate a case-mix classification system, the "PsyCMS," for predicting concurrent and future mental health (MH) and substance abuse (SA) healthcare costs and utilization. Subjects: Subjects included 914,225 veterans who used Veterans Administration (VA) healthcare services during fiscal year 1999 (FY99) with any MH/SA diagnosis (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] codes 290.00-312.99, 316.00-316.99). Methods: We derived diagnostic categories from ICD-CM codes using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition definitions, clinical input, and empiric analyses. Weighted least-squares regression models were developed for concurrent (FY99) and prospective (FY00) MH/SA costs and utilization. We compared the predictive ability of the PsyCMS with several case-mix systems, including adjusted clinical groups, diagnostic cost groups, and the chronic illness and disability payment system. Model performance was evaluated using R-squares and mean absolute prediction errors (MAPEs). Results: Patients with MH/SA diagnoses comprised 29.6% of individuals seen in the VA during FY99. The PsyCMS accounted for a distinct proportion of the variance in concurrent and prospective MH/SA costs (R² = 0.11 and 0.06, respectively), outpatient MH/SA utilization (R² = 0.25 and 0.07), and inpatient MH/SA utilization (R² = 0.13 and 0.05). The PsyCMS performed better than other case-mix systems examined with slightly higher R-squares and lower MAPEs. Conclusions: The PsyCMS has clinically meaningful categories, demonstrates good predictive ability for modeling concurrent and prospective MH/SA costs and utilization, and thus represents a useful method for predicting mental health costs and utilization.