title: Development and validation of a psychiatric case-mix system.
abstract: 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.
  
  
  METHODS
  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 (R=0.11 and 0.06, respectively), outpatient MH/SA utilization (R=0.25 and 0.07), and inpatient MH/SA utilization (R=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.
authors.affiliation: VA Puget Sound Health Care System, and the Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington 98108-1597, USA. Kevin.Sloan@med.va.gov
authors.forename: Kevin L
authors.initials: KL
authors.lastname: Sloan
authors.forename: Maria E
authors.initials: ME
authors.lastname: Montez-Rath
authors.forename: Avron
authors.initials: A
authors.lastname: Spiro
authors.forename: Cindy L
authors.initials: CL
authors.lastname: Christiansen
authors.forename: Susan
authors.initials: S
authors.lastname: Loveland
authors.forename: Priti
authors.initials: P
authors.lastname: Shokeen
authors.forename: Lawrence
authors.initials: L
authors.lastname: Herz
authors.forename: Susan
authors.initials: S
authors.lastname: Eisen
authors.forename: James N
authors.initials: JN
authors.lastname: Breckenridge
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
mesh_terms: D000368:Aged; D005260:Female; D006296:Health Services / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000191:economics* / Q000628:therapy*; D008875:Middle Aged; D015233:Models, Statistical; D020379:Risk Adjustment / Q000706:statistics & numerical data*; D019966:Substance-Related Disorders / Q000191:economics / Q000628:therapy; D014728:Veterans