title: Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population.
abstract: BACKGROUND
  Although most widely used risk adjustment systems use diagnosis data to classify patients, there is growing interest in risk adjustment based on computerized pharmacy data. The Veterans Health Administration (VHA) is an ideal environment in which to test the efficacy of a pharmacy-based approach.
  
  
  OBJECTIVE
  To examine the ability of RxRisk-V to predict concurrent and prospective costs of care in VHA and compare the performance of RxRisk-V to a simple age/gender model, the original RxRisk, and two leading diagnosis-based risk adjustment approaches: Adjusted Clinical Groups and Diagnostic Cost Groups/Hierarchical Condition Categories.
  
  
  METHODS
  The study population consisted of 161,202 users of VHA services in Washington, Oregon, Idaho, and Alaska during fiscal years (FY) 1996 to 1998. We examined both concurrent and predictive model fit for two sequential 12-month periods (FY 98 and FY 99) with the patient-year as the unit of analysis, using split-half validation.
  
  
  RESULTS
  Our results show that the Diagnostic Cost Group /Hierarchical Condition Categories model performs best (R2 = 0.45) among concurrent cost models, followed by ADG (0.31), RxRisk-V (0.20), and age/sex model (0.01). However, prospective cost models other than age/sex showed comparable R2: Diagnostic Cost Group /Hierarchical Condition Categories R2 = 0.15, followed by ADG (0.12), RxRisk-V (0.12), and age/sex (0.01).
  
  
  CONCLUSIONS
  RxRisk-V is a clinically relevant, open source risk adjustment system that is easily tailored to fit specific questions, populations, or needs. Although it does not perform better than diagnosis-based measures available on the market, it may provide a reasonable alternative to proprietary systems where accurate computerized pharmacy data are available.
authors.affiliation: VA Puget Sound Health Care System, Seattle, Washington 98108, USA. Ann.Sales@med.va.gov
authors.forename: Anne E
authors.initials: AE
authors.lastname: Sales
authors.forename: Chuan-Fen
authors.initials: CF
authors.lastname: Liu
authors.forename: Kevin L
authors.initials: KL
authors.lastname: Sloan
authors.forename: Jesse
authors.initials: J
authors.lastname: Malkin
authors.forename: Paul A
authors.initials: PA
authors.lastname: Fishman
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.forename: Susan
authors.initials: S
authors.lastname: Loveland
authors.forename: W
authors.initials: W
authors.lastname: Paul Nichol
authors.forename: Norman T
authors.initials: NT
authors.lastname: Suzuki
authors.forename: Edward
authors.initials: E
authors.lastname: Perrin
authors.forename: Nancy D
authors.initials: ND
authors.lastname: Sharp
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Todd-Stenberg
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D016303:Clinical Pharmacy Information Systems; D003953:Diagnosis-Related Groups / Q000191:economics*; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D017723:Drug Utilization Review; D005260:Female; D005544:Forecasting / Q000379:methods; D017048:Health Care Costs / Q000639:trends*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D015205:Northwestern United States; D010594:Pharmacies / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000379:methods*; D014481:United States; D014493:United States Department of Veterans Affairs; D014728:Veterans / Q000706:statistics & numerical data*