title: The Medicaid Rx model: pharmacy-based risk adjustment for public programs.
abstract: BACKGROUND
  Risk adjustment models typically use diagnoses from claims or encounter records to assess illness severity. However, concerns about the availability and reliability of diagnostic data raise the potential for alternative methods of risk adjustment. Here, we explore the use of pharmacy data as an alternative or complement to diagnostic data in risk adjustment.
  
  
  OBJECTIVE
  To develop and test a pharmacy-based risk adjustment model for SSI and TANF Medicaid populations.
  
  
  METHODS
  Pharmacological review combined with empirical evaluation. We developed the Medicaid Rx model, a system that classifies a subset of the National Drug Codes into categories that can be used for risk-assessment and risk-adjusted payment.
  
  
  METHODS
  Subjects consisted of 362,370 persons with disability and 1.5 million AFDC and TANF beneficiaries in California, Colorado, Georgia, and Tennessee during 1990-1999.
  
  
  METHODS
  We compare pharmacy and diagnostic classification for three chronic diseases. We also compare R2 statistics and use simulated health plans to evaluate the performance of alternative models.
  
  
  RESULTS
  Pharmacy and diagnostic classification vary in their ability to identify specific chronic disease. Using simulated plans, diagnostic models are better at predicting expenditures than are pharmacy-based models for disabled Medicaid beneficiaries, although the models perform similarly for TANF Medicaid beneficiaries. Models that combine diagnostic and pharmacy data have superior overall performance.
  
  
  CONCLUSIONS
  The performance of risk adjustment models using a combination of pharmacy and diagnostic data are superior to that of models using either data source alone, particularly among TANF beneficiaries. Concerns regarding variations in prescribing patterns and the incentives that may follow from linking payment to pharmacy use warrant further research.
authors.affiliation: Department of Family and Preventive Medicine, University of California, San Diego 92093-0622, USA. tgilmer@ucsd.edu
authors.forename: T
authors.initials: T
authors.lastname: Gilmer
authors.forename: R
authors.initials: R
authors.lastname: Kronick
authors.forename: P
authors.initials: P
authors.lastname: Fishman
authors.forename: T G
authors.initials: TG
authors.lastname: Ganiats
mesh_terms: D000328:Adult; D000367:Age Factors; D002140:California; D002648:Child; D002908:Chronic Disease / Q000145:classification* / Q000188:drug therapy / Q000191:economics; D003120:Colorado; D016208:Databases, Factual; D003954:Diagnostic Services / Q000145:classification* / Q000706:statistics & numerical data; D006233:Persons with Disabilities / Q000706:statistics & numerical data; D005845:Georgia; D005102:Health Expenditures; D006801:Humans; D008484:Medicaid / Q000191:economics / Q000706:statistics & numerical data*; D017059:Models, Econometric; D010593:Pharmaceutical Services / Q000145:classification* / Q000706:statistics & numerical data; D011446:Prospective Studies; D012044:Regression Analysis; D020379:Risk Adjustment / Q000379:methods*; D012720:Severity of Illness Index; D013714:Tennessee; D014481:United States