The Medicaid Rx Model: Pharmacy-Based Risk Adjustment for Public Programs

• Published in: Medical care 2001-11, Vol.39 (11), p.1188-1202
• Main Authors: Gilmer, Todd, Kronick, Richard, Fishman, Paul, Ganiats, Theodore G.
• Format: Article
• Language: English
• Subjects: Adult; Age Factors; California; Child; Chronic Disease - classification; Chronic Disease - drug therapy; Chronic Disease - economics; Colorado; Databases, Factual; Diagnostic Services - classification; Diagnostic Services - statistics & numerical data; Disabled Persons - statistics & numerical data; Georgia; Health Expenditures; Humans; Medicaid - economics; Medicaid - statistics & numerical data; Models, Econometric; Pharmaceutical Services - classification; Pharmaceutical Services - statistics & numerical data; Prospective Studies; Regression Analysis; Risk Adjustment - methods; Severity of Illness Index; Tennessee; United States
• ISSN: 0025-7079; 1537-1948
• DOI: 10.1097/00005650-200111000-00006

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. OBJECTIVES.To develop and test a pharmacy-based risk adjustment model for SSI and TANF Medicaid populations. RESEARCH DESIGN. 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. SUBJECTS.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. MEASURES.We compare pharmacy and diagnostic classification for three chronic diseases. We also compare R 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.