Predicting Costs of Care Using a Pharmacy-Based Measure Risk Adjustment in a Veteran Population

Anne E. Sales · Chuan‐Fen Liu · Kevin L. Sloan · Jesse D. Malkin · Paul Arthur Fishman · Amy K. Rosen +5 more

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Healthcare Policy and Management Pharmaceutical Practices and Patient Outcomes Medical Coding and Health Information

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.

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2000Using diagnoses to describe populations and predict costs.1996Diagnosis-based risk adjustment for Medicare capitation payments.2000Principal inpatient diagnostic cost group model for Medicare risk adjustment.1996The ratio of costs to charges: how good a basis for estimating costs?1989Adjusting Medicare capitation payments using prior hospitalization data.1996Risk-adjusted Medicare capitation rates using ambulatory and inpatient diagnoses.1996Refinements to the Diagnostic Cost Group (DCG) model.1983On the Air

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2005Predictive Modeling of Total Healthcare Costs Using Pharmacy Claims Data

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