Electronic Theses and Dissertations

Date of Award

2012

Document Type

Dissertation

Degree Name

M.S. in Pharmaceutical Science

First Advisor

John P. Bentley

Second Advisor

Jeffrey Hallam

Third Advisor

Yi Yang

Abstract

The purpose of this study was to develop and test a measure of outpatient prescription utilization (medication exposure measure, or MEM) that may be coupled with the CMS-HCC and CMS-RxHCC methodologies to improve risk-adjusted payments to Medicare Part C and Part D plans. Studies have identified prescription measures that predict future expenditures; however, many are easily manipulable by health plans or practitioners, thus limiting their utility as risk-adjusters. The addition of a non-manipulable prescription utilization measure to existing risk-adjustment models may improve prediction, reducing adverse risk selection incentives by health plans. A secondary objective of this study was to evaluate the utility of adding prescription measures to the Charlson's Comorbidity Index, Elixhauser's Index, and RxRisk to predict year-2 expenditures, hospitalization counts, emergency department visits, and mortality. Study Design: The study design utilized a retrospective cohort from the 5% Medicare national sample, which used year-1 (2007) inputs to predict the year-2 (2008) economic and clinical outcomes. The sample included beneficiaries with continuous enrollment in fee-for-service Medicare Parts A, B, and D for a minimum of 12 months in the base year and a minimum of 1 month in year-2. An interaction between the end-of-year Medicare Part D benefit phase and the prescription measures was included to account for the influence of the coverage gap (i.e., "donut hole") on the prescription measures. Results: Overall, the addition of the prescription-based measures to risk-adjustment models resulted in enhanced predictive validity for the economic and clinical outcomes tested compared to the risk-adjustment model alone. The addition of any prescription measure to the risk-adjustment models did not meaningfully improve model performance in predicting year-2 medical expenditures; however, the prescription measures, particularly the MEM, markedly improved prediction of year-2 pharmacy expenditures. Conclusions: Although adding MEM to the HCC models used to predict medical expenditures does not appear to be a useful method of enhancing risk-adjusted payments, the MEM performed particularly well with the RxHCC predicting year-2 pharmacy expenditures. Incorporating the MEM into Medicare Part D risk-adjustment models (i.e., with RxHCC) would improve risk-adjusted capitated payments from both the perspectives of CMS and the health plans.

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