{"id":{"repo_id":"mississippi","oai_identifier":"oai:egrove.olemiss.edu:etd-1210"},"canonical_url":"https://search.dev.ndltd.org/etd/mississippi/oai:egrove.olemiss.edu:etd-1210","repository":{"repo_id":"mississippi","name":"University of Mississippi","base_url":"https://egrove.olemiss.edu/do/oai/"},"display":{"title":"Development and Comparative Predictive Validity of an Outpatient Medication Exposure Measure for Risk Adjustment Using Retrospective Claims Data","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Null, Kyle Dennis"],"institution":null,"degree_name":"M.S. in Pharmaceutical Science","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["John P. Bentley","Jeffrey Hallam","Yi Yang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T03:05:15Z","subjects":["Capitation","Hcc","Health Care Financing","Medicare","Risk-Adjustment","Rxhcc","Pharmacy and Pharmaceutical Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://egrove.olemiss.edu/etd/211","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John P. Bentley","Jeffrey Hallam","Yi Yang"]},{"key":"dc:creator","label":"Author","values":["Null, Kyle Dennis"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-01T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Pharmaceutical Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Capitation","Hcc","Health Care Financing","Medicare","Risk-Adjustment","Rxhcc","Pharmacy and Pharmaceutical Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://egrove.olemiss.edu/etd/211"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Development and Comparative Predictive Validity of an Outpatient Medication Exposure Measure for Risk Adjustment Using Retrospective Claims Data"]}]}],"canonical_facts":{"dc:contributor":["John P. Bentley","Jeffrey Hallam","Yi Yang"],"dc:creator":["Null, Kyle Dennis"],"dc:date.available":["2019-01-01T08:00:00Z"],"dc:description.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."],"dc:identifier":["https://egrove.olemiss.edu/etd/211"],"dc:subject":["Capitation","Hcc","Health Care Financing","Medicare","Risk-Adjustment","Rxhcc","Pharmacy and Pharmaceutical Sciences"],"dc:title":["Development and Comparative Predictive Validity of an Outpatient Medication Exposure Measure for Risk Adjustment Using Retrospective Claims Data"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Pharmaceutical Science"]},"updated_at":"2026-07-24T03:05:15Z"}