Outcome-Based Reimbursement: The Solution to High Drug Spending?

• 成果类型: Article
• 署名作者: Xu, Liang; Li, Hongmin; Zhao, Hui
• 署名单位: University of Nebraska System; University of Nebraska Lincoln; Arizona State University; Arizona State University-Tempe; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2021.1051
• 发表日期: 2022
• 页码: 2029-2047
• 关键词: outcome-based reimbursement; risk sharing; Drug pricing; formulary design; risk aversion; multinomial logit models

摘要

Problem definition: The continuously soaring prices of new drugs and their uncertain effectiveness in clinical practice have put substantial risks on insurers/payers. To induce insurer coverage of their new drugs, manufacturers start to propose an innovative outcome-based reimbursement (OBR) scheme under which manufacturers refund insurers (and possibly patients) if the drugs fail to achieve a prespecified treatment target. We investigate the impact of OBR on insurers, manufacturers, and patients. Academic/practical relevance: Although OBR sounds intuitively appealing, its true impact is under much debate and depends particularly on the design of OBR. Our study sheds light on the optimal design of OBR and the debate around OBR, considering key trade-offs and key elements not covered in prior literature. Methodology: We develop a Stackelberg game under which the manufacturer designs a rebate scheme for its drug, either non-OBR or OBR, considering the trade-off between a favorable formulary position and the rebate provided. The insurer subsequently determines its formulary for the drug as well as other alternative drugs within the same disease category considering the trade-off between its spending and patient health benefits. Using data on 14 drugs treating a common disease, hyperlipidemia, we estimate through a multinomial logit model the demand of the 14 drugs and conduct counterfactual analyses on the impact of OBR. Results: Under the optimal OBR, the manufacturer lowers the insurer's risk but inflates the wholesale price (hence, may not reduce insurer spending). OBR also induces a better formulary position for the manufacturer, which, hence, improves patient access to new drugs. Further, rebates to the insurer and patients affect demand through different mechanisms. Including patient rebates in OBR lowers patient expenses and increases drug demand but further increases insurer spending. Managerial implications: We demonstrate the structure of an optimal formulary and its application in practice. We caution insurers/payers who are seeking OBR to reduce their spending.