基于结果的报销:高药费的解决方案?

Liang Xu, Hongmin Li, H Zhao
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引用次数: 2

摘要

问题定义:新药价格的持续飙升及其在临床实践中的不确定有效性给保险公司/付款人带来了巨大的风险。为了吸引保险公司为他们的新药提供保险,制造商开始提出一种创新的基于结果的报销(OBR)计划,根据该计划,如果药物未能达到预定的治疗目标,制造商将退款给保险公司(可能还有患者)。我们调查了OBR对保险公司、制造商和患者的影响。学术/实践相关性:尽管OBR听起来很吸引人,但其真正的影响仍存在很多争议,尤其是取决于OBR的设计。我们的研究揭示了OBR的优化设计和围绕OBR的争论,考虑了先前文献中未涉及的关键权衡和关键因素。方法:我们开发了一个Stackelberg游戏,在这个游戏中,制造商为其药物设计一个回扣方案,无论是非OBR还是OBR,考虑到有利的处方位置和提供的回扣之间的权衡。随后,保险公司考虑到其支出与患者健康利益之间的权衡,确定其药物处方以及同一疾病类别内的其他替代药物。利用治疗高脂血症这一常见疾病的14种药物的数据,我们通过多项逻辑模型估计了这14种药物的需求,并对OBR的影响进行了反事实分析。结果:在最优OBR下,制造商降低了保险公司的风险,但提高了批发价格(因此,可能不会减少保险公司的支出)。OBR还为制造商提供了更好的处方位置,从而改善了患者获得新药的途径。此外,对保险公司和患者的回扣通过不同的机制影响需求。将患者回扣纳入OBR降低了患者费用,增加了药品需求,但进一步增加了保险公司的支出。管理意义:我们展示了最优公式的结构及其在实践中的应用。我们提醒寻求OBR的保险公司/付款人减少他们的支出。
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Outcome-Based Reimbursement: The Solution to High Drug Spending?
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.
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