中文摘要
二十年来,仿制药短缺给美国制药业和政府带来了重大挑战,造成严重后果并引起广泛关注。人们已投入大量努力识别短缺的成因,其中一些研究将短缺与医疗保险(Medicare)在医院药品报销中采用平均销售价格(ASP)政策联系起来。然而,鲜有研究把握报销政策、药品短缺与集团采购组织(GPO)的影响三者之间的联系,而 GPO 在代表医院与制造商议定价格方面发挥着关键作用。鉴于报销政策通过影响供应链各方的决策而影响短缺,我们分析了一个药品供应模型,该模型刻画了药品批发价决策中的关键要素与权衡。我们发现,在 ASP 政策下,两种方向相反的效应——搭便车效应与协调效应——的相互作用引导批发定价决策,并影响短缺结果。我们刻画了影响这些效应的关键因素,并表明 ASP 政策实际上对曾出现短缺的药品具有抵御短缺的能力。通过克服数据透明度有限(尤其是 GPO 相关数据)的挑战,我们进一步结合多种数据来源(借助机器学习)开展数值分析,以获得更多洞见。除了证实 ASP 对短缺的抵御能力之外,数值分析还定量考察了政策参数和 GPO 相关参数的影响。我们基于理论和数值研究结果对政策含义进行了深入讨论。值得注意的是,与我们分析中作为基准建模的既往固定价格报销政策相比,ASP 是一种以市场数据为驱动的政策。这一区别凸显了深入理解此类政策对未来政策评估和缓解短缺的重要性。本文由 Jayashankar Swaminathan(运营管理)接受发表。补充材料:在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2023.02223 获取。
Abstract
Generic drug shortages have posed significant challenges to the U.S. pharmaceutical industry and the government for two decades, causing severe consequences and drawing widespread attention. Substantial efforts have been devoted to identifying the causes, with some linking shortages to Medicare’s adoption of the average sales price (ASP) policy for hospital drug reimbursement. However, little research has captured the connections among reimbursement policies, shortages, and the influence of group purchasing organizations (GPOs), which play a key role in representing hospitals to set prices with manufacturers. Recognizing that the reimbursement policy influences shortages through affecting supply chain parties’ decisions, we analyze a drug supply model that captures the essential elements and tradeoffs in drug wholesale price decisions. We find that under the ASP policy, the interplay of two opposing effects, the free-ride effect and the aligning effect, guides wholesale pricing decisions and affects shortage outcomes. We capture key factors influencing these effects and show that the ASP policy actually possesses resistance to shortages of drugs that have experienced shortages. Overcoming the challenges of limited data transparency, especially on GPOs, we further conduct numerical analysis incorporating various data sources (with the aid of machine learning) to gain additional insights. In addition to confirming ASP’s resistance to shortages, the numerical analysis quantitatively investigates the impact of policy and GPO-related parameters. We provide a thorough discussion of policy implications based on our theoretical and numerical findings. Notably, compared with the previously adopted fixed-price reimbursement policy, which we model as a benchmark in our analysis, ASP represents a market-data-driven policy. This distinction underscores the importance of a deep understanding of such policies for future policy evaluation and shortage mitigation. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02223 .