Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to Track-and-Pay to Learn-and-Predict.
Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to Track-and-Pay to Learn-and-Predict.
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DOI：
10.1002/cpt.2471
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发表时间：
2022-01
影响因子：
6.7
通讯作者：
中科院分区：
文献类型：
--
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基础科学家和药物开发人员正在加速创新，以实现精准医疗的目标。监管机构为患者及时获得精准药物创造了途径，包括个性化治疗。医疗保健支付方承认变革的必要性，但下游覆盖和报销方面的创新只是断断续续地出现。与精准医疗相关的绩效不确定性、高价格标签、支付时间和精算风险问题给支付方带来了新的财务挑战。在传统的药物报销框架中，支付是基于假设的随机对照试验（RCT）对现实世界有效性的预测，这是一种“试验+项目”策略；患者的临床获益通常不能通过收集真实世界的数据（RWD）来确定。为了减轻临床表现不确定性带来的财务风险，制造商和付款人设计了“跟踪和支付”框架（即跟踪与财务后果相关的预先商定的治疗结果）。尽管一些跟踪和支付的安排已经取得了成功，但其固有的弱点包括潜在的激励不一致、引导患者的风险，以及未能使用生成的RWD来持续学习治疗方法。“精确报销”（PR）旨在克服简单的跟踪和薪酬计划的固有弱点。将RWD的收集与先进的分析（例如，人工智能和机器学习）相结合，生成可操作的现实世界证据，与所有利益相关者（包括提供者和患者）的前瞻性激励对齐，以及预先商定的使用和传播生成的信息，PR成为一种“学习-预测”的绩效支付模式。我们在这里详细描述公关的概念，并列出使其成为现实的后续步骤。
Basic scientists and drug developers are accelerating innovations toward the goal of precision medicine. Regulators create pathways for timely patient access to precision medicines, including individualized therapies. Healthcare payors acknowledge the need for change but downstream innovation for coverage and reimbursement is only haltingly occurring. Performance uncertainty, high price‐tags, payment timing, and actuarial risk issues associated with precision medicines present novel financial challenges for payors. With traditional drug reimbursement frameworks, payment is based on an assumed randomized controlled trial (RCT) projection of real‐world effectiveness, a “trial‐and‐project” strategy; the clinical benefit realized for patients is not usually ascertained ex post by collection of real‐world data (RWD). To mitigate financial risks resulting from clinical performance uncertainty, manufacturers and payors devised “track‐and‐pay” frameworks (i.e., the tracking of a pre‐agreed treatment outcome which is linked to financial consequences). Whereas some track‐and‐pay arrangements have been successful, inherent weaknesses include the potential for misalignment of incentives, the risk of channeling of patients, and a failure to use the RWD generated to enable continuous learning about treatments. “Precision reimbursement” (PR) intends to overcome inherent weaknesses of simple track‐and‐pay schemes. In combining the collection of RWD with advanced analytics (e.g., artificial intelligence and machine learning) to generate actionable real‐world evidence, with prospective alignment of incentives across all stakeholders (including providers and patients), and with pre‐agreed use and dissemination of information generated, PR becomes a “learn‐and‐predict” model of payment for performance. We here describe in detail the concept of PR and lay out the next steps to make it a reality.
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影响因子：
6.7
作者：
Eichler, H-G;Bloechl-Daum, B.;Bauer, P.;Bretz, F.;Brown, J.;Hampson, L. V.;Honig, P.;Krams, M.;Leufkens, H.;Lim, R.;Lumpkin, M. M.;Murphy, M. J.;Pignatti, F.;Posch, M.;Schneeweiss, S.;Trusheim, M.;Koenig, F.
通讯作者：
Koenig, F.
影响因子：
6.7
作者：
Hirsch G
通讯作者：
Hirsch G
影响因子：
6.7
作者：
Eichler HG;Pignatti F;Schwarzer-Daum B;Hidalgo-Simon A;Eichler I;Arlett P;Humphreys A;Vamvakas S;Brun N;Rasi G
通讯作者：
Rasi G
影响因子：
6.7
作者：
Eichler, H-G;Baird, L. G.;Hirsch, G.
通讯作者：
Hirsch, G.
影响因子：
120.1
作者：
Eichler, Hans-Georg;Abadie, Eric;Bloechl-Daum, Brigitte
通讯作者：
Bloechl-Daum, Brigitte