Medical University of Vienna, Vienna, Austria.
Massachusetts Institute of Technology Center for Biomedical Innovation, Cambridge, Massachusetts, USA.
Clin Pharmacol Ther. 2022 Jan;111(1):52-62. doi: 10.1002/cpt.2471. Epub 2021 Nov 17.
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.
基础科学家和药物开发者正在加速创新，以实现精准医学的目标。监管机构为患者及时获得精准药物（包括个体化治疗）开辟了途径。医疗保健支付方认识到需要进行变革，但覆盖范围和报销的下游创新只是在缓慢进行。与精准药物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下，支付是基于对真实世界疗效的假设随机对照试验（RCT）预测，即“试验和预测”策略；患者实际获得的临床获益通常不会通过收集真实世界数据（RWD）来事后确定。为了减轻临床绩效不确定性带来的财务风险，制造商和支付方设计了“跟踪和支付”框架（即，跟踪预先约定的治疗结果，这与财务后果相关联）。虽然一些跟踪和支付安排取得了成功，但固有的弱点包括激励措施可能不一致、患者渠道化的风险以及未能利用生成的 RWD 来实现对治疗方法的持续学习。“精准报销”（PR）旨在克服简单跟踪和支付方案的固有弱点。通过将 RWD 收集与先进的分析（例如人工智能和机器学习）相结合，生成可操作的真实世界证据，同时在所有利益相关者（包括提供者和患者）之间前瞻性地调整激励措施，并预先约定信息的使用和传播，PR 成为一种基于绩效的支付的“学习和预测”模式。我们在这里详细描述了 PR 的概念，并提出了下一步的实施步骤，以使其成为现实。
