Mark R. Trusheim

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.

Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single‐sponsor, single‐drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real‐world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints—aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.

Quality healthcare, as measured by outcomes and costs, needs high-quality diagnostics whose use governs the evidence-based flow of patients from screening to treatment to outcomes monitoring. The current patent, regulatory and reimbursement environment may be inadequate to spur their development, thereby placing in jeopardy the goals of healthcare reform and the aspirations of personalized medicine. Policy actions to ensure consistent quality standards and to increase development incentives through research support, reimbursement reform, increased intellectual property protection and market-making activities may be required to obtain the well-characterized, clinically proven diagnostics that US healthcare requires.