Precision Reimbursement for Precision Medicine: Using Real‐World Evidence to Evolve From Trial‐and‐Project to Track‐and‐Pay to Learn‐and‐Predict

Abstract

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.

Delivering the precision oncology paradigm: reduced R&D costs and greater return on investment through a companion diagnostic informed precision oncology medicines approach

“…The increased success of precision oncology medicine’s FDA approval rates not only helps to reduce R&D spend, but also reinforces the rationale for targeting the underlying genomic mechanisms of oncogenesis [ 8 , 31 ]. Decreased R&D spend and increased effectiveness of precision oncology medicines will likely accelerate the reimbursement process and also require a rethink of market access strategies [ 3 , 32 – 34 ].…”

Prognostication in inflammatory bowel disease

“…Another barrier to clinical implementation is the complexity of reimbursement; precision testing often does not align with traditional payment schemes as precision care by its very nature requires more upfront costs for a theoretical, long-term pay off. It has been postulated that payment systems and data collection would need to be overhauled to allow for precision integration in clinics across the world ( 82 ).…”

Orphan drugs’ clinical uncertainty and prices: Addressing allocative and technical inefficiencies in orphan drug reimbursement

“…However, in the presence of high-quality RCT evidence, the average clinical benefit and effect size for the treatment-eligible population can be quantified ex ante and payers can base their P&R negotiations on the expected average clinical outcome for a group of insured patients, including non-responders. While the power of RCT results to predict treatment success under conditions of everyday clinical practice is less than perfect ( Eichler et al, 2022 ), available RCT evidence is expected to at least mitigate, if not eliminate, technical inefficiency. Contrast this to a situation where clinical evidence about a novel orphan product is limited to a small, uncontrolled case series of patients, often coupled to relatively short observation periods in the clinical trial setting.…”

“…It is difficult to quantify the value of such learnings and we are not proposing to formally account for learnings during P&R negotiations of orphan drugs but it is the authors’ belief that every effort should be made to ensure maximum knowledge generation from every reimbursement contract covering high-price orphan drugs. The clinical outcomes of orphan drug treatment should be considered a “commons” and we would argue that, for example, pay-for-performance or other managed entry agreements (MEA) that keep confidential clinical outcome data are not just a wasted opportunity but are ethically unacceptable ( Eichler et al, 2022 ).…”