Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to...

Area of Science:

• Biomedical innovation
• Health economics
• Regulatory science

Background:

• Precision medicine innovations are advancing rapidly, with regulatory pathways established for patient access.
• Healthcare payors face challenges in covering and reimbursing precision medicines due to performance uncertainty and high costs.
• Traditional reimbursement models (trial-and-project) do not adequately ascertain real-world effectiveness using real-world data (RWD).

Purpose of the Study:

• To introduce and detail the concept of Precision Reimbursement (PR) as an advanced payment model for precision medicines.
• To address the limitations of existing reimbursement frameworks, including track-and-pay models.
• To propose a learn-and-predict payment strategy that leverages RWD and advanced analytics.

Main Methods:

• Conceptualizing Precision Reimbursement (PR) as a framework.
• Integrating RWD collection with advanced analytics (AI, machine learning) for actionable real-world evidence.
• Ensuring prospective alignment of incentives among all stakeholders (manufacturers, payors, providers, patients).

Main Results:

• PR aims to overcome weaknesses in current track-and-pay models, such as misaligned incentives and limited learning from RWD.
• PR facilitates a "learn-and-predict" payment for performance model.
• The proposed model emphasizes pre-agreed use and dissemination of generated evidence.

Conclusions:

• Precision reimbursement represents a significant advancement over traditional and track-and-pay models for precision medicines.
• PR enables continuous learning and prediction by utilizing RWD and advanced analytics.
• Further development and implementation steps are outlined to realize PR as a practical payment solution.