A Review of the Data-Driven Policy Making of Medical Financial Assistance for Rare Diseases: Current Situation, Trends and Opportunities

The inherent clinical uncertainties, substantial costs, and small patient cohorts of orphan drugs limit the applicability of randomized controlled trial (RCT)-based health technology assessments (HTAs) in guiding coverage criteria, sustainable financing models, and equitable reimbursement frameworks for medical financial assistance policies for rare diseases.The digital transformation in healthcare system leads to solutions to the challenges in designing the policy by using data-driven decision-making. This article summarizes the decision-making issues in policy design, discusses the current status and trends of digital transformation, and analyzes the important new opportunities for AI-driven policy design for medical financial assistance policies for rare diseases. Decision-making that is digital intelligence driven and using techniques such as big data analytics and real-world research methods will enhance targeting efficiency, improve the quality of financing, and realize the performance-based reimbursement in the medical financial assistance, providing significant value in facilitating the policy reform and development for rare diseases healthcare.