Presentation (CTI)

OBJECTIVES: The framework governing health technology assessment (HTA), pricing, and reimbursement (P&R) decisions varies between countries, giving rise to distinct archetypes, including value-based, budget-driven, cost-effectiveness, and free-market approaches. The perception and application of artificial intelligence (AI) might vary across these archetypes. This study explores the trends in how AI is viewed and utilized within each archetype.
METHODS: The study used desk research (search for relevant literature, published reports, and policy documents), interviews, and a survey to gather insights from payers and experts across archetypes (e.g., value-based: Germany, France; budget-driven: Italy, Spain; cost-effectiveness: UK; and free-market: US). Participants were selected based on professional roles, expertise, and geographic representation using our professional network and targeted outreach. In-depth interviews explored perspectives on AI’s role in P&R, while the survey determined perceptions of how AI currently supports HTA decisions.
RESULTS: Findings reveal limited experience and policy development regarding the use of AI in P&R. Participants from value-based countries reported interest in AI for evidence synthesis. Participants representing budget-driven systems prioritized AI use for pricing negotiations. Participants from cost-effectiveness markets reported interest in AI's role in predictive modeling and cost savings. Participants from free-market systems expressed interest in AI for competitive intelligence and dynamic pricing strategies. Across all P&R archetypes, there was a strong focus on leveraging AI to enhance efficiency. However, substantial challenges persist, including concerns about quality assurance, reliability, and the need for adequate training.
CONCLUSIONS: As global interest in leveraging AI to enhance P&R efficiency and decision-making grows, manufacturers should tailor their P&R strategies to align with specific archetypes. This includes using AI to enhance launch materials and aligning with how payers utilize AI. Common challenges have been identified across archetypes, highlighting the need for collaborative efforts to develop reliable and effective AI tools that address the unique requirements of each archetype.