AI for Pricing Reimbursement: When to Green-Light, When to Red-Light?
Author(s)
Juliette Torres Ames, MSc1, Emanuele Arca`, Sr., MSc2, Grace E. Fox, PhD3, Nita Santpurkar, MSc4, Jed Avissar, MSc1.
1OPEN Health HEOR & Market Access, London, United Kingdom, 2OPEN Health HEOR & Market Access, Rotterdam, Netherlands, 3OPEN Health HEOR & Market Access, New York, NY, USA, 4OPEN Health HEOR & Market Access, Thane, India.
1OPEN Health HEOR & Market Access, London, United Kingdom, 2OPEN Health HEOR & Market Access, Rotterdam, Netherlands, 3OPEN Health HEOR & Market Access, New York, NY, USA, 4OPEN Health HEOR & Market Access, Thane, India.
OBJECTIVES: Artificial intelligence (AI) can be employed to support industry with the development of internal and external materials used for pricing and reimbursement (P&R), from evidence gap analyses to budget impact models. However, payer acceptance of materials whose development was aided by AI remains uncertain. To explore when and how industry should integrate AI for P&R materials to enhance market access strategy and promote payer acceptance.
METHODS: A survey was conducted with payers in France (n=1), Germany (n=1), Italy (n=2), Spain (n=1), and the US (n=1) to gather perspectives on AI use in P&R activities, AI’s perceived benefits and challenges, and AI’s role in P&R decision-making. In-depth interviews provided further insights in France (n=1) and Italy (n=1).
RESULTS: Payers think AI could be beneficial for P&R processes, including pricing predictions, budget impact analyses, and evidence synthesis, but not for handling subjective elements such as political and contextual factors considered in value-based pricing and negotiation decisions. For external P&R materials, payers believed AI could improve economic modelling and evidence synthesis, thereby accelerating health technology assessments (HTAs), but specified inclusion of human oversight in decision-making. Key concerns include bias, lack of transparency, and the potential for AI to diminish the role of human expertise within the process. Payers currently perceived limited usefulness of AI for value-based pricing or negotiation decisions. For internal use of AI, payers saw value for pricing predictions, competitor analysis, and reimbursement pathway navigation, but again with human oversight deemed essential.
CONCLUSIONS: AI can support the efficient development of P&R materials for industry; however, it should be used strategically with human expertise. Integrating AI into internal P&R processes can offer several benefits, and AI could also be used to develop HTA materials if the process is transparent, reliable, and overseen by human experts.
METHODS: A survey was conducted with payers in France (n=1), Germany (n=1), Italy (n=2), Spain (n=1), and the US (n=1) to gather perspectives on AI use in P&R activities, AI’s perceived benefits and challenges, and AI’s role in P&R decision-making. In-depth interviews provided further insights in France (n=1) and Italy (n=1).
RESULTS: Payers think AI could be beneficial for P&R processes, including pricing predictions, budget impact analyses, and evidence synthesis, but not for handling subjective elements such as political and contextual factors considered in value-based pricing and negotiation decisions. For external P&R materials, payers believed AI could improve economic modelling and evidence synthesis, thereby accelerating health technology assessments (HTAs), but specified inclusion of human oversight in decision-making. Key concerns include bias, lack of transparency, and the potential for AI to diminish the role of human expertise within the process. Payers currently perceived limited usefulness of AI for value-based pricing or negotiation decisions. For internal use of AI, payers saw value for pricing predictions, competitor analysis, and reimbursement pathway navigation, but again with human oversight deemed essential.
CONCLUSIONS: AI can support the efficient development of P&R materials for industry; however, it should be used strategically with human expertise. Integrating AI into internal P&R processes can offer several benefits, and AI could also be used to develop HTA materials if the process is transparent, reliable, and overseen by human experts.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
HPR18
Topic
Health Policy & Regulatory, Health Technology Assessment, Methodological & Statistical Research
Topic Subcategory
Reimbursement & Access Policy
Disease
No Additional Disease & Conditions/Specialized Treatment Areas