Artificial Intelligence in Pharmaceutical Payer Decision Making—Assessing Current Use, Strategic Intent, and Infrastructure Readiness
Author(s)
Brett Gardiner, MPhil1, Rahael Maladwala, PGDip2, Cal Foden, PhD2.
1Access Infinity Ltd, London, United Kingdom, 2Access Infinity, London, United Kingdom.
1Access Infinity Ltd, London, United Kingdom, 2Access Infinity, London, United Kingdom.
OBJECTIVES: Assess the current state of Artificial Intelligence (AI) adoption in pharmaceutical payer decision-making across thirteen markets. Specifically, investigating AI use across three domains: current use within pricing and reimbursement (P&R) decision making, strategic ambitions for implementation, and infrastructure readiness.
METHODS: A comprehensive secondary research approach was employed, analysing publicly available documents including (but not limited to): policy papers, industry publications, conference posters, and public comments from key payer/government stakeholders.
RESULTS: Across markets, AI use is generally in its infancy, but there is a willingness to incorporate it into payer decision making in the future. The UK stands out as most advanced, demonstrating progress through in both current use (i.e., use of pilot programs (National language processing [NLP] to support a systematic literature review [SLR] within ovarian cancer) and strategic ambitions (i.e., published guidance on use of AI - NICE’s Statement on AI in Evidence Generation" [8/24] and "Statement of Intent for AI" [11/24]). In markets such as Germany and France there is minimal evidence of current use of AI, but early publications highlight strategic ambitions and possible uses (i.e., machine learning for SLRs and study selection). Evidence also points to emerging cross-country collaboration between markets, such as IQWiG inviting member of NICE to conduct workshops on the use of AI in SLRs, suggesting a shared interest in harmonizing AI application and methodological development within P&R decision making.
CONCLUSIONS: While AI integration in payer decision-making is still in its early phases, several major markets have signalled strategic intent to use AI within the P&R process. International collaboration signals a collective effort towards to understanding how AI can impact payer decision making. Further efforts are needed to establish robust frameworks to fully understand and leverage AI’s potential in optimizing decisions for market access.
METHODS: A comprehensive secondary research approach was employed, analysing publicly available documents including (but not limited to): policy papers, industry publications, conference posters, and public comments from key payer/government stakeholders.
RESULTS: Across markets, AI use is generally in its infancy, but there is a willingness to incorporate it into payer decision making in the future. The UK stands out as most advanced, demonstrating progress through in both current use (i.e., use of pilot programs (National language processing [NLP] to support a systematic literature review [SLR] within ovarian cancer) and strategic ambitions (i.e., published guidance on use of AI - NICE’s Statement on AI in Evidence Generation" [8/24] and "Statement of Intent for AI" [11/24]). In markets such as Germany and France there is minimal evidence of current use of AI, but early publications highlight strategic ambitions and possible uses (i.e., machine learning for SLRs and study selection). Evidence also points to emerging cross-country collaboration between markets, such as IQWiG inviting member of NICE to conduct workshops on the use of AI in SLRs, suggesting a shared interest in harmonizing AI application and methodological development within P&R decision making.
CONCLUSIONS: While AI integration in payer decision-making is still in its early phases, several major markets have signalled strategic intent to use AI within the P&R process. International collaboration signals a collective effort towards to understanding how AI can impact payer decision making. Further efforts are needed to establish robust frameworks to fully understand and leverage AI’s potential in optimizing decisions for market access.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
HTA42
Topic
Health Policy & Regulatory, Health Technology Assessment, Methodological & Statistical Research
Topic Subcategory
Systems & Structure
Disease
No Additional Disease & Conditions/Specialized Treatment Areas