Presentation (CTI)

OBJECTIVES: Understanding differences in health technology assessment (HTA) outcomes across markets remains challenging due to variation in assessment frameworks and the volume of available decision documentation. This study used an AI-enabled market access intelligence platform to identify and compare lung cancer assessment outcomes across England and Germany, and to explore potential drivers of outcome variation.
METHODS: An AI-enabled market access intelligence platform incorporating large language model-assisted evidence extraction, with traceability to the supporting source paragraph(s), and structured cross-market comparison was used to analyse lung cancer assessments published by NICE in England and G-BA in Germany between January 2022 and May 2026.The platform identified eligible lung cancer products and extracted assessment outcomes and relevant decision information from publicly available HTA documentation. Products assessed in both markets enabled cross-market comparison of outcomes and assessment patterns.
RESULTS: 25 lung cancer assessments were identified in England and 51 in Germany, with 11 products evaluated in both markets.In England, 84% of products received a positive recommendation, while 16% did not. In Germany, 13% of products demonstrated an additional benefit (3% major, 3% considerable, 2% minor, and 5% non-quantifiable), while 87% showed no demonstrated additional benefit.Among products assessed in both markets, variation was observed in assessment outcomes and decision drivers. Two of 11 products received a negative recommendation in England but were assessed in Germany, reflecting differences in HTA frameworks, including the role of economic evidence, and differences in assessment approaches, such as the influence of health-related quality of life and biomarker-related outcomes.
CONCLUSIONS: The platform enabled rapid identification and comparison of lung cancer assessment outcomes across England and Germany using publicly available HTA documentation. The analysis demonstrated the potential of AI-supported evidence extraction to facilitate cross-market HTA pattern identification that may inform future investigation of access outcomes.