From GVD to HTA: Preparing for Success in the EU JCA Era With GenAI-Enhanced Efficiency
Author(s)
Larisa Gofman, PhD1, Molebedi Segwagwe, BSc, MSc2, Sahil Sharma, M.Pharm3.
1ZS Associates, Princeton, NJ, USA, 2ZS, London, United Kingdom, 3ZS Associates, Gurugram, India.
1ZS Associates, Princeton, NJ, USA, 2ZS, London, United Kingdom, 3ZS Associates, Gurugram, India.
OBJECTIVES: With Joint Clinical Assessment (JCA) being applicable to new oncology and advanced therapeutic medicinal products (ATMPs) in the EU since January 2025, health technology developers (HTDs) face unprecedented pressures to deliver timely, aligned evidence packages. These findings explore how Generative-AI (Gen-AI) can be leveraged to accelerate and optimize the end-to-end submission process and highlight the urgency to synchronize Global Value Dossier (GVD) and JCA dossier development.
METHODS: We obtained historical data development timeframes for a clinical section of an Oncology GVD, synthesizing all relevant evidence and comparators for the submission, developed fully by human-in-the-loop absent of any AI technological enablers. We compared this with a Gen-AI accelerated approach, reproducing the clinical section of the same GVD and estimated the differences in time and effort to produce a document of the comparable quality and accuracy. Emphasis was placed on using Gen-AI to streamline repetitive, time-intensive drafting processes.
RESULTS: Deployment of Gen-AI to support GVD development led to efficiency in literature review summarization, clinical evidence harmonization, and drafting of value messages for JCA-relevant modules. Clinical sections were assessed to be of comparable quality for the traditional and Gen-AI developed GVDs, with limited hallucinations and errors in the Gen-AI output.
CONCLUSIONS: The convergence of regulatory and HTA timelines increases the cost of rework and delays. Gen-AI-driven tools help overcome resourcing bottlenecks and enable rapid iteration of JCA-compliant content while maintaining consistency across internal and affiliate-facing deliverables. Strategic Gen-AI use also supports faster adaptation to national HTA nuances post-JCA. Success in the JCA era demands not only early planning and cross-functional coordination but also intelligent automation. By embedding Gen-AI into the GVD-to-HTA workflow, HTDs can accelerate evidence readiness, reduce inefficiencies, and improve the likelihood of timely market access across Europe.
METHODS: We obtained historical data development timeframes for a clinical section of an Oncology GVD, synthesizing all relevant evidence and comparators for the submission, developed fully by human-in-the-loop absent of any AI technological enablers. We compared this with a Gen-AI accelerated approach, reproducing the clinical section of the same GVD and estimated the differences in time and effort to produce a document of the comparable quality and accuracy. Emphasis was placed on using Gen-AI to streamline repetitive, time-intensive drafting processes.
RESULTS: Deployment of Gen-AI to support GVD development led to efficiency in literature review summarization, clinical evidence harmonization, and drafting of value messages for JCA-relevant modules. Clinical sections were assessed to be of comparable quality for the traditional and Gen-AI developed GVDs, with limited hallucinations and errors in the Gen-AI output.
CONCLUSIONS: The convergence of regulatory and HTA timelines increases the cost of rework and delays. Gen-AI-driven tools help overcome resourcing bottlenecks and enable rapid iteration of JCA-compliant content while maintaining consistency across internal and affiliate-facing deliverables. Strategic Gen-AI use also supports faster adaptation to national HTA nuances post-JCA. Success in the JCA era demands not only early planning and cross-functional coordination but also intelligent automation. By embedding Gen-AI into the GVD-to-HTA workflow, HTDs can accelerate evidence readiness, reduce inefficiencies, and improve the likelihood of timely market access across Europe.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
HTA152
Topic
Health Technology Assessment, Methodological & Statistical Research, Organizational Practices
Topic Subcategory
Decision & Deliberative Processes, Systems & Structure, Value Frameworks & Dossier Format
Disease
Oncology