AI-ENABLED EVIDENCE SYNTHESIS FOR EU JOINT CLINICAL ASSESSMENT (JCA): ADVANCING TIMELY, CONSISTENT, AND HTA-READY SUBMISSIONS
Author(s)
Rashi Tomer, M.Pharm1, Upasna Gaba, MPH2, Ashish Pandey, M.Pharm3, Sahil Sharma, M.Pharm4, Sayyeda Anam, M.Sc5, Rajanpreet Singh, M.Pharm4, George Agathangelou, BA6, Molebedi Segwagwe, BSc, MSc7, Rajpal Singh, PhD4.
1Associate Consultant, ZS Associates, Noida, India, 2ZS Associates, Bangalore, India, 3ZS Associates, Noida, India, 4ZS Associates, Gurugram, India, 5ZS Asssociates, Noida, India, 6ZS Associates, London, United Kingdom, 7ZS, London, United Kingdom.
1Associate Consultant, ZS Associates, Noida, India, 2ZS Associates, Bangalore, India, 3ZS Associates, Noida, India, 4ZS Associates, Gurugram, India, 5ZS Asssociates, Noida, India, 6ZS Associates, London, United Kingdom, 7ZS, London, United Kingdom.
OBJECTIVES: The EU JCA, now operational with its first report published, marks a significant advancement in standardizing the clinical assessment component of HTA across member states. The JCA aims to reduce duplication and promote consistent, transparent evidence-based decision-making through a single systematic literature review (SLR) conducted according to standardized methodological and reporting requirements. This review evaluated the role of AI-driven evidence synthesis in supporting timely, consistent, and high-quality EU JCA submissions.
METHODS: A targeted literature search was conducted in PubMed and Google Scholar, supplemented with grey literature from HTA bodies and organizations (e.g., NICE and ISPOR). Publications from 2021-2025 on JCA methodological requirements, multi-PICO evidence synthesis challenges, and AI-enabled systematic review tools were included. Findings were synthesized narratively.
RESULTS: The literature highlighted substantial methodological and operational challenges associated with JCA submissions and the emerging role of AI in evidence synthesis. The requirement to set SLR search cut-offs within three months of dossier submission (vs. six months in frameworks such as NICE) creates significant time pressure, complicating project management and may increase the risk of errors, particularly when reviews require updating following PICO refinements. Addressing multiple final JCA-aligned PICOs within compressed timelines requires substantial resources. AI-enabled tools demonstrated potential to improve efficiency across screening, data extraction, quality assessment, and reporting. With appropriate human oversight, these tools may help improve efficiency, reduce workload, and enhance the accuracy and completeness of evidence reviews. However, available guidance from HTA and professional organizations remains limited and emphasize need for transparency, bias mitigation, validation, and expert review to ensure methodological rigor and reliability.
CONCLUSIONS: Compressed JCA timelines and the complexity of addressing multiple final JCA-aligned PICOs necessitate more efficient evidence synthesis approaches. AI-enabled literature reviews, supported by transparent methodologies and human-in-the-loop validation, may improve efficiency, consistency, and readiness of evidence package for timely JCA submissions.
METHODS: A targeted literature search was conducted in PubMed and Google Scholar, supplemented with grey literature from HTA bodies and organizations (e.g., NICE and ISPOR). Publications from 2021-2025 on JCA methodological requirements, multi-PICO evidence synthesis challenges, and AI-enabled systematic review tools were included. Findings were synthesized narratively.
RESULTS: The literature highlighted substantial methodological and operational challenges associated with JCA submissions and the emerging role of AI in evidence synthesis. The requirement to set SLR search cut-offs within three months of dossier submission (vs. six months in frameworks such as NICE) creates significant time pressure, complicating project management and may increase the risk of errors, particularly when reviews require updating following PICO refinements. Addressing multiple final JCA-aligned PICOs within compressed timelines requires substantial resources. AI-enabled tools demonstrated potential to improve efficiency across screening, data extraction, quality assessment, and reporting. With appropriate human oversight, these tools may help improve efficiency, reduce workload, and enhance the accuracy and completeness of evidence reviews. However, available guidance from HTA and professional organizations remains limited and emphasize need for transparency, bias mitigation, validation, and expert review to ensure methodological rigor and reliability.
CONCLUSIONS: Compressed JCA timelines and the complexity of addressing multiple final JCA-aligned PICOs necessitate more efficient evidence synthesis approaches. AI-enabled literature reviews, supported by transparent methodologies and human-in-the-loop validation, may improve efficiency, consistency, and readiness of evidence package for timely JCA submissions.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
HTA71
Topic
Health Technology Assessment
Topic Subcategory
Systems & Structure
Disease
No Additional Disease & Conditions/Specialized Treatment Areas