Health Technology Assessment (HTA) in the European Union (EU) is entering a new phase with the introduction of the EU Joint Clinical Assessment (JCA). The goal of the new HTA regulation is to improve the availability of innovative health technologies in the EU by ensuring efficient resource use and strengthening the scientific quality of HTA across Member States (MS).

At the heart of this process is the JCA scope, which consolidates diverse evidence requests from all MS into the PICO (Population, Intervention, Comparator, Outcome) framework. Anticipating these policy-driven PICO requests is critical for a successful JCA submission and can turn into a complex, time- and labor-intensive exercise. In addition to understanding the potentially diverse clinical practices across the MS, it demands an in-depth assessment of the different national HTA evidence requirements. Teams working on PICO predictions need a clear mapping of what evidence has been accepted, questioned, or rejected across the different HTA systems. Building that mapping is multifaceted.

Why HTA landscaping is challenging

HTA landscaping requires careful review of past HTA decisions to understand what evidence leads to positive HTA outcomes. This involves identifying relevant patient populations, accepted comparators, and meaningful outcomes. It also requires going deeper in the HTA documentation, uncovering why certain choices were criticized or dismissed.

Much of this information is hidden in long reports, potentially including appendices. These HTA documents are written in different languages, follow different formats, and often include subtle but important contextual details that unravel the HTA critiques and reasoning for specific evidence requests. As a result, landscaping is still largely manual, time-consuming, and difficult to scale.

What makes agentic AI different

Agentic AI offers a new way to approach this problem. Instead of simply summarizing documents or answering one-off questions, agentic systems are designed to carry out structured tasks. They can follow a defined set of instructions, extract specific types of information, and organize results in a consistent way.

This makes them particularly suited for HTA landscaping, where the goal is not just to read documents, but to systematically extract comparable insights across multiple sources.

Our research: Using AI agents for HTA extraction

In our recent research, which will be presented at ISPOR US this May, we explored how autonomous AI agents can support HTA landscaping for EU JCA.

We developed two large language model–based agents designed to extract structured information from HTA reports using a set of 21 expert-defined questions. These questions covered both standard PICO elements, such as population, comparators, and outcomes, as well as more context-specific insights. This included methodological requirements, reasons for rejecting certain outcomes or comparators, and other critique points raised by HTA bodies.

The two agents differed in how they were guided. The first used a general prompt, while the second incorporated additional clarification within selected questions to improve contextual understanding.

How we evaluated performance

To test the agents, we used publicly available HTA reports for osimertinib (in locally advanced or metastatic NSCLC with EGFR T790M mutation) from Spain, the Netherlands, and France. These reports varied in length, structure, and language, providing a realistic test of performance.

Local HTA experts applied a strict scoring framework that assessed both accuracy and completeness. Importantly, any answer containing hallucinated content was automatically scored as zero. This ensured that reliability remained central to the evaluation.

What we found

Both agents were able to complete the full extraction across all HTA reports, and around 90% of responses were generated without hallucinations. The second agent performed better overall, achieving a higher number of fully correct answers and fewer partially correct responses.

The first agent, while still effective, produced some hallucinated content, particularly in the Spanish report. The second agent avoided hallucinations entirely in this evaluation. Both agents performed best on the French HTA report, suggesting that clearer structure and language can improve AI performance.

One of the most important findings was the impact of prompt design. Adding targeted clarification significantly improved the agent’s ability to interpret and extract complex HTA information.

What this means for EU JCA landscaping

These results suggest that agentic AI can meaningfully improve how HTA landscaping is performed. By automating structured extraction, it becomes possible to review multiple reports more quickly and consistently. This allows teams to build a more comprehensive understanding of the landscape in less time.

Importantly, this approach goes beyond standard PICO elements. It captures the context-specific insights that often drive HTA decisions, such as methodological concerns or other reasons for rejecting evidence. This is critical for developing realistic PICO scenarios in the context of JCA.

Another key advantage is the ability to work across languages. Since EU HTA involves multiple jurisdictions, multilingual capability removes a major barrier and enables a more unified analysis.

The role of human expertise

Despite these advances, AI alone is not enough. Some limitations remain, including occasional hallucinations and variability depending on the source material. For this reason, human oversight continues to be essential.

The most effective approach is to combine agentic AI with human HTA expertise. AI can handle large-scale extraction and structuring of information, while experts validate the outputs and ensure that interpretations are accurate and relevant.

Looking ahead

Agentic AI is unlikely to replace HTA professionals, but it will fundamentally reshape how they work. By reducing the burden of manual review, it frees experts to focus on higher-value activities such as interpretation, strategic planning, and decision-making.

In the context of EU JCA, this shift brings clear advantages. It enables faster, more scalable landscaping and PICO predictions, helping to identify potential evidence gaps earlier in the process. As the methodology evolves, further testing will expand the integration of HTA reports from additional MS into the agent-driven workflows. At the same time, engineering adaptations may be needed to accommodate ongoing changes in local HTA documents as they continue to evolve together with the JCA reports.

Interested in learning more?

Manuel Cossio and Lilia Leisle will be presenting their poster “Accelerating Dynamic HTA Landscaping in Oncology Through Autonomous Generative AI-Driven Multilingual Data Extraction” at ISPOR US on May 18 at 4 PM. We hope to see you there!

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