Getting Ahead of EU HTA: How our predictive PICO Simulator Capabilities Support Smarter Market Access

Authored by: Louise McEntee-Richardson, Sylvaine Barbier, Simon Pannett, Clément Francois, Eric Auger

In our previous blogs, we explored how artificial intelligence can serve as a strategic input rather than a stand-alone solution, how real-world claims data combined with AI can power equity-driven action, and why critical human thinking remains indispensable in pharmaceutical commercialization. Building upon those insights, this new post turns to the structured framework of PICO (Population, Intervention, Comparator, Outcome) simulation as a bridge between data and strategic decision-making. By embedding PICO into simulation modelling, we can deepen our understanding of evidence generation, sharpen scenario analyses, and empower market-access professionals to craft actionable strategies rooted in both quantitative rigor and human judgement.

The European market access environment is undergoing a fundamental transformation. Evidence requirements are becoming more complex, with the introduction of the Joint Clinical Assessment (JCA)¹ adding another dimension of challenge since January 2025. While JCA is designed to harmonize clinical evaluations across EU Member States, it creates new demands on how evidence is generated, aligned, and presented.

Manufacturers now face compressed timelines and must anticipate critical questions earlier in the development cycle. The need to define and align on PICOs scenarios well before pivotal trial design is reshaping strategic planning. With just 100 days from PICO scoping to dossier submission in a standard case, the margin for error is narrowing considerably.

At Putnam, we are helping clients navigate this complexity through predictive, data-driven approaches to evidence planning – methods that leverage AI and analytics to anticipate likely PICO requirements and enable proactive market-access strategy; helping manufacturers anticipate and address the evolving evidence requirements of the EU Health Technology Assessment (HTA) Regulation, particularly with the introduction of Joint Clinical Assessment (JCA). It highlights the need for predictive, data-driven planning to navigate complex timelines and diverse national needs.

The Reality of Divergent Needs

Further complicating matters, divergent national requirements², for example, differing accepted standards of care across EU 27 countries. The perspectives of stakeholders, including payers, prescribers, and patients, often make a one-size-fits-all approach difficult.

Manufacturers must now adopt a predictive, data-driven approach to evidence planning that is flexible enough to meet EU HTA and local market needs.

Evidence Planning as a Strategic Imperative

In this evolving environment, manufacturers are increasingly asking:

• How do we design trials without limiting national flexibility?
• How can evidence serve multiple purposes?
• Given the short reaction time, what is flexible enough to meet EU HTA and local markets needs?

Addressing these questions requires not only new methodologies but also a mindset shift. Evidence must be viewed as a strategic asset across the product lifecycle, rather than a compliance exercise at the point of submission.

Predictive Tools and the Role of AI

One emerging area of interest that research and development teams may be familiar with is the use of predictive modelling to anticipate likely PICO scenarios before formal scoping. By consolidating insights from clinical guidelines, HTA databases, stakeholder perspectives, and evolving competitor landscapes, simulation tools can flag potential evidence gaps early. This allows trial design to be adapted proactively, aligning with both regulatory expectations and future market access goals.

Such approaches are not just about meeting JCA deadlines; they represent a broader shift towards smarter, more adaptive evidence planning considering HTA needs. As new data becomes available, predictive tools can be refreshed to provide updated scenarios, ensuring that strategies remain resilient in a fast-moving environment.

Beyond JCA: A Broader Perspective

While JCA is an immediate catalyst for change, the implications extend well beyond EU submissions. The same predictive and adaptive planning approaches can support:

• Integration of evidence planning across R&D, Medical Affairs, and commercial functions.
• Differentiated strategies for national HTA requirements.
• Stronger value communication in pricing and reimbursement negotiations.

Human Insight in a Data-Driven Era

Ideally, AI tools must be grounded in clinical realities, HTA experience, and payer expectations. Crucially, predictive models and AI tools must be underpinned by an expert who can interpret the insights they generate. The value of simulation lies not only in technology but also in its application. This combination of human expertise and machine intelligence is what enables truly forward-looking, strategic evidence planning.

Advancing Predictive PICO Modelling Within our Strategic Intelligence Platform

Our PICO Simulator is a first-of-its-kind tool that blends AI technology with deep HTA expertise to model and prioritise the most relevant PICO scenarios across Europe. It’s an integrated data platform that allows manufacturers to plan ahead of EU HTA requirements, reducing the risk of evidence gaps and positioning their assets for success.  Already being applied in client engagements, this evolving capability supports proactive planning by helping manufacturers anticipate and prioritize the most relevant PICO frameworks across Europe.

How it works:

• Aggregates data in a usable, structured way from an extensive range of validated public sources, including clinical guidelines, HTA and JCA assessments, patient association contributions/patient perspectives, alongside company proprietary information.
• Runs simulations across key EU markets to consolidate varying requirements from clinicians, payers, and patients using the EU HTA scoping guidance to mimic future actual scoping.
• Identifies the most likely PICO scenarios and flags potential evidence gaps.

Key benefits:

• Anticipate JCA expectations before formal scoping begins.
• Allow design of pivotal trials that align with both regulatory and market access goals.
• Highlights the risk of evidence gaps that can delay access or limit reimbursement.
• Support adaptive strategies for national HTA submissions and post-launch value communication.
• Quick and easy update of the PICO prediction as new critical information becomes available (for example, phase III results of competitors or assets, or a change in guidelines).

Powered by People, Accelerated by AI

The strength of our approach lies in the combination of human insight and machine intelligence. Backed by deep experience across HTA submissions (including over 200 dossiers submitted via Putnam and over 1000+ HTA dossiers reviewed) and extensive networks of payers and clinical experts, our consultants ensure that all AI-generated outputs are grounded in a real-world context.

This human-AI partnership enables a faster, more robust, and forward-looking solution to empower clients to plan with confidence and adapt quickly in an unpredictable landscape.

Conclusion: Be Ready for What’s Next

JCA is fundamentally reshaping how evidence is developed and assessed across Europe. As timelines shorten and expectations grow, proactive, data-driven scenario planning is no longer a nice-to-have; it’s essential.

With predictive PICO-modelling capabilities, we are enabling manufacturers to plan ahead of EU HTA requirements, reduce the risk of evidence gaps, and position their assets for success.

To explore how the PICO Simulator can support your next EU HTA submission or for more information on AI at Putnam, please contact us.

References:

1. Joint Clinical Assessment. (n.d.). Health.ec.europa.eu. Retrieved October 9, 2025, from https://health.ec.europa.eu/health-technology-assessment/implementation-regulation-health-technology-assessment/joint-clinical-assessments_en
2. Divergent national requirements. (n.d.). Mdpi.com. Retrieved October 9, 2025, from https://www.mdpi.com/2001-6689/13/3/32