Day Two

• Understand the design spectrum with external control arms, hybrid studies, and traditional randomised trials
• Learn when regulators see these designs as acceptable and how to present them with confidence
• Hear case studies of real-world data being used instead of historical trial data in control arm designs

• Use RWE to inform study design, feasibility, and decision-making to prevent costly delays and reduce early clinical delays
• Close evidence gaps before launch to secure smoother pricing and reimbursement pathways strengthening HTA readiness
• Deploy targeted RWE to demonstrate real-world value and support confident market entry.

• Explore emerging RWD opportunities and overcome data silos while moving towards EHDS
• Understand the additional value of new RWD opportunities, as well as their (current) limitations
• Learn how these approaches can support internal decision making and accelerate patient access

• Understanding how to overcome role confusion and shifting responsibilities in cross-functional RWE projects by embedding robust IEP governance
• Exploring frameworks to balance standardisation and agility, ensuring continuity despite team turnover or organisational change
• Gaining strategies to prioritise projects effectively, align evidence generation with stakeholder needs, and prevent duplication of efforts across functions
• Incorporating AI in the development of integrated evidence plans – what are the pros and cons and how can we move forward?

This interactive discussion will explore how to ensure Integrated Evidence Plans convert into tangible decision-making influence across development and commercialization. Join peers to share practical approaches for accelerating evidence delivery, securing internal stakeholder buy-in, and demonstrating clear business impact, from reducing protocol amendments to improving market access readiness.

Join this roundtable to discuss:

• How do we ensure IEPs actually influence development decisions vs. sitting on a shelf?

• What governance structures enable rapid change when evidence needs shift?

• How can RWE teams demonstrate impact in real time (not just post-launch)?

• Where have teams successfully avoided expensive amendments using early RWE?

• Leverage demographic and epidemiological transitions to forecast future unmet needs and guide product strategy
• See how segmentation using regression-tree AI models can predict cost and disease burden at both national and indication levels
• Understand how enriched administrative + clinical RWD supports transparent, evidence-based planning for market access and healthcare decision-makers

• Benchmark how pharma are validating AI algorithms for structuring unstructured data (clinician notes, oncology staging, biomarkers) while ensuring reproducibility and regulatory alignment
• Learn practical strategies for human-in-the-loop review to balance automation with trust, avoiding “hallucinations” and privacy breaches in sensitive patient datasets
• See early examples of AI use cases accepted by regulators or HTA bodies and what lessons they hold for wider adoption

As AI models rapidly advance, their real-world applicability remains limited by fragmented data, regulatory uncertainty, and lack of clinical alignment. This roundtable will bring together clinicians, RWE experts, and data scientists to debate how AI can meaningfully support research questions today, including regulatory-relevant analyses, patient-centric endpoints, and expanded access data generation, while overcoming the challenges of standardisation, accuracy, and trust.

Join this roundtable to discuss:

• Fit-for-Purpose AI Models in RWE: When does AI truly add value, and how can clinicians and data scientists co-define research questions that models can reliably address?
• Regulatory Readiness & Trustworthiness: How should AI algorithms be validated for decision-making, given regulatory bodies’ limited comfort with model-based inferences?
• Data Fragmentation, Bias & Standardisation Gaps: How do we improve data quality and consistency, across wearables, biometrics, unstructured records, to ensure AIdriven insights are reproducible?
• Patient-Centric Evidence Generation with Advocacy Partners: What role can patient groups play in defining outcomes and providing meaningful data for AIenabled research, such as treatment satisfaction or preference studies?

• Identify the cultural and organisational impediments slowing down RWE integration, including confusion over ownership and traditional comfort zones
• Learn practical approaches for “rewiring” organisations so clinical, medical, and commercial functions view RWE as a shared opportunity
• Discover strategies for engaging stakeholders early, building trust in data use, and aligning evidence generation with broader business objectives

• Translate complex real-world data into clear, impactful narratives that resonate with payers, patients, and healthcare systems
• Discover practical approaches to bridge medical, access, and commercial teams for unified value communication while exploring how evidence-driven storytelling can strengthen stakeholder trust and long-term market success in an evolving valuebased landscape
• Conclude by looking ahead to a future where being more efficacious and safer is no longer enough and real-world impact defines success