Archives: Agenda

Medtech innovators increasingly face a paradox: devices that achieve regulatory success under MDR, FDA, or the AI Act still fail to secure EU HTA endorsement, CMS coverage, or global payer adoption. This session reveals how to engineer evidence strategies that bridge this gap, ensuring that what is clinically compelling also becomes economically undeniable.

We will outline how to design reimbursement-ready clinical programs that anticipate payer requirements without duplicating effort; how to build integrated regulatory-clinical pathways that satisfy Notified Bodies, FDA reviewers, AI Act constraints, and HTA evaluators; and how to leverage PMCF/PMPF, real-world data, and economic modelling to translate regulatory clearance into coding, coverage, payment, and sustainable market access.

Through cross-market case studies and practical frameworks, attendees will learn how to convert approval into adoption, scale, and lasting commercial success.

• Create lean data collection strategies that deliver quality insights
• Insights from case based lessons learnt
• Room for improvement in context of PMCF regulations

• Examining the “business behind clinical” and how operations leaders can demonstrate ROI, efficiency, and scalability
• Meeting regulatory expectations while supporting commercial growth
• Translating clinical results into business impact: internal KPIs that matter
• Creating adaptable infrastructure for long-term clinical evidence generation

• Exploring options when traditional clinical data requirements cannot be fully met at submission
• Establishing a robust clinical strategy using early dialogue, risk-based justification and post-market commitments
• Leveraging expert panels, NB consultations and scientific advice to de-risk approval pathways

Moderator: Dr. Fatima Bennai-Sanfourche, Senior Director of QA & RA Compliance for Medical Devices and eHealth, Bayer

• Future-proof your CERs against class shifts and MDR updates
• Address challenges when intended purpose evolves with technology
• Build defensible clinical arguments for AI-enabled systems

• Sharing a practical case study on using retrospective clinical data to meet evidence requirements for CE marking
• Demonstrating how to align historical data with current MDR expectations for robust clinical evaluation
• Highlighting lessons learned, common pitfalls, and tips for ensuring Notified Body acceptance