The importance of measuring patient experience in clinical research is widely recognized, yet limited research has asked patients directly what matters most to them when participating in a trial. This presentation shares findings from a national study designed to capture patient perspectives on their clinical trial experiences and highlight what they want sponsors, sites, and CROs to know. This session will elevate the voice of patients themselves—revealing what they identify as critical to feeling cared for, supported, and respected throughout their participation. Attendees will gain actionable insights that can inform quality improvement initiatives, strengthen engagement strategies, and ultimately foster more patient-centered trial designs.

Real-world data is vast and expanding, but fragmented. By applying AI techniques, we can bridge gaps, reduce complexity, and create actionable real-world evidence more efficiently. Drawing on a pharmaceutical RWD/RWE program, this talk covers implementation of AI tools across key workflows: programming assistants (SQL, R/SAS/Python), protocol and report authoring, and systematic literature review. The session also describes the design of fit‑for‑purpose knowledge bases to ground LLMs in organizational standards.

Novo Nordisk is building cross-country collaborations with healthcare providers to maximize the value of real-world data. This session will highlight case studies from the US, UK, Denmark, and Sweden, showing how Pharma–HCP partnerships advance evidence generation, enable broader patient insights, and address practical challenges in governance, data access, and alignment, delivering scalable models for global research.

GSK has developed innovative collaborations with healthcare providers in the US and UK to unlock the value of real-world data. This session will share case studies demonstrating how Pharma–HCP partnerships enhance evidence generation, streamline data access, and address key challenges—highlighting lessons learned, mutual benefits, and emerging solutions for advancing research beyond traditional trials.

In today’s value-driven healthcare environment, regulatory approval is no longer the finish line—timely reimbursement and broad patient access are now essential measures of success. Yet many development programs still delay market access planning until late stages, risking misaligned evidence, launch delays, and restricted coverage. This opening talk launches SCOPE’s inaugural Market Access, Pricing & Reimbursement track by spotlighting why access strategy must be integrated early and how it directly shapes study design, endpoint selection, and data capture. It explores how real-world data (RWD) and artificial intelligence (AI) are accelerating the shift toward access-ready trials. From early economic modeling to AI-driven evidence synthesis, these technologies help anticipate payer needs, align evidence strategies early, and support faster, smarter launch decisions. Embedding market access into clinical development from the start is now a strategic imperative for launch success.

To optimize market access, clinical trials must align with payer expectations beyond regulatory requirements. This includes selecting payer-relevant comparators, ensuring trial sites in key markets, and incorporating long-term follow-up for robust outcomes. Moreover, tokenization can enable real-world data collection during and post-trial. Collecting healthcare resource use (HCRU) and implementing comprehensive patient-reported outcomes (PROs) further support the demonstration of economic and patient value.

Market access success is not won at launch—it is built years earlier in the clinic. Smart decisions regarding clinical trial design can address the needs of regulators as well as payers in the US and globally. The alternative, risks delayed reimbursement, restricted access, and costly post-hoc studies to fill evidence gaps. Breaking down silos between clinical development, medical, HEOR, and market access functions is no longer optional; it is the decisive factor in determining whether innovation reaches patients rapidly and at target prices. This session will share case-based lessons from global launches where both market access and development milestones were accelerated by the way trials were designed.

Case Study 1: The First Tissue-Agnostic Global Oncology Launch—Leading Innovation in HTA  

The first global launch of a tissue-agnostic oncology therapy moved rapidly through regulators, but payers demanded data the trials were not designed to generate. While the product was historic in its clinical development program—taking only three years to FDA approval—delays in reimbursement threatened to keep patients waiting and required shifting traditional ways of addressing HTA evidence requirements without increasing the cost, complexity, or time required for drug development.

Case Study 2: Third-to-Market Oncology Asset—Smart Trial Design Wins 

Despite being third to market in a crowded class, smart trial design—careful endpoint selection, careful timing and selection of the endpoint hierarchy and OS readouts, and integration of supportive real-world evidence—enabled a differentiated value story that secured superior reimbursement in the U.S. and with challenging HTA organizations globally. Insights into the role of early payer engagement, cross-functional leadership and dialogue, efficient alignment across regions in evidence planning, and trial design that matters at launch will be provided. Attendees will leave with practical insights and frameworks they can apply immediately to better align clinical trial programs with the realities of global market access.

Too often, clinical trial protocols are held together by a patchwork of disconnected platforms and manual workarounds, a system built on repeated data entry, mismatched formats, and limited data consistency. It’s inefficient, error-prone, and a major barrier to accelerating study start-up. This session will explore how biopharmaceutical companies are changing that by digitalizing protocol design and authoring. Using an open-source, vendor-agnostic data model, organizations are automating data flow, improving interoperability across systems, and gaining insight to inform better study design. Join us to see how this approach can unlock new use cases from registry submission to statistical analysis; automate data flow and interoperability between systems; reduce study start-up effort, cycle time, and complexity; improve quality and compliance​.

At Amgen, we are transforming our analytics ecosystem with AI-driven tools that enable near real-time, self-service insights for Global Development stakeholders. Our next-generation analytics engine features a rebuilt metrics platform, centralized data governance, and a streamlined dashboard landscape leveraging Power BI with Co-Pilot and custom AI interfaces. By implementing AI/ML bots and natural language queries, we are shifting from static reporting to a dynamic, AI-driven experience.