Session (CTI)

Modern real-world data can surface insights and opportunities earlier than traditional trials and registries, yet evidence strategy is often shaped by answers that arrive months, or even years, late. As a result, HEOR teams may commit to post-launch evidence plans before emerging patterns in real-world practice are fully understood.
This session explores how artificial intelligence (AI) can accelerate exploratory analysis of real-world data. This session will explore approaches for using artificial intelligence (AI) to support exploratory analysis of real-world data. Using AI, HEOR teams can generate analytical insights from real-time, longitudinal real-world data in minutes rather than months. These insights allow teams to explore, refine, and prioritize research questions quickly to inform future evidence generation strategies.
Rather than replacing formal evidence generation, AI-enabled insights from real-world data help inform and accelerate early hypothesis generation and exploratory phases of evidence planning. Attendees will learn how iterative exploration of real-world data reveals current care patterns and helps identify priorities for future evidence generation.
Using GLP-1 therapies as a real-world example, the session will illustrate how HEOR teams can:

• Refine and prioritize research questions across patient populations and settings
• Detect shifts in utilization following changes in coverage or formulary status

Inform the design of methodologically rigorous real-world studies Attendees will leave with a clear understanding of how AI enabled insights from real-world data can accelerate exploratory evidence planning and help focus downstream evidence generation strategies.
Sponsored by Corporate Partner, Truveta