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Most AI isn't built for research. It's built for chat, for dashboards, for demos. In HEOR, that gap shows up fast. Fragmented data. Opaque models. Manual workflows that break the moment a regulator or peer reviewer asks how an answer was produced.
This session outlines a different approach: healthcare-native AI designed specifically for rigorous, defensible evidence. Three pieces, working together. A Healthcare Map of 60+ curated sources with validated representativeness across payers, regions, and care settings. A research-specific architecture with unified visit consolidation, 95%+ cost fill, and continuous enrollment, built so methodology holds up under scrutiny. And an AI engine with visible code generation, stepwise validation, and explicit articulation of every limitation.
The result is RWE that's faster, scalable, and trusted. Cohort identification compressed from hours to minutes. Descriptive statistics that used to take months. Industry standard R-package integration for inferential methods like propensity score matching, survival modeling, and weighted analyses, all version-controlled and replicable. Ready for the moments that matter most: market access discussions, internal strategic decision making, peer reviewed publications, and guideline directed medical therapy for real patients.
Key Learning Objectives
By the end of this session, attendees will be able to:
Objectives:By the end of this presentation, participants will be able to:
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Strong innovation doesn’t automatically lead to commercial success. Many science-led companies struggle to translate capability into revenue. This session introduces a platform that identifies commercial gaps and turnsinnovation into a clear, execution-ready growth strategy.