As healthcare shifts toward digital reporting, payers must overcome challenges posed by inconsistent, fragmented clinical data to achieve reliable, automated measurement. This session explores how artificial intelligence (AI) is enabling scalable, accurate mapping of real-world clinical data to Fast Healthcare Interoperability Resources (FHIR), a critical capability for executing digital quality measures (dQMs) such as HEDIS®.

Presenters will share insights into the technical architecture and real-world applications of AI-driven solutions—including natural language processing (NLP), terminology services, and validation workflows—that transform unstructured and variable data into standardized, computable formats. Attendees will learn how these tools reduce manual burden, improve measure completeness, and support accurate identification of numerator events, denominators, and exclusions.

The session will provide a practical roadmap for implementing AI-enhanced FHIR mapping across payer systems, addressing integration, privacy, and data quality considerations. Participants will leave with actionable strategies to improve digital measurement readiness, streamline operations, and enhance performance in value-based care programs.