As healthcare organizations accelerate AI adoption, many are discovering a foundational challenge: AI is only as effective as the data and clinical definitions used to train, guide, and govern it. Inconsistent value sets, fragmented terminology, and manual processes can introduce variability that limits the accuracy, scalability, and trustworthiness of both operational workflows and AI-enabled solutions.
This webinar explores two dimensions of value set management: how AI can accelerate the creation and maintenance of clinical code lists and definitions, and how high-quality value sets can improve the performance and reliability of AI-driven healthcare workflows.
Attendees will learn how organizations can leverage AI to reduce the time and effort required to develop value sets while maintaining quality through structured clinical review. We'll also examine how standardized, clinically validated value sets can serve as a foundation for AI readiness, providing consistent clinical logic that helps improve cohort identification, prior authorization, care management, analytics, and AI-powered decision support.
Designed for informatics, data, product, and clinical leaders across payer and digital health organizations, this session will highlight practical approaches for combining AI-enabled value set development with structured clinical review to balance speed with trust.
AI-assisted value set creation can significantly improve efficiency while keeping the informaticist at the center of the process, fostering greater collaboration, transparency, and a more effective terminology management workflow.
In this webinar, learn:
- How AI can help streamline the creation and maintenance of clinical code lists and definitions while reducing manual effort
- Why clinically governed value sets remain essential for accuracy, reviewability, and trust
- How inconsistent clinical definitions impact prior authorization, care management, analytics, and other healthcare workflows
- How value sets can serve as a foundation for AI readiness by grounding AI models and workflows in consistent clinical logic
- How organizations can improve the accuracy, consistency, and reliability of AI-enabled workflows through standardized value set management
- Best practices for balancing AI-driven efficiency with clinical oversight and governance
- Real-world applications of AI-enabled value set management in payer and digital health environments