Healthcare organizations are investing rapidly in AI, but many efforts remain fragmented across applications, data pipelines, and workflows. This makes it difficult to govern AI consistently, evaluate results in context, and build the trusted foundation needed for enterprise adoption.
This moderated panel will examine the digital backbone required for an AI-ready health system. Three informatics leaders will discuss how organizations can connect structured and unstructured data, improve semantic consistency, preserve identity, provenance, and clinical context, and govern privacy, consent, equity, and appropriate use.
Attendees will gain practical perspectives on what research and implementation experience are showing about data readiness, AI evaluation in context, and trusted adoption with greater confidence, consistency, and accountability.
Intended Audience
The webinar is designed for:
- Chief medical, nursing, and health informatics officers
- Clinical and operational informaticians
- Data, analytics, AI, and interoperability leaders
- Health system digital and technology executives
- Clinicians leading AI evaluation or implementation
- Researchers and learning health system leaders
- Professionals responsible for data governance, privacy, safety, or quality
Learning Objectives
At the conclusion of the webinar, participants will be able to:
- Identify the data, interoperability, semantic, and governance capabilities that support an AI-ready digital backbone.
- Explain why clinical AI requires data that is accurate, complete, timely, traceable, representative, and clinically contextualized.
- Distinguish reusable enterprise data capabilities from use-case-specific quality and validation requirements.
- Describe how identity, provenance, privacy, consent, equity, oversight, and monitoring influence safe AI use.
- Evaluate research-informed strategies for moving from isolated AI efforts to sustainable enterprise adoption.
