Name
HDU and AI: Hype vs Reality - What Can We Realistically Accomplish in the Next Two Years?
Description
As generative and agentic AI rapidly evolve, their application in Health Data Utilities (HDUs) and Health Information Exchange (HIE) systems is becoming more tangible. This session explores realistic advancements over the next two years, including AI-driven data standardization, predictive analytics, automation of care coordination, and real-time decision support. With AI optimizing interoperability, population health insights, and workflow efficiency, we’ll discuss achievable milestones and the challenges of ensuring ethical, secure, and equitable implementation.
Presentation Learning Objectives
1) Understand the Role of Health Data Utilities (HDUs) in AI-Driven Healthcare Transformation. Attendees will learn how integrating social and clinical data within HDUs creates a foundation for ethical AI implementation, improving interoperability, patient outcomes, and system efficiency.
2) Explore Real-World AI Use Cases in Health Data Exchange. Participants will analyze key AI applications, including automated care coordination, predictive analytics, real-time interoperability, and government program efficiency, and understand their impact on cost reduction and resource optimization.
3) Develop Strategies for Ethical and Equitable AI Deployment. Attendees will gain insights into the ethical considerations of agentic AI, including bias mitigation, data governance, and patient consent frameworks, to ensure AI-driven solutions enhance equity rather than reinforce disparities.
2) Explore Real-World AI Use Cases in Health Data Exchange. Participants will analyze key AI applications, including automated care coordination, predictive analytics, real-time interoperability, and government program efficiency, and understand their impact on cost reduction and resource optimization.
3) Develop Strategies for Ethical and Equitable AI Deployment. Attendees will gain insights into the ethical considerations of agentic AI, including bias mitigation, data governance, and patient consent frameworks, to ensure AI-driven solutions enhance equity rather than reinforce disparities.
Speaker Name
Rajib Ghosh
Speaker Credentials
MS, MBA
Speaker Title
CEO
Speaker Organization
Health Roads, LLC
Speaker 2 Name
Kevin Dawson
Speaker 2 Credentials
MBA
Speaker 2 Title
Chief Information Officer
Speaker 2 Organization
LANES
Speaker 3 Name
David Lewis
Speaker 3 Credentials
MPA
Speaker 3 Title
VP of Strategy
Speaker 3 Organization
Eccovia
Speaker 4 Name
Ahmad Alkasir
Speaker 4 Title
Policy Program Manager
Speaker 4 Organization
Ellison Institute of Technology