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In this third episode of a three-week series of HIMSSCast, we again speak with UNC Health’s System Director for Analytical Solutions Greg Kuhnen. This time, he is joined by his colleague Ram Rimal, UNC’s manager of data science engineering.
Kuhnen and Rimal discuss tips and best practices for efficient and effective deployments of artificial intelligence – generative AI, large language models (LLMs), predictive models and more. They discuss how UNC Health is ensuring responsible adoption of AI tools, and how HIMSS Analytics Maturity Assessment Model has helped the health system on that journey.
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Talking points:
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How and where UNC is using AI, and where it’s finding most success
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The areas and use cases where it’s scoring early wins
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Operational and administrative uses of automation, vs. clinical applications
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How it’s working to ensure responsible adoption of AI tools
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How its AI projects are rolled out and scaled up
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Early lessons learned along the way, challenges and successes
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AI use cases UNC hasn’t yet pursued, but is eyeing down the road
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How the HIMSS Analytics Maturity Assessment Model, which focuses on real-time data and predictive AI in Stage 6, has been helpful in this journey
More about this episode:
HIMSSCast: Fundamentals of data governance – lessons from UNC Health, part 1
HIMSSCast: Fundamentals of data governance – lessons from UNC Health, part 2
UNC Health Care shares how it reached peak analytics maturity
UNC Health reaches Stage 7 with advanced EMR, analytics capabilities
UNC Health’s CIO talks generative AI work with Epic and Microsoft
Responsible AI governance is needed now, says UNC Health chief analytics officer
Creating a path for sustainable AI governance
At HIMSS24 APAC, the Adoption Model for Analytics Maturity gets facelift
New HIMSS Analytics Maturity Assessment Model supports smart AI deployments
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