Most health systems have an AI governance committee. Far fewer have AI governance. This report is about the difference, and how to build the second one.
The wrong half: standing up a committee, writing a policy, and stopping there, while the AI runs unsupervised between meetings and accountability stays undefined.
The half that controls anything: an operating model that defines who decides, who reviews, who owns the data and the risk, and how those decisions get enforced in the system and evidenced automatically.
Effective governance does not pile everything on one committee that meets monthly.
This is the move that separates governance that works from governance that does not, and it is the lesson regulated DevOps learned the hard way.
because pieces of governance are now law
The regulatory ground has shifted from HIPAA and good intentions to specific, enforceable requirements.
Stand up the four pillars with real mandates and the right composition, including ethics.
Inventory every AI system and tier it by risk. The tier drives oversight, validation, and human-in-the-loop.
Turn Model Review Board requirements into deployment gates, capture HTI-1 source attributes automatically, and enforce risk-tier controls in the system.
Make sure clinicians know the policies, the accountability model, and the disclosure obligations.
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