Healthcare AI Governance
Questions Executives Are Actually Asking About AI Governance
Substantive answers to the governance questions that surface when a board asks, a surveyor arrives, a vendor pitches, or something goes wrong. Written for healthcare executives, not AI developers.
17 questions answered · 7 more in development
Liability & Risk
Who is liable when an AI clinical decision support tool contributes to a misdiagnosis or adverse patient outcome?
Liability falls on the hospital and the physician, not the vendor. How that exposure is structured, where it's growing, and what reduces it.
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Do our malpractice insurance policies cover liability from AI-assisted clinical decisions?
Healthcare AI is creating coverage gaps that many organizations don't discover until a claim. What your insurer is asking for, how AI Policy Riders work, and what governance documentation reduces your exposure.
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How should we document AI-assisted decisions in the medical record to protect against malpractice claims?
Documentation that demonstrates independent clinical judgment is your primary defense. What to record, what state laws now require, and how institutional governance supports your malpractice posture.
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- Coming soonWhat happens to our organization if a state AI audit finds we deployed a biased algorithm that affected care decisions?
Governance Structure
What AI governance framework should a mid-size hospital adopt if we are starting from scratch?
The AMA's 8-step toolkit is the right starting point. How to use it, how to supplement it with CHAI guidance, and what year one should look like.
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How do we build an AI governance committee and what roles need to be on it?
The minimum viable committee structure, the two-tier model that scales, and the four barriers that stall most programs.
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How do we create and maintain an AI tool inventory for our health system?
A practical guide to enterprise AI tool inventory: how to define 'AI tool' so departments report consistently, what fields to track, and how to govern tools discovered without formal approval.
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- Coming soonHow do we ensure human-in-the-loop oversight as AI tools become more autonomous in clinical and operational workflows?
Regulatory & Accreditation
What does the Joint Commission's AI guidance mean for my health system?
What TJC's co-branded CHAI guidance means for your accreditation posture and what surveyors are likely to ask.
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What will a Joint Commission surveyor ask about our AI governance?
Surveyors are already asking AI governance questions under existing Leadership and Performance Improvement standards. The specific questions to expect and how to prepare your documentation.
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What AI disclosure and consent requirements do we need to comply with under new state laws like Texas, California, and Illinois?
Texas HB 149, California AB 3030, Illinois HB 1806, and other state laws now require healthcare providers to disclose AI use to patients. What your organization needs to know and do.
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What is the FDA's current regulatory stance on clinical decision support software and how does it affect our AI tools?
Not all clinical decision software is regulated as a medical device. The FDA's distinction between locked and adaptive AI, and what the 21st Century Cures Act exemptions mean for your tool portfolio.
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- Coming soonWhat are CMS and ONC requiring for algorithm transparency in prior authorization and claims processing?
- Coming soonWhich AI use cases in our health system would be considered high-risk under emerging state and federal regulations?
Board & Executive Oversight
Vendor & Procurement
How do we evaluate an AI vendor's claims and what questions should we ask before signing a contract?
Vendor accuracy claims routinely come from ideal conditions or misleading methodologies. The questions to ask before signing, and the contract language that actually protects you.
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Does our vendor's BAA actually prevent them from using our patient data to train their AI models?
A BAA alone doesn't reliably prevent AI vendors from using patient data for model training. How vendors exploit standard contractual language, and what provisions actually provide protection.
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What should we require from AI vendors as a condition of deployment in our health system?
Five categories of vendor requirements that protect your organization: evidence and validation standards, contractual protections, data governance, ongoing monitoring cooperation, and accountability provisions.
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Deployment & Monitoring
- Coming soonHow do we monitor AI algorithms for bias and performance drift after they are deployed in clinical workflows?
- Coming soonHow do we detect and manage shadow AI use by clinicians and staff across our health system?
Generative & Emerging AI
- Coming soonHow do we create an acceptable-use policy for generative AI tools like ChatGPT that protects PHI and HIPAA compliance?
About this resource
Every question on this page came from a real conversation with a healthcare executive, a regulatory filing, or a board agenda. We publish detailed answers because the generic version of this content already exists. What most organizations need is the specific version: what does this mean for a mid-size hospital, a community health system, a safety-net provider.
Answers are written by Teresa and Jim Younkin of Mosaic Life Tech and draw on our knowledge base of primary regulatory and guidance sources. Pages are updated when the underlying guidance changes. This is not legal advice — organizations should consult qualified counsel on exposure specific to their circumstances.
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We're actively building this resource. If you're working through a governance question that isn't covered yet, reach out. Direct conversations with executives shape what we write next.
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