Healthcare AI governance that holds up to the board and the bedside
Committee structures, decision rights, model inventories, and review cadences that survive scrutiny from auditors and from the clinicians who have to use the thing.
We work with health system executives, chief data and digital officers, and boards on the oversight that decides whether clinical AI reaches production and stays there. Most programs do not fail on the model. They fail on ownership, on data nobody trusts, and on the conversation that never happened before go-live. See our perspective on who owns AI oversight in a health system, healthcare data governance, and responsible AI in healthcare.
Source: HFMA and Eliciting Insights, survey of 233 health systems, Q2 2025.
Four Ways The Work Usually Starts
Oversight Architecture and Decision Rights
Who approves a model, who can stop one, and what happens when clinical and legal disagree. Committee structures and named owners that hold up under audit and under pressure.
Model Inventory and Review Cadence
Know every model running in your organization, who owns it, what it touches, and when it was last reviewed. Most health systems cannot answer the first question.
AI Vendor and Build-vs-Buy Review
Independent technical and oversight review of AI vendors, partnership terms, and the claims underneath them. Evaluate the system without becoming somebody reference architecture.
Board and Audit Committee Education
Working sessions for leadership teams and audit committees: the questions to ask, the answers worth listening for, and the risks the board itself owns.
Outcomes You Can Expect From Oversight That Works
Oversight is supposed to speed decisions up. Built well, approvals get faster, because the argument was settled before the model reached the room.
A Decision Path That Exists
Every model has a named owner and a route to yes or no.
Faster Approvals, Not Slower
Settled questions stop being relitigated at every review.
Clinician Trust You Can Point To
Clinicians shape the deployment instead of absorbing it.
An Answer When The Auditor Asks
Inventory, ownership, and review history in one place.
Common Questions
What does an AI governance consulting firm actually do?
Decide who owns which decision, build the committee and review structure that carries those decisions, and put controls where models get selected and deployed rather than where incidents get reported. The deliverable is not a policy document. It is a set of decisions that survive the next reorganization.
We have no AI oversight today. Where do we start?
One model, one owner, one review. Pick the AI tool your clinicians already argue about, take it end to end, and let that become the template. Enterprise-wide programs that begin enterprise-wide are the ones that stall, because there is no early win to point at.
How is this different from an AI policy or an oversight platform?
A policy states intent and a platform records decisions. Neither one makes them. Tools are worth buying once ownership is settled and close to worthless before, which is why so much oversight software ends up documenting a disagreement instead of resolving it.
Do you work with organizations outside the United States?
Yes. Chris Hutchins has built and led enterprise data and analytics functions across health systems in the US and the UK, and the advisory work spans both. Structures differ by market. The failure modes rarely do.
What should a CMIO look for in an AI oversight partner?
Someone who has been answerable for a number, not only for advising on one. Ask how they handle the moment a clinical leader disputes a model output, because that meeting is the job. Read healthcare data governance for how we approach it.
Put a decision path under your AI.
A 30-minute call is enough to tell whether we are the right fit.