Healthcare AI governance: the operator's complete guide

Healthcare AI governance is the discipline of being able to answer, for every AI system touching care: what does it do, who approved it against what evidence, who watches it, what happens when it degrades, and where is the record. Most published material stops at frameworks. This guide is the operational map — each section links to the working reference, the free instrument, or the template that does the job.

Start with what you actually run

Every governed program begins with an inventory, and every first inventory is a surprise: AI arrives inside EHR upgrades and vendor products, not just purchase orders, so an intake triggered by procurement can only ever produce a short register. The AI use-case registry template is the working artifact; for regulated device software, the Transparency Registry lets you verify any vendor’s FDA clearances in seconds — 558 of them, refreshed weekly, facts only.

The accountable body

Governance without a chartered committee is a shared spreadsheet with opinions. The committee charter covers the eleven seats and the reporting line; minutes that survive a survey covers the record the committee must leave behind, because minutes only accumulate forward — they cannot be written retroactively.

Approval, baselines, and the locked number

A model enters service against a stated performance baseline, recorded and insert-only. Without the locked number, later monitoring has no reference point and later disputes have no anchor. The model validation memo template is the artifact; the monitoring plan template is its companion.

Monitoring that includes the patients averages hide

Aggregate accuracy can hold steady while one patient subgroup quietly fails — which is why subgroup monitoring against locally chosen equity thresholds is the least optional part of the program. The concept is explained in the demographic parity gap reference; the bias assessment template operationalizes it. Our governance platform enforces it automatically — see a real equity suspension in the public demo.

Governing AI that acts, not just predicts

Scheduling agents, prior-auth bots, and ambient scribes act rather than suggest — and governance built for predictive models does not survive that difference. The Agent Privileging Framework is the open, complete treatment: scope of action, proctoring, action-level audit, automatic suspension, revocation.

Certification: the clock that is already running

The Joint Commission’s Responsible Use of AI in Healthcare certification turned governance from good practice into a surveyable standard, and the evidence it examines comes in two kinds — draftable and accumulative — only one of which can be produced on demand. Where do you stand? Score your readiness in ten minutes, or face the Mock Surveyor. For the two major frameworks side by side: CHAI vs the Joint Commission. Our founder’s full argument on the evidence clock ran on KevinMD: Why AI governance in health care cannot be backdated.

The record itself

Everything above produces evidence, and evidence needs a durable, portable home. The unit of that record is the model card — one document per system answering “show me this model.” The Model Governance Record is our open, vendor-neutral format for one system’s complete governed history (CC BY 4.0); HAI-OS is the record keeping itself — registry, monitoring, sign-off chains, and survey binders as a byproduct of operating. For how long the whole preparation takes and in what order, see the certification timeline.

Where to start on Monday

Inventory first. Charter second, because minutes accumulate forward. Baseline your highest-risk model third. Then let the monitoring cadence run. Documents last — they are the only part that can be rushed. The full free instrument set is on one page.