Governance Template

Health System AI Governance Committee Charter Template

A formal document that defines the scope, membership, decision authority, review cadence, and escalation paths for overseeing clinical and administrative AI tools.

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A health system AI governance committee charter is a formal document that defines the scope, membership, decision authority, review cadence, and escalation paths for overseeing clinical and administrative AI tools. Most health systems build this charter as an extension of existing clinical informatics, IT governance, and patient safety committee structures rather than as a standalone body, and align it with frameworks such as NIST’s AI RMF and vendor transparency requirements under ONC/ASTP’s HTI-1 rule.

What a Health System AI Governance Committee Charter Establishes

An AI governance committee charter is the founding document that gives a review body formal standing within a health system. It states why the committee exists, what it has authority over, who sits on it, and how its decisions relate to other governance structures already in place, such as clinical informatics committees, IT governance boards, and patient safety committees.

Without a charter, health systems deploying AI tools tend to rely on informal or inconsistent review, which leaves gaps in who approved a tool, what evidence was reviewed, and who is accountable if the tool underperforms or causes harm. The charter closes that gap by making oversight a documented, repeatable process rather than an ad hoc one.

Regulatory and Accreditation Context Shaping Charter Structure

Several developments over the past 12 months have pushed health systems toward more formal AI oversight documentation. ONC’s HTI-1 final rule requires certified health IT developers to disclose intended use, development data, and performance information for predictive decision support interventions, with compliance dates extending into 2025. This creates a direct dependency between vendor-supplied transparency documentation and how a governance committee evaluates AI-enabled tools embedded in certified EHR systems.

Separately, the Joint Commission and the Coalition for Health AI (CHAI) announced a collaboration in 2024 to develop guidance and a certification pathway for responsible AI use in healthcare organizations, though finalized certification requirements had not been published at the time of this writing. CHAI has also published a Blueprint for Trustworthy AI Implementation Guidance and Assurance addressing governance, risk assessment, and local validation.

NIST’s AI Risk Management Framework, organized around Govern, Map, Measure, and Manage functions, is commonly referenced as a structural model for internal AI oversight, though it remains voluntary. No single government agency or accreditation body has published a standardized charter template specific to health systems, so most charter language is adapted from established committee governance practice combined with these frameworks.

Scoping the Charter: Clinical, Administrative, and Third-Party AI

A charter’s scope statement should distinguish between categories of AI tools because oversight obligations and risk tolerance differ across them:

  • Clinical AI, including diagnostic and predictive decision support, generally carries higher patient safety exposure and may include FDA-regulated AI/ML-based Software as a Medical Device, which the FDA tracks through a public list of authorized devices.
  • Administrative and operational AI, such as scheduling, revenue cycle automation, or documentation support, typically carries lower clinical risk but still requires oversight for operational and data governance reasons.
  • Third-party or vendor-embedded AI, including AI features bundled into EHR platforms, is increasingly treated as its own scope category because health systems often have limited visibility into the training data and validation methods behind these tools.

A charter that fails to separate these categories tends to apply the same review depth to every tool, which slows low-risk approvals and under-scrutinizes high-risk ones.

Membership Composition and Decision Authority

Common membership recommendations include representation from clinical informatics, IT and security, compliance and legal, clinical leadership, data science or analytics, and patient safety, reflecting the range of risk exposure an AI tool can create.

Decision authority is typically structured in tiers: the committee holds binding authority to approve or reject certain categories of AI tools, while higher-risk or regulatory-ambiguous cases escalate to an executive or board-level body. Charters commonly name an accountable executive, such as a CMIO, CIO, or Chief Quality Officer, who is distinct from the committee chair and provides organizational authority behind committee decisions. This separation matters because committee recommendations without executive backing are easier to bypass when operational pressure to deploy a tool is high.

Charter Components at a Glance

These core components give the charter operational clarity and make oversight consistent across tool types and risk levels.

Scope Statement

Defines which AI tools are covered: clinical, administrative, and vendor-embedded.

Membership

Cross-functional representation from informatics, IT, compliance, and patient safety.

Decision Authority

Specifies what the committee can approve versus what escalates upward.

Review Cadence

Tiered review frequency based on tool risk level.

Escalation Triggers

Defined thresholds, such as safety events or performance drift, that force out-of-cycle review.

Core Sections to Include in the Charter

Use the following sections as a practical outline when drafting or refreshing a health system AI governance committee charter.

  • Purpose and scope statement, including explicit inclusion or exclusion of FDA-regulated devices
  • Membership composition and defined roles, including a named accountable executive sponsor
  • Decision authority, specifying what the committee can approve, reject, or must escalate
  • Intake process for submitting a new AI tool for review, including required documentation
  • Review cadence tied to risk tier, with defined monitoring for performance drift
  • Escalation paths with specific triggers, such as safety events or regulatory changes

Extend Governance Decisions Into Runtime Oversight

A charter defines who approves an AI tool and under what conditions. Sustaining that oversight after deployment requires visibility into how the tool actually behaves in production.

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