Building Your Practice's AI Governance Policy

Most practices have AI use scattered across front desk, billing, and clinical teams before leadership realizes how wide the footprint has become. In this episode, we outline the policy structure that turns ad hoc adoption into a governed system without killing useful experimentation.

The 5 Core Policy Components

• Scope and inventory: define which tools count as AI, who can approve them, and maintain a current approved-tool register
• Data handling rules: specify when PHI can be entered, what requires a BAA, and where outputs may be stored or copied
• Clinical use boundaries: separate administrative assistance from decision support and define mandatory human review points
• Audit standards: log usage, vendor changes, exceptions, and near-misses on a recurring schedule
• Retirement triggers: remove tools that drift in performance, change terms, or lose security attestations

Board-Level Oversight and Vendor Review

Board-level oversight does not mean the board selects products. It means leadership owns the practice's risk appetite and receives regular reporting on what is approved, what is in pilot, and what still needs remediation.

• Assign one executive, physician leader, or compliance owner to maintain accountability for AI governance
• Give the board a quarterly dashboard with approved tools, active pilots, incidents, and open corrective actions
• Use a vendor evaluation framework that scores security posture, workflow fit, clinical risk, explainability, and contract protections
• Require evidence for training-data policies, data retention windows, subprocessor lists, and support escalation paths

Incident Response and Staff Training

An AI incident response plan should work like any other patient-safety process: contain the issue, assess harm, document facts, and prevent recurrence. Near-misses count, because repeated near-misses usually become reportable events later.

• Define who freezes tool access, who notifies compliance, and who performs chart review when an AI output is wrong
• Preserve prompts, outputs, screenshots, and version details for internal investigation
• Set escalation rules for patient safety events, privacy events, and billing or compliance events
• Require role-based staff training before access is granted and annual refreshers afterward
• Train staff on approved use cases, prohibited use, override expectations, and how to report suspected hallucinations or data leakage

The point of governance is not paperwork. The point is making safe use easier than unsafe use.