Building an AI Governance Framework for Your Practice

Most practices adopt AI tools the way they adopt any new technology: someone tries it, it works, everyone starts using it. No policy. No training. No oversight.

This approach worked when the worst outcome of a bad tool was wasted time. With AI in clinical workflows, the worst outcome is patient harm, regulatory violation, or malpractice liability.

A governance framework isn't bureaucracy — it's risk management. Here's how to build one that's practical enough to actually use.

The Four Pillars of AI Governance

1. Tool Approval Process — Before any AI tool touches patient data, it goes through a structured evaluation. Who approves new tools? What criteria must they meet? How is the decision documented?

2. Usage Policies — Clear guidelines for how AI tools can and cannot be used. Which clinical scenarios are appropriate? What requires human override? How are AI outputs documented in the medical record?

3. Training Requirements — Every staff member who uses an AI tool needs training — not just on how to use it, but on its limitations, failure modes, and compliance requirements.

4. Monitoring and Audit — Regular review of AI tool performance, accuracy, and compliance. Incident reporting for AI-related errors or near-misses.