What security checks should a healthcare AI pilot pass?

What this guide helps you decide

Decide whether the pilot can prove value without PHI before designing the full system. Document data boundaries, vendor obligations, and reviewer responsibilities early. Keep clinical or operational judgment with qualified humans.

Separate no-PHI pilots from PHI workflows

Many useful pilots can start with synthetic data, de-identified examples, public policies, or non-sensitive operational documents. This can shorten the first learning cycle and reduce compliance friction.

If PHI is required, confirm the client's agreements, vendor review process, data retention expectations, and system boundaries before building.

Design for review and auditability

The pilot should make it clear who reviewed an AI output, what sources were used, what changed, and when an output was escalated. This matters for patient intake, chart summarization, care navigation, and revenue-cycle workflows.

A reviewer should be able to see source context, edit the output, and reject uncertain suggestions without fighting the interface.

Keep the rollout narrow

Start with one site, one team, one document type, or one queue. A narrow rollout makes it easier to catch failure modes before they become operational risk.

The expansion plan should include training, feedback capture, support ownership, and a clear path for disabling the workflow if quality drops.

Putting the guide into practice

Use the following steps as a working review list. Assign an owner and record the decision for every item before launch.

1. Decide whether the first pilot requires PHI.
2. Confirm vendor agreements, data retention, and approved systems.
3. Map user roles, access levels, and reviewer responsibilities.
4. Log sources, generated outputs, reviewer edits, and escalations.
5. Define what users should do when the system is uncertain.
6. Limit the first rollout to a workflow with measurable operational value.