A practical healthcare AI pilot
By ChatGPT Health. Educational synthesis prepared with AI assistance. Sources checked September 28, 2026. Clinically reviewed by Harvey Castro, MD, MBA, on September 28, 2026. Approved as educational content. This page is not a clinical protocol or a claim of independent clinical peer review.
Start with a specific problem and a comparison you can measure. An AI pilot should test the complete workflow, including the people who respond to the output.
Write a short pilot charter
State the current problem, intended users, patient population, proposed change and excluded uses. Name the person who can approve changes and the person who can stop the pilot. Specify the time and resources available without promising a benefit before evaluation.
Choose outcomes before launch
AHRQ groups implementation considerations around product selection, monitoring and workforce readiness. AHRQ: implementation considerations. The following worksheet turns those concerns into decisions for a local team; it is an editorial planning aid, not a validated scoring system.
- Baseline: how does the current workflow perform?
- Primary measure: which result would make adoption worthwhile?
- Safety measures: what errors, delays or unequal effects would be unacceptable?
- Burden: how much review, training, support and integration work is required?
- Cost: licensing, implementation, maintenance and correction, not just subscription price.
- Comparison: how will you separate the tool effect from changes in staffing or case mix?
Test the fallback before the tool
Document what staff do when the service is unavailable, the output is implausible or a patient declines the workflow. Use realistic test cases in an approved environment. A human approval step only helps when the reviewer has the information, time and authority to challenge the output.
Make the decision reversible
Before launch, define the evidence needed to continue, expand, modify or stop. Include patient and frontline feedback. Keep a record of tool versions and workflow changes so the team can interpret the results. A pilot that identifies a poor fit can still be useful if it prevents a wider unsafe or expensive rollout.
Sources and further reading
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