How to Run a Healthcare AI Pilot

Define success before you start

A pilot without a clear definition of success becomes a demo that never ends. Before signing anything, write down the specific outcome you expect: less documentation time, a higher clean claim rate, fewer no-shows, or faster prior authorization. Attach a number and a timeframe to each. Vague goals produce vague results.

Pick metrics you can actually measure

Choose a small set of metrics you can collect on your own systems, not just the vendor's dashboard. Useful examples include clinician edit time, note completion time, denial overturn rate, days in A/R, no-show rate, and staff hours saved. Establish a baseline first so you can tell whether the tool changed anything.

Scope data access narrowly

Give the pilot the minimum data it needs. Decide what data the vendor can access, whether it leaves your environment, whether it is used for model training, and how it is deleted at the end. Confirm HIPAA handling and a signed BAA before any PHI is shared. See What to Check Before Using AI with PHI.

Test the hard cases, not the demo

Demos use clean, easy inputs. Your pilot should use messy reality: complex visits, edge-case payers, noisy rooms, unusual specialties, and the workflows your staff actually follow. A tool that only performs on ideal inputs will disappoint in production.

Decide on evidence, not enthusiasm

At the end of the pilot, compare results against your baseline and your written success criteria. Keep a human in the loop for any clinical or financial decision the tool influences, and make the go or no-go call on measured outcomes. If the evidence is weak, it is cheaper to walk away now than after a full rollout.