Tool

Healthcare AI Deployment Readiness

Most healthcare AI dies in the demo. This is how I check whether a project is actually ready for production: score it across the five things that decide it, and get an honest read in real time.

Data and PHI governance 25%

PHI flows are mapped, minimized, and access-controlled A signed BAA or equivalent covers every vendor touching PHI Data is de-identified or governed wherever the use case allows

Evaluation and guardrails 25%

A labeled evaluation set exists for the top intents or tasks Pass bars and hard safety rules are written down, not implied A regression suite re-runs on every prompt, model, or integration change

Human in the loop 20%

A human reviews or approves anything that writes to the record There is a clear escalation path to a person, with context intact

Monitoring and rollback 15%

Live quality and safety metrics are tracked in production You can roll back a model or a prompt in minutes

Clinical and workflow fit 15%

The tool fits an existing clinical workflow, not the other way around A real business outcome, not a demo metric, defines success

0/ 100

Toggle items to begin

Governance

Evaluation

Human

Monitoring

Fit

Ready does not mean perfect. It means safe to put in front of patients, with a way to watch it and pull it back.

Pairs with my writing on deploying AI in healthcare. Prefer paper? Download the checklist PDF.

Clear-eyed analysis and advisory on healthcare AI, patient access, and product.

Pittsburgh, PA

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