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Healthcare AI Vendor Evaluation Checklist

Forty-one questions across seven review areas, organized by which internal reviewer asks them. Request the answers before the demonstration, not after.

Updated September 18, 2026

The short answer

Evaluate a healthcare AI vendor across seven areas: intended use and clinical claim, evidence and validation population, data handling, integration, model change management, post-deployment monitoring, and commercial terms. Request the documentation before the demonstration. What a vendor can produce in week one predicts the implementation more reliably than anything they show you.

Explained at three levels

1 Plain English

Before you let anyone demonstrate anything, ask for paperwork. What exactly does this tool claim to do, who did they test it on, where does our data go, how does it connect to our systems, what happens when they change it, how will we know if it stops working, and what does it really cost. A vendor who has sold to hospitals before will have all of that ready. One who has not will take three weeks to assemble it, and that is useful information by itself.

2 Informed buyer

The checklist is organized by reviewer rather than by topic on purpose. Each of your internal reviews will ask its own subset, and routing the questions that way means the answers arrive in the form the reviewer needs instead of being re-extracted from a general vendor response by someone with less context.

3 Technical and professional detail

Two questions carry disproportionate weight and are the most frequently answered vaguely: the validation population relative to yours, and the model change notification and revalidation process. The first determines whether published performance is likely to transfer. The second determines whether the product you approved is the product you will be running in eighteen months.

1. Intended use and clinical claim

  • What is the stated intended use, in the vendor’s own words, written down?
  • Is the output advisory, or does it act, reorder, or notify autonomously?
  • What clinical decision is it expected to influence, and whose decision is it?
  • What is explicitly out of scope?
  • Is a clinician always in the loop, and at which step?
  • What happens to the workflow when the tool is unavailable?

Get this in writing before anything else. Almost every later disagreement traces to an intended use that was described loosely at the start.

2. Evidence and validation

  • What population was the model developed on? Size, sites, years, demographics, care setting.
  • What population was it validated on, and was that validation external to the development data?
  • How does that population compare with ours?
  • What performance measures are reported, and at what operating threshold?
  • What is the expected false positive and false negative behavior at our prevalence?
  • Has subgroup performance been assessed, and what was found?
  • Is the evidence peer reviewed, vendor-generated, or both, and is it available to read in full?

Vendor-generated evidence is not disqualifying. Vendor-generated evidence presented as though it were independent is.

3. Data handling and privacy

  • What data does the product receive, and does it include protected health information?
  • Where is it processed and where is it stored?
  • How long is it retained, and can we set that?
  • Is our data used to train or improve models, and can that be contractually disabled?
  • Which subcontractors and cloud providers sit downstream?
  • Is there a signable Business Associate Agreement?
  • What happens to our data at termination, and on what timeline?

The training question is the one most often answered verbally and needs to be answered contractually.

4. Security posture

  • Current SOC 2 Type II report, with the scope section, not just the opinion.
  • HITRUST certification if held, with the assessment type named.
  • Penetration testing cadence and summary findings.
  • Breach history and notification commitments.
  • Access control model and audit logging available to us.
  • Business continuity and disaster recovery commitments.

5. Integration

  • Which integration model: read-only feed, write-back, in-context launch?
  • Which standards, which versions, which resources? FHIR support alone is not an answer.
  • Does it require acceptance into the EHR vendor’s application program, and where is that in progress?
  • How many hours of our integration analyst time, and across what elapsed period?
  • What breaks on an EHR upgrade, and who fixes it?
  • Reference customers running the same EHR version and integration model.

This is the largest source of schedule risk in the whole evaluation and the item most often underestimated in proposals.

6. Model change management

  • How often is the model updated?
  • Are we notified before a change, after it, or not at all?
  • Can we decline or defer an update?
  • What revalidation does a change require on our side, and who pays for it?
  • Is there a version history we can audit?
  • How would we roll back?

A product that silently changes behavior is a governance problem regardless of how well it performs.

7. Monitoring and support after go-live

  • What performance data does the vendor provide us continuously?
  • Can we monitor independently, on our own data?
  • What drift detection exists and who is alerted?
  • What is the support model, and who owns the relationship after implementation?
  • What does the vendor commit to if performance degrades?
  • Is there a documented deprovisioning procedure?

See post-deployment validation requirements for how to turn these answers into a monitoring plan your governance body will accept.

8. Commercial terms

  • Pricing basis: per clinician, per encounter, per site, per study, flat?
  • What happens to price at scale, and is there a documented tier structure?
  • What is included in implementation, and what is billed separately?
  • Pilot terms, and specifically what converts and at what price.
  • Term, renewal, and price escalation.
  • Exit terms, including data return.

How to use this

Send it before the demonstration. Not as a test, and not as a procurement formality, but because the answers are what your five internal reviewers will each need anyway, and collecting them once in a usable form is the difference between a three-month evaluation and a nine-month one.

What comes back is also diagnostic. A vendor experienced in health systems returns most of this in days. A vendor that has sold mainly outside healthcare will take weeks and will be missing the same three items every time: subcontractor list, model change notification terms, and independent monitoring access.

Where this goes next

Topics: evaluation, checklist, clinical ai, vendor selection

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