Procurement Guide

Clinical AI Vendor Evaluation Scorecard

A scorecard framework for healthcare teams comparing clinical AI vendors during shortlist, pilot, or committee review.

Updated May 23, 20267 minute readFor Clinical AI evaluation committees, procurement teams, informatics leaders, and health system executives.

Direct Answer

A clinical AI vendor scorecard should weight evidence and workflow fit more heavily than demo polish. The strongest scorecards compare vendors across clinical validation, EHR fit, privacy and security, governance, implementation burden, support, and measurable value.

Source: Clinical AI Report, 2026

Key takeaways

  • -Use weighted criteria so a slick demo does not outweigh clinical evidence or security gaps.
  • -Score the product, vendor operating model, and customer implementation burden separately.
  • -Require comments for low or high scores so committee decisions remain auditable.
  • -Rescore after pilot testing, because workflow fit often changes once clinicians use the tool.

CDS solution examples

How this applies to Vera Health, OpenEvidence, and UpToDate

  • -Vera Health should be scored on workflow breadth, source-linked reasoning, differential and treatment support, dosing context, calculators, EHR fit, and enterprise implementation effort.
  • -OpenEvidence should be scored on citation quality, speed, adoption, journal and guideline coverage, mobile access, advertising model, and limits outside literature synthesis.
  • -UpToDate should be scored on curated content depth, GRADE evidence, institutional familiarity, pricing, mobile usability, AI-native capability, and point-of-care speed.

Score what matters to clinicians and operators

The scorecard should reflect how the product will perform in daily work, not only how persuasive the demo was.

  • -Give clinical evidence and workflow fit the highest combined weight.
  • -Separate product capability from implementation services and customer support.
  • -Use a consistent 1 to 5 scale with written evidence required for each score.

Make evidence review explicit

Clinical AI tools often have uneven evidence across use cases. The scorecard should force reviewers to evaluate the specific use case they plan to deploy.

  • -Score peer-reviewed evidence, customer pilots, benchmark validity, and subgroup reporting separately.
  • -Record whether the evidence is vendor-sponsored, independent, prospective, or retrospective.
  • -Lower the score when limitations are not disclosed clearly.

Include implementation drag

A product with strong clinical value can still become expensive if it requires extensive custom integration, training, or operational monitoring.

  • -Score customer staffing requirements and time to launch.
  • -Identify new workflows, approvals, or documentation steps created by the tool.
  • -Ask whether the vendor has live customers using the same integration pattern.

Use the scorecard as a decision record

The final score should not be the only artifact. Committee notes should preserve why the team accepted, deferred, or rejected the vendor.

  • -Capture unresolved risks, conditions for approval, and post-pilot metrics.
  • -Assign owners for legal, security, clinical governance, and operational follow-up.
  • -Update the score after a pilot so final procurement reflects observed performance.

Suggested evaluation weights

CriterionWeight

Clinical validation

Evidence strength, population fit, clinical limitations, and use-case-specific outcomes.

25%

Workflow and EHR fit

Time saved, point-of-care usability, EHR launch context, FHIR support, and documentation burden.

20%

Privacy, security, and compliance

BAA readiness, PHI handling, retention, subprocessors, access controls, and audit logs.

20%

Governance and safety monitoring

Model update process, issue escalation, reporting cadence, and rollback options.

15%

Implementation and support

Launch timeline, customer staffing, training, support model, and customer references.

10%

Commercial value

Pricing model, contract flexibility, measurable outcomes, and total cost of ownership.

10%

Questions to ask

  • QWhich criterion would disqualify a vendor even if the total score is high?
  • QAre scores based on evidence reviewed by clinicians, IT, legal, and security?
  • QWhich assumptions must be validated during a pilot?
  • QDoes the scorecard distinguish current functionality from roadmap promises?
  • QWhat post-launch metrics will determine renewal or expansion?

Red flags

  • !The vendor receives a high score without written evidence attached to each criterion.
  • !Workflow fit is scored by non-clinical reviewers only.
  • !Security and legal review happens after finalist selection rather than before it.
  • !The scorecard does not include a way to revise scores after pilot testing.