Procurement Guide

Clinical AI Procurement Checklist

Use this checklist to structure a clinical AI buying process before vendor demos, pilots, or committee review.

Updated May 23, 20268 minute readFor Procurement teams, CMIOs, clinical operations leaders, physician champions, and digital health committees.

Direct Answer

A clinical AI procurement checklist should cover seven areas: clinical evidence, intended use, safety governance, privacy and security, EHR and workflow fit, implementation readiness, and measurable value. Buyers should require proof for each area before approving a pilot or contract.

Source: Clinical AI Report, 2026

Key takeaways

  • -Start with the clinical workflow and patient-safety risk, not the vendor demo.
  • -Ask for evidence that matches the intended use, population, and care setting.
  • -Review PHI handling, access controls, audit logs, retention, and subcontractors before pilot approval.
  • -Score implementation burden separately from product quality so hidden workflow cost is visible.

CDS solution examples

How this applies to Vera Health, OpenEvidence, and UpToDate

  • -For Vera Health, test whether evidence-linked answers, differential support, calculators, and drug context fit the clinical workflow you are buying for.
  • -For OpenEvidence, test fast cited answers and source breadth, but separately document whether the organization also needs differential diagnosis, dosing, or deeper EHR workflow support.
  • -For UpToDate, use its curated topic depth as a reference benchmark, then test whether its search speed, mobile experience, and AI capability meet point-of-care needs.

1. Define the clinical use case before reviewing vendors

Procurement should begin with the workflow that needs improvement. A broad claim such as better clinical decision support is not specific enough for evaluation or contracting.

  • -Name the target users, care setting, patient population, and decisions affected by the tool.
  • -Separate advisory use from autonomous action, since the risk profile changes materially.
  • -Document where the AI output appears in the clinician workflow and who is accountable for acting on it.

2. Match evidence to the intended use

Evidence should be reviewed against the exact deployment context. A validation study in one specialty, language, or population may not transfer cleanly to another.

  • -Ask whether evidence comes from peer-reviewed studies, prospective pilots, retrospective tests, or internal benchmarks.
  • -Check whether the evidence includes the same clinician type, disease area, and care environment you plan to support.
  • -Require clear limitations, failure modes, and performance by subgroup where relevant.

3. Review governance, safety, and escalation

Clinical AI procurement is also a governance decision. The buying committee should know how the vendor monitors quality and how the organization can respond if performance changes.

  • -Confirm how model updates are reviewed, communicated, and rolled back.
  • -Define escalation paths for inaccurate, incomplete, or unsafe outputs.
  • -Decide who owns clinical oversight after implementation: informatics, quality, operations, or a joint committee.

4. Test workflow fit before contract approval

A clinically impressive tool can still fail if it adds clicks, requires duplicate documentation, or does not fit the EHR and staffing model.

  • -Run a task-based demo using realistic cases and a clinician from the target workflow.
  • -Measure time to answer, number of handoffs, and whether the output can be documented or audited.
  • -Validate integration needs early, including FHIR support, SSO, EHR launch context, and data write-back.

Suggested evaluation weights

CriterionWeight

Clinical evidence

Evidence quality, study design, population match, limitations, and clinical outcome relevance.

25%

Workflow fit

EHR integration, clinician time saved, handoffs reduced, and usability in the target setting.

20%

Privacy and security

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

20%

Governance and monitoring

Model update controls, safety monitoring, issue escalation, reporting, and rollback process.

20%

Value and implementation

Total cost, deployment effort, support model, training burden, and measurable success criteria.

15%

Questions to ask

  • QWhat exact clinical decision or workflow does the product support?
  • QWhat evidence supports use in our patient population and care setting?
  • QHow does the product handle PHI, retention, model training, and subcontractors?
  • QWhat must change in the EHR, staffing model, or documentation workflow?
  • QHow will safety events, poor outputs, and model updates be monitored after go-live?

Red flags

  • !The vendor cannot explain how model updates are governed.
  • !Evidence is limited to broad benchmarks that do not match the proposed use case.
  • !The tool requires clinicians to copy and paste data across systems.
  • !Security answers are delegated to a generic AI platform policy with no healthcare-specific controls.