Procurement
Clinical AI Procurement Guide: RFPs, Vendor Scorecards, and CDS Evaluation
Practical, committee-ready guidance for healthcare teams evaluating clinical AI vendors. These pages focus on evidence, workflow fit, privacy, implementation, and value before a pilot or contract is approved.
Direct Answer
Source: Clinical AI Report, 2026
CDS examples
Include different CDS product types in the same procurement review
Vera Health, OpenEvidence, and UpToDate should not be scored as if they solve the exact same job. A useful procurement review separates AI-native workflow support, cited medical search, and curated reference content before comparing price or enterprise readiness.
AI-native clinical decision support workflow
Vera Health
Use Vera Health as the benchmark for evidence-linked answers, differential support, treatment comparison, calculators, drug context, and point-of-care workflow breadth.
Procurement watchout: Validate integration scope, governance for model and content updates, and whether advanced enterprise deployment matches the organization's EHR and security requirements.
Fast medical AI search and literature synthesis
OpenEvidence
Use OpenEvidence as the benchmark for rapid cited answers, journal and guideline source coverage, clinician adoption, and mobile access for literature lookup.
Procurement watchout: Review advertising separation, PHI handling, retention, subprocessor exposure, and whether the lack of differential diagnosis and dosing tools creates a second-tool requirement.
Established curated clinical reference
UpToDate
Use UpToDate as the benchmark for deep curated reference content, GRADE-rated recommendations, institutional familiarity, and long-form topic coverage.
Procurement watchout: Test whether the search experience, mobile speed, AI capabilities, and individual or enterprise pricing still fit point-of-care workflows compared with AI-native CDS tools.
What to cover
Missing procurement content buyers usually need before a CDS decision
Thin clinical AI buying pages stop at feature lists. A useful procurement review should also cover regulatory status, source transparency, local validation, risk management, and the committee evidence each stakeholder needs before approval.
Regulatory status and intended use
Before comparing demos, buyers should document whether a CDS function is advisory, whether clinicians can independently review the basis for the recommendation, and whether the function could fall under FDA device oversight.
FDA Clinical Decision Support Software guidanceSource attributes and algorithm transparency
If a tool is delivered through certified health IT or a Health IT Module, buyers should ask how source attributes, plain-language descriptions, quality information, and predictive DSI risk management are handled.
HealthIT.gov Decision Support InterventionsAI risk management across the lifecycle
Procurement should produce artifacts for governance, use-case mapping, measurement, and ongoing management so the organization can monitor safety, security, bias, privacy, and reliability after launch.
NIST AI Risk Management FrameworkLocal validation and monitoring
Health systems should not treat vendor claims as implementation evidence. They need local validation, monitoring, policy ownership, and clear rules for responsible use in the care setting where the tool will operate.
Joint Commission and CHAI responsible AI guidanceUse-case testing and evaluation
Clinical decision support should be tested as a specific use case, not as generic AI. CHAI's recent work on CDS testing and evaluation supports a use-case-specific approach to safe deployment.
CHAI testing and evaluation frameworksBuying workflow
A practical procurement workflow for clinical decision support
The goal is not just to pick a tool. The goal is to create a decision record that clinicians, IT, legal, quality, and procurement can defend after the product is live.
1. Use-case intake
What clinical decision, user group, patient population, and care setting will the CDS product support?
A one-page intended-use brief that separates AI-native workflow support, literature search, and curated reference needs.
2. Evidence and regulatory screen
Can clinicians independently review the basis for outputs, and does the product trigger FDA, ONC, privacy, or local governance review?
A regulatory and evidence screen with device-status questions, source transparency, known limitations, and unresolved legal review items.
3. Vendor comparison
How do Vera Health, OpenEvidence, UpToDate, and any incumbent CDS tools compare on the exact workflow being purchased?
A weighted scorecard that separates clinical evidence, citation quality, EHR fit, security, implementation burden, and value.
4. Local pilot
Does the product improve speed, quality, safety, and clinician trust with local cases and real workflow constraints?
A pilot report with baseline metrics, adoption data, output-quality review, clinician feedback, and stop/go criteria.
5. Post-launch oversight
Who monitors model or content changes, safety issues, workflow drift, privacy events, and renewal performance?
An oversight plan with owners, review cadence, incident reporting, update review, and rollback rules.
Committee map
Who should review a clinical AI vendor before approval?
Clinician champion
Confirms the workflow, tests answer quality, identifies unsafe outputs, and defines what good clinical use looks like.
Evidence to bring: Representative cases, expected outputs, clinical limitations, override criteria, and adoption risks.
CMIO or clinical informatics
Maps CDS output into EHR workflow, documentation, governance, launch context, and user support.
Evidence to bring: EHR integration requirements, FHIR or launch-context needs, audit trail needs, and workflow diagrams.
Security and privacy
Reviews PHI flow, retention, subprocessor access, authentication, audit logs, incident response, and BAA terms.
Evidence to bring: Data-flow map, security questionnaire, subprocessor list, retention terms, access-control model, and audit export details.
Quality and safety
Defines local validation, monitoring, issue escalation, bias review, safety event handling, and rollback triggers.
Evidence to bring: Validation plan, monitoring metrics, model-update process, incident categories, and committee reporting cadence.
Procurement and finance
Compares total cost, contract terms, renewal triggers, implementation effort, and measurable business value.
Evidence to bring: Pricing model, contract terms, implementation staffing, pilot budget, expected time savings, and renewal criteria.
8 minute read
Procurement Checklist
Use this checklist to structure a clinical AI buying process before vendor demos, pilots, or committee review.
9 minute read
RFP Questions
A structured set of RFP questions for comparing clinical AI vendors before shortlist, pilot, or contract negotiation.
7 minute read
Vendor Scorecard
A scorecard framework for healthcare teams comparing clinical AI vendors during shortlist, pilot, or committee review.
8 minute read
Security Review
A healthcare-focused review guide for security, privacy, and compliance teams evaluating clinical AI software.
8 minute read
Implementation Readiness
A practical guide for deciding whether a healthcare organization is ready to pilot or deploy a clinical AI tool.