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Do not start with a product demo. Start by defining the decision the AI will influence, the patients and staff affected, the evidence that it works in a comparable setting, and the controls that make errors detectable and reversible. A scheduling assistant and a model that informs diagnosis or treatment need different levels of validation, oversight, contracting, and regulatory review.
Hospital use of predictive AI is expanding, but adoption is not proof of safety, effectiveness, or local fit. In the 2024 AHA IT Supplement, 71% of non-federal acute care hospitals reported predictive AI integrated with their EHR, up from 66% in 2023. Those figures describe predictive models embedded in or launched through EHRs, not every healthcare AI product or all generative AI.
Use a risk-proportionate procurement process
Before requesting proposals, classify the proposed system by what it does and what can happen when it is wrong. An administrative tool that drafts a billing code may be reviewed mainly for workflow, privacy, and financial controls. A deterioration alert, diagnostic aid, or treatment recommendation can affect clinical decisions and requires stronger evidence, subgroup analysis, human oversight, and rollback planning.
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- The task or decision the system supports.
- The users and their training level.
- The patient population, care setting, and geographic scope.
- The inputs the system receives and the output it produces.
- Expected benefits and measurable success criteria.
- Explicitly excluded uses and situations in which the output must not be used.
- The consequences of a false positive, false negative, delayed result, or unavailable service.
This prevents a broad marketing description from silently becoming a higher-risk clinical use after purchase.
1. What exact problem are we buying it to solve?
Ask the internal sponsor and supplier to describe the use case in one testable sentence: “For this population, in this setting, the system will provide this output to these users so they can take this action.” Require the same wording in the business case, evaluation plan, implementation design, and contract.
Define the baseline workflow
Document how the work is done without AI, including time, staffing, data sources, handoffs, and existing error rates. A product cannot demonstrate improvement unless the hospital defines what it is being compared with. For example, a follow-up model should be compared with the current outreach process, not with an abstract “no intervention” scenario if staff already use rules or registries.
Separate use cases that carry different risks
ASTP/ONC hospital data distinguish use cases such as inpatient risk prediction, outpatient follow-up, billing, and scheduling. In the 2024 survey, reported use for billing rose from 36% in 2023 to 61% in 2024, while scheduling rose from 51% to 67%. These are adoption figures, not evidence of effectiveness or return on investment. Treat each workflow as a separate procurement decision even when one platform supplies all of them.
Set success measures before seeing a sales forecast
Choose measures that reflect the intended benefit and possible harm. Examples include time to complete a task, missed follow-ups, clinically confirmed events, unnecessary escalations, override rates, staff workload, patient access, and equity-related differences. A target such as “improve efficiency” is not an acceptance criterion; “reduce median referral-processing time without increasing incorrect routing” is measurable.
2. What evidence supports the claims, and does it apply here?
Request the complete evidence package for the exact version and configuration being offered. Do not accept a slide showing an impressive area under a curve without knowing the population, comparator, outcome definition, and deployment conditions.
Ask for the study design
- Was the evaluation retrospective, prospective, silent, or performed during live clinical use?
- Was the data collected at the vendor’s site, at independent institutions, or from your own patients?
- Were outcomes measured against clinician judgment, an existing rule, usual care, or another model?
- How were missing records, repeat encounters, transfers, and excluded cases handled?
- Was the tested version locked, and can the vendor identify it by version and date?
- What limitations did the investigators report?
Vendor-only testing can be useful for early screening, but it should not be the final basis for operational reliance on a high-impact tool.
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Require local evaluation
Ask whether the supplier supports a local silent or limited pilot in which the output is recorded but does not yet drive care. Use representative local data and workflows, then compare predictions with outcomes that are defined in advance. ASTP/ONC’s survey specifically asked whether hospitals evaluated predictive models with their own data for accuracy, bias, and post-implementation monitoring. That makes local evaluation an important procurement question, but it does not establish one universal validation threshold for every task.
Check transportability
Performance can change when coding practices, equipment, referral patterns, prevalence, staffing, or patient mix differ. Ask the vendor to identify the populations and settings in which the model was developed and tested, and to explain known out-of-scope conditions. A model validated in one hospital department should not automatically be treated as validated across the entire health system.
3. Who benefits, who may be harmed, and how do errors behave?
Ask for task-appropriate performance information rather than a single headline metric. Depending on the use case, the package may need sensitivity, specificity, positive and negative predictive value, calibration, precision-recall performance, time-to-event performance, or measures of ranking quality. Require definitions and operating thresholds for every reported number.
Examine false positives and false negatives
Ask what happens after each type of error. A false-positive sepsis alert can create alarm fatigue, unnecessary tests, and treatment exposure; a false negative may delay care. A false-positive billing suggestion may be corrected in review, while a false negative in a cancer-screening workflow may have more serious consequences. Require an escalation path, an override mechanism, and a way to record and investigate errors.
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Request results for populations relevant to your service, such as age groups, sex, race and ethnicity where lawfully collected, language, disability, insurance status, geography, pregnancy status, and clinical comorbidity. The appropriate groups depend on the task and the data available. Ask how missing or unknown demographic fields are handled and whether performance gaps are monitored after deployment.
ONC’s health IT transparency materials describe information intended to help clinical users assess fairness, appropriateness, validity, effectiveness, and safety. ASTP/ONC’s hospital survey gives false-positive parity across groups as one example of a bias evaluation. Neither source makes one fairness metric sufficient for every clinical or operational use.
Understand the training data
Ask whether training data included demographic, social-determinant, utilization, or proxy variables; how labels were created; and whether historical treatment decisions could reproduce past inequities. A model can be statistically accurate while optimizing a target that is an imperfect proxy for need. Require the supplier to explain which variables are essential, which are optional, and how changes in data availability affect outputs.
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4. What is the product’s regulatory status?
Regulatory status depends on the precise function, claims, and intended use—not on the presence of the word “AI.” Ask which feature you are buying and whether it is a medical-device function, general administrative software, clinical documentation support, or another category.
Ask for a precise regulatory explanation
- Which FDA pathway, exemption, or enforcement policy does the supplier believe applies?
- What is the exact cleared, authorized, or approved intended use?
- Does the marketed workflow stay within that intended use?
- Which version and configuration were covered by the submission?
- What post-market reporting, complaint handling, and correction processes apply?
For a device, verify the current FDA clearance, authorization, or approval and the specific indications rather than relying on a broad statement that the company is “FDA compliant.” The hospital’s legal and regulatory teams should document their own determination.
Plan for changes to AI-enabled devices
FDA’s August 2025 final guidance on predetermined change control plans (PCCPs) says a plan for an AI-enabled device should describe planned modifications, how they will be developed, validated, and implemented, and the impacts of those modifications. The guidance applies to covered devices reviewed through 510(k), De Novo, and PMA pathways; it does not make every healthcare software product a regulated device.
Ask the supplier whether updates occur automatically, how release notes identify model changes, what validation evidence accompanies each release, and whether the hospital can defer or reject an update. Contract terms should require notice, testing windows, rollback capability, and documentation of material performance changes.
5. What happens to our data?
Map the complete data flow before signing: source systems, interfaces, processing locations, backups, support access, subprocessors, retention, deletion, and exports. Review the actual data-processing and security terms with privacy, legal, information-security, and clinical-risk teams.
Questions for the data-flow review
- What fields are collected, and are all of them necessary?
- Where are data processed and stored, and in which jurisdictions?
- Who can access identifiable records, for what purpose, and with what audit trail?
- Are customer data used to train, fine-tune, validate, or improve a shared product?
- Can the hospital opt out of secondary use?
- How long are inputs, outputs, logs, and backups retained?
- How are records deleted at contract termination, including copies held by subprocessors?
- How quickly will the supplier notify the hospital of a suspected or confirmed incident?
- Can the hospital retrieve its data, configurations, prompts, rules, and audit logs in a usable format?
Do not treat a general security brochure as a substitute for a review of the proposed architecture and contract. The 2025 SAFER System Management guidance can help structure questions about EHR infrastructure, configuration, validation, maintenance, and interfaces, but it is not a certification of a particular vendor.
6. How will the tool change clinical work and patient communication?
Draw the workflow on a screen or process map. Mark where the output appears, what context accompanies it, who must act, how quickly, and what happens when the user disagrees. Specify whether the output is advisory, requires confirmation, or can trigger an automated action.
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Design meaningful human review
“A clinician remains in the loop” is not enough if alerts arrive without supporting evidence, appear after the decision deadline, or are too numerous to review. Define the reviewer’s authority, the information they receive, the response time, and the record they must keep. HHS Health Sector AI Commitments describe human review as a possible mitigation depending on risk; they are commitments, not a universal legal mandate.
Prepare staff and patients
- Identify which roles need training and what competency must be demonstrated.
- Explain known limitations, out-of-scope uses, and override procedures.
- Provide a route for staff to report unsafe or misleading outputs.
- Decide when patients should be told that AI assisted a process or recommendation.
- Review patient-facing language for accuracy, accessibility, language coverage, and inappropriate certainty.
Test downtime and degraded modes
Specify what clinicians do if the interface, model, network, or upstream EHR data is unavailable. The fallback should preserve safe care, not merely hide the AI button. Test downtime procedures during implementation and after major interface or model changes.
7. Who owns the tool after go-live?
Name one accountable executive and a cross-functional group with responsibilities proportionate to risk. Typical participants include clinical leadership, operations, health information technology, data science, patient safety, privacy, security, legal, procurement, compliance, and representatives of affected staff.
In 2024, 74% of hospitals reported that multiple entities were accountable for evaluating predictive AI. Reported entities included a model-specific committee or task force (66%), division or department leaders (60%), and IT staff (41%). These figures describe a common governance pattern, not a mandated committee structure.
Assign decisions, not just attendees
| Decision | Named owner | Evidence or record |
|---|---|---|
| Approve intended use and scope | Clinical and operational sponsor | Use-case statement and risk classification |
| Authorize technical deployment | IT and security leadership | Architecture, access, interface, and security review |
| Accept clinical performance | Designated clinical governance body | Local validation and subgroup results |
| Review incidents and unsafe outputs | Patient-safety owner | Incident log, investigation, and corrective action |
| Approve or defer updates | Product owner with clinical authority | Version assessment and change record |
| Pause, roll back, or retire | Accountable executive | Trigger decision and continuity plan |
8. How will we monitor, update, and stop it?
Put monitoring and intervention duties in the implementation plan and contract. Define what is measured, how often, who reviews it, and what happens when a threshold is crossed.
Monitor more than accuracy
- Overall performance on current local data.
- Performance and error rates for relevant subgroups.
- Calibration or threshold behavior as prevalence changes.
- Missing-data rates, interface failures, and stale inputs.
- Overrides, ignored alerts, duplicate alerts, and alert burden.
- Workflow time, staffing effects, and unintended workarounds.
- Patient complaints, safety reports, and confirmed adverse events.
- Changes in outcomes after model or workflow updates.
ASTP/ONC reports that hospitals conduct post-implementation evaluation, although fewer evaluate all or most predictive models. Treat monitoring coverage as a capability to improve, not as evidence that every deployed model is safe.
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Examples include a material subgroup performance gap, unexplained drift, repeated incorrect recommendations, an unacceptable alert burden, a security incident, a failed interface, or a supplier update that has not completed required validation. The trigger should identify who can suspend the tool, how users are notified, and which manual process takes over.
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Require advance notice of material changes, access to relevant logs and outputs, cooperation during incident investigations, and a tested export and transition process. State how the hospital will retrieve data and configurations, how deletion will be verified, and how support continues during migration. A rollback or termination right is useful only if the organization has a documented alternative workflow.
Compare vendors with one scorecard
Use identical questions for every supplier, an internal build, and a do-nothing option. Score evidence and fit separately so a polished interface cannot compensate for weak validation.
| Evaluation axis | Questions to score |
|---|---|
| Intended-use fit | Does the feature match the defined population, task, setting, and user roles? |
| Evidence quality | Are study design, comparator, outcomes, version, limitations, and local-validation support disclosed? |
| Safety and equity | Are error consequences, subgroup results, missing-data behavior, escalation, and override controls clear? |
| Regulatory position | Is the exact function’s status documented, with update and change-control responsibilities? |
| Data governance | Are processing, retention, training use, subprocessors, access, deletion, and incident notice acceptable? |
| Interoperability and operations | Can it integrate with current systems, operate during downtime, and avoid unsafe duplicate work? |
| Human factors | Are training, patient communication, review time, overrides, and feedback channels workable? |
| Lifecycle control | Who monitors it, approves updates, investigates incidents, and can stop it? |
| Commercial terms | Do price, implementation effort, liability, service levels, audit rights, and exit provisions match the risk? |
Model source—EHR-developed, third-party, or self-developed—is another comparison axis identified in ASTP/ONC hospital data. It should inform diligence, not replace evidence about performance and fit.
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A practical pre-purchase checklist
- Write the intended-use statement, excluded uses, users, population, setting, and success measures.
- Classify the harm that could result from incorrect, delayed, or unavailable output.
- Obtain the exact-version evidence package and document population, comparator, outcomes, and limitations.
- Design a local silent or limited pilot with predefined acceptance criteria.
- Review subgroup performance, missing-data behavior, false-positive and false-negative consequences, and mitigation controls.
- Confirm the precise regulatory status and obtain documentation for any covered device function.
- Map data flows and complete privacy, security, legal, and procurement review.
- Design the user workflow, training, patient communication, downtime process, and feedback route.
- Assign accountable owners for approval, monitoring, incidents, updates, and suspension.
- Put monitoring, change notice, validation, rollback, data export, deletion, and termination rights in the contract.
- Run a go-live review that verifies the system behaves as evaluated, not merely that the interface connects.
- Schedule post-deployment reviews and record the decision to continue, modify, pause, or retire the tool.
What the major guidance documents can—and cannot—tell you
The 2025 SAFER Guides, updated on the ASTP/ONC page on February 27, 2026, add explicit attention to AI-enabled EHR features in the Organizational Responsibilities guide. The broader guides address system configuration and validation, contingency planning, patient identification, decision support, results follow-up, and clinician communication. They are practical EHR safety guidance, not a complete healthcare-AI procurement standard or a product certification.
FDA’s August 2025 PCCP guidance is relevant when the product is an AI-enabled device within the specified regulatory pathways. It supports asking for planned modification, validation, implementation, and impact-assessment details; it does not establish universal thresholds for every healthcare algorithm.
Health Sector AI Commitments describe voluntary commitments such as risk analysis, testing, ongoing monitoring, and human review where appropriate. Use them as a reference for supplier questions, not as a replacement for regulation, contractual controls, or the hospital’s own legal assessment.
ONC transparency requirements summarized in its healthcare-AI explainer apply to certain certified health IT products. Do not assume that the same disclosures are legally required of every healthcare software vendor.
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