Ask about the exact use, local evidence, patient safety, regulation, data, governance, and exit plan before signing a healthcare-AI contract. A scheduling assistant and a model that influences diagnosis may both be marketed as “AI,” but their failure consequences are very different. Procurement should therefore scale its scrutiny to the decision the software can affect, not to the novelty of the technology.
Hospitals are adopting predictive tools faster than they are proving that those tools work in every local setting. The questions below turn a vendor demonstration into a documented, testable decision.
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Why a rigorous question set matters
ASTP/ONC Data Brief 80, published in September 2025 from the 2023–2024 American Hospital Association IT Supplement, found that 71% of non-federal acute-care hospitals reported predictive AI integrated with their EHR in 2024, up from 66% in 2023. The survey covers predictive models embedded in or launched through EHRs; it does not describe every healthcare-AI product or all generative-AI deployments.
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Reported use also expanded in routine operations: hospitals using predictive AI for billing rose from 36% in 2023 to 61% in 2024, while scheduling use rose from 51% to 67%. Those are adoption figures, not proof of effectiveness, safety, savings, or return on investment. The same brief notes continuing difficulty demonstrating real-world performance and engaging clinical workforces.
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Governance is becoming more distributed. In 2024, 74% of hospitals said multiple entities were accountable for evaluating predictive AI. The reported entities included a model-specific committee or task force (66%), division or department leaders (60%), and IT staff (41%). This is a description of common practice, not a mandated committee structure.
Use these figures as a reason to ask better questions, not as a reason to buy quickly. A product that performs acceptably in a vendor study can fail after a change in patient mix, documentation, staffing, workflow, or data interface.
First classify the decision and the potential harm
Before comparing suppliers, classify what the system can change. This determines the depth of validation, oversight, contracting, and monitoring you need.
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|---|---|---|
| Administrative assistance | Scheduling, coding support, billing work queues | Productivity measurement, data access, error correction, downtime and human review |
| Clinical workflow support | Follow-up queues, documentation prompts, care coordination | Alert burden, missed cases, escalation rules, subgroup performance and workflow fit |
| Risk prediction or monitoring | Who receives an intervention or closer observation | Calibration, false positives and negatives, local validation, drift and equity effects |
| Diagnostic or treatment support | Interpretation, diagnosis, treatment selection or timing | Regulatory status, clinical evidence, explainability, override, incident response and rollback |
A tool may move between categories as features are enabled or claims change. Record the precise feature, user, patient population, setting and decision for which you are evaluating it.
1. What exact problem are we buying it to solve?
Define the intended use in one sentence
Require the internal sponsor and supplier to state the intended use, affected task or decision, users, patient population, operating setting, expected benefit and excluded uses. “Improves outcomes with AI” is not an intended use. “Prioritizes adult inpatients for pharmacist review using information available in the EHR at 6 a.m.” is specific enough to test.
Document the baseline workflow
Map how the work is done now, including inputs, handoffs, delays, exceptions and who is accountable. Record the baseline measure you will compare with the product: time to schedule, coding rework, follow-up completion, medication errors or another outcome appropriate to the use. Do not let a vendor choose a success metric that cannot be measured in your environment.
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Set boundaries before a demonstration becomes a commitment
- List populations, departments, sites and languages included in the first release.
- State decisions the software may inform and decisions it may not make.
- Specify whether the output is advisory, queue-prioritizing, blocking or automatically executed.
- Define what happens when required data are missing or contradictory.
2. What evidence supports the claims, and does it apply here?
Demand the complete evaluation record
Ask for study design, data sources, dates, sample size, intended population, comparator, outcomes, model version, limitations and the exact configuration tested. Clarify whether evidence is vendor-only, retrospective, prospective, externally replicated or independently audited. A polished accuracy number without its denominator, threshold and operating context is not enough to support a purchase.
Check transportability
Compare the tested population and workflow with your own. Differences in age, disease prevalence, payer mix, documentation habits, device data, staffing and referral patterns can change performance. Ask which data elements are required, how often they are refreshed and whether your interfaces produce them in the same format and timing as the evaluation data.
Plan a local, staged evaluation
Ask whether the supplier supports a local silent or limited pilot before operational reliance. A sensible sequence is retrospective testing on representative local data, silent-mode observation, restricted deployment with review, and only then broader use if predefined criteria are met. ASTP/ONC’s hospital survey asked about evaluation with a hospital’s own data for accuracy, bias and post-implementation monitoring; it supports local evaluation as a procurement question but does not set one universal validation threshold.
3. Who benefits, who may be harmed, and how do errors behave?
Request task-appropriate performance measures
Ask for sensitivity, specificity, positive and negative predictive value, calibration, area-under-curve measures or other metrics suited to the task. For a queue-ranking model, calibration and workload at different thresholds may matter more than a single accuracy figure. For an alarm, false-alarm frequency and missed-event consequences may dominate. Do not treat one metric as sufficient for every use.
Examine false positives and false negatives
Require concrete examples of each error. A false positive may consume scarce clinical capacity, trigger an invasive test or stigmatize a patient. A false negative may delay treatment or omit a patient from follow-up. Ask who sees the result, what action follows, how quickly an error can be corrected and whether the event is logged for review.
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Request results for populations relevant to your service, such as age bands, sex, race and ethnicity where legally and ethically appropriate, disability, language, geography, insurance status, comorbidity and socioeconomic or social-determinant variables. Ask whether those variables were used in training, as labels, as predictors or only for evaluation. ASTP/ONC gives false-positive parity across groups as an example of a bias evaluation; the right analysis depends on the use and the harm.
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Ask how missing data are handled
Find out whether the model imputes, ignores or rejects missing values, and whether missingness itself changes the result. Confirm the user-facing message when inputs are out of range, stale or unavailable. A system that silently produces a score from incomplete data needs a different control from one that stops and requests review.
Review transparency information where it applies
ONC’s health-IT transparency framework describes information for clinical users about fairness, appropriateness, validity, effectiveness and safety for certain certified health-IT products. Ask the supplier which transparency obligations apply to the specific product; do not assume the same requirements cover every healthcare software vendor.
4. What is the product’s regulatory status, and how can it change?
Identify the regulated function, not the marketing category
Ask which exact feature you are buying and whether it is a medical-device function. Request the applicable FDA pathway or exemption rationale and supporting documentation. If the function is a device, verify the current clearance, authorization or approval and its precise intended use. “FDA registered” or a broad company statement is not a substitute for identifying the reviewed function.
Understand change control
Ask how model versions are numbered, what changes trigger a new validation, how customers are notified and how a previous version can be restored. FDA’s August 2025 final guidance on predetermined change control plans (PCCPs) applies to AI-enabled device software functions reviewed through 510(k), De Novo and PMA pathways. For a covered device, a PCCP should describe planned modifications, how they will be developed, validated and implemented, and the impacts of those modifications.
Separate device obligations from general software governance
Many administrative, analytics and generative systems are not regulated as medical devices simply because they use machine learning. Regulatory status depends on function, claims and pathway. Have regulatory, clinical, legal and quality specialists document the conclusion for the feature and version being purchased.
5. What happens to our data?
Draw the actual data flow
Ask what information is collected, where it is processed and stored, who can access it, and which subcontractors or subprocessors handle it. Map movement from EHR, laboratory, imaging, claims, scheduling and user devices into the service and back. Include support access, backups, logs and test environments.
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Negotiate use, retention and deletion
- Is customer data used to train, fine-tune or improve a shared model?
- Can the supplier use outputs, prompts, metadata or de-identified records for other purposes?
- How long are production, backup and audit copies retained?
- What is the deletion process at contract end, and how is deletion verified?
- Which geographic regions host the data, and can that location change?
Require legal, privacy and security review of the actual contract and data-processing terms. No generic assurance can determine compliance for an unspecified supplier or replace your own regulatory assessment.
Set security and incident expectations
Specify authentication, least-privilege access, encryption, vulnerability management, audit logs, penetration testing and notification deadlines. Define what counts as a security incident, privacy incident, model-integrity incident or unsafe output. The 2025 SAFER System Management guidance is useful for questions about EHR infrastructure, configuration, validation, maintenance and interfaces, but it is practical safety guidance rather than a healthcare-AI product certification.
6. What changes for clinicians and patients?
Design the point of use
Document where the output appears, what patient context accompanies it, how prominently it is displayed and whether the user can inspect the source data. A score detached from its time stamp, population or confidence can invite overreliance. Ask how a clinician records disagreement and whether the system learns from overrides or merely logs them.
Define human review proportionate to risk
Specify who must review an output, within what time, and what action is required when the reviewer disagrees. HHS Health Sector AI Commitments describe human review as a possible mitigation depending on risk; they are commitments, not a universal legal mandate. For high-consequence decisions, identify a qualified reviewer and a safe process when that person is unavailable.
Plan training and patient communication
Train users on intended use, limitations, missing-data behavior, escalation and downtime. Decide when patients are told that AI assisted a process, how questions are answered and how a patient can request correction or human review. Keep training current when interfaces or model behavior change.
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Make downtime safe
Write the manual workflow for outages, delayed interfaces, stale scores and vendor incidents. Clarify whether staff can continue care without the tool, how queued work is recovered and how duplicate or conflicting outputs are reconciled. The 2025 SAFER Guides address contingency planning, clinical communication and related EHR safety issues, including explicit attention to AI-enabled EHR features in the Organizational Responsibilities guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Who owns the tool after go-live?
Name accountable roles
Assign an executive owner and a cross-functional evaluation group appropriate to the use: clinical leaders, operations, informatics, IT, quality and safety, privacy, security, legal, procurement and affected frontline users. The group should approve deployment, review incidents, assess updates and authorize suspension. A committee without decision rights is not governance.
Put responsibilities in writing
| Responsibility | Owner to name | Evidence to retain |
|---|---|---|
| Intended use and acceptance criteria | Clinical or operational sponsor | Approved use statement and baseline measures |
| Interface, access and uptime | IT or digital operations | Architecture, access review and service reports |
| Performance, subgroup outcomes and drift | Clinical safety or analytics lead | Monitoring reports and review minutes |
| Privacy, security and incidents | Privacy and security officers | Risk assessment, logs and incident records |
| Version changes and release approval | Product owner with clinical sign-off | Change notices, validation and rollback record |
Define how frontline staff report unsafe behavior and how quickly the owner must respond.
8. How will we monitor, update and stop it?
Specify post-launch measures
Monitor real-world performance, calibration where relevant, subgroup outcomes, false positives and negatives, overrides, alert or queue burden, workflow time, downtime, complaints and unintended effects. Set review intervals before launch and increase them during the first weeks or after a material change.
Set action thresholds
Write triggers for investigation, corrective action, restricted use, suspension and permanent termination. Thresholds may involve safety events, unexplained drift, subgroup disparity, excessive override rates, unavailable inputs, security incidents or failure to meet the contracted service level. A threshold should name the decision-maker and the time allowed for action.
Contract for updates and rollback
Require advance notice of model, data, interface and hosting changes; release notes describing expected impact; validation evidence; and a way to defer or reject an update. For covered devices, FDA’s PCCP guidance provides a framework for planned modification, validation, implementation and impact assessment. It does not establish universal contractual thresholds for every AI system.
Make exit practical
Require export of configuration, audit logs, results and patient-facing records in usable formats. Define assistance, timing and costs for migration, data deletion and transition to a manual process or replacement system. A termination right without usable data and a safe fallback is not a workable exit plan.
How to compare competing options
Use one scorecard for every supplier, an internal build, an EHR-native feature and a status-quo option. Score evidence and local fit separately from marketing claims.
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|---|---|
| Purpose and fit | Does the feature solve the defined problem for the intended population, setting and users? |
| Evidence | What was tested, against what comparator, in which version and population? Can local validation be performed? |
| Safety and equity | How do errors behave? Are subgroup results, missing-data behavior and mitigation controls available? |
| Regulatory and change control | What status applies to this function? How are versions validated, communicated and rolled back? |
| Data and security | What is collected, retained, shared, trained on, deleted and protected? |
| Implementation | What interfaces, staffing, training, downtime procedures and workflow changes are required? |
| Governance | Who monitors, approves updates, investigates incidents and can pause use? |
| Commercial and exit terms | What are the total costs, service commitments, audit rights, termination terms and migration support? |
| Measured value | Which local outcomes will determine continuation, expansion or cancellation? |
Hospitals report using EHR-developed, third-party and self-developed predictive models. Model source is a useful comparison field, but it cannot substitute for evidence about performance, safety or fit.
Contract terms that turn answers into controls
Do not leave critical answers in a sales presentation. Attach them to acceptance criteria, service levels and change procedures.
Quick Recap
- Defined use: feature, population, setting, users, permitted and prohibited decisions.
- Evidence package: tested version, data description, limitations, subgroup results and local-pilot plan.
- Performance commitments: measurable uptime, interface reliability, response time and support obligations.
- Data terms: permitted processing, training use, subprocessors, locations, retention, deletion and audit rights.
- Safety reporting: incident categories, notification times, investigation cooperation and corrective actions.
- Change management: notice period, validation material, customer approval or deferral rights and rollback.
- Monitoring access: logs, outputs, version identifiers and data needed for independent evaluation.
- Exit: export formats, transition assistance, continued access during migration and verified deletion.
Red flags that should pause a purchase
- The supplier cannot state the intended use or the population represented in testing.
- Only a single accuracy number is provided, with no threshold, comparator, confidence interval or error analysis.
- Subgroup performance, missing-data behavior and override handling are unavailable.
- The supplier treats all future updates as automatic and provides no version history or rollback.
- Data-use language permits broad model training or onward sharing without a clear boundary.
- No named clinical owner can suspend the system, and no downtime workflow exists.
- The contract offers no incident-notification duty, monitoring access or practical exit path.
- Adoption by other hospitals is presented as proof of safety or benefit.
A practical approval sequence
- Write the use statement. Identify the decision, users, population, setting, benefit and exclusions.
- Assign risk and owners. Name the accountable sponsor and the clinical, operational, technical, safety, privacy and security reviewers required.
- Request the evidence and data-flow package. Include version, evaluation design, subgroup results, regulatory rationale, architecture and contract terms.
- Test locally. Use representative data and a staged pilot with predefined safety and workflow measures.
- Review the contract. Convert monitoring, incident response, update control, data use and exit requirements into enforceable language.
- Approve a limited launch. Train users, publish escalation and downtime procedures, and monitor closely.
- Reassess against measured outcomes. Expand, restrict, remediate or stop based on the agreed thresholds rather than enthusiasm or sunk cost.
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