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Evaluate the use case before evaluating the algorithm. An AI system that drafts an eligibility letter is not assessed like one that recommends treatment. Define the task, affected people, data flows, evidence, human controls, legal duties and monitoring plan, then require product-specific documentation and a reversible pilot. A “HIPAA compliant” badge, a strong general benchmark or a polished demonstration is not proof that a tool is suitable for your records or workflow.
1. Define the decision, users and consequences
Write a one-sentence intended-use statement before speaking with vendors. It should name the task, users, population, setting, inputs, output and decisions that remain with a qualified person. For example: “The system summarizes claims and encounter notes for a nurse who reviews the summary before contacting an enrollee.” That is materially different from “The system identifies patients who should receive a particular treatment.”
Classify the use by risk
- Administrative support: drafting correspondence, coding assistance, scheduling or summarizing records. Errors can still affect benefits, payments or access, but the workflow may allow straightforward human correction.
- Clinical decision support: risk scores, diagnostic suggestions, medication prompts or treatment recommendations. An incorrect or poorly explained result can cause direct clinical harm and requires stronger validation, oversight and regulatory analysis.
- Patient-facing use: chat, personalized explanations or self-management advice. Plan for health literacy, language, accessibility, crisis escalation and the possibility that users treat an answer as medical advice.
List the people who could be affected, including patients, caregivers, clinicians, claims staff and people whose records are used for model development. Describe the consequence of a false positive, false negative, omission, delay and plausible misuse. Decide in advance which errors require immediate escalation and who has authority to stop the system.
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Ask for the vendor’s intended-use statement, excluded uses, contraindications, known limitations and version history. The documentation should say which decisions must not be automated and what evidence a user is expected to inspect. If the vendor cannot state what the product is not designed to do, treat that as an unresolved safety issue rather than filling the gap with assumptions.
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2. Establish whether your records fit the evidence
“Health records” are not a single, consistent dataset. Medicare claims, electronic health records, pharmacy data, laboratory results, social-care information and patient-entered information differ in coding, completeness, timing and provenance. Ask exactly which sources and time periods were used to develop and validate the system, and why they represent your intended population and workflow.
Check population and setting match
- Age, disability status, comorbidity, language, geography, insurance arrangement and care setting of the validation population.
- Whether records came from fee-for-service Medicare, Medicare Advantage, other payers or a mixed population.
- Clinical specialties, facility types, documentation systems and coding conventions represented.
- Whether the validation period predates important coding, treatment, policy or workflow changes.
- Differences between the people used to validate the model and the people on whom you intend to use it.
A model can perform well on a vendor’s population yet fail after deployment because your records contain different missingness patterns, terminology, referral practices or claim timing. Request results from a population and workflow that resemble yours, not only an overall average.
Inspect record quality and provenance
Ask how the system handles missing, conflicting, duplicated, delayed and stale information. A claims record may show that a service was billed without providing the clinical context needed to interpret it. An encounter note may be copied forward, while a medication list may not reflect what a patient is actually taking. The system should identify the source and date of important facts, distinguish observed information from inference, and expose uncertainty instead of silently filling gaps.
FDA’s Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (final guidance, July 2024) explains considerations for assessing EHR and medical-claims data in clinical studies supporting regulatory decisions about effectiveness or safety. It is useful context for questions about data relevance, reliability and traceability; it is not a universal certification checklist for every AI product.
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3. Demand validation that answers your real question
Request the complete validation protocol and results, not a single accuracy number. You need to know what the system was asked to do, what reference standard was used, how cases were selected, and what happened when the model was uncertain.
What to request
- The exact task definition, prediction horizon and unit of analysis.
- Training, tuning and test-set separation, including protection against patient or institution overlap.
- The reference standard: expert adjudication, verified outcome, chart review, claim status or another method.
- Confusion-matrix measures and clinically relevant thresholds, such as sensitivity, specificity, positive and negative predictive values, calibration and the rate of unclassified cases.
- Results by relevant subgroup, site, workflow, language, age range, race or ethnicity where lawfully and appropriately assessed, disability status and data-completeness pattern.
- Error analysis showing common false positives, false negatives, omissions, unsupported statements and harmful failure modes.
- External or independent evaluation, with enough methodological detail to reproduce the interpretation.
- A plan for monitoring performance after deployment and repeating evaluation after model, data-source or workflow changes.
Ask whether reported results measure the model alone or the model-plus-human workflow. A clinician may catch some errors in a controlled study that users miss during production. Conversely, a tool may save time without improving decisions. Require evidence for the outcome you care about: turnaround time, coding accuracy, appropriate referrals, safety events or another defined measure.
Interpret benchmark claims cautiously
General language or reasoning benchmarks do not establish reliability on Medicare claims or clinical records. A test set drawn from one health system does not establish performance at another. Small subgroup samples can make apparent differences unstable, but hiding subgroup results prevents you from judging that uncertainty. Ask for confidence intervals or other measures of uncertainty where appropriate and document which results are too limited to support a deployment decision.
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Whether HIPAA governs a particular use depends on the organizations involved and their relationship to the data, not simply on the fact that information originated in Medicare or another medical record. HIPAA generally regulates covered entities and business associates. Information that a person voluntarily enters into a consumer app that is not offered by or for a regulated entity may fall outside HIPAA even when the person copied it from a medical record.
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HHS Office for Civil Rights explains this distinction in its guidance on online tracking technologies (reviewed June 26, 2024) and consumer health information (reviewed September 14, 2023). The FTC Act and the FTC Health Breach Notification Rule may apply to some non-HIPAA apps and services. Determine the status of each party, every transfer and each purpose with qualified privacy and legal advisers. A vendor’s statement that it is “HIPAA compliant” is not a legal determination for your specific arrangement.
Identify the contractual relationship
Document which organization is the data owner or controller, who is processing the information, whether a business-associate relationship exists, and which agreement governs access and disclosure. The contract should address permitted uses, subcontractors, security controls, breach and incident reporting, assistance with individual rights, return or deletion of data, audit rights and what happens when the contract ends.
Account for secondary uses
Clarify whether prompts, uploaded records, outputs, logs or feedback are used to train or improve a model. “De-identified” should be defined by the applicable legal and technical standard, not used as a vague assurance. Ask whether humans or contractors can view content, whether data leave your approved environment, and whether a customer’s information can influence answers shown to another customer.
5. Examine the actual data flow and security controls
Obtain a diagram showing collection, transmission, processing, storage, backups, logging, support access, model-training systems and deletion. HHS recommends mapping data flows, purposes, recipients and retention, applying safeguards and meaningful consent where appropriate, and ensuring public statements match actual practices.
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Questions the architecture should answer
- What fields are collected, and are free-text notes or identifiers sent unnecessarily?
- Where are data processed and stored, including disaster-recovery copies?
- Who can access raw records, prompts, outputs and logs, and how is access reviewed?
- What encryption, key management, authentication, network isolation and tenant separation are used?
- How long are each of the input, output, log and backup categories retained?
- Can an authorized administrator delete or export data, and how is deletion verified across replicas?
- How are vulnerabilities, model abuse, data leakage and security incidents detected, investigated and reported?
- Do browser extensions, analytics tools, tracking pixels or support channels transmit health information to additional recipients?
Test the answers against the product configuration you will actually use. A security white paper describes an option; it does not prove that the option is enabled in your tenant.
6. Design human accountability into the workflow
Human oversight must be an operating procedure, not a sentence in a brochure. Assign a qualified reviewer for each consequential output, define what the reviewer must check, and record the decision and any correction. The interface should make it easy to inspect supporting source material, distinguish quoted facts from generated text and report an error.
Set escalation and stop rules
- Route uncertain, contradictory or high-impact cases to a named clinical, benefits or privacy lead.
- Provide a correction path that reaches the responsible team and preserves an audit trail.
- Define conditions for pausing a feature, such as unexplained error spikes, data leakage, unsafe recommendations or a major source-system change.
- Give users a way to bypass the tool and continue the underlying workflow.
- Train users not to treat fluent language as evidence and not to paste information into unapproved tools.
CMS’s Guidance for Responsible Use of Artificial Intelligence (AI) at CMS (last reviewed August 26, 2025) states: “Users are fully responsible for the impact and accuracy of all content produced with GATs at CMS.” That guidance is directed to CMS employees, contractors and people working on behalf of CMS; it is a useful public-sector example of accountability, not a binding rule for every private organization.
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7. Check regulatory and professional boundaries
If software informs diagnosis, treatment, triage or another clinical decision, determine whether the particular function may be regulated as medical-device software and what FDA requirements or evidence apply. Do not infer the answer from the vendor’s product category or from a general claim that the system is “just decision support.” Ask for the product-specific regulatory position, applicable documentation and evidence supporting it.
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Also check state licensure, delegation and scope-of-practice rules. A tool cannot transfer professional responsibility to an unqualified user. Patient-facing outputs may require additional review, disclosures, accessibility measures and emergency escalation. If the vendor cannot explain how its intended use fits your jurisdiction and profession, obtain specialist advice before deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Compare candidate tools with a weighted decision record
Use the same questions and evidence threshold for every candidate. Weight the criteria according to the harm that a failure could cause; do not let a low price or attractive demo compensate for an unaddressed safety or privacy gap.
| Comparison axis | Evidence to record | Decision question |
|---|---|---|
| Intended use and population fit | Task definition, exclusions, validated populations, settings and time periods | Is this the same problem and population we have? |
| Data provenance and quality | Source systems, lineage, missingness, conflicts, freshness and record-level citations | Can users tell what the output is based on? |
| Validation and error profile | Reference standard, subgroup results, calibration, false-positive and false-negative analysis | Are errors acceptable for this consequence and workflow? |
| Privacy and security | Data-flow map, contract, access controls, retention, training use and incident process | Do actual practices match our obligations and policy? |
| Human control | Reviewer role, escalation, correction, audit logs and disablement procedure | Who remains accountable and can anyone stop it? |
| Regulatory and professional duties | Product-specific regulatory analysis, licensure and scope-of-practice review | Is the proposed use lawful and professionally permissible? |
| Monitoring and change management | Drift metrics, update notices, revalidation triggers and incident response | How will we know when the original evidence no longer applies? |
NIST’s AI Risk Management Framework and its frequently asked questions describe trustworthiness characteristics including validity and reliability, safety, security, accountability, transparency, explainability, privacy and fairness. NIST emphasizes that these characteristics involve contextual trade-offs; no single score or label settles the procurement decision.
9. Use a controlled pilot before production
- Limit the scope. Choose one documented task, a defined user group and the minimum data needed. Exclude autonomous actions and high-risk cases until evidence supports them.
- Set a baseline. Measure the current workflow’s accuracy, time, backlog, correction rate and safety incidents so that “faster” is not mistaken for “better.”
- Run silent or shadow evaluation. Where feasible, generate outputs without showing them to decision-makers, then compare them with an approved reference process.
- Review representative cases. Include missing, conflicting, rare and complex records, as well as relevant demographic and site subgroups. Have qualified reviewers classify errors and harms.
- Test privacy controls. Verify access permissions, logging, retention, deletion, export, tenant separation and behavior after an account is disabled.
- Define go/no-go thresholds. Set acceptable error patterns, review time, escalation rates and security conditions before looking at results.
- Document the decision. Record evidence, limitations, residual risk, responsible owners, user training, approval date and the next review date.
10. Monitor, revalidate and retire responsibly
Approval is not permanent. Monitor input drift, missingness, subgroup performance, override and correction rates, escalation volume, unsafe outputs, privacy events, response latency and changes in the underlying workflow. Review a sample of outputs regularly and preserve enough information to reconstruct what data and model version produced a result.
Trigger reevaluation when the vendor changes the model, prompt layer, data source, thresholds or interface; when your EHR or claims system changes; when coding or clinical practice changes; when a new population or site is added; or when monitoring shows a material performance or safety shift. Require advance notice of updates where possible, a rollback plan and a named person who can suspend the tool. Retire the system when its evidence no longer matches the use, when risks cannot be controlled, or when a safer process replaces it.
11. Vendor due-diligence checklist
Request written answers and supporting evidence to these questions:
- What exact task is validated, and which tasks are outside intended use?
- Which populations, settings, sources and time periods were included, and how do they match ours?
- How are missing, conflicting and stale records handled?
- Can the system show the source material and dates supporting each consequential output?
- What are the measured errors and limitations by relevant group and workflow?
- What independent evaluation exists, and who performed it?
- What monitoring and revalidation occur after model or data changes?
- What information is collected, transmitted, retained or used for model training and improvement?
- Who receives the data, including subprocessors and support personnel?
- Which contract governs access, disclosure, deletion, incident reporting and any business-associate relationship?
- Who reviews consequential outputs, how are corrections handled, and who can disable the system?
- Does this function raise FDA, licensure or scope-of-practice requirements, and what product-specific evidence supports the answer?
12. Red flags that should pause a purchase
- A demo is offered instead of a written intended-use statement and validation report.
- The vendor gives one overall accuracy figure without a reference standard, subgroup results or error analysis.
- Marketing says “HIPAA compliant,” “secure” or “certified” but will not describe data flows, retention or training use.
- The product cannot identify source records, dates or uncertainty for important statements.
- Users cannot correct an output, escalate a case, see an audit trail or turn the feature off.
- Updates occur without version notices, change documentation or a revalidation plan.
- The proposed workflow allows an AI output to become a benefits or clinical decision without qualified review.
Any one of these may be fixable, but deployment should wait until the gap has an owner, evidence and a documented control. The final decision should state not only why the tool is acceptable, but also where it must not be used and what would cause you to reconsider.
Quick Recap
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