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No “HIPAA compliant,” “secure,” or “certified” label proves that a product is appropriate or accurate. Use the process below to determine whether a specific tool is suitable for a specific Medicare, Medicaid, EHR, claims, or patient-generated-data workflow—and when the evidence is too weak to proceed.
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1. Define the decision before reviewing features
Write a one-page intended-use statement before speaking with vendors. It should identify:
- Task: what the system actually does, such as extracting diagnoses from claims, drafting a denial letter, flagging a possible care gap, ranking referrals, or answering a beneficiary’s question.
- Users: clinicians, coding staff, case managers, call-center agents, patients, caregivers, or another group.
- Inputs and outputs: which record types enter the system and exactly what a user receives.
- Decision boundary: which decisions the output may influence and which decisions remain with a qualified person.
- Consequences of error: delayed care, an incorrect bill, an improper coverage decision, disclosure of sensitive information, or merely an editing inconvenience.
- Operating setting: the organization, geography, language, population, workflow, and technical environment in which the tool will run.
Ask the vendor for intended-use documentation, out-of-scope uses, known limitations, required qualifications, and the controls expected around each output. If those documents are vague, the product cannot be evaluated against a stable target.
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CMS’s Technology-Enabled Care and Artificial Intelligence materials identify privacy protection, human oversight, and continuing accuracy and safety monitoring as safeguards. Treat those as design requirements, not as proof that any particular product meets them.
2. Classify the risk of the use case
Risk depends on what people will do with an output, not on whether the vendor calls the product “assistive.” Classify the proposed use before setting an evidence threshold.
Administrative and low-consequence assistance
Examples include formatting a referral, extracting fields for review, or drafting nonbinding correspondence. A person should still verify the result, but a mistake may be reversible without affecting access to care. Test accuracy on the fields that matter and make clear that generated text is a draft.
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Scheduling, outreach, utilization management, and care-gap queues can change who receives attention first. Evaluate missed cases, false alerts, waiting-time effects, and subgroup differences—not just the percentage of records processed.
Clinical decision support
A system that summarizes a record, suggests a diagnosis, proposes a medication, or influences treatment carries a higher burden. Require clinically meaningful validation, visible source evidence, qualified review, escalation for uncertainty, and a way to stop use quickly.
Coverage, payment, or eligibility decisions
Automated or semi-automated decisions can affect benefits, appeals, provider payment, and legal rights. Define the review standard, notice to affected people, correction path, audit trail, and authority for reversing a result before deployment.
Patient-facing advice
Beneficiaries may treat a fluent answer as medical advice. Test plain-language communication, languages and accessibility, emergency escalation, privacy notices, and the system’s behavior when a question is outside its scope. A patient-facing interface should not imply that a generated answer replaces a clinician or emergency service.
3. Establish that the records fit the question
Medicare information may include claims, encounter data, enrollment and eligibility fields, pharmacy events, laboratory results, notes, imaging reports, or linked EHR data. These sources answer different questions. Claims can show that a service was billed; they may not show the clinical rationale, severity, or outcome. An EHR note may contain context that is absent from a claim, while a note can also be incomplete or copied forward.
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Request a data dictionary and a diagram showing every source, transformation, linkage, and output field. For each important variable, ask:
- What is the source system and observation period?
- How soon after an event does the data become available?
- What proportion is missing, duplicated, conflicting, or stale?
- How are changes, corrections, and late-arriving claims handled?
- How are patients matched across sources, and what happens when matching is uncertain?
- Can the user see the source record or passage supporting an output?
Define a “freshness” rule for the use case. A model that is acceptable with a one-day delay for an administrative queue may be unsafe when a clinician assumes that medication information is current. Require the system to display the date and provenance of material facts, or to signal when the available record is too old or incomplete.
FDA’s July 2024 final guidance, Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products, discusses relevance, reliability, and data-quality assessment for regulatory studies. It is useful context for asking about provenance and fitness, but it is not a universal certification checklist for every AI product.
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A vendor’s general benchmark, demonstration, or accuracy claim does not establish performance in your setting. Request a validation report that states the question, population, time period, data sources, reference standard, sample construction, metrics, uncertainty, and exclusions.
Population and setting
Check whether the evaluation includes the ages, disability status, dual-eligible status, languages, care settings, plan types, rural or urban locations, and clinical conditions present in your intended population. A result from one health system or a commercial dataset may not transfer to Medicare beneficiaries or your workflow.
Reference standard
Ask who decided what counted as correct and how disagreements were resolved. For extraction, the reference may be a chart abstraction; for a risk flag, it may be a defined outcome during a specified follow-up period. “Human review” is insufficient unless the reviewers, instructions, and adjudication process are described.
Metrics and errors
Require metrics that fit the decision. Sensitivity and specificity may matter for a screening alert; positive predictive value matters when staff must act on every alert; calibration matters when a score is interpreted as a probability. For generated summaries, assess omission, contradiction, unsupported statements, and harmful wording. Request a confusion matrix or equivalent counts, not only a single accuracy percentage.
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Subgroup and workflow results
Look for performance by relevant demographic, clinical, language, and operational groups, plus differences between sites, specialties, and record completeness levels. Examine false negatives and false positives separately. A low overall error rate can conceal systematic failures in a small but important group.
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Independent evaluation and change testing
Give preference to evaluations by people who did not build the model, using data that were not used for development. Ask how the tool performed after a source-system change, coding update, new clinical guideline, or model version. Require a plan for revalidation whenever the model, prompt, data pipeline, or user workflow changes.
5. Check privacy coverage instead of assuming HIPAA applies
HIPAA status depends on the parties and the data flow, not merely on the fact that information originated in Medicare or another medical record. The HHS Office for Civil Rights explains that HIPAA generally governs covered entities and their business associates. Health information that a person voluntarily enters into a consumer app not offered by or for a regulated entity may fall outside HIPAA, even when the person copied it from a medical record.
Other rules can still apply. The Federal Trade Commission Act and the FTC Health Breach Notification Rule may cover some non-HIPAA apps and services. Determine which organization controls the data, who receives it, why it is used, and which laws and contracts govern that relationship. Obtain advice from your privacy and legal teams for the actual arrangement; a vendor slogan is not a legal determination.
HHS OCR’s guidance on online tracking technologies, reviewed June 26, 2024, and its consumer-health-information guidance, reviewed September 14, 2023, are useful reminders to examine tracking pixels, analytics tools, application programming interfaces, and other disclosures around the AI feature—not only the model itself.
6. Inspect the complete data lifecycle
Request a written data-flow and security description. It should answer:
- What information is collected, including prompts, uploaded files, metadata, identifiers, logs, and feedback?
- Where is data processed and stored, and which subprocessors or model providers receive it?
- Who can access raw records, prompts, outputs, and audit logs?
- Are records, prompts, or outputs used to train or improve a shared model? Is that use optional, contractually prohibited, or unavoidable?
- How long is each data type retained, and can the customer set a shorter period?
- How are deletion, correction, export, legal holds, backups, and account closure handled?
- What encryption, identity controls, segregation, vulnerability management, and incident-detection measures protect the service?
- How quickly will the vendor notify you of a suspected or confirmed incident, and what assistance will it provide?
Map the flow yourself and compare it with the vendor’s privacy notice, contract, and user interface. HHS recommends documenting purposes, recipients, retention, safeguards, and meaningful consent where appropriate, while ensuring public statements match actual practices. A “no training” promise is useful only when the contract and technical controls make it enforceable and verifiable.
7. Put human accountability into the workflow
Human-in-the-loop is not a checkbox. Specify who reviews which output, within what time, using what evidence, and with what authority to correct or override it. The reviewer must be able to see the relevant source material and recognize uncertainty rather than being pressured to accept a confident-looking answer.
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Define an escalation route for disagreements, urgent risks, suspected bias, privacy concerns, and repeated failures. Record the output, source-data version, model version, reviewer action, correction, and final decision when the use is consequential. Decide who can pause a feature, disable an integration, or revert to a manual process. Train users not to paste information into an unapproved service or treat generated prose as verified fact.
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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 statement applies to CMS work, not automatically to private organizations, but it illustrates why responsibility must be assigned to people rather than implied to belong to the software.
8. Check regulatory and professional boundaries
If a function informs diagnosis, treatment, monitoring, or another clinical decision, ask whether that particular software function may be subject to medical-device requirements and what product-specific evidence supports the vendor’s answer. Do not infer regulatory status from the words “AI,” “clinical,” or “decision support.” The answer can depend on the function, claims made, degree of clinician control, and jurisdiction.
Also check licensure, credentialing, delegation, documentation, informed-consent, accessibility, and scope-of-practice obligations. A tool may be technically lawful to operate while a proposed workflow still assigns a decision to someone who is not authorized to make it. Have compliance and clinical leadership approve the intended use, not merely the purchase.
9. Compare candidates on the same axes
Use one scorecard for every candidate. Record evidence and open questions rather than awarding points for marketing language.
| Evaluation axis | What to compare | Minimum evidence to request |
|---|---|---|
| Intended use and population fit | Task, users, setting, record types, exclusions, and similarity to your population | Intended-use statement, limitations, population description, workflow requirements |
| Data provenance and quality | Source systems, linkage, missingness, conflicts, freshness, and traceability | Data dictionary, flow diagram, quality report, source citations in outputs |
| Validation | Reference standard, design, metrics, uncertainty, subgroup and site performance | Full validation report, error analysis, independent evaluation, change-retest plan |
| Privacy and data use | Applicable entities and laws, permitted uses, training, disclosure, retention, deletion | Contract, privacy terms, business-associate terms where applicable, deletion procedure |
| Security | Access, encryption, isolation, monitoring, subprocessors, and incident response | Security architecture, audit evidence, incident-notification commitments |
| Human accountability | Review, escalation, correction, auditability, and shutdown authority | Workflow design, roles, training, logs, rollback procedure |
| Regulatory and professional duties | Potential medical-device status, clinical governance, licensure, notices, accessibility | Product-specific regulatory analysis and internal approvals |
| Monitoring and updates | Drift, model releases, data-pipeline changes, incidents, and reevaluation triggers | Monitoring dashboard, thresholds, release notes, suspension and revalidation policy |
NIST’s AI Risk Management Framework and its FAQs describe trustworthiness characteristics including validity and reliability, safety, security, accountability, transparency, explainability, privacy, and fairness. NIST also cautions that these characteristics involve contextual trade-offs; no single score or label settles the decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.10. Questions to put in the vendor record
- What exact task is the system designed and validated to perform, and which tasks are outside its intended use?
- Which populations, settings, record sources, and time periods were included in validation, and how do they compare with ours?
- How are missing, conflicting, duplicated, and stale records handled?
- Can a user inspect the source material supporting every consequential output?
- What are the measured error rates, uncertainty ranges, known limitations, and subgroup differences?
- What independent evaluation has been completed, and what remains unevaluated?
- What changes trigger revalidation: model updates, prompts, data sources, coding systems, interfaces, or workflow changes?
- What information is collected or transmitted, who receives it, how long is it retained, and is it used to train or improve models?
- Which entity is responsible for the data, and what contract governs access, disclosure, deletion, incident reporting, and any business-associate relationship?
- Who reviews consequential outputs, how can a user or patient challenge a result, and who can pause or disable the tool?
- Does the use case raise FDA or clinical-practice requirements, and what product-specific evidence supports the answer?
Require written answers and supporting documents. Treat an unanswered question as a risk to resolve, not as an implied “no.”
11. Run a controlled pilot before production
A pilot should test the real workflow without allowing unverified outputs to determine care, payment, or access. Use a representative, appropriately governed sample and compare the AI-assisted process with the current process.
- Set acceptance criteria: define allowable error types, review time, escalation rates, subgroup thresholds, privacy controls, and conditions that automatically stop the pilot.
- Freeze the test plan: document the model version, prompts, data window, users, workflow, and reference standard before examining results.
- Use a silent or shadow phase: generate outputs without exposing them to decision-makers, then measure misses, false alerts, unsupported statements, and operational burden.
- Conduct structured review: have qualified reviewers label errors and severity, record the source evidence, and examine cases with incomplete or contradictory records.
- Test adversarial and boundary cases: conflicting medications, duplicate patients, unusual codes, language variation, missing dates, emergencies, and requests outside scope.
- Decide explicitly: expand, redesign, restrict, or stop based on the predeclared criteria. Document who made that decision and why.
Do not let a successful demonstration substitute for a pilot. Demonstrations usually use clean, selected examples and do not reveal how the tool behaves when records are incomplete or users are busy.
12. Monitor after deployment
Approval is the start of surveillance. Assign an owner and review cadence for:
- Input-data completeness, freshness, linkage failures, and distribution changes.
- Output error rates, overrides, appeals, complaints, safety events, and privacy incidents.
- Performance by relevant demographic, clinical, language, and site groups.
- Changes in workload, wait times, alert volume, clinician behavior, and patient outcomes where measurable.
- Model, prompt, software, vendor, subprocessor, coding, guideline, or data-pipeline releases.
Set thresholds that require investigation, restricted use, rollback, or suspension. Reevaluate after a material change rather than waiting for an annual review. Keep a versioned inventory of models and integrations so an incident can be traced to the output that was actually used.
13. Recognize stop signs and common failure modes
Marketing replaces evidence
Claims such as “enterprise-grade,” “HIPAA compliant,” or “clinically proven” appear without a defined task, population, reference standard, or error analysis. Ask for the missing documents; do not fill the gap with the label.
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A high score on a public dataset says little about your Medicare claims, local documentation, language mix, or downstream workflow. Reject evidence that cannot be connected to the intended use.
Source records are invisible
If reviewers cannot inspect the passages, dates, and codes behind an output, they cannot reliably correct it. Restrict the tool to low-consequence drafting or stop until traceability exists.
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Users begin accepting recommendations without review because the interface is fast or confident. Add mandatory review for consequential outputs, visible uncertainty, and audit logs; retrain or redesign when bypasses occur.
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Prompts, logs, or uploaded records are retained or sent to an undisclosed provider. Pause transmission, investigate the contract and flow, notify the appropriate privacy and security leads, and do not resume until the use is authorized.
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A vendor changes a model or data pipeline and the organization learns about it after performance shifts. Contract for advance notice, release documentation, testing access, and a rollback path.
14. Apply a go, restrict, or stop decision
Proceed only when the intended use is clear, the data are fit, validation is relevant, privacy and security terms are enforceable, qualified people remain accountable, and monitoring can detect harm. Restrict the tool when evidence supports only a narrower task, population, or nonbinding draft. Stop when the vendor cannot explain data handling, source evidence, error severity, responsible reviewers, or change controls.
Document the decision, conditions, owner, review date, approved populations, prohibited uses, and suspension triggers. Requiring a narrower deployment is not a failure; it is often the correct response when evidence does not cover the proposed scope.
15. Keep an evaluation file
For each tool, retain:
- Intended-use statement and prohibited-use list.
- Data inventory, lineage, quality assessment, and retention schedule.
- Validation, subgroup, error, and independent-evaluation reports.
- Privacy, security, contractual, subprocessor, and incident-response documents.
- Regulatory, clinical, accessibility, and professional-scope determinations.
- Pilot protocol, raw review results, acceptance decision, and training materials.
- Model and software versions, release notices, monitoring reports, incidents, corrections, and suspension decisions.
This file lets a new reviewer understand what was approved, on what evidence, and under which limits. It also provides the record needed to investigate a complaint or reassess the tool when circumstances change.
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