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Evaluate an AI tool in the context where it will actually operate—not by its feature list or an unqualified “HIPAA compliant” badge. Define the intended use, users, data, outputs and decision authority; map every data flow and contract term; determine whether communications fall under HIPAA’s marketing rules; test consumer-facing privacy claims under FTC principles; and require evidence, human controls and ongoing monitoring using a lifecycle framework such as NIST’s AI Risk Management Framework (AI RMF). Then assess whether the product’s specific function creates any FDA considerations.
What a sound evaluation must establish
A healthcare marketing or patient-engagement system can draft a newsletter, choose an audience, personalize a reminder, answer a patient’s question, schedule an appointment or recommend a clinical action. Those are different uses with different consequences. A writing assistant used on de-identified campaign copy does not present the same exposure as a chatbot that receives identifiable symptoms and sends answers directly to patients.
Before comparing vendors, write a one-sentence context of use: who operates the system, who receives its output, what information it processes, what decisions it may influence and whether it communicates directly with patients. Record the intended benefit, prohibited uses, required approvals and the point at which a human must take over. This context becomes the reference for privacy, security, performance and regulatory decisions.
| Example function | Primary output | Questions that change the risk |
|---|---|---|
| Content drafting | Copy for staff review | Does the prompt contain protected health information (PHI)? Can staff publish without review? |
| Audience selection | Campaign segment or list | Which attributes are used, and does the selection reveal or infer a health condition? |
| Personalized messaging | Patient-specific message | What purpose does the message serve, and is authorization required for the information used? |
| Patient questions | Automated conversational response | What happens with urgent, ambiguous or clinical questions? Is escalation immediate? |
| Scheduling and navigation | Appointment or care-navigation action | Can the system alter care access, and are identity, consent and audit controls sufficient? |
| Clinical recommendation | Suggested diagnosis, treatment or triage | Does the intended use move the product into a clinical or device-related regulatory scope? |
Step 1: Define intended use and boundaries
Document the workflow
Describe the complete path from input to outcome. Identify the staff role that configures the system, the role that reviews its output and the person who receives it. State whether the tool drafts, ranks, selects, personalizes, answers, schedules or recommends. “AI for patient engagement” is too broad to support a meaningful risk assessment.
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- Users: name internal operators, administrators, reviewers and any external recipients.
- Inputs: list message history, demographics, appointment data, diagnoses, medications, preferences, free-text questions and any inferred attributes.
- Outputs: specify copy, audience lists, classifications, replies, appointment actions or recommendations.
- Decision authority: state whether the output is advisory, automatically sent, able to change a record or able to affect access to care.
- Boundaries: prohibit uses such as diagnosis, emergency triage or sensitive inference unless separately assessed and approved.
- Escalation: define the trigger, destination, response time and staff ownership for uncertainty, distress, complaints and urgent symptoms.
NIST describes its AI RMF as a voluntary way to incorporate trustworthiness into AI design, development, use and evaluation. That lifecycle orientation supports assessing the system in its real workflow rather than treating “AI” as a single risk category.
Separate marketing from service communications
Classify each message by its purpose before selecting a tool. A campaign encouraging recipients to purchase or use a product or service can be HIPAA marketing. Other communications may concern a covered entity’s own health-related services, treatment or care coordination and may fall within specified exceptions. Appointment reminders, care coordination and patient education are not automatically marketing, and they are not automatically exempt: the purpose, content, recipient and information used matter.
Step 2: Map health information, permissions and contracts
Trace data end to end
Create a data-flow diagram that follows information from collection through prompts, model processing, logs, analytics, support access, backups, deletion and every subprocessor. For each field, record whether it identifies a person or can reasonably be linked back to one. Include free text, images, voice, device identifiers, cookies and inferred segments, not just formal medical-record fields.
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Ask these questions for every transfer:
- What exact categories enter the tool?
- Where are processing and storage located, and which vendor entities or subprocessors can access them?
- How long are prompts, outputs, logs and backups retained?
- Can the organization configure retention and deletion, and how is deletion verified?
- Are inputs or outputs reused to train, fine-tune or improve a shared model?
- What contractual purpose limits prevent secondary use?
- Who can retrieve data for support, quality review or legal requests?
Apply HIPAA marketing rules to the actual communication
HHS’s Office for Civil Rights defines HIPAA marketing as a communication about a product or service that encourages recipients to purchase or use it. HHS explains that use or disclosure of PHI for marketing generally requires an individual authorization, subject to specified exceptions. The analysis belongs to the proposed communication and data flow—not to a product label.
For each workflow, document the purpose, the PHI involved, the recipient, the authorization or exception relied on and the minimum information needed. Review whether a business-associate relationship and appropriate contractual terms apply to the vendor. HHS privacy materials also address minimum-necessary use, so limit fields and access to what the workflow requires.
Do not classify all patient-facing messages in one bucket. A message about a patient’s treatment or care coordination can have a different analysis from a message promoting a third-party product, even when both are delivered through the same chatbot.
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Check obligations beyond HIPAA
HIPAA does not cover every business that handles health-related information. HHS and the Federal Trade Commission (FTC) describe FTC Act duties concerning deceptive or unfair health-data practices, including practices by organizations outside HIPAA’s covered-entity and business-associate categories.
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Step 3: Evaluate privacy, security and patient controls
Require configurable controls
Look for controls that an administrator can set and verify, rather than promises in a sales presentation:
- Role-based access, single sign-on and multifactor authentication.
- Separate production, test and support environments.
- Field-level or workspace-level restrictions on PHI.
- Configurable retention, deletion and export procedures.
- Subprocessor inventory and notification of changes.
- Immutable audit records for prompts, edits, approvals, sends and administrative access.
- Incident detection, notification, containment and recovery procedures.
- Back-up, availability and business-continuity commitments appropriate to the workflow.
Ask how the controls are tested, how often access is reviewed and what evidence is supplied after a change. “Encrypted” alone does not explain who can decrypt data, how keys are managed or whether sensitive prompts appear in logs.
Inspect the patient experience
View the exact disclosure and consent language a patient sees. It should identify the organization operating the service, explain what information is collected and why, state retention and sharing practices in understandable terms, and provide an accessible route for questions or opt-out where applicable. Test whether a patient can withdraw a preference and whether that choice reaches downstream campaign and messaging systems.
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Step 4: Use NIST’s AI RMF to test governance and evidence
NIST released the voluntary AI RMF on January 26, 2023. Its four functions provide a practical structure for procurement and operation; the companion AI RMF Playbook supplies suggested actions. The framework is not a product certification or legal safe harbor.
| AI RMF function | Questions for a healthcare marketing or engagement deployment | Evidence to request |
|---|---|---|
| Govern | Who owns risk acceptance, privacy decisions, content approval, incidents and vendor changes? | Named owners, approval policy, training records, escalation matrix and change-notification process. |
| Map | What context, populations, harms, dependencies and prohibited uses are in scope? | Intended-use statement, data-flow diagram, stakeholder analysis, threat model and impact assessment. |
| Measure | How are accuracy, harmful output, privacy leakage, subgroup performance and drift tested? | Evaluation protocol, representative test data description, error analysis, thresholds and monitoring dashboards. |
| Manage | How are identified risks mitigated, accepted, escalated and re-evaluated after updates? | Risk register, corrective-action records, incident playbooks, rollback procedure and review schedule. |
Demand use-case-specific performance evidence
Generic benchmark scores rarely answer whether a system is safe for your population and workflow. Request the evaluation method, data characteristics, language coverage, known limitations, false-positive and false-negative patterns, and results for relevant subgroups. For a chatbot, test ambiguous questions, misspellings, low health literacy, multiple languages, distress, requests for diagnosis and attempts to obtain another person’s information.
For marketing selection, test whether the model infers sensitive conditions, excludes people unfairly or exposes a condition through a message subject line or audience list. For drafting, measure unsupported claims, omitted disclaimers, incorrect eligibility language and the rate at which reviewers must rewrite output.
Verify human accountability
Specify which outputs require review before sending, who may override the model, how overrides are recorded and what happens when a reviewer disagrees. A human-in-the-loop description is incomplete unless the reviewer has enough time, context and authority to stop or correct an output. Escalation should route clinical or safety questions to qualified staff rather than merely appending a disclaimer.
Step 5: Determine whether FDA materials apply
FDA relevance follows product function and intended use, not the presence of the word “AI.” A campaign writer or scheduling assistant may not fit the same category as software intended to support diagnosis, treatment or another device-related function. Document the scope analysis instead of assuming that every healthcare product requires the same FDA pathway.
The FDA’s January 2025 document, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, is a draft with non-binding recommendations and is expressly not for implementation. Its risk-based credibility approach addresses a model in a particular context of use supporting regulatory decisions for drugs and biological products. It is not a universal evaluation standard for marketing software.
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For device-related questions, consult the FDA’s current Guidances with Digital Health Content index and match the product’s actual claims and function to the relevant guidance. The index can change, so record the version or access date used in your assessment.
Compare vendors on documented evidence
When more than one supplier remains under consideration, score each against the same use case, data set, workflow and acceptance thresholds. A feature checklist is not a risk comparison. Keep the underlying evidence with the score so a low price or polished demonstration cannot outweigh an unresolved data-use or escalation gap.
| Comparison axis | What to examine |
|---|---|
| Data use and control | Input categories, PHI handling, retention, deletion, training reuse, subprocessors and contractual purpose limits. |
| Privacy and communication controls | Authorization support, consent records, audience rules, preference and opt-out handling, and patient-facing disclosures. |
| Use-case evidence | Evaluation methods, representative populations, limitations, error analysis and post-deployment monitoring. |
| Human accountability | Required review, escalation, override, audit records and ownership when output causes harm. |
| Security and resilience | Access management, safeguards, incident response, recovery, availability and change control. |
| Governance and transparency | Intended-use documentation, model or product changes, risk owners and limitation notices. |
| Regulatory scope | Whether the specific function and intended use create additional HIPAA, FTC or FDA analysis. |
Questions to put in the vendor review
- What exact patient or marketing task is the system designed to perform, and which uses are out of scope?
- What health information is sent to the vendor and each subprocessor?
- Are prompts, outputs, logs or feedback retained, reused or used for model training?
- Can the organization configure purpose limits, access, retention, deletion and communication preferences?
- Which proposed messages or disclosures could be HIPAA marketing, and how are authorization and exceptions assessed?
- What does a patient see about collection, use, retention and sharing, and how can the patient opt out where applicable?
- What evidence supports performance for this workflow, language set and population?
- What errors and failure modes are known, and how are they detected after launch?
- What human review is required before a message, audience decision or recommendation reaches a patient?
- How are model updates, security incidents, complaints and performance drift communicated?
- What is the rollback or shutdown procedure if the system behaves unsafely?
- What documented intended use supports the organization’s assessment of FDA digital-health relevance?
These are procurement prompts derived from privacy and risk-management frameworks. They help structure diligence; they are not a complete quotation of every regulatory requirement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical approval sequence
- Write the use-case record. Capture users, recipients, inputs, outputs, authority, prohibited uses and escalation.
- Classify the information. Mark PHI, identifiable data, inferred health attributes, confidential business data and de-identified material.
- Draw the data flow. Include vendor systems, subprocessors, logs, support access, backups, retention and deletion.
- Analyze communication purpose. Determine whether each message is marketing, treatment, care coordination, service information or another category; document authorization or an applicable exception.
- Review contracts and controls. Resolve purpose limits, business-associate terms where applicable, training reuse, security, incident response, audit rights and change notices.
- Test the patient interface. Verify disclosures, consent, opt-out, identity checks, accessibility and escalation behavior.
- Measure the intended workflow. Run representative and adversarial scenarios, record errors and set go/no-go thresholds.
- Assign governance. Name risk owners, reviewers, incident leads and the person who can pause the system.
- Assess FDA scope. Compare actual claims and function with current FDA digital-health materials; do not infer status from “AI.”
- Approve with monitoring. Define launch limits, audit cadence, drift indicators, complaint handling and reapproval triggers after material changes.
Common evaluation failures and fixes
“The vendor says it is HIPAA compliant.”
Why it fails: A generic claim does not reveal the data flow, authorization analysis, contractual role or configured safeguards.
Fix: Request specific terms, diagrams, controls and evidence, then analyze the proposed communication and organization’s role.
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Why it fails: Purpose and information used determine the analysis. A promotional communication can differ from treatment or care-coordination content.
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Fix: Classify each message and document the authorization or exception relied on.
“HIPAA is the only privacy screen.”
Why it fails: FTC Act principles can reach deceptive or unfair health-data practices outside HIPAA coverage.
Fix: Reconcile public statements, consent language, sharing practices and safeguards; review them periodically.
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“A benchmark proves the chatbot is safe.”
Why it fails: Aggregate scores may not represent your languages, population, escalation needs or failure costs.
Fix: Test the intended workflow, relevant subgroups, edge cases and post-deployment drift with human review.
“An FDA AI document applies to every healthcare tool.”
Why it fails: The January 2025 FDA draft has a narrow scope and is non-binding.
Fix: Start with the product’s claims and function, then consult the current digital-health guidance relevant to that scope.
When to pause or reject a deployment
- The vendor cannot explain where prompts, outputs or logs go.
- Training reuse or secondary purposes cannot be disabled or contractually limited.
- There is no reliable deletion, access review, audit trail or incident process.
- The organization cannot identify a reviewer with authority to stop harmful output.
- Performance evidence excludes the intended population or the workflow’s highest-risk cases.
- Patient disclosures conflict with actual collection, sharing or retention.
- The proposed communication’s HIPAA marketing analysis is unresolved.
- The product’s intended use has expanded into clinical recommendation without a new assessment.
A safe decision can be “not yet.” Restricting the tool to non-PHI drafting, requiring manual approval, or running a monitored pilot can reduce exposure while unresolved questions are answered. Any expansion to new data, audiences, automation or clinical influence should trigger a fresh review.
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