The Tool Desk
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An AI label, a healthcare marketing page, or a claim that a product is “secure” does not by itself authorize PHI processing. A provider should approve a tool only after a repeatable privacy, security, contractual, and governance review, with human oversight of the system’s outputs.
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What patient information counts as PHI?
PHI is individually identifiable health information created, received, maintained, or transmitted by a covered entity or business associate. It can concern a person’s health condition, treatment, or payment for care and can appear in a diagnosis, progress note, referral, billing record, image, voice recording, email, spreadsheet, prompt, generated answer, or system log. The medium does not change the analysis: information can be PHI on paper, in a database, in a chat transcript, or in an AI vendor’s support ticket.
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Classify every part of the AI interaction
- Inputs: prompts, notes, images, audio, laboratory values, device data, documents, and identifiers.
- Outputs: drafts, summaries, risk scores, recommendations, codes, or generated messages that may retain patient details.
- Context and metadata: account names, timestamps, IP addresses, device identifiers, case numbers, and conversation titles.
- Connected records: information retrieved from an electronic health record, document store, scheduling system, or billing system.
- Operational copies: vendor logs, backups, caches, error reports, support attachments, and monitoring data.
Classify the whole data flow, not only the text a user can see. A prompt with a patient’s initials may be linkable to a case through an attached file name or an account identifier.
Can I put patient information into an AI tool?
Only after your organization has established that the specific use, recipient, and safeguards are permitted. There is no universal “yes” or “no” based on whether a service calls itself generative AI, clinical AI, or an enterprise product. The decision depends on the parties, the information, the purpose, the configuration, the contract, and the laws that apply.
Use this approval sequence
- State the task. Write one sentence describing what the system will do, such as drafting a patient-education explanation from an approved clinical note or classifying incoming administrative requests. Define who will use the result and whether a licensed professional must review it.
- List the data. Enumerate fields, files, images, recordings, prompts, outputs, and logs. Mark which elements identify a person and whether the system can connect to internal records.
- Determine the legal relationship. Identify your organization, the AI provider, any cloud or model host, subcontractors, and support personnel. Assess whether the service is acting for a covered entity or business associate and whether a business associate agreement or other contractual terms are required.
- Map the destinations. Record where data travels, where it is stored, who can access it, and whether it leaves the United States or your approved environment. Include telemetry, abuse monitoring, human review, and customer-support paths.
- Check permitted purposes. Confirm that collection, use, disclosure, and retention match the stated task and your policies. Ask whether prompts or outputs are used to train a general model, advertise to users, or create profiles unrelated to care or operations.
- Approve safeguards and controls. Require authentication, least-privilege access, encryption, auditability, retention and deletion settings, incident procedures, and workforce training appropriate to the risk.
- Document the decision. Keep the data inventory, risk analysis, contract review, configuration, test evidence, owner, approval date, and conditions for re-review. Do not treat a one-time approval as permanent if the model, vendor, data, or purpose changes.
If any critical answer is missing, pause the deployment and use a non-PHI workflow until the uncertainty is resolved by your privacy, security, compliance, and legal functions.
How to minimize what the AI receives
Data minimization is both a privacy control and a security control. Send only the information needed for the defined task, and design the workflow so users do not have to make that judgment from scratch each time.
Reduce fields and granularity
- Replace a full name with a local, non-identifying case key when the model does not need the name.
- Use age ranges instead of exact birth dates when an exact date is unnecessary.
- Remove addresses, phone numbers, email addresses, insurance identifiers, and account numbers from a drafting task that needs only clinical facts.
- Transmit the relevant excerpt rather than an entire chart, inbox, or encounter history.
- Crop images to the area needed for analysis and remove embedded metadata when it is not required.
- Separate a patient lookup from model processing so the model receives a controlled record rather than direct access to the production database.
Prevent accidental oversharing
Provide approved prompt templates, field-level filters, upload restrictions, and warnings when a user attempts to paste a medical record. Disable copy-and-paste from unrestricted applications where feasible, and train staff to recognize that free-text narratives often contain identifiers. A template that asks for “age band, symptoms, relevant medications, and question” is safer than an open box that invites a complete note.
Minimization does not make data anonymous. If a recipient can reasonably link the remaining details to a person, treat the information as identifiable unless a valid de-identification method has been applied.
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How to de-identify patient data before using AI
Removing names alone is not HIPAA de-identification. The U.S. Department of Health and Human Services describes two methods in its guidance: Expert Determination and Safe Harbor. Choose and document the method rather than relying on casual redaction.
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A qualified expert applies generally accepted statistical and scientific principles to determine that the risk of re-identifying an individual is very small, considering the context and available information. The organization should retain the expert’s analysis, assumptions, data elements considered, and conclusion. Re-identification risk can change when data sets are combined or when a recipient has outside information.
Safe Harbor
Safe Harbor removes specified categories of identifiers, including direct identifiers and certain geographic, date, contact, and unique identifying elements, subject to the method’s requirements. A workforce member should not improvise a partial list or assume that deleting a visible name satisfies Safe Harbor. Have the privacy function apply the complete method and address any actual knowledge that the remaining information could identify someone.
Keep de-identification separate from ordinary masking
Tokenization, pseudonyms, redaction, aggregation, and synthetic examples can reduce exposure, but they do not automatically meet either HIPAA method. Keep the re-identification key in a separately controlled environment, restrict who can use it, and document whether the AI provider can access it. If the tool must return a patient-specific answer, the workflow may involve PHI even when the model sees a token.
Is this AI tool HIPAA compliant?
“HIPAA compliant” is not a universal product category or a substitute for an organization’s own risk analysis. A vendor’s statement may describe its controls, but it does not establish that every configuration, use case, subcontractor, retention setting, or customer workflow meets your obligations. Ask for evidence and evaluate the complete service.
Review the recipient and data flow
Obtain a current architecture and data-flow description showing the model endpoint, application layer, storage, backups, monitoring, support access, and subprocessors. Confirm whether prompts, files, outputs, and logs are retained; for how long; in which locations; and for what purposes. Ask whether data is used to train or tune models, whether a customer can opt out, and whether deletion propagates to backups and derived data.
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Review contracts and accountability
Determine whether the relationship requires a HIPAA business associate agreement and whether the proposed agreement covers the actual services and subcontractors. Check permitted uses and disclosures, safeguards, incident and breach-notification duties, return or destruction of PHI, access for compliance review, and restrictions on secondary use. Contract language should match the technical configuration; a promise on paper cannot override a setting that retains prompts indefinitely.
Request security evidence proportionate to risk
- Identity and access management, including multifactor authentication and role-based permissions.
- Encryption in transit and at rest, with a clear description of key management.
- Tenant isolation and controls preventing one customer’s data from appearing in another customer’s results.
- Vulnerability management, secure development, penetration testing, and independent assessments where appropriate.
- Detailed audit logs for administrator activity, data access, exports, configuration changes, and deletion requests.
- Business continuity, disaster recovery, backup protection, and tested incident-response procedures.
- Controls for support personnel, model operators, and subcontractors who could view content.
Ask for dates, scope, exceptions, and remediation plans rather than accepting a logo or a one-page marketing summary. Your security team should verify that the evidence covers the edition and environment you will actually use.
Apply HIPAA Security Rule safeguards
The Security Rule calls for administrative, physical, and technical safeguards for ePHI. NIST SP 800-66 Rev. 2, published February 14, 2024, is an implementation resource that organizations of different sizes can use to organize those activities. It does not replace HIPAA, your risk analysis, or professional legal advice.
Administrative safeguards
- Assign an owner for AI privacy and security, with authority to approve, suspend, and retire use cases.
- Perform and document a risk analysis covering confidentiality, integrity, and availability of ePHI in prompts, outputs, integrations, and logs.
- Define workforce authorization, supervision, training, sanctions, and termination procedures.
- Maintain policies for acceptable AI use, prohibited data, human review, corrections, retention, and incident escalation.
- Use agreements with vendors and subcontractors where required, and review them when services or data flows change.
Physical safeguards
- Control physical access to workstations, servers, backup media, and printed AI outputs.
- Use managed devices, screen-locking, secure disposal, and restrictions on local downloads or removable media.
- Verify the physical and environmental protections of hosted infrastructure through appropriate vendor evidence.
Technical safeguards
- Use unique user accounts, strong authentication, least privilege, and timely access termination.
- Separate development, testing, and production data; prohibit live PHI in experimentation unless specifically approved.
- Encrypt connections and stored content, and protect keys separately from the data they protect.
- Log access to prompts, files, outputs, administrative functions, exports, and deletion actions. Review logs for unusual activity.
- Use network segmentation, application allowlists, endpoint protection, and data-loss-prevention rules appropriate to the workflow.
- Validate model and integration changes before release, including tests for unauthorized disclosure and prompt manipulation.
Control access, logging, and human use
AI privacy failures often come from ordinary account and workflow weaknesses rather than a novel model attack. Give users access to only the approved tool, workspace, data set, and functions they need. Prohibit shared accounts. Require multifactor authentication for administrative and remote access, and review privileges when a person changes role or leaves.
Logs should answer who accessed which patient-related content, what was sent, what was returned, which integrations ran, and who changed a policy or retention setting. Protect logs from alteration, restrict their visibility, and define a review schedule. Remember that logs can themselves contain PHI; apply the same retention and access rules.
Keep a qualified human responsible for decisions that affect diagnosis, treatment, medication, benefits, or patient communication. Verify facts, citations, calculations, and patient identity before an output enters the record or reaches a patient. Record when AI assisted a task when your policy or professional rules require it, and provide a way to correct an erroneous output.
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Check federal, state, and local requirements
HIPAA is not the only law that may apply. Federal requirements outside HIPAA, state medical-privacy and consumer-privacy laws, confidentiality rules for particularly sensitive information, public-sector requirements, professional licensing rules, labor obligations, and local policies can change what is permitted. Some rules apply to organizations or data that fall outside the HIPAA covered-entity and business-associate categories.
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For each use case, identify the patient population, service location, organization type, vendor location, data types, and disclosure purpose. Ask counsel or the responsible privacy officer to check applicable retention, consent, access, correction, cross-border, and breach-notification rules. Do not infer that a HIPAA analysis resolves state or local obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build an AI governance process, not a one-time approval
Privacy controls work best inside a broader governance program. The World Health Organization’s 2021 guidance on ethics and governance of artificial intelligence for health describes six consensus principles and related recommendations centered on accountability, safety, transparency, inclusiveness, and human rights. Use that framing to examine effects on patients and communities, not only technical compliance.
Set a lifecycle
- Intake: capture the purpose, owner, users, data classes, expected benefit, and affected patients.
- Assessment: perform privacy, security, clinical, safety, equity, accessibility, and legal reviews proportional to risk.
- Pilot: use a limited population, test data where possible, explicit human review, and measurable stop conditions.
- Production: enforce the approved configuration, training, access controls, monitoring, and documentation.
- Continuous review: reassess after model updates, vendor changes, new integrations, incidents, complaints, or changes in law.
- Retirement: disable accounts and integrations, retrieve or delete data as required, preserve necessary records, and document closure.
Measure more than accuracy
Track unauthorized access attempts, inappropriate disclosures, retention exceptions, deletion failures, user complaints, correction rates, and disparities in performance. Review whether staff are bypassing the approved workflow or copying results into unapproved tools. A model that performs well on a benchmark can still create unacceptable privacy or safety risk in your setting.
Compare two AI tools on the same use case
Do not compare products by slogans or a generic feature list. Give each candidate the identical task, data-flow diagram, and risk assumptions, then record evidence for the following axes:
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| Evaluation area | Questions to answer |
|---|---|
| Data involved | Does the workflow handle PHI or ePHI? Which inputs, outputs, metadata, and logs are included? |
| Collection and retention | What is collected, where is it stored, how long is it retained, and how are backups and derived data handled? |
| Access | Who can view prompts, outputs, logs, support tickets, and administration functions? Are permissions and multifactor authentication available? |
| Purpose and permissions | What may the provider do with customer content? Is model training, human review, advertising, or profiling enabled? |
| Contractual protection | Are required agreements available, and do they cover the actual service, subprocessors, incidents, deletion, and compliance cooperation? |
| Security and auditability | What encryption, isolation, vulnerability management, incident response, and audit logs are provided? |
| Deletion and exit | Can the organization delete content, verify deletion, export records, and terminate integrations without leaving uncontrolled copies? |
| Legal and governance fit | Does the proposed use fit federal, state, local, professional, ethical, and human-rights requirements? |
Score only what you can substantiate with current documentation, configuration evidence, testing, or contract language. Mark unresolved items as approval conditions rather than silently treating them as satisfied.
Common failure modes and fixes
“We removed the patient’s name.”
Why it fails: Other identifiers, dates, geography, rare conditions, file names, or combinations of facts can identify a person. Fix: apply Expert Determination or Safe Harbor when relying on HIPAA de-identification, document the method, and control any re-identification key.
“The vendor says it is HIPAA compliant.”
Why it fails: A label does not reveal retention, training use, subcontractors, access paths, or whether your configuration is covered. Fix: review the data flow, evidence, settings, and required agreement for the exact service.
“We will delete the chat later.”
Why it fails: Copies may exist in logs, backups, exports, browser caches, support systems, or model-development pipelines. Fix: set retention before launch, restrict local storage, obtain deletion and backup terms, and test the process.
“The clinician will notice if the answer is wrong.”
Why it fails: Plausible output can carry incorrect facts or expose details to the wrong recipient. Fix: define human-review checkpoints, verify identity and source data, prohibit autonomous high-impact decisions, and provide correction and escalation paths.
“A pilot is exempt from governance.”
Why it fails: A small trial can still expose real PHI and create reportable incidents. Fix: use approved test data where possible and apply the same minimum privacy, security, contract, and incident controls before the first live record.
What to do if information is sent to the wrong AI service
- Stop further transmission and disable the account, integration, token, or workspace involved.
- Preserve relevant logs, prompts, files, recipients, timestamps, and configuration without circulating the exposed content.
- Notify your privacy and security incident teams immediately under your organization’s policy.
- Ask the recipient to suspend processing and delete the information, and obtain written confirmation where feasible; do not assume deletion resolves legal duties.
- Assess whether PHI or another protected data category was involved, whose information was affected, and whether the event meets breach-notification or other reporting thresholds.
- Remediate the control that failed, retrain users, and document the decision, notifications, and corrective actions.
Do not investigate by copying the exposed data into another unapproved tool. Use your established incident channel and access-controlled evidence store.
A practical pre-launch checklist
- The use case, owner, users, intended benefit, and human-review point are documented.
- Inputs, outputs, metadata, logs, integrations, and storage locations are inventoried and classified.
- The minimum necessary data set and approved prompt or API pattern are defined.
- De-identification, if used, follows Expert Determination or Safe Harbor and has supporting documentation.
- The recipient, subprocessors, access paths, geographic locations, retention, training use, and deletion process are understood.
- Required agreements and permitted-use terms are reviewed for the exact service and configuration.
- Administrative, physical, and technical safeguards are implemented, including risk analysis, least privilege, authentication, encryption, and audit controls.
- State, local, federal, professional, and ethical requirements have been checked for the organization and patient population.
- Users are trained, prohibited workflows are enforced, and an incident-response path is tested.
- Monitoring, reapproval triggers, and retirement procedures are assigned to named owners.
The bottom line for healthcare providers
Safe healthcare AI is a controlled data-processing activity, not an informal conversation with a clever application. Treat prompts, outputs, and logs as potential patient information; minimize what leaves your environment; verify the recipient, contract, safeguards, retention, and secondary uses; and apply HIPAA de-identification methods when you rely on de-identification. Then add the state, local, federal, clinical, ethical, and human-rights requirements that fit your setting. Approve only uses that your organization can explain, monitor, audit, and stop.
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