Checklist

Healthcare & Patient Care — Safety & Compliance Checklist

Interactive checklist to help clinical and operational teams confirm safety, privacy, clinical validation, and regulatory readiness when applying AI in healthcare. Collects decisions, evidence, owners, risk level, and review dates so teams can record and track compliance steps.

Interactive Tool

Healthcare & Patient Care — Safety & Compliance Checklist

This checklist helps clinical and operational teams run AI projects that meet safety, privacy, clinical validation, and regulatory requirements. Use it to record decisions, attach evidence (summaries or links), assign owners, and set next-review dates. The checklist is intentionally practical — it complements your local SOPs, privacy policies, and regulatory workflows rather than replacing them.

How to use: answer each item, add supporting details or links, name a responsible owner, and choose a next review date. Save the checklist to build an auditable trail.

A clinical validation plan should define datasets, endpoints, performance targets, population coverage, bias/ fairness checks, prospective or retrospective validation approach, and success criteria.
Summarize validation datasets, metrics achieved, who reviewed results, and provide links to reports or registration numbers. If validation is incomplete, describe the expected timeline and gaps.
Confirm PHI flows have been mapped, consent/legal basis documented, and de-identification/encryption controls specified. Include whether data use agreements or Business Associate Agreements are required.
Describe which data elements are PHI, where they move, who sees them, and how consent or legal basis is recorded. Link to consent forms or DUA if available.
Define roles (clinician-in-loop, supervisor, safety officer), decision authority, and escalation path for model outputs that require human review.
List named roles, responsibilities, how overrides are logged, and training requirements for clinicians or staff involved in oversight.
Confirm whether the product or workflow requires premarket submissions, notifications, or local regulatory filings and whether required documentation (risk assessment, clinical evidence, labeling) is prepared.
Note applicable regulations (e.g., device classification, region-specific rules), submission statuses, and links to regulatory dossiers or internal compliance records.
Assess the potential for patient harm if the AI fails or produces incorrect output. Use your internal risk taxonomy where available.
Describe safety mitigations, performance monitoring frequency, alerting thresholds, rollback procedures, and how you will detect degradation or dataset shift.
Indicate if a PIA or equivalent data protection impact assessment has been completed and where it is stored.
Have you measured model performance across relevant subpopulations and addressed any disparities?
Confirm that user-facing guidance, limitations, and training materials are available for clinicians/staff who will use or interpret AI outputs.
Person accountable for this checklist and follow-up actions.
Schedule a next review. If monitoring thresholds or performance targets are time-based, choose a date consistent with that cadence.
Paste links to reports, SOPs, dataset inventories, regulatory filings, or internal tickets. Use your internal document store if required by policy.
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