Clinical RAG Safety Checklist
A comprehensive safety checklist covering input validation, output safety, escalation protocols, knowledge governance, and incident response for clinical RAG systems.
Why Safety Checklists Matter
Clinical AI systems require systematic safety review. Unlike general-purpose AI applications, medical RAG systems interact with information that can directly influence healthcare decisions. A safety checklist provides a structured framework for teams to evaluate their system across all relevant dimensions before deployment and during ongoing operation.
This checklist is designed as a starting point. Each deployment context is unique, and your institution's clinical governance, IT security, and legal teams should review and adapt these items to your specific requirements. For a complementary evaluation framework focused on answer quality, see our Clinical RAG Evaluation Checklist.
Input Validation
Ensure that the system safely handles all types of input:
- [ ] Query sanitization: Remove or escape potentially harmful content from user queries (injection attempts, malformed input).
- [ ] PHI detection: Implement checks to detect and handle queries that may contain Protected Health Information. Consider whether to reject, anonymize, or flag such queries.
- [ ] Adversarial prompt detection: Identify and safely handle prompts designed to bypass safety constraints, such as "ignore previous instructions" or role-play scenarios.
- [ ] Out-of-scope detection: Identify queries that fall outside the knowledge base scope and respond with an appropriate refusal message rather than attempting to answer from insufficient context.
- [ ] Query length limits: Set reasonable limits on query length to prevent resource exhaustion or prompt injection via extremely long inputs.
- [ ] Rate limiting: Implement rate limiting to prevent abuse and ensure fair access for all users.
Output Safety
Ensure that all generated responses meet safety standards:
- [ ] Disclaimers: Every response includes an appropriate disclaimer noting that the information is for educational or decision-support purposes only and does not constitute medical advice.
- [ ] No specific dosing without context: The system avoids providing specific medication dosages without referencing the source guideline and noting that dosing should be determined by a qualified healthcare professional.
- [ ] High-risk claim flagging: Responses that address high-risk topics (e.g., emergency protocols, contraindicated drug combinations) are clearly flagged and include source citations.
- [ ] Confidence display: The system displays a confidence level (HIGH/MEDIUM/LOW) based on the quality and quantity of retrieved evidence, helping users calibrate their trust in the response.
- [ ] Refusal behavior: When the system cannot find sufficient information to answer a question, it clearly states this rather than generating a speculative response.
- [ ] Structured output validation: If the system produces structured output (e.g., JSON), validate the structure before returning it to the user to prevent malformed responses.
Escalation Protocols
Define clear pathways for escalating AI outputs to human review:
- [ ] Low-confidence flagging: Responses with LOW confidence scores are automatically flagged for human review and logged for follow-up.
- [ ] User reporting mechanism: Users can easily flag responses they believe are incorrect, incomplete, or unsafe.
- [ ] Review SLA: Define service-level agreements for how quickly flagged responses are reviewed by the clinical team (e.g., within 24 hours for high-priority flags).
- [ ] Clinician override: Provide a mechanism for clinicians to override or annotate system responses with corrections or additional context.
- [ ] Escalation contact list: Maintain an up-to-date list of who to contact for different types of safety issues (clinical accuracy, data privacy, system malfunction).
Monitoring and Alerting
Establish ongoing monitoring to detect issues before they affect users:
- [ ] Query volume monitoring: Track query volume over time and set alerts for unusual spikes or drops that may indicate system issues.
- [ ] Confidence score distribution: Monitor the distribution of confidence scores across queries. A shift toward lower confidence may indicate a knowledge base gap.
- [ ] Error rate tracking: Track the rate of system errors, timeouts, and failed retrievals. Set alert thresholds for abnormal error rates.
- [ ] User feedback analysis: Regularly review user feedback and flagged responses to identify patterns of error or confusion.
- [ ] Anomaly detection: Implement automated anomaly detection to identify unusual query patterns, such as repeated queries about a single topic that may indicate a system failure.
- [ ] Periodic safety audits: Conduct formal safety audits at regular intervals (quarterly recommended) using a structured evaluation framework. See our RAG Evaluation Sheet for a testing workbook.
Knowledge Base Governance
The quality of your knowledge base directly determines the safety of your RAG outputs:
- [ ] Source verification: Every document in the knowledge base has been verified as coming from an authoritative, trusted source (e.g., recognized medical organizations, peer-reviewed journals).
- [ ] Update schedule: Define and follow a regular schedule for reviewing and updating knowledge base content.
- [ ] Version control: Track which version of each document is in the knowledge base. When a guideline is updated, replace the old version and log the change.
- [ ] Deprecated content handling: Clearly mark or remove superseded guidelines, retracted studies, and withdrawn drug approvals. Do not rely on the retrieval system to distinguish current from outdated content.
- [ ] Specialist review: New documents added to the knowledge base should be reviewed by a subject-matter expert before inclusion, especially for high-risk clinical areas.
- [ ] Duplicate management: Regularly check for and resolve duplicate content across documents to prevent retrieval confusion.
Incident Response
Prepare for when things go wrong:
- [ ] Documented incident response plan: Maintain a written plan that defines what constitutes an incident, who is responsible for each step, and how incidents are communicated to stakeholders.
- [ ] Known failure modes catalog: Maintain a living document of known failure modes, their root causes, and the mitigation strategies in place. Update this catalog after each incident.
- [ ] Rollback procedures: Define how to roll back knowledge base updates or system configuration changes if a deployed change introduces safety issues.
- [ ] Stakeholder communication templates: Prepare templates for communicating safety incidents to users, clinical leadership, and (if applicable) regulatory bodies.
- [ ] Post-incident review: After every incident, conduct a structured review to identify root causes, update the failure modes catalog, and implement preventive measures.
Disclaimer: This checklist is a starting point and does not constitute medical, legal, or compliance advice. Each deployment should be reviewed by the institution's clinical governance, IT security, and legal teams. Requirements may vary based on jurisdiction, institutional policies, and the specific clinical use case.