The Starter Checklist for Safer AI-Assisted Triage Tools

Executive Overview

AI-assisted triage tools can reduce workload, but they introduce new risks around accuracy, bias, and accountability. This starter checklist helps GP practices evaluate and deploy AI triage safely, meeting NHS guidance and protecting patients.

Confirm the Use Case and Accountability

Define which symptoms, pathways, or patient groups the AI will support.
Identify who reviews AI outputs (clinician sign-off, escalation to duty GP) and when manual overrides apply.
Document responsibilities in the DCB0160 safety file and governance minutes.

Assess Supplier Assurances

Request DCB0129 Clinical Safety Case Reports, hazard logs, and evidence addressing AI-specific risks.
Ask for transparency on training data, known limitations, and bias mitigation steps.
Confirm compliance with DTAC, DSPT, and relevant medical device regulations.
Verify how the supplier monitors model drift and updates algorithms.

Conduct Local Risk Assessment

Map potential failure modes (under-triage, over-triage, language limitations, accessibility barriers) in the hazard log.
Define risk controls (mandatory clinician review for high-risk categories, patient safety-netting messages, escalation triggers).
Involve diverse clinical staff to spot equality and health inclusion risks.

Plan for Safe Deployment

Run a pilot comparing AI recommendations with clinician decisions; log discrepancies and adjust workflows.
Develop standard operating procedures covering triage review, overrides, patient communications, and incident reporting.
Train staff on how the tool works, its limitations, and when to override recommendations.
Update patient-facing materials to explain AI use, response times, and emergency guidance.

Monitor Performance Continuously

Track metrics: agreement rate between AI and clinicians, escalation frequency, incident/near miss counts, patient feedback, and response times.
Schedule joint reviews with the supplier (initially weekly, then monthly) to discuss performance data and upcoming changes.
Log updates to the model or decision thresholds in the safety file; retest critical workflows after each update.

Ensure Governance and Ethics

Review AI use against NHS AI governance frameworks, focusing on transparency, fairness, and accountability.
Check for algorithmic bias by analysing outcomes across demographic groups.
Maintain a clear audit trail of decisions and overrides in the EPR.
Provide patients with options to opt out or request human triage where appropriate.

Scenario: Westbrook Practice

Westbrook piloted an AI symptom checker with a two-week shadowing period. Clinicians compared AI outputs to traditional triage, documented discrepancies, and fed them back to the supplier. They implemented clinician oversight for red-flag categories, published patient guidance, and monitored agreement rates weekly. Adjustments to cardiovascular weighting were made before public launch, reducing risk and boosting clinician confidence.

Pitfalls to Avoid

Blind trust in automation: retain human oversight and test realistic scenarios before launch.
Lack of transparency: if a supplier cannot explain how the AI works or provide safety evidence, reconsider adoption.
No monitoring: without ongoing metrics, you cannot spot drift or emerging bias.
Ignoring patient communication: inform patients how AI is used and reinforce emergency pathways.

Action Checklist

Define the AI triage use case, responsibilities, and oversight model.
Gather supplier safety, data, and regulatory evidence.
Complete a local risk assessment and document controls in the safety file.
Pilot the tool with clinician comparison, training, and patient communications.
Monitor performance, engage with the supplier, and update governance documents regularly.

Resources to Bookmark

Key Takeaways

AI-assisted triage requires rigorous oversight, clear accountability, and continuous monitoring. With structured checks, the technology can support safe, efficient care while maintaining trust with patients and staff.