How to Define Escalation Criteria for an AI-Assisted Laboratory Workflow

Describe what the AI-assisted step produces, what happens next and which decisions could affect quality, safety, records or people.

Do not treat a low-effort administrative suggestion and a consequential laboratory decision as the same risk.

Define triggers staff can recognise, such as missing required inputs, conflicting records, output outside an approved scope, repeated uncertainty or a failed control.

Avoid vague instructions such as escalate when it looks wrong. Use terms tied to the validated workflow and local procedures.

Assign a role with the competence and authority to review each trigger, plus an alternative for absence or workload.

AI output must not substitute for qualified human judgement where professional, regulatory or safety oversight is required.

State whether work pauses, reverts to an approved manual process or is quarantined from downstream use while review takes place.

Do not let an escalation queue silently become approval through delay. Time limits and ownership should be explicit.

Capture the trigger, relevant inputs and output version, reviewer, decision, rationale and any corrective action under appropriate access controls.

Minimise personal or sensitive data and follow applicable information-governance requirements.

Use approved scenarios to check that staff recognise triggers and know how to respond. Review false alarms, missed escalations and workflow changes.

Changes should pass the organisation's validation, change-control and approval processes before operational use.

Should every unusual output be discarded? Follow the approved process and qualified review. Can AI approve its own escalation? Not where independent human oversight is required. Is this clinical advice? No.