Healthcare AI systems increasingly detect clinical risks and generate alerts for healthcare professionals.
However, a technically accurate alert does not guarantee that an appropriate clinical response will follow.
This creates an important design and evaluation challenge: how do we ensure that AI-generated alerts lead to meaningful action?
The alert-to-action gap
Consider an AI system that detects potential patient deterioration.
The system generates an alert and sends it to a clinical team. However, the notification may remain unread, reach an unavailable professional, or become buried among numerous other alerts.
The model may have performed correctly, but the clinical workflow has failed to deliver the intended response.
Model the alert lifecycle
A robust healthcare AI workflow should distinguish between several stages.
An alert is generated when the system detects a concern. It is delivered when it successfully reaches the intended recipient. It is acknowledged when the recipient confirms receipt. It is reviewed when the concern receives appropriate assessment. It is acted upon when an intervention or decision follows. It is resolved when the concern has been appropriately addressed or formally closed.
These stages should not be treated as interchangeable.
Implications for agentic AI
An agentic system may be able to route alerts, identify responsible teams, monitor acknowledgement, and initiate authorised escalation pathways.
However, it must respect clinical authority and must not assume that successful message delivery equals clinical resolution.
Clear ownership, escalation rules, audit trails, and human oversight are essential.
What should we evaluate?
Beyond sensitivity and specificity, evaluation should include acknowledgement rates, time to review, time to appropriate intervention, unresolved alerts, escalation effectiveness, alert fatigue, and relevant patient outcomes.
These measures help determine whether an AI system improves clinical response rather than merely generating accurate notifications.
The design principle
Healthcare AI should be designed around the complete journey from detection to appropriate clinical action.
Do not measure only what your AI detects. Measure what happens because it detected it.
