Medical ML research and competitions often optimize ROC-AUC as the primary performance metric.

However, in real hospital environments, the central question is not classification accuracy — it is escalation timing.

In deterioration detection systems: • A noisy alert creates alarm fatigue. • A late alert costs lives. • A static classifier may fail to reflect dynamic physiology.

I’ve been exploring a framework that introduces: • Dual-threshold activation (high/low) • Temporal stability validation • False-alarm suppression logic • Governed escalation timing

The aim is to shift from probability scoring toward structured decision triggering.

I’m curious how others here would approach modeling escalation timing in a clinically responsible way.

Would love perspectives from ML engineers and clinicians.