Clinical decision support & deterioration alerting

A machine-learning sepsis alert deployed across five hospitals of an academic health system, evaluated on 590,736 patients — the largest prospective study of its kind. Its mortality benefit was real but conditional: it accrued only to patients whose alert a provider confirmed within three hours, and the alert alone did nothing. The evaluation was prospective and peer-reviewed, but observational and run by the developer who commercialized the model.

An in-hospital deterioration model running around the clock across 21 hospitals, firing about twelve hours ahead of predicted deterioration. Its NEJM-measured mortality benefit is inseparable from where the alert goes: not to the bedside, but to a dedicated regional tier of critical-care virtual nurse consultants who screen every alert before escalating. The governance here is not a checkbox — it is a staffed, 24/7 subsystem with a payroll.

A deep-learning sepsis detector scoring every emergency-department patient every five minutes, fronted by rapid-response nurses on treatment-bundle timers. Its fault line is an authority split: the nurse who receives the alert is not the physician empowered to act on it. An independent ethnography found the system worked only because nurses did hidden, undervalued repair work to make a risk score actionable across a professional hierarchy.

A proprietary sepsis-prediction model shipped inside a common EHR and switched on across hundreds of hospitals — then externally validated to catch only a third of sepsis cases at roughly 109 alerts per true case, a real-world performance the vendor had not fully examined before selling it, shielded behind a firewall from outside scrutiny. This is the domain's failure arc: deployment at scale ahead of independent validation, alert fatigue, and vendor opacity — corrected only after outside criticism forced a retune.