Fordel StudiosClinical Alert Prioritization System
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
ICU alert fatigue — 300+ monitoring alerts per unit per day, 68% false alarms. Critical alerts were buried in the noise. The base monitoring infrastructure generated threshold alerts with no contextual intelligence, and staff were silencing alarms as a coping mechanism.
A single ICU patient generates 20,000+ data points per hour across vitals sensors. False alarms arise from patient movement, lead detachment, post-procedure transient changes, and normal variation in sedated patients that sits outside default threshold ranges. Distinguishing these from genuine deterioration requires patient baseline, current clinical context, recent interventions, and trend patterns — not just a single reading. The four bedside monitoring vendors used different HL7 message structures, requiring a normalization pipeline before any unified processing was possible. Building a model with sufficient precision on the critical event class (which is rare by definition) required a carefully annotated training set that the facility did not have — creating it was a significant pre-engineering effort.
How we
built it
- 01
Analysed 90 days of historical ICU alert data to characterise the false alarm distribution — which sensor combinations, patient states, and time-of-day patterns generated the highest false alarm rates — before defining the model architecture.
- 02
Built a trend analysis layer that evaluated vitals in context of the preceding 4–6 hours rather than threshold-crossing on a single reading, dramatically reducing alerts triggered by transient measurement artefacts.
- 03
Integrated patient history from the EHR to provide context-aware baselines — a heart rate that is alarming for one patient is normal post-procedure for another, and the system needed to know the difference.
- 04
Deployed a confidence-tiered alert system: high-confidence critical events go to the full unit, medium-confidence events notify the assigned nurse only, low-confidence events are logged but not surfaced — with all thresholds reviewable by the clinical team.
This engagement addressed a patient safety problem caused by expanding ICU capacity without corresponding improvements to alert intelligence. The existing bedside monitoring systems generated raw threshold alerts — any single reading outside a configured range triggered an alarm — with no understanding of patient baseline, recent interventions, or trend context. The solution was positioned as a clinical decision support tool rather than an alert management system, which was both a technical and regulatory constraint: all base hardware alerts had to continue reaching clinical staff. The AI layer adds urgency scoring and clinical context, not filtering. Four different monitoring hardware vendors across the facility required a normalization layer before any AI processing was viable.
What we
delivered
AI layer that analyzes vitals trend patterns in context of patient history to reduce false alarm volume and prioritize genuine critical events. The system adds a priority layer on top of existing hardware alerts — it does not suppress base alarms, ensuring no events are lost.
Measurable
outcomes
- Critical alert response time improved 41% as staff attention shifted from filtering noise to responding to genuine events.
- Alert precision reached 91% — staff now act on the majority of alerts rather than routinely dismissing them.
- False alarm volume reduced 31%, with the full 68% baseline false alarm rate as the improvement baseline.
“The false alarm rate was a genuine patient safety issue — staff were silencing monitors as a coping mechanism. The AI layer changed the signal-to-noise ratio enough that nurses are paying attention to alerts again.”
— ICU Clinical Director, Regional Medical CentreReady to build
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