By deploying Predictive AI trained on historical admissions, local events, and real-time vitals, a 12-hospital network now forecasts bed demand hours in advance and auto-assigns triage priority — cutting ED wait times by 22% and lifting bed turnover by 15%.
Average ED wait reduction
Faster patient flow
Real-time network view
Demand prediction horizon
Across 12 hospitals, bed managers relied on spreadsheets and phone calls. By the time a surge was detected, it was already too late — ambulances were diverted, patients waited in hallways, and staff were stretched thin.
Reactive, not predictive
Decisions made after crises hit — not before
Fragmented data
EHR, ADT, vitals, and local events lived in separate systems
Triage inconsistency
Priority scores varied by shift, nurse, and hospital
Ambulance diversions
23% of monthly inbound transfers were turned away
Average ED wait before bed assignment
Ambulance diversions per month
Average bed turnover time
Annual cost of inefficiency
Three integrated AI layers — forecasting, real-time monitoring, and automated triage — work together to give every hospital in the network a shared, forward-looking view of capacity.
Gradient-boosted models trained on 5 years of admissions, local event calendars, weather, flu trends, and historical ED arrivals forecast bed demand 4–24 hours ahead.
Streaming vitals, bed status, and ADT feeds flow through a unified event bus. Every hospital sees the same live picture — updated every 30 seconds across the entire network.
A rules + ML hybrid assigns triage priority (P1–P4) based on vitals, chief complaint, and predicted LOS. High-acuity patients are auto-matched to the nearest appropriate bed.
EHR, ADT, vitals monitors, weather APIs, local event feeds, and historical admissions stream in continuously.
ML models run every 15 minutes, producing a rolling 24-hour forecast of admissions, discharges, and bed needs per unit.
Incoming patients are auto-scored P1–P4 based on acuity, predicted LOS, and specialty needs. High-priority patients surface first.
The system recommends the optimal bed across the network — balancing acuity, specialty, proximity, and predicted discharge timing.
Every 30 seconds, the operations dashboard refreshes with the latest patient flow, bed status, and forecast confidence. Charge nurses, bed managers, and executives see the same single source of truth.
Network-wide visibility
All 12 hospitals, all units, all beds — one screen.
Proactive alerts
Predicted surges trigger early staffing and transfer recommendations.
Clinician-in-the-loop
Every auto-assignment can be reviewed or overridden by staff.
NETWORK SNAPSHOT
--:--:--
OCCUPIED
338/480
IN ED QUEUE
24
P1 CRITICAL
7
FORECAST (4H)
+18
AI: Surge predicted at St. Mary's in 2h 14m · 3 transfers recommended
"We went from reacting to crises to preventing them. The forecast dashboard is the first thing I look at every morning. Last month, we avoided 11 ambulance diversions that would have happened under the old system."
Dr. Marcus Kowalski
Chief Medical Officer, Metro Health Network
Operational savings
Patients served faster
Nurse hours saved monthly
NPS improvement
See how predictive AI, real-time monitoring, and automated triage can transform your hospital network's patient flow.