Hospital Patient Flow Optimization
AI command center that orchestrates patient movement, reducing wait times by 30% and increasing bed capacity by 20%.
Executive Summary
A metropolitan hospital deployed an AI command center that orchestrates patient movement and predicts discharges. The system reduced ER wait times by 30%, increased effective bed capacity by 20%, and improved patient satisfaction to 98%.
Background & Context
Hospital capacity is a patient safety issue. When beds are unavailable, patients 'board' in the ER, surgeries are delayed, and care quality suffers. Yet most hospitals manage capacity with whiteboards and phone calls, lacking real-time visibility into patient flow. The hospitals that can predict discharges, optimize bed assignments, and coordinate across departments unlock hidden capacity without building new rooms.
The Challenge
A busy metropolitan hospital was facing severe overcrowding. The ER was backed up, surgeries were delayed due to lack of beds, and patients spent hours waiting for discharge.
Staff relied on whiteboards, phone calls, and intuition to manage bed assignments. There was no real-time view of capacity.
This 'gridlock' compromised patient safety and frustrated staff, leading to high turnover.
Our Approach
We built an AI 'command center' that acts as air traffic control for the hospital. The system tracks every patient in real-time, predicts discharges 24 hours in advance with 90% accuracy, and intelligently allocates beds based on clinical needs. Automated notifications coordinate housekeeping, transport, and nursing to eliminate 'dead time' between patients.
Solution Workflow
The diagram below shows how our AI Command Center tracks patients, predicts discharges, and orchestrates bed allocation across the hospital.
The Solution
Syvoq implemented a centralized AI Command Center that acts as the 'air traffic control' for the hospital.
- Real-Time Patient Tracking: Visualizes the status and location of every patient in the system.
- Discharge Prediction: Predicts which patients will be discharged in the next 24 hours with 90% accuracy, allowing staff to prep beds in advance.
- Intelligent Bed Allocation: Matches patients to the right bed based on clinical needs, isolation requirements, and staffing ratios.
- Automated Communication: Triggers alerts to environmental services and transport teams immediately when a bed is vacated.
Key Technologies
Training & Deployment
The model was trained on 3 years of ADT (Admission, Discharge, Transfer) data to understand flow patterns and bottlenecks.
We simulated various surge scenarios (e.g., flu season, mass casualty) to stress-test the logic.
Technical Architecture
Real-Time Dashboard
Displays capacity status on large screens in every nursing station.
Digital Twin Simulation
Runs scenarios to optimize staffing schedules.
EHR Interface
Bi-directional sync with Epic/Cerner.
The Impact
The hospital unlocked hidden capacity without building a single new room.
Operational Flow
- •Reduced ER wait times by 30%.
- •Increased effective bed capacity by 20% through faster turnover.
- •Reduced average length of stay (LOS) by 15%.
Patient & Staff
- •Patient satisfaction scores rose to 98%.
- •Reduced 'boarding' in the ED by 50%.
- •Improved nurse satisfaction by reducing chaos and overtime.
Orchestrated Care
By synchronizing housekeeping, transport, nursing, and doctors, we eliminated the 'dead time' that traps patients in the hospital.
Key Takeaway
Efficient patient flow is a patient safety issue. AI provides the visibility and foresight needed to keep the hospital moving.
Optimize your hospital operations.
Discover how our Patient Flow solution can improve capacity and care.