AI-Optimised Patient Flow & Bed Management
Use AI to predict patient admissions, optimise bed allocation, and reduce emergency department boarding times.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Bed management relies on morning huddles and manual bed boards. Discharge predictions are based on physician estimates that are often inaccurate. Emergency department patients wait 4-8 hours for inpatient beds. Surgical cancellations due to bed unavailability cost the hospital $500K+ annually.
After
AI predicts next-day admissions and discharges with 85%+ accuracy, enabling proactive bed preparation. Real-time dashboards show bed availability, predicted discharges, and patient flow bottlenecks. ED boarding time drops by 40%. Surgical cancellations due to bed shortage decrease by 70%.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Analyse Current Flow
2 weeksMap patient flow from ED arrival through admission, inpatient stay, and discharge. Identify bottlenecks, delay causes, and data sources. Calculate current metrics: ED boarding time, bed turnover time, length of stay variation, and surgical cancellation rate.
Build Prediction Models
5 weeksDevelop ML models for: (1) admission volume prediction using historical patterns, seasonal factors, and external data; (2) discharge readiness scoring based on clinical indicators and care plan completion; (3) length of stay prediction by diagnosis and patient characteristics.
Create Command Centre Dashboard
3 weeksBuild a real-time operations dashboard showing: current bed status, predicted admissions/discharges, patient flow bottlenecks, and AI-recommended actions. Design for use by bed management, nursing supervisors, and hospital operations.
Pilot in Target Units
4 weeksLaunch AI-assisted bed management in 2-3 high-volume units (typically surgical and medical floors). Compare AI predictions against actual outcomes. Train charge nurses and bed managers on using AI recommendations. Iterate based on feedback.
Scale Hospital-Wide
3 weeks + ongoingExpand to all inpatient units. Integrate with ED tracking for seamless admission flow. Add automated alerts for predicted capacity crunches. Connect with housekeeping and transport for faster bed turnover.
Tools Required
Expected Outcomes
Reduce ED boarding time by 30-40%
Predict admissions and discharges with 85%+ accuracy
Decrease surgical cancellations due to bed shortage by 70%
Improve bed turnover time by 20-30%
Reduce average length of stay by 0.3-0.5 days through better discharge planning
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Core data includes: historical admission/discharge/transfer (ADT) records, ED tracking data, surgical schedules, and patient acuity scores. Enhanced predictions add: weather data (affects ED volumes), local event calendars, and flu surveillance data. Most hospitals have sufficient historical data in their EHR/HIS.
Well-calibrated models achieve 85-90% accuracy for next-day admission predictions and 80-85% for discharge predictions. Accuracy improves over time as the model learns your hospital's specific patterns. Even at 85%, AI predictions significantly outperform the traditional "clinical intuition" approach.
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