Bed Management Agent

The work it takes off your plate

Hospitals routinely struggle with ED boarding, uneven unit occupancy, and delayed bed turnover when bed control relies on manual phone calls and outdated census boards. Discharge predictions are often inaccurate, causing orphaned beds and cascading delays for elective admissions and transfers. Staff spend hours reconciling bed requests across ICUs, med-surg, and specialty units without a shared real-time view of capacity. These gaps increase length of stay, ambulance diversion risk, and patient experience scores while inflating overtime and opportunity cost from unused beds.

How it works

The Bed Management Agent continuously ingests ADT events, census, predicted discharges, and pending transfers to maintain a live capacity model across units. It scores bed requests against clinical acuity, isolation needs, gender, and service-line rules, then recommends optimal assignments and escalates blockers. Predictive discharge signals and housekeeping turnaround data feed automated alerts so bed control and care teams can free capacity before demand peaks.

1. Step 1: Ingest Real-Time Census and Demand Signals

   • Pull ADT, bed status, and pending transfer queues from the EHR bed board
   • Capture ED boarding, OR hold, and direct-admit request volumes
   • Normalize isolation, acuity, and service-line placement constraints

   Result: A unified, near real-time capacity picture across all inpatient units and holding areas.

2. Step 2: Predict Discharges and Bed Availability

   • Score discharge likelihood from orders, notes, and care-team milestones
   • Estimate housekeeping turnaround and bed clean-ready times
   • Flag high-probability early discharges and delayed barriers

   Result: Hourly forecasts of available beds by unit type with confidence scores for bed control planning.

3. Step 3: Optimize Assignment and Placement

   • Match pending patients to eligible beds using clinical and operational rules
   • Balance unit load, nurse staffing ratios, and cohorting preferences
   • Recommend reassignments when higher-acuity demand arrives

   Result: Ranked bed assignment recommendations that minimize boarding and avoid inappropriate placements.

4. Step 4: Coordinate Execution and Continuous Learning

   • Push assignment tasks to bed control, transport, and EVS workflows
   • Escalate blocked discharges and long-boarding cases to charge nurses
   • Learn from acceptance rates and boarding outcomes to refine models

   Result: Faster bed turns, lower ED boarding hours, and improved throughput with auditable placement decisions.

Why use it

• Lower Boarding Times. Cuts ED and PACU boarding by matching patients to capacity as soon as beds free.
• Predictive Throughput. Anticipates discharges hours ahead so units prepare rooms proactively.
• Cross-Unit Coordination. Aligns bed control, EVS, transport, and nursing on one live capacity plan.
• Staff Efficiency. Replaces phone-tag bed hunting with ranked, rule-aware recommendations.