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Bed Management Agent

Clinical OperationsCapacity & Throughput

Optimizes inpatient bed assignment, predicts discharges, and reduces boarding times by coordinating capacity across units in real time.

4
Process steps
2
Integrations
3
Data inputs
The Problem

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with ED boarding, uneven unit occupancy, and delayed bed turnover when bed control relies on manual phone calls and outdated census boards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.

Process steps
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
Outcome: A unified, near real-time capacity picture across all inpatient units and holding areas.
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
Outcome: Hourly forecasts of available beds by unit type with confidence scores for bed control planning.
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
Outcome: Ranked bed assignment recommendations that minimize boarding and avoid inappropriate placements.
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
Outcome: Faster bed turns, lower ED boarding hours, and improved throughput with auditable placement decisions.
Capabilities & integrations
Epic
ADT events, bed board status, and care-team discharge milestones via FHIR R4 and Caboodle census fee
Cerner
Millennium capacity management, encounter location updates, and EVS turnaround status via HL7 and FH