AI Patient Flow Orchestration

This AI solution optimizes patient transfers across the continuum of care—from admission to discharge—by predicting bed availability, identifying bottlenecks, and orchestrating handoffs between units and facilities. It continuously tracks patient progress, recommends next-best care settings, and automates routing and communication, reducing wait times and length of stay while improving capacity utilization and care coordination.

The Problem

Predict and coordinate patient transfers to cut LOS and unlock bed capacity

Organizations face these key challenges:

1

ED boarding due to downstream bed uncertainty and late discharges

2

Transfer delays caused by missing readiness criteria, incomplete tasks, and manual paging

3

Inaccurate unit-level capacity forecasts and reactive staffing/bed decisions

4

Post-acute placement friction (SNF/home health) leading to avoidable inpatient days

Impact When Solved

Accelerated patient transfersEnhanced capacity forecastingStreamlined discharge planning

The Shift

Before AI~85% Manual

Human Does

  • Daily bed huddles
  • Phone and pager coordination
  • Reactive decision-making based on incomplete information

Automation

  • Basic forecasting using historical averages
  • Manual status updates
  • Static dashboards for monitoring
With AI~75% Automated

Human Does

  • Final approvals for transfers
  • Handling complex cases and exceptions
  • Strategic oversight of patient flow processes

AI Handles

  • Predictive modeling for bed turnover
  • Automated readiness status tracking
  • Coordinated task management for transfers
  • Dynamic capacity forecasting

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

Capacity Forecast Dashboard Starter

Typical Timeline:Days

Stand up a lightweight forecasting and bottleneck view using existing ADT feeds and census snapshots to predict near-term discharges and bed availability by unit. The output is a daily/shift dashboard and simple alerts for likely capacity shortfalls, enabling earlier bed huddles and discharge focus without changing workflows.

Architecture

Rendering architecture...

Key Challenges

  • ADT event quality (late discharges, cancelled transfers) impacting forecast trust
  • Unit/bed taxonomy mismatches across source systems
  • Operational adoption: aligning forecasts to shift cadence
  • Small data volume for rare units/service lines

Vendors at This Level

Small community hospitalsCritical access hospitals

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Market Intelligence

Technologies

Technologies commonly used in AI Patient Flow Orchestration implementations:

Key Players

Companies actively working on AI Patient Flow Orchestration solutions:

Real-World Use Cases