RTLS & Digital Twins for
Patient Flow Management

Understanding where time is spent in the patient's journey is critical for improving throughput. RTLS-based patient flow management solutions provide granular visibility into every stage of patient movement, revealing opportunities for optimization that were previously invisible.

Journey Analytics

• Stage-by-Stage Timing: Measure time spent in triage, waiting, exam, treatment, and discharge
• Bottleneck Identification: Automatically identify stages with excessive wait times
• Comparative Analysis: Compare journey times by shift, day of week, and season
• Provider Performance: Measure throughput by provider and team
• Process Improvement Tracking: Measure before/after impact of workflow changes

Optimization Outcomes

• 25–40% reduction in total journey time
• 50% faster identification of bottlenecks
• 30% improvement in process standardization
• Data-driven justification for staffing and resource allocation decisions

Bed availability is often the primary constraint on patient throughput. RTLS provides real-time visibility into bed status, turnover times, and utilization patterns, enabling data-driven optimization of your most valuable resource.

Bed Analytics

• Turnover Time Analysis: Measure time from discharge to bed ready by unit and shift
• Utilization Rates: Track occupancy rates by department, time of day, and day of week
• Discharge Prediction: AI models predict discharge timing 2–4 hours in advance
• Boarding Analysis: Measure ED boarding hours and identify root causes
• Capacity Planning: Forecast bed needs based on historical patterns and current census

Financial Impact

Optimizing bed utilization delivers significant financial returns. A 400-bed hospital reducing turnover time by 30 minutes gains 5–8% effective capacity, equivalent to adding 20–30 beds without construction. Reducing ED boarding by 25% can recover $2–4M annually in lost revenue and avoid diversion penalties.

Documented workflows often differ significantly from actual practice. RTLS-based hospital patient flow management reveals how patients actually move through your facility, exposing inefficiencies and opportunities for standardization.

Workflow Insights

• Process Mining: Automatically discover actual patient flow patterns from location data
• Variation Analysis: Identify deviations from standard workflows and their impact
• Handoff Coordination: Measure delays in patient handoffs between departments
• Transport Efficiency: Analyze transport request-to-completion times
• Parallel Processing: Identify opportunities for concurrent activities to reduce journey time

Improvement Results

Workflow analysis enables targeted process improvements. Hospitals report a 35% reduction in unnecessary patient movements, a 40% improvement in handoff coordination, a 25% decrease in transport delays, and standardization of best practices across shifts and departments.

Reactive staffing and resource allocation lead to either overstaffing (wasted costs) or understaffing (poor patient experience). A patient flow management system with AI-powered forecasting enables proactive resource planning based on predicted patient volumes.

Forecasting Capabilities

• ED Volume Prediction: Forecast arrivals 4–24 hours in advance with 85%+ accuracy
• Admission Rate Forecasting: Predict ED-to-inpatient admission rates by time and season
• Discharge Prediction: Forecast discharge timing and volume for capacity planning
• Seasonal Pattern Recognition: Identify flu season, summer trauma, and cyclical trends
• Special Event Planning: Adjust forecasts for community events, weather, and holidays

Operational Benefits

Predictive volume forecasting enables proactive staffing adjustments, reducing overtime costs by 20–30% while improving patient experience. Hospitals report a 40% reduction in unexpected capacity crises, a 25% improvement in staff satisfaction, and the ability to avoid ED diversions during predicted surges.

By the time a bottleneck is obvious, patient experience has already suffered. Patient flow management software uses AI models to analyze real-time flow patterns and predict bottlenecks 30 to 60 minutes before they occur, enabling proactive intervention.

Prediction Capabilities

• Capacity Threshold Alerts: Predict when departments will reach 85–90% capacity
• Wait Time Escalation: Forecast when wait times will exceed acceptable thresholds
• Resource Shortage Prediction: Anticipate bed, staff, or equipment shortages
• Downstream Impact Analysis: Predict how current bottlenecks will affect downstream departments
• Intervention Recommendations: AI suggests specific actions to prevent predicted bottlenecks

Proactive Management

Predictive bottleneck detection transforms flow management from reactive to proactive. Hospitals report a 50% reduction in severe bottleneck incidents, a 35% improvement in patient satisfaction scores, a 40% decrease in staff stress during high-volume periods, and an ability to maintain flow during unexpected surges.

Resource allocation decisions are often based on historical averages or gut feel. AI-powered demand prediction provides granular forecasts of resource needs, enabling optimal allocation and reducing waste.

Demand Forecasting

• Staffing Optimization: Predict nurse, physician, and support staff needs by shift
• Equipment Demand: Forecast equipment needs by department and time
• Bed Demand by Acuity: Predict ICU, telemetry, and med-surg bed needs
• Ancillary Service Demand: Forecast lab, imaging, and pharmacy workload
• Transport Resource Needs: Predict patient transport demand for optimal staffing

Efficiency Gains

Resource demand prediction enables right-sizing of staffing and resources. Hospitals report 20–25% reduction in overtime costs, 30% improvement in resource utilization, elimination of last-minute staffing scrambles, and data-driven justification for resource allocation decisions.

Operational changes are expensive and risky. Digital twin simulation supports hospital patient flow management by allowing you to test workflow modifications, layout changes, and resource allocation strategies in a risk-free virtual environment before committing resources.

What-If Scenarios

• What if we add 2 triage nurses during peak hours?
• How would a fast-track area impact overall ED throughput?
• What's the optimal bed allocation between ICU and step-down?
• How will a new surgical tower affect patient flow?
• What's the ROI of adding a discharge lounge?

Planning Benefits

Digital twin simulation reduces planning time by 40–60%, improves decision confidence by providing quantitative impact predictions, eliminates costly implementation mistakes, and provides data-driven justification for capital investments and operational changes.

Capacity planning decisions have multi-million dollar implications. Digital twin simulation enables you to model different capacity scenarios and predict their impact on throughput, patient experience, and financial performance.

Capacity Scenarios

• Bed Expansion Analysis: Model impact of adding beds to specific units
• Flex Capacity Strategies: Simulate flex bed activation during surges
• Unit Conversion: Evaluate converting med-surg beds to ICU or vice versa
• Observation Unit Sizing: Determine optimal observation unit capacity
• Seasonal Capacity Adjustments: Plan for flu season and other predictable surges

Strategic Value

Capacity planning simulation prevents costly mistakes. One hospital avoided a $15M bed tower expansion by using simulation to identify operational improvements that achieved the same throughput increase. Others have optimized bed mix to reduce boarding without adding total beds.

Process improvement initiatives often fail because changes are implemented without understanding their full impact. Digital twin simulation enables you to test workflow modifications and predict their effect on throughput, quality, and patient experience.

Workflow Scenarios

• Triage Process Changes: Model impact of split-flow or rapid triage approaches
• Discharge Process Optimization: Simulate discharge lounge, early discharge rounds, etc.
• Transport Workflow: Model centralized vs. decentralized transport teams
• Bed Assignment Logic: Test different bed assignment algorithms
• Layout Modifications: Simulate impact of physical layout changes on flow

Optimization Results

Workflow simulation enables evidence-based process improvement. Hospitals report 50% reduction in failed improvement initiatives, 40% faster implementation of successful changes, quantitative prediction of improvement impact, and elimination of unintended consequences from workflow changes.