Digital Twins for Decision Making: Combining Ambulance and ED Facility Resources

Area of Science:

• Healthcare Operations Research
• Health Systems Engineering
• Emergency Medicine

Background:

• Emergency department (ED) patient flow models often analyze walk-ins and ambulance arrivals separately.
• The interdependence between patient arrival streams and fixed treatment space (TS) capacity is frequently overlooked.
• Understanding these dynamics is crucial for effective ED resource management and patient care.

Purpose of the Study:

• To develop and utilize a digital twin (DT) based on discrete event simulation to model ED patient flow.
• To investigate the impact of varying treatment space (TS) capacities on patient off-stretcher time (POST) and resource utilization.
• To assess the sensitivity of ED patient flow to resource constraints and identify potential bottlenecks.

Main Methods:

• A discrete event simulation algorithm was adapted to create a digital twin (DT) of an ED.
• The DT was driven by patient arrival streams, simulating patient off-stretcher time (POST), ambulance bay, and treatment space (TS) occupation over 24 hours.
• The model was calibrated using 19,570 ambulance records from a 250-bed hospital (2021-2022) and tested across scenarios with different TS capacities (43, 38, 34) and five ambulance bays.

Main Results:

• Simulations revealed distinct patterns of treatment space (TS) depletion and recovery under different capacity scenarios.
• Analysis showed that limited TS capacity can lead to prolonged patient off-stretcher times (POST) and resource bottlenecks.
• In some scenarios, TS recovery within a 24-hour period was not achieved, indicating critical resource limitations.

Conclusions:

• Digital twin (DT) based simulation is a valuable tool for understanding complex ED patient flow dynamics.
• ED patient flow is highly sensitive to fixed treatment space (TS) capacity, necessitating careful capacity planning.
• Optimizing ED resource allocation requires considering the interplay between patient arrivals and available treatment space to prevent system overload.