Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin

2026-05-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) has been developed to simulate highly configurable Emergency Department (ED) environments, addressing challenges in patient care and resource management. The model's expressivity was validated by matching its key performance indicators and metrics against known literature values, using configurations for ED sizes, patient load, and staffing derived from real-world studies. The study implemented scientifically established and practice-proven resource optimization strategies, demonstrating that the DES-ABM simulation effectively replicates real-world ED dynamics under interventions. Furthermore, a Proof-of-Concept multi-agent system (MAS) was integrated, enabling autonomous exploration of resource allocation strategies within the simulated ED environment based on a temporal ledger of ED event records. This modular DES-ABM-MAS framework provides a robust tool for exploring resource optimization in emergency departments.

Key takeaway

For hospital administrators and operations researchers tasked with optimizing emergency department efficiency, this validated DES-ABM-MAS framework offers a powerful tool to explore resource allocation strategies. You can use this digital twin to test interventions and staffing changes virtually, predicting their impact on patient care and resource utilization before real-world implementation, thereby reducing operational risks and improving outcomes.

Key insights

A hybrid DES-ABM-MAS framework effectively simulates and optimizes emergency department resource allocation.

Principles

• Hybrid models enhance simulation realism.
• Validation against literature is crucial.
• MAS can autonomously optimize resource allocation.

Method

The method involves developing a hybrid DES-ABM, validating it against real-world data and literature, implementing optimization strategies, and integrating a multi-agent system for autonomous exploration of resource allocation.

In practice

• Simulate ED patient flow and resource use.
• Test new staffing models virtually.
• Evaluate impact of interventions.

Topics

• Multi-Agent Systems
• Emergency Departments
• Discrete Event Simulation
• Agent-Based Modeling
• Resource Optimization

Best for: AI Scientist, Research Scientist

Related on AIssential

• Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin — A hybrid DES-ABM-MAS framework effectively simulates ED dynamics for resource optimization and strategy exploration.
• Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems — Adaptive multi-agent systems exhibit topological overfitting and illusory coordination, limiting their generalization.
• SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model — SOM improves LLM agent prediction by separating opponent modeling and prediction using a Structural Causal Model.
• H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows with FHIR Integration — H-AdminSim provides a FHIR-integrated multi-agent simulation for evaluating LLM automation in complex hospital administrative workflows.
• DEpiABS: Differentiable Epidemic Agent-Based Simulator — DEpiABS is a differentiable agent-based model that balances mechanistic detail, computational efficiency, and interpretability for epidemic simulation.
• LLM-Based Multi-Agent System with Retrieval-Augmented Generation for Medical Care Planning Generation in Sickle Cell Disease — RAG-enhanced multi-agent LLMs can improve safety and clinical quality in high-risk medical care planning.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.