Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918499602923520 |
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| author | Wenzel, Markus Strapatsas, Tobias Kress, Jessika Sauer, Dorothea Gessler, Nele Hahn, Horst K. |
| author_facet | Wenzel, Markus Strapatsas, Tobias Kress, Jessika Sauer, Dorothea Gessler, Nele Hahn, Horst K. |
| contents | Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and flexible model and develop a hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) simulating highly configurable ED environments. We specifically focus on the validation of the modeling approach. We derive configurations for ED sizes, patient load, and staffing from real-world studies. We then validate the model expressivity by matching its key performance indicators and metrics with their values known from literature. We proceed by implementing scientifically established and practice-proven resource optimization strategies. Comparing the documented real-world outcomes with our model's results demonstrates that the DES-ABM based simulation can effectively replicate real-world ER dynamics under interventions. We lastly integrate a Proof-of-Concept multi-agent system (MAS) that can autonomously explore resource allocation strategies within the simulated ER environment based on a temporal ledger of ED event records. This modular DES-ABM-MAS framework offers a powerful tool to explore resource optimization strategies in emergency departments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13345 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin Wenzel, Markus Strapatsas, Tobias Kress, Jessika Sauer, Dorothea Gessler, Nele Hahn, Horst K. Artificial Intelligence Multiagent Systems 37 Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and flexible model and develop a hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) simulating highly configurable ED environments. We specifically focus on the validation of the modeling approach. We derive configurations for ED sizes, patient load, and staffing from real-world studies. We then validate the model expressivity by matching its key performance indicators and metrics with their values known from literature. We proceed by implementing scientifically established and practice-proven resource optimization strategies. Comparing the documented real-world outcomes with our model's results demonstrates that the DES-ABM based simulation can effectively replicate real-world ER dynamics under interventions. We lastly integrate a Proof-of-Concept multi-agent system (MAS) that can autonomously explore resource allocation strategies within the simulated ER environment based on a temporal ledger of ED event records. This modular DES-ABM-MAS framework offers a powerful tool to explore resource optimization strategies in emergency departments. |
| title | Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin |
| topic | Artificial Intelligence Multiagent Systems 37 |
| url | https://arxiv.org/abs/2605.13345 |