From Coordination to Personalization: A Trust-Aware Simulation Framework for Emergency Department Decision Support

• URL: http://arxiv.org/abs/2510.15896v1
• Date: Tue, 09 Sep 2025 18:00:44 GMT
• Title: From Coordination to Personalization: A Trust-Aware Simulation Framework for Emergency Department Decision Support
• Authors: Zoi Lygizou, Dimitris Kalles,
• Abstract summary: This study proposes a simulation-based framework for modeling doctors and nurses as intelligent agents guided by computational trust mechanisms.<n>The proposed framework demonstrates the potential of computational trust for evidence-based decision support in emergency medicine.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background/Objectives: Efficient task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient care quality, yet the complexity of staff coordination poses significant challenges. This study proposes a simulation-based framework for modeling doctors and nurses as intelligent agents guided by computational trust mechanisms. The objective is to explore how trust-informed coordination can support decision making in ED management. Methods: The framework was implemented in Unity, a 3D graphics platform, where agents assess their competence before undertaking tasks and adaptively coordinate with colleagues. The simulation environment enables real-time observation of workflow dynamics, resource utilization, and patient outcomes. We examined three scenarios - Baseline, Replacement, and Training - reflecting alternative staff management strategies. Results: Trust-informed task allocation balanced patient safety and efficiency by adapting to nurse performance levels. In the Baseline scenario, prioritizing safety reduced errors but increased patient delays compared to a FIFO policy. The Replacement scenario improved throughput and reduced delays, though at additional staffing cost. The training scenario forstered long-term skill development among low-performing nurses, despite short-term delays and risks. These results highlight the trade-off between immediate efficiency gains and sustainable capacity building in ED staffing. Conclusions: The proposed framework demonstrates the potential of computational trust for evidence-based decision support in emergency medicine. By linking staff coordination with adaptive decision making, it provides hospital managers with a tool to evaluate alternative policies under controlled and repeatable conditions, while also laying a foundation for future AI-driven personalized decision support.

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