标题： 从协调到个性化：一种信任感知的急诊科决策支持仿真框架 显示英文标题

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

摘要： 背景/目的：医院急诊科（ED）的高效任务分配对于运营效率和患者护理质量至关重要，但员工协调的复杂性带来了重大挑战。 本研究提出了一种基于仿真的框架，将医生和护士建模为由计算信任机制引导的智能代理。 目的是探讨信任驱动的协调如何支持ED管理中的决策制定。 方法：该框架在Unity（一个3D图形平台）中实现，其中代理在执行任务前评估自身能力，并与同事进行自适应协调。 仿真环境能够实时观察工作流程动态、资源利用情况和患者结果。 我们考察了三种情景——基准情景、替换情景和培训情景——反映了不同的员工管理策略。 结果：信任驱动的任务分配通过适应护士绩效水平，平衡了患者安全和效率。 在基准情景中，优先考虑安全性的策略减少了错误，但与FIFO策略相比增加了患者延误。 替换情景提高了吞吐量并减少了延误，尽管需要额外的人员成本。 培训情景促进了低绩效护士的长期技能发展，尽管短期内出现了延误和风险。 这些结果突显了在ED人员配置中即时效率收益与可持续能力建设之间的权衡。 结论：所提出的框架展示了计算信任在急诊医学中基于证据的决策支持的潜力。 通过将员工协调与自适应决策制定相结合，它为医院管理者提供了一个工具，在受控和可重复的条件下评估替代政策，同时也为未来基于人工智能的个性化决策支持奠定了基础。 显示英文摘要

摘要： 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.