用AI优化灾难伤员转运，提升医院资源分配效率。
Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform
- 基于深度强化学习构建决策代理，综合伤情、医院容量与运输条件。
- AI辅助下非专家表现达专家水平，且显著优于人工独立决策（p<0.001）。
- 适用于应急培训与真实灾害响应，尤其适合缺乏经验的医护人员。
大规模伤亡事件（MCIs）会迅速压垮医疗系统，需在高压下快速准确地完成伤员-医院匹配。本文开发并验证了一个基于深度强化学习的决策支持AI代理，用于模拟场景中优化伤员转运决策，平衡患者伤情严重度、专科治疗需求、医院容量及运输物流。为集成该AI，我们构建了MasTER——一个可网页访问的指挥仪表盘，用于MCIs管理模拟。通过30名参与者（6名创伤专家，24名非专家）的对照实验，在多伦多大区20人和60人规模的模拟事件中评估了三种交互方式（纯人工、人机协作、纯AI）。结果表明，增加AI参与度显著提升决策质量与一致性。该AI代理表现优于创伤外科医生（p < 0.001），并在辅助下使非专家达到专家级表现，而其独立操作时表现显著更差（p < 0.001）。研究证实该AI决策支持系统在提升灾备训练与实际应急响应中的潜力。
原文摘要 · Abstract (English)
Mass casualty incidents (MCIs) overwhelm healthcare systems and demand rapid, accurate patient-hospital allocation decisions under extreme pressure. Here, we developed and validated a deep reinforcement learning-based decision-support AI agent to optimize patient transfer decisions during simulated MCIs by balancing patient acuity levels, specialized care requirements, hospital capacities, and transport logistics. To integrate this AI agent, we developed MasTER, a web-accessible command dashboard for MCI management simulations. Through a controlled user study with 30 participants (6 trauma experts and 24 non-experts), we evaluated three interaction approaches with the AI agent (human-only, human-AI collaboration, and AI-only) across 20- and 60-patient MCI scenarios in the Greater Toronto Area. Results demonstrate that increasing AI involvement significantly improves decision quality and consistency. The AI agent outperforms trauma surgeons (p < 0.001) and enables non-experts to achieve expert-level performance when assisted, contrasting sharply with their significantly inferior unassisted performance (p < 0.001). These findings establish the potential for our AI-driven decision support to enhance both MCI preparedness training and real-world emergency response management.
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