数据驱动的队列建模:对急诊室拥挤的模拟案例研究
Adrien Wartelle1, Farah Mourad-Chehade2, Farouk Yalaoui2
1IRIT, Université de Toulouse, CNRS, Toulouse INP, INU Champollion, Toulouse, France adrien.gj.wartelle@gmail.com.
BMJ health & care informatics
|January 8, 2026
概括
一种新的数据驱动排队方法准确地模拟了急诊室拥挤的情况. 这种方法弥合了预测和优化之间的差距,为系统动态提供了更好的理解.
科学领域:
- 医疗保健 运营 研究 研究 研究
- 紧急医疗管理管理
- 计算统计学 计算统计学
背景情况:
- 紧急部门拥挤是一个复杂的问题,影响患者护理和运营效率.
- 现有的拥挤模型方法在预测准确性 (机器学习) 和评估能力 (排队/模拟) 之间存在分歧.
- 在能够预测拥挤和评估运营变化影响的方法论中存在重大差距.
研究的目的:
- 实施和验证一种新的数据驱动排队方法,用于应急部门拥挤.
- 为了弥合机器学习预测和队列/模拟评估方法之间的差距.
- 在现实世界的模拟案例研究中证明方法的适用性.
主要方法:
- 开发了一种排队过程的统计模型,专注于患者离院率和概率.
- 创建了一个基于数据的排队网络模型,使用来自主要紧急部门的数据.
- 通过同步模拟算法验证并应用模型.
主要成果:
- 该模型准确地捕捉了患者到达和医疗保健人员分配 (医生,护士) 的复杂相互作用.
- 它提供了对长期急诊室拥挤情况的公正而准确的测量.
- 量化了新的非计划照顾服务对拥挤水平的影响.
结论:
- 这种新的数据驱动排队方法有效地模拟和量化紧急部门的复杂拥挤动态.
- 这种方法成功地弥合了建模差距,为预测各种操作变量下的系统拥挤提供了一个工具.
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