马尔科夫决策优化医疗服务资源的两个类患者队列在急诊室通过粒子群优化算法粒子群优化算法
Chia-Hung Wang1,2, Rong Tian1, Kun Hu1
1College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou City, 350118, Fujian Province, China.
Scientific reports
|January 23, 2025
概括
这项研究开发了一个优化的急救部门 (ED) 资源分配系统,显著减少了患者等待时间和队列长度. 该模型有效地管理床位和服务器,以提高紧急护理效率和患者安全.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 排队理论 排队理论
背景情况:
- 合理分配急救部门 (ED) 的资源对于有效应急响应,患者安全和优质医疗服务至关重要.
- 目前的ED资源分配模式经常面临效率,成本和患者结果平衡方面的挑战.
研究的目的:
- 设计和优化基于优先级的患者分类和资源分配系统,用于急诊室.
- 通过考虑患者等待时间,拒绝成本和操作费用,尽量减少总成本.
主要方法:
- 开发了多服务器的先发性和非先发性优先排队模型,对紧急排队的容量有限制.
- 构建了一个混合整数编程模型,以尽量减少总成本.
- 应用粒子群集优化来确定最佳的服务器数量,服务率和床位容量.
主要成果:
- 与之前的模型相比,拟议的模型显著减少了患者等待时间 (例如,一个指标的74.44%减少) 和队列长度 (例如,一个指标的78%减少).
- 通过确定最佳数量的床位和服务器,实现了优化资源配置.
- 有效平衡成本降低,改善患者流动和减少等待时间.
结论:
- 开发的优先排队和优化模型为紧急部门的资源配置提供了有效的解决方案.
- 该系统提高了ED效率,减少了患者等待时间,并控制了运营成本.
- 提供有价值的管理见解,以优化紧急护理的提供.
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