A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs
A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs
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DOI：
10.1016/j.artmed.2016.03.001
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发表时间：
2016-03-01
影响因子：
7.5
通讯作者：
Gorunescu, Florin
中科院分区：
文献类型：
--
作者：
Belciug, Smaranda;Gorunescu, Florin
目的：探索如何有效的智能决策支持系统，易于理解和直接实施，可以帮助现代医院管理者优化床位占用率和利用costs.Methods和材料：本文提出了一种混合遗传算法排队多隔间模型的病人在医院的流量。将具有相位型服务分布的有限容量排队模型与分区模型相结合，建立了相应的费用模型。一种基于进化的方法用于提高优化床位管理和相关成本的能力。此外，“假设分析”显示了如何改变模型参数可以提高性能，同时控制成本。本研究利用伦敦圣乔治医院老年医学部1969-1984年和2000年1月的床位占用率数据。结果：混合模型显示，床位占用率超过91%，意味着病人拒绝率约为1.1%。相同的持有和罚款成本，但显著不同的床位分配（分别为156与184个工作人员床位，8与9个无人床位）将导致显著不同的成本（755磅与1172英镑）。此外，一旦到达率超过7个病人/天，与有限容量系统相关联的成本变得明显小于与Erlang B排队模型相关联的成本（134磅vs.947）磅。结论：遗传算法通过染色体对排队系统和成本模型提供的全部信息进行编码，代表了优化床位分配和相关成本的有效工具。该方法可以扩展到不同的医疗部门的结构和参数化的微小修改。(C)2016爱思唯尔B. V.保留所有权利。
Purpose: Explore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs.Methods and materials: This paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a "What-if analysis" shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine - St. George's Hospital, London, period 1969-1984, and January 2000.Results: The hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (755 pound vs. 1172) pound. Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (134 pound vs. 947) pound.Conclusion: Encoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization. (C) 2016 Elsevier B.V. All rights reserved.