基于患者两阶段医疗服务过程的病床资源优化

doi:10.16381/j.cnki.issn1003-207x.2020.03.010

中国管理科学 ›› 2020, Vol. 28 ›› Issue (3): 93-102.doi: 10.16381/j.cnki.issn1003-207x.2020.03.010

基于患者两阶段医疗服务过程的病床资源优化

宋鸿芳¹, 褚宏睿², 张文思³

1. 河北经贸大学管理科学与工程学院, 河北石家庄 050061;
2. 首都经济贸易大学管理工程学院, 北京 100070;
3. 中国海洋大学经济学院, 山东青岛 266100

收稿日期:2018-11-30 修回日期:2019-04-23 出版日期:2020-03-20 发布日期:2020-04-08
通讯作者: 褚宏睿(1987-),男(汉族),山东枣庄人,首都经济贸易大学管理工程学院,讲师,研究方向:医疗运作管理,E-mail:chuhongrui@cueb.edu.cn. E-mail:chuhongrui@cueb.edu.cn
基金资助:
国家自然科学基金青年项目（71901089）；北京市智能物流系统协同创新中心开放课题（BILSCIEC-2019KF-17）；教育部人文社会科学研究项目（18YJCZH247）

Hospital Inpatient Bed Management Based on Two-stage Medical Service Process

SONG Hong-fang¹, CHU Hong-rui², ZHANG Wen-si³

1. School of Management Science and Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China;
2. School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China;
3. School of Economics, Ocean University of China, Qingdao 266100, China

Received:2018-11-30 Revised:2019-04-23 Online:2020-03-20 Published:2020-04-08

摘要/Abstract

摘要： 随着医疗需求的日益增长，供需不平衡的矛盾日益显著，病床资源短缺已经成为制约医疗服务发展的重要因素之一。针对病床资源供需失衡的问题，本文构建患者两阶段医疗服务过程的病床资源优化模型。考虑患者的医疗服务是一个两阶段串联排队过程，第一阶段是强制治疗阶段，第二阶段是康复阶段。在患者排队系统中，服务台总数固定的前提下，本文利用增补变量方法给出了该排队系统的概率等式，通过求解偏微分方程得出系统的状态概率，并推导出医院拒绝患者的概率、平均康复时间等相关指标。考虑部分患者因病床资源不足而无法入院和康复阶段患者提前出院两种风险因素，本文基于排队指标构建多病种间的病床分配模型，再利用动态规划的求解思路得到病床分配的最优解。最后，本文采用2017年卫生统计数据和模拟数据相结合进行数值分析。研究结果表明与传统单阶段模型相比，两阶段模型对不同病种间病床数差别影响显著，并且患者入院率对病床分配影响较大。进一步，基于数值结果给出了医疗管理建议和研究展望。

关键词: 病床管理, 排队论, 能力分配, 医疗运作

Abstract: The gap between supply and demand in healthcare service is becoming more and more serously with the improvement of medical requirements. Moreover, one of the most significant problems is the shortage of bed capacity around the world. Furthermore, most patients should go through a treatment stage and recovery stage in medical service procedure which has not been studied sufficiently. In this paper, a resource allocation study of healthcare beds is present that formulates patient’s medical service process by a two-stage queuing model. The two-stage queuing model includes a compulsory treatment stage and a recovery stage. Firstly, dividing the state of healthcare service system into three situations, the probability equations of the treatment queuing are gotten by supplementary variable method under the fixed service number constraint respectively, then the partial differential equations is solved by characteristic equation method with boundary conditions and the probability expression of healthcare service system state. Using this expression, the customer deny probability and average recovery service time can be further calculated. Furthermore, considering the risk factors that a new patient is unable to get treatment and the patients who has recovered are early discharged due to bed capacity shortage, the medical bedroom allocation model is developed based on the queuing indexes, and dynamic programming analysis is given. Finally, the numerical analysis is to implement according to Chinese Health Statistics data and the simulation data. The result shows that the traditional one-stage model has a significant difference allocation result compared with our two-stage model, and patient’s arrival rate has significant impact on optimal bed allocation in the proposed model. Furthermore, management insights are provided according to numerical result and further work is given in conclusion. This study can be used to improve the efficiency of healthcare resource and be extend to support the similar researches about two-stage service system.

Key words: hospital bed management, queuing, capacity analysis, healthcare operations management

中图分类号:

C93.3
O224

宋鸿芳, 褚宏睿, 张文思. 基于患者两阶段医疗服务过程的病床资源优化[J]. 中国管理科学, 2020, 28(3): 93-102.

SONG Hong-fang, CHU Hong-rui, ZHANG Wen-si. Hospital Inpatient Bed Management Based on Two-stage Medical Service Process[J]. Chinese Journal of Management Science, 2020, 28(3): 93-102.

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基于患者两阶段医疗服务过程的病床资源优化

Hospital Inpatient Bed Management Based on Two-stage Medical Service Process

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