考虑患者止步行为的动态入院接收决策模型

doi:10.12005/orms.2022.0284

运筹与管理 ›› 2022, Vol. 31 ›› Issue (9): 49-55.DOI: 10.12005/orms.2022.0284

考虑患者止步行为的动态入院接收决策模型

姜艳萍¹, 杨飞飞², 孙灿¹

1.东北大学工商管理学院,辽宁沈阳 110167;
2.北京工业大学经济与管理学院,北京 100124

收稿日期:2020-11-07 出版日期:2022-09-25 发布日期:2022-10-21
通讯作者: 杨飞飞(1988-),女,河北邢台人,讲师,博士,研究方向:医疗运作管理,管理决策分析。
作者简介:姜艳萍(1968-),女,辽宁沈阳人,博士,教授,博士生导师,研究方向:管理决策分析、运筹与管理等。
基金资助:
国家自然科学基金资助项目(71871048,72202010);中国博士后科学基金资助项目(2022M710275)

Dynamic Admission Decision Model Considering Patient Balking Behavior

JIANG Yan-ping¹, YANG Fei-fei², SUN Can¹

1. School of Business Administration, Northeastern University, Shenyang 110167, China;
2. School of Economics and Management, Beijing University of Technology, Beijing 100124, China

Received:2020-11-07 Online:2022-09-25 Published:2022-10-21

摘要/Abstract

摘要： 大型公立医院病床供需矛盾日益突出,医院作为服务系统有必要考虑由于病床需求响应速度不及时而引起的患者策略性行为。针对患者到达时间的随机性与住院时长的不确定性,本文提出考虑患者止步行为的动态入院接收决策问题,制定了适用于可等待慢性病患者的入院接收决策方法,旨在提高患者的就医满意度,有效权衡多类患者的接收数量,降低由于科室响应速度过慢引发的患者止步频率。首先,本文对考虑患者止步行为的动态入院接收决策问题进行数学描述及符号定义;然后,对患者止步行为的影响因素进行分析并构建止步概率函数;进一步地,构建考虑患者止步行为的动态入院接收马尔可夫决策过程(MDP)模型,并针对模型特点设计值迭代算法,最后通过数值算例验证本文所提方法的可行性与有效性。

关键词: 入院接收决策, 止步行为, 马尔可夫决策过程, 值迭代算法

Abstract: The contradiction between supply and demand of beds in large public hospitals is increasingly prominent. As a service system, it is necessary for hospitals to consider the patient's strategic behavior caused by untimely response speed of the hospital bed demand. In this paper, a dynamic admission decision problem considering patient balking behavior is proposed, and an admission decision method suitable for waiting patients with chronic diseases is developed. The purpose is to improve the patients' satisfaction, and effectively weigh the number of multiple patients received, and reduce the frequency of patient balking behavior caused by the slow response speed in the department. Firstly, this paper gives the mathematical description of the dynamic admission decision problem considering balking behavior and defines the symbol. Then we analyze the influence factors of the patient balking behavior and construct the balking probability function. Further, we establish the Markov decision process (MDP) model of the dynamic admission decision considering patient balking behavior and design value iteration algorithm to solve it according to the characteristics of this model. Finally, we design a numerical example to verify the feasibility and effectiveness of the method.

Key words: admission decision, balking behavior, Markov decision process, value iteration algorithm

中图分类号:

C934

姜艳萍, 杨飞飞, 孙灿. 考虑患者止步行为的动态入院接收决策模型[J]. 运筹与管理, 2022, 31(9): 49-55.

JIANG Yan-ping, YANG Fei-fei, SUN Can. Dynamic Admission Decision Model Considering Patient Balking Behavior[J]. Operations Research and Management Science, 2022, 31(9): 49-55.

参考文献

[1] 国家卫生健康委员会.中国卫生健康统计年鉴[M].北京:中国协和医科大学出版社,2019.
[2] 梁峰,徐苹.基于MDP和动态规划的医疗检查预约调度优化方法研究[J].运筹与管理,2020,29(5):17-25.
[3] Izady N, Mohamed I. A clustered overflow configuration of inpatient beds in hospitals[J]. Manufacturing & Service Operations Management, 2021, 23(1): 139-154.
[4] Lu Y, Xie X, Jiang Z. Dynamic appointment scheduling with wait-dependent abandonment[J]. European Journal of Operational Research, 2018, 265(3): 975-984.
[5] Kuo Y H, Balasubramanian H, Chen Y. Medical appointment overbooking and optimal scheduling: tradeoffs between schedule efficiency and accessibility to service[J]. Flexible Services and Manufacturing Journal, 2020, 32(1): 72-101.
[6] 杨飞飞,姜艳萍.考虑患者爽约和插队的医疗检查能力分配策略[J].系统工程学报,2019,34(3):422-432.
[7] Jiang Y P, Yang F F, Tang Z P, Li Q L. Admission control of hospitalization with patient gender by using Markov decision process[J]. International Transactions in Operational Research, 2023, 30(1): 70-98.
[8] Huh W T, Liu N, Truong V A. Multiresource allocation scheduling in dynamic environments[J]. Manufacturing & Service Operations Management, 2013, 15(2): 280-291.
[9] Samiedaluie S, Kucukyazici B, Verter V, et al. Managing patient admissions in a neurology ward[J]. Operations Research, 2017, 65(3): 635-656.
[10] 王康周,江志斌,李娜,李国威.不耐烦行为生产库存系统最优生产和分包控制[J].工业工程与管理,2012,17(4):65-70.
[11] Sun W, Li S Y, Tian N S. Comparisons of customer behavior in Markovian queues with vacation policies and geometric abandonments[J]. RAIRO-Operations Research, 2020, 54(3): 615-636.
[12] Kremer M, Debo L. Inferring quality from wait time[J]. Management Science, 2016, 62(10): 3023-3038.
[13] Osadchiy N, Diwas K C. Are patients patient? the role of time to appointment in patient flow[J]. Production & Operations Management, 2017, 26(3): 469-490.
[14] LeBlanc L J, Hoot N R, Hones L, et al. Simulation models for just-in-time provision of resources in an emergency department[J]. SSRN Electronic Journal, 2008: 1-19.
[15] Zacharias C, Armony M. Joint panel sizing and appointment scheduling in outpatient care[J]. Management Science, 2017, 63(11): 3978-3997.
[16] Porteus E L. Foundations of stochastic inventory theory[M]. California: Stanford University Press, 2002.
[17] Chen X, Gao X, Pang Z. Preservation of structural properties in optimization with decisions truncated by random variables and its applications[J]. Operations Research, 2018, 66(2): 340-357.

考虑患者止步行为的动态入院接收决策模型

Dynamic Admission Decision Model Considering Patient Balking Behavior

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