东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (4): 604-608.DOI: 10.12068/j.issn.1005-3026.2017.04.030

• 管理科学 • 上一篇    

门诊到住院过程中病床负荷公平性的病人路由策略

陈丹1,2, 朱华波3, 雒兴刚1   

  1. (1. 东北大学 信息科学与工程学院, 辽宁 沈阳110819; 2. 辽宁科技学院 管理学院, 辽宁 本溪117004;3.辽宁石油化工大学 经济管理学院, 辽宁 抚顺113001)
  • 收稿日期:2015-04-09 修回日期:2015-04-09 出版日期:2017-04-15 发布日期:2017-04-11
  • 通讯作者: 陈丹
  • 作者简介:陈丹(1980-),女,辽宁凤城人,东北大学博士研究生; 雒兴刚(1971-),男,新疆奇台人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金青年基金资助项目(71501033); 国家科技重大专项 (2012ZX07505-002).

Patient Routing Based on Beds Fairness from Outpatient Department to Internal Wards

CHEN Dan 1,2, ZHU Hua-bo 3, LUO Xing-gang 1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. School of Management, Liaoning Institute of Science and Technology, Benxi 117004, China; 3. School of Economics Mangement, Liaoning Shihua University, Fushun 113001, China.
  • Received:2015-04-09 Revised:2015-04-09 Online:2017-04-15 Published:2017-04-11
  • Contact: ZHU Hua-bo
  • About author:-
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摘要: 在调研分析住院病人历史数据和医院业务流程基础上,建立了病人流路由分配的单排队多服务台队列倒V模型.以病房工作负荷公平性为目标,分析了RMI (randomized most-idle)随机路由分配策略的马尔科夫过程在倒V模型中的应用,得到了系统的阻塞概率及病床占用率等相关性能指标参数,从理论上分析了RMI路由分配策略的公平性.对比了两服务台队列占用率、单位时间内服务的病人数量,结果验证了RMI策略的公平性.

关键词: 医疗服务, 路由分配, 随机过程, 马尔科夫, 公平性

Abstract: Based on theoretical analysis of flowchart and mass patient flow data, an inverted-V model with single patient queue and several heterogeneous server pools was established to describe patient routing allocation process. With the goal of routing allocation fairness for the beds of internal wards, the application of Markov process of randomized most-idle (RMI) routing policy in the inverted-V model was analyzed. The occupied and service rates of sever pools under different routing policies were obtained, and the routing policy fairness was analyzed theoretically. The experimental results showed the occupied and service rates for different server pool under RMI policy are more balanced than that of the average allocation policy.

Key words: healthcare, routing allocation, random, Markov, fairness

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