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Dynamic Scheduling Policy for patient flow in a Smart Environment

Chinese Journal of Electronics 2017年第3期26卷 530-536页

作者： CHEN Xiao DING Jie Nigel Thomas School of Computer Science and Communication Engineering Jiangsu University State Key Laboratory of Software Development Environment Beihang University School of Information Engineering Yangzhou University School of Computing Science Newcastle University

Given the popularity of smart environment in a hospital involving the participation of a large group of patients,scheduling of patients towards their desired destination is still being a challenging *** the development of Internet of Things(Io T),smart environments are increasingly being deployed in various public scenarios and in particular,hospitals are an important target environment for smart *** paper models a dynamic scheduling policy based on a patient flow scenario in order to solve the queuing issue and facilitate people’s *** dynamic scheduling policy aims to forward incoming patients to dissolve jams especially at the peaks by giving accurate predication of appointment *** main modelling technique is a formal method—Performance evaluation process algebra(PEPA).The findings show that the dynamic scheduling policy is able to efficiently improve the patient flow.

关键词： Dynamic scheduling patient flow Smart environment Formal method Performance evaluation process algebra(PEPA)

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Performance Modelling of patient flow Scheduling Through a Formal Method

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Journal of Shanghai Jiaotong university(Science) 2017年第1期22卷 66-71页

作者：陈潇 THOMAS Nigel 丁杰 School of Computer Science and Communication Engineering Jiangsu University School of Computing Science Newcastle University School of Information Engineering Yangzhou University

Smart environment is being used in many areas to deliver more services to individuals in a physical space, such as a hospital. In the UK, the National Health Service(NHS) provides free and high quality healthcare service for all residents. Smart hospital environment is able to support NHS and provide more convenience. patient flow scheduling is a crucial section in a smart hospital environment. Smart hospital environment aims to provide a smart environment in the hospital to facilitate individual experience and improve the quality of healthcare *** of all, this paper investigates a real world patient flow scenario of a hospital in the UK and models a general scheduling scheme based on the scenario using a compositional formal approach, i.e. performance evaluation process algebra(PEPA). This scheduling scheme uses an easy-implemented solution(the grouping scheme) to reduce the waiting queue in the hospital. Secondly, fluid flow analysis is used for the performance analysis by generating a set of ordinary differential equations(ODEs) in terms of the PEPA model.

关键词： scheduling performance valuation patient flow performance evaluation process algebra (PEPA) ordinary differential equations (ODEs) TP 302 TP 391 A

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The Analysis of Feedback patient flow as A Retrial Queueing System in Chinese Outpatient Department

The Analysis of Feedback Patient Flow as A Retrial Queueing ...

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第32届中国控制与决策会议

作者： Huabo Zhu Xudan Liu School of Economics ＆ Management Liaoning Shihua University

In China,hospitals are not allowed to reject outpatients and are thus confronted with large patient ***,analysis of patient flow and optimization of patient flow are important to improve the efficiency of managing hospital *** on the flow chart in outpatient department（OD）,the diagnosis department has feedback patient flow as retrial customer in the retrial queuing *** retrial queueing system with feedback patient flow model is *** process analysis is carried out for the model,and the performance index of the system is *** influence of feedback patient flow on system performance is analyzed by retrial queuing *** the feedback of the change of patient flow probability,the analysis on the performance of the system is verified by numerical *** study has a certain guiding significance for the healthcare patient flow operations management in OD.

关键词： patient flow retrial queuing Markov

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Forecasting patient demand at urgent care clinics using explainable machine learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 712-733页

作者： Teo Susnjak Paula Maddigan School of Mathematical and Computational Sciences Massey UniversityAucklandNew Zealand

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient *** delays arising from inadequate staffing levels during these periods have been linked with adverse clinical *** research into forecasting patient flows has mostly used statistical *** studies have also predominately focussed on short‐term forecasts,which have limited practicality for the resourcing of medical *** study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient *** research uses datasets covering 10 years from two large urgent care clinics to develop long‐term patient flow forecasts up to one quarter ahead using a range of state‐of‐the‐art algo-rithms.A distinctive feature of this study is the use of eXplainable Artificial Intelligence(XAI)tools like Shapely and LIME that enable an in‐depth analysis of the behaviour of the models,which would otherwise be *** analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVID‐19 pandemic lockdowns and to identify the most impactful variables,resulting in valuable insights into their *** results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting,into an ensemble,delivered the most accurate and consistent solutions on *** approach generated improvements in the range of 16%-30%over the existing in‐house methods for esti-mating the daily patient flows 90 days ahead.

关键词： data mining explainable AI forecasting machine learning patient flow urgent care clinics

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Lean techniques for the improvement of patients' flow in emergency department

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World Journal of Emergency Medicine 2014年第1期5卷 24-28页

作者： HY Chan SM Lo LLY Lee WYL Lo WC Yu YF Wu ST Ho RSD Yeung JTS Chan Emergency Department Alice Ho Miu Ling Nethersole Hospital

BACKGROUND: Emergency departments(EDs) face problems with overcrowding, access block, cost containment, and increasing demand from patients. In order to resolve these problems, there is rising interest to an approach called "lean" management. This study aims to(1) evaluate the current patient flow in ED,(2) to identify and eliminate the non-valued added process, and(3) to modify the existing ***: It was a quantitative, pre- and post-lean design study with a series of lean management work implemented to improve the admission and blood result waiting time. These included structured re-design process, priority admission triage(PAT) program, enhanced communication with medical department, and use of new high sensitivity troponin-T(hsTnT) blood test. Triage waiting time, consultation waiting time, blood result time, admission waiting time, total processing time and ED length of stay were ***: Among all the processes carried out in ED, the most time consuming processes were to wait for an admission bed(38.24 minutes; SD 66.35) and blood testing result(mean 52.73 minutes, SD 24.03). The triage waiting time and end waiting time for consultation were significantly decreased. The admission waiting time of emergency medical ward(EMW) was significantly decreased from 54.76 minutes to 24.45 minutes after implementation of PAT program(Ppatient flow in ED. Acquiescence to the principle of lean is crucial to enhance high quality emergency care and patient satisfaction.

关键词： Lean Triage Waiting time patient flow Emergency department

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数据驱动的大型医院门诊系统服务效率与患者流研究

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管理科学 2020年第7期

作者：张润彤赵红梅许媛张雄王超尚小溥

关于医院管理的相关研究大多集中在住院系统的资源优化和配置，或是具有预约模式的门诊医疗服务资源优化配置。对于不需要预约的门诊服务系统研究较少，尤其是关于这类门诊服务中隐藏的运行规律方面的研究。　　在重负载的医院门诊场景... 详细信息

关于医院管理的相关研究大多集中在住院系统的资源优化和配置，或是具有预约模式的门诊医疗服务资源优化配置。对于不需要预约的门诊服务系统研究较少，尤其是关于这类门诊服务中隐藏的运行规律方面的研究。　　在重负载的医院门诊场景下，使用探索性数据分析方法，在数据分析过程中使用回归拟合和关联关系分析等方法。采用医院信息系统中记录的患者在接受门诊服务过程中产生的真实时间戳数据，分析并挖掘医生服务效率与患者流之间的关系，得到服务效率-患者等待队列长度-等待时间三者之间的关系。在此基础上，提出一种具有普适性的描述具有多服务阶段的门诊患者流模型，为医院门诊管理者提供洞察患者流规律的方法。　　研究结果表明，医生的服务效率随患者等待队列长度的增加而增加，并在到达一定程度后下降，呈倒U形关系；随着患者等待队列长度的增加，患者等待时间呈阶梯形的增长曲线。服务效率与患者流之间存在潜在的关联关系，这一关系可以被用于调整整个门诊系统中不同阶段的服务效率和患者流。　　研究结果启示医院管理者需要从系统化的高度实现对具有多服务阶段的门诊系统进行运营管理，对于提升医院门诊的服务效率、缓解医疗服务资源供需紧张的状况具有重要作用。

关键词：服务效率患者流数据驱动队列长度等待时间 service efficiency patient flow data driven length of queue waiting time

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