A comprehensive review of emergency department simulation applications for normal and disaster conditions

Gül, Muhammet; Güneri, Ali Fuat

doi:10.1016/j.cie.2015.02.018

Cited by 152 publications

(86 citation statements)

References 109 publications

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“…A recent overview in [17] outlines this variety in terms of resulting research questions. Many of the existing papers in this area study reasons for excessive patient waits and discuss strategies to overcome crowded emergency departments [25,30,34,36].…”

Section: Pathway Redesign In Emergency Departmentsmentioning

confidence: 99%

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Redesigning the diagnostic pathway for chest pain patients in emergency departments

Rachuba

Salmon

Zhelev

et al. 2017

Health Care Manag Sci

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Patients presenting with chest pain at an emergency department in the United Kingdom receive troponin tests to assess the likelihood of an acute myocardial infarction (AMI). Until recently, serial testing with two blood samples separated by at least six hours was necessary in order to analyse the change in troponin levels over time. New high-sensitivity troponin tests, however, allow the inter-test time to be shortened from six to three hours. Recent evidence also suggests that the new generation of troponin tests can be used to rule out AMI on the basis of a single test if patients at low risk of AMI present with very low cardiac troponin levels more than three hours after onset of worst pain. This paper presents a discrete event simulation model to assess the likely impact on the number of hospital admissions if emergency departments adopt strategies for serial and single testing based on the use of high-sensitivity troponin. Data sets from acute trusts in the South West of England are used to quantify the resulting benefits.

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Section: Pathway Redesign In Emergency Departmentsmentioning

confidence: 99%

“…Common outcome measures of ED models appear to be the time spent in department, patient throughput and resource utilisation [6]. On the other hand, the costs of Emergency Departments appears to be an under researched topic [17]. The vast majority of models are very unit specific.…”

Section: Pathway Redesign In Emergency Departmentsmentioning

confidence: 99%

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Rachuba

Salmon

Zhelev

et al. 2017

Health Care Manag Sci

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“…In numerous developing nations, such as Pakistan, almost all health organizations lack a set Appointment System, resulting in long waiting. The former works have emphasized on evaluating and improving patient flow and scheduling (Bhattacharjee and Ray, 2014) in different departments of the hospital including inpatients (Proudlove et al, 2007), outpatients (Cayirli and Veral, 2003), emergency (Gul and Guneri, 2015) and surgical/operations (May et al, 2011). However, there are only a limited number of studies which particularly assessed the flow of walk-in patients in health centres (Fetter and Thompson 1966;Rising et al, 1973;Ashton et al, 2005;Cayirli and Gunes 2014).…”

Section: Excessive Queuing In Developing Countriesmentioning

confidence: 99%

“…Waiting time -Difference between arrival time and the time consultation/service begins (Adeleke et al, 2008;Gul and Guneri 2015;Sorup et al, 2015 (Welch 1964;Hill-Smith 1989;Brahimi and Worthington 1991;Adeleke et al 2009;Harper and Gamlin 2003;Khori et al, 2012;Mankowska et al, 2014) Number of Resources -The number of resources available upon arrival of patients mainly including personnel (doctors/nurses/other staff) and beds (Lane et al, 2000;Griffiths et al, 2005;Gunal 2012;Mankowska et al, 2014;Gul and Guneri 2015) No-shows -When patients do not arrive for their appointments (Hassin and Mendel 2008;Klassen and Yoogalingham 2009;Feldman et al, 2014) Average Queue length -The length of the queue at a particular time (Huarng et al 1996;Cote 1999;Silvester et al, 2004;Gul and Guneri 2015) Idle time of doctors -The amount of time that the doctors are not busy (Fetter and Thompson 1966;Klassen and Rohlder 1996;Cayirli et al, 2008) Taking into account the variables considered by the DEA and queue management studies, and the objective of the current study which is to assess the patient flow system using DEA, the inputs and outputs for the proposed DEA-queuing Model are shown in Table 4. Since the objective is to improve the efficiency of the queue system, an input-oriented DEA model will be run in order to reduce the waiting time of patients as well as other inputs, while keeping the level of availability of the doctors at the same level.…”

Section: Queuing Variables Definitionmentioning

confidence: 99%

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

2015

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Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widelyused techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients' department of a large public hospital in a developing country where appointment systems do not exist.The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

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“…M&S has been used extensively to study EDs (Gul and Guneri 2015). The majority of studies take a discrete-event simulation approach (DES; e.g.…”

Section: Introductionmentioning

confidence: 99%

Using simulation to help hospitals reduce emergency department waiting times: Examples and impact

Monks¹,

Meskarian²

2017

2017 Winter Simulation Conference (WSC)

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In recent years, all acute hospitals in the UK have experienced unprecedented emergency department waiting times and hospital bed pressures. The consequences are overcrowded emergency departments, ambulance shortages, cancelled elective operations, low staff morale and financial penalties. To deal with the increasing numbers of patient admissions and delayed discharges hospitals must turn now to modelling and simulation to help increase their flexibility and ability to deal with demand variation. Hospitals face several issues that reduce their flexibility including the need for extreme value-for-money and specialization of care. This talk presents three ED case studies undertaken by an analytics team in the UK. The paper considers the impact of the work and challenges arising from their experiences of simulation modelling in acute hospitals. Final thoughts consider the future of ED simulation. INTRODUCTIONEmergency department (ED) overcrowding is common in the United Kingdom's (UK) National Health Service (NHS). In England, performance of EDs are publicly reported using the (infamous) four hour waiting (cycle) time target. The target was originally set to a 100% level of compliance, but was quickly relaxed to 98% in 2004 and then 95% from 2010. The pressure felt in an NHS ED is closely linked to bed pressure within the main hospital (Cooke et al. 2004). Bed pressures and subsequent poor ED performance are due to systemic issues ranging from capacity in community based care, such as social services and nursing homes, lack of investment in public health, and the aging population. While the long term solutions to the systemic problem are sought, hospitals must find ways to increase their short term flexibility to deal with variation in demand and length of patient stay. Many of the flexibility issues experienced by hospitals can be conceptualized as queuing problems. Modelling and Simulation (M&S) therefore has a key role to play in finding solutions that ease the pressure on ED's and hospitals. M&S has been used extensively to study EDs (Gul and Guneri 2015). The majority of studies take a discrete-event simulation approach (DES; e.g. Coats and Michalis 2001;Günal and Pidd 2009;Paul and Lin 2012;Chavis et al. 2016;Oh et al. 2016;Wong et al. 2016;Yang et al. 2016). DES studies have typically focused on the number and scheduling of resources within the ED (Saghafian et al. 2015). Few studies appear to have worked with hospitals to examine the flexibility of the ED and associated processes to deal with variation in demand. With the exception of a few large centers in the UK, hospitals are unlikely to have the finance for substantial increases in their ED staffing. Hence the efficiency and flexibility of processes has become ever more important. This paper first presents some of the challenges UK hospitals face in managing demand and the options they have used in order to have the flexibility to meet waiting time targets in the face of hospital bed pressures. We describe available data sources for M&S and deta...

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A comprehensive review of emergency department simulation applications for normal and disaster conditions

Cited by 152 publications

References 109 publications

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

Using simulation to help hospitals reduce emergency department waiting times: Examples and impact

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