Simulation-based models of emergency departments:

Zeltyn, Sergey; Marmor, Yariv N.; Mandelbaum, Avishai; Carmeli, Boaz; Greenshpan, Ohad; Mesika, Yossi; Wasserkrug, Sergev; Vortman, P.; Shtub, Avraham; Lauterman, Tirza; Schwartz, Dagan; Moskovitch, Kobi; Tzafrir, Sara; Basis, Fuad

doi:10.1145/2000494.2000497

Cited by 76 publications

(20 citation statements)

References 20 publications

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“…In this case, the results evidenced that this measure was shortened from 192 to 112 min. Other studies combining simulation and queuing theory can be found in Ferrand et al [105] and Zeltyn et al [128]. Another related study was presented by Chen and Wang [102] who proposed an integrated approach integrating non-dominated sorting particle swarm optimization (NSPSO), multi-objective computing budget allocation (MOCBA) and discrete-event Simulation (DES) aiming at meeting the government LOS targets in Sunnybrook Hospital emergency department.…”

Section: Resultsmentioning

confidence: 99%

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Ortíz‐Barrios

Alfaro-Saiz

2020

IJERPH

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The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

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Section: Resultsmentioning

confidence: 99%

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Ortíz‐Barrios

Alfaro-Saiz

2020

IJERPH

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“…The ED resource allocation problems examined in the current study are also known as medical resource planning or capacity allocation planning problems (Zeltyn et al [5]; Hulshof et al [6]). Zeltyn et al [5] studied the staffing capacity planning problem in the ED of a large Israeli hospital from operational, tactical, and strategic perspectives via a flexible ED simulator.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zeltyn et al [5] studied the staffing capacity planning problem in the ED of a large Israeli hospital from operational, tactical, and strategic perspectives via a flexible ED simulator. Hulshof et al [6] comprehensively reviewed the typical decisions made in resource capacity planning and control for six health care services, namely, ambulatory cases, emergency care, surgical care, inpatient care, home care, and residential service, on the basis of OR method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, the current study adopted the operational research solution, which entails applying models or approaches of operational research (OR) and developing an emergency flow simulation model to examine and solve the medical resource allocation problem of EDs given limited medical resources. This issue is also called the medical resource planning (Ahmed and Alkhamis [3]; Xu et al [4]) or the capacity dimensioning/allocation planning problem (Zeltyn et al [5]; Hulshof et al [6]). This issue involves efficiently allocating and resizing medical resources to minimize patient delays, waiting time, or total cost under a stochastic environment, such as patient arrivals or service time.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Feng

Chen

2015

Health Care Manag Sci

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The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

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“…In recent years, these technologies have been applied to emergency care settings to forecast crowding [22][23][24][25][26], quantify the effects of patients who leave without being seen (LWBS) [27][28][29][30][31], assess triage and patient streaming mechanisms [4,5,[32][33][34], optimize staffing [1,[35][36][37][38][39], examine the impact of reducing boarding times [33,40], and analyze the financial consequences of crowding [14][15][16]. However, resources that provide this support in a single toolkit are not typically available to ED managers, and due to high technical barriers to entry, systems methods and tools remain broadly underutilized by decision-makers in emergency care settings [20,41].…”

Section: Introduction Backgroundmentioning

confidence: 99%

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

Hurwitz

Lopiano

Bohrmann

et al. 2016

Preprint

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Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict sitespecific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods:The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated.Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion:This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-anderror approaches and become a useful and accessible tool for healthcare managers challenged to make operational decisions in environments of increasingly scarce resources.

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Simulation-based models of emergency departments:

Cited by 76 publications

References 20 publications

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

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