Using the “Floating Patients” method to balance crowding between the hospital emergency department and other departments

Wachtel, Guy; Elalouf, Amir

doi:10.1016/j.cie.2017.06.023

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Elalouf and Wachtel [ 70 , 71 ] developed fully polynomial-time approximation scheme (FPTAS) algorithms to assist ED decision-makers in optimally scheduling evaluations for waiting patients. The authors used these algorithms in combination with the “floating patients” method discussed above and evaluated their efficacy in simulations with real-life data [ 71 , 72 ]. Memari et al [ 219 ] used genetic algorithms to optimize service allocations and patient flow in EDs.…”

Section: Scientific Modeling Methodsmentioning

confidence: 99%

“…In subsequent work, Elalouf and Wachtel [ 71 ] incorporated the method into an algorithm aimed at optimizing the scheduling of patient examinations, assuming a constraint on the maximal LOS allowed in the ED. In 2017, they analyzed a holistic approach to the dynamic patient allocation method, considering crowding in the ED and other hospital departments [ 72 ]. They also considered the extent to which information has been made available about the patient’s condition in addition to other factors such as its severity and the effect of crowding on treatment time.…”

Section: Practical Approaches To Managing Ed Queueingmentioning

confidence: 99%

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Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Elalouf

Wachtel

2021

Oper. Res. Forum

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Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

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Section: Scientific Modeling Methodsmentioning

confidence: 99%

Section: Practical Approaches To Managing Ed Queueingmentioning

confidence: 99%

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Elalouf

Wachtel

2021

Oper. Res. Forum

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“…2012 ; Visintin et al. 2019 ; Wachtel and Elalouf 2017 ). The management of ED overcrowding is one of the most critical challenges faced by many hospital establishments which require significant human and material resources (unfortunately these latter’s are limits).…”

Section: Introductionmentioning

confidence: 99%

Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework

Kadri

Dairi

Harrou

et al. 2022

J Ambient Intell Human Comput

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Recently, the hospital systems face a high influx of patients generated by several events, such as seasonal flows or health crises related to epidemics (e.g., COVID’19). Despite the extent of the care demands, hospital establishments, particularly emergency departments (EDs), must admit patients for medical treatments. However, the high patient influx often increases patients’ length of stay (LOS) and leads to overcrowding problems within the EDs. To mitigate this issue, hospital managers need to predict the patient’s LOS, which is an essential indicator for assessing ED overcrowding and the use of the medical resources (allocation, planning, utilization rates). Thus, accurately predicting LOS is necessary to improve ED management. This paper proposes a deep learning-driven approach for predicting the patient LOS in ED using a generative adversarial network (GAN) model. The GAN-driven approach flexibly learns relevant information from linear and nonlinear processes without prior assumptions on data distribution and significantly enhances the prediction accuracy. Furthermore, we classified the predicted patients’ LOS according to time spent at the pediatric emergency department (PED) to further help decision-making and prevent overcrowding. The experiments were conducted on actual data obtained from the PED in Lille regional hospital center, France. The GAN model results were compared with other deep learning models, including deep belief networks, convolutional neural network, stacked auto-encoder, and four machine learning models, namely support vector regression, random forests, adaboost, and decision tree. Results testify that deep learning models are suitable for predicting patient LOS and highlight GAN’s superior performance than the other models.

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“…There is a continuous increase in demands for ED services (medical and surgical treatments) and EDs are usually confronting an influx of patients around the world [5] , [6] , [7] , [8] . The EDs overcrowding is manifested by a prolonged waiting time and increasing patient length of stays in these healthcare establishments [9] , [10] . In addition, inadequate management of overcrowding affects the job satisfaction of medical staff and the quality of treatment of patients [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting emergency department overcrowding: A deep learning framework

Harrou

Dairi

Kadri

et al. 2020

Chaos, Solitons & Fractals

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Highlights Developed deep learning-based models to forecast emergency department overcrowding. Eight deep learning models have been compared for ED visits forecasting. Real Datasets from the ED at CHRU Lille, France, investigated to serve the case study. Two types of forecasting are conducted: one- and multi-step-ahead forecasting. Results show the superior performance of the VAE compared to the other algorithms.

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Using the “Floating Patients” method to balance crowding between the hospital emergency department and other departments

Cited by 5 publications

References 17 publications

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework

Forecasting emergency department overcrowding: A deep learning framework

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