A simulation model of patient flow through the emergency department to determine the impact of a short stay unit on hospital congestion

Chavis, John; Cochran, Amy L.; Kocher, Keith E.; Washington, Valerie; Zayas‐Cabán, Gabriel

doi:10.1109/wsc.2016.7822243

Cited by 9 publications

(8 citation statements)

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“…From an operations perspective, the ultrasound center is a queueing network of multi-class customers and servers. Among the numerous papers on simulation models in healthcare settings, our work is most relevant to those studying emergency department (ED) and inpatient department for hospitals in North America or others [20,13,18,1]. [20] developed an open queueing network with blocking to capture the situation when a hospital is full and patients have to be sent to another hospital.…”

Section: Previous Workmentioning

confidence: 99%

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

Pan¹,

Xu²,

Jin³

et al. 2021

Preprint

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We collaborate with a large teaching hospital in Shenzhen, China and build a high-fidelity simulation model for its ultrasound center to predict key performance metrics, including the distributions of queue length, waiting time and sojourn time, with high accuracy. The key challenge to build an accurate simulation model is to understanding the complicated patient routing at the ultrasound center. To address the issue, we propose a novel two-level routing component to the queueing network model. We apply machine learning tools to calibrate the key components of the queueing model from data with enhanced accuracy.

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Section: Previous Workmentioning

confidence: 99%

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

Pan¹,

Xu²,

Jin³

et al. 2021

Preprint

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“…Complete hospitals models are quite rare and researchers often focus on a department in particular. For instance, (Chavis et al 2016) studies the impact of a post-emergency unit on ED patient's length of stay. Indeed, EDs are probably the most studied department using simulation.…”

Section: Related Workmentioning

confidence: 99%

Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

Lay

Augusto

Xie

et al. 2020

2020 Winter Simulation Conference (WSC)

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Bed occupancy ratio reflects the state of the hospital at a given time. It is important for management to keep track of this figure to proactively avoid overcrowding and maintain a high level of quality of care. The objective of this work consists in proposing a decision-aid tool for hospital managers allowing to decide on the bed requirements for a given hospital or network of hospitals on a short-medium term horizon. To that extent we propose a new data-driven discrete-event simulation model based on data from a French university hospital to predict bed and staff requirements. We propose a case study to illustrate the tool's ability to monitor bed occupancy in the recovery unit given the admission rate of ED patients during the pandemic of Sars-Cov-2. These results give an interesting insight on the situation, providing decision makers with a powerful tool to establish an enlightened response to this situation.

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“…The discrete event simulation (DES) has been widely employed in modelling healthcare systems [30] and investigating emergency departments [31]. The patient flow through the ED was improved by integrating the ED with other units such as the short stay unit (SSU) and inpatient units (IU) [32]. In this paper, the impact of the SSU and IU in reducing the ED congestion was investigated using the ED-Simulation model, in which statistical tests were proposed to establish the patient arrival times and service times.…”

Section: Introductionmentioning

confidence: 99%

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

et al. 2021

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One of the most critical objectives in the healthcare system is maximising patient flows in the emergency care patient pathway. Patient emergency flow analysis indicates that the timetabling of a patient's movement from one activity to another through the Emergency Department (ED) is critical for treating patients. The ED deals with the patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, and decisions to admit or discharge the patient. Any delayed activities in patient flow reduce the service level of healthcare. To address these challenges, this paper develops a stochastic ED Simulation-Optimisation approach by considering stochastic variables, such as patient interarrival times and treatment times, using statistical distributions. This type of distribution depends on two main elements: day shifts and patient categories. A hybrid evolutionary algorithm is integrated with the simulation to find a satisfactory solution for this stochastic optimisation problem in real time. Computational experiments show that the proposed approach can serve more patients in specific time windows or provide the same quality of the service with the use of fewer medical resources.

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A simulation model of patient flow through the emergency department to determine the impact of a short stay unit on hospital congestion

Cited by 9 publications

References 0 publications

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

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