Changing the perspective of system crowding evaluation using a new congestion measure: application to the Emergency Department

Adrien Wartelle (), Farah Mourad-Chehade (), Farouk Yalaoui (), David Laplanche () and Stéphane Sanchez ()
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Adrien Wartelle: Mines Saint-Etienne, Univ. Clermont Auvergne, CNRS, UMR 6158 LIMOS
Farah Mourad-Chehade: Université de Technologie de Troyes (UTT)
Farouk Yalaoui: Université de Technologie de Troyes (UTT)
David Laplanche: Centre Hospitalier de Troyes
Stéphane Sanchez: Centre Hospitalier de Troyes

Operational Research, 2024, vol. 24, issue 4, No 1, 35 pages

Abstract: Abstract System crowding relates to the general congestion phenomenon of clients, products or patients waiting concurrently, and its adequate representation and measurement is a key step in the monitoring and optimization of system performances in response to an arrival demand. Traditional crowding and queuing measures such as occupation and waiting time are insufficiently studied in a data-driven context, especially with their transient behaviors on a short temporal scale. Furthermore, central to the study of Emergency Department systems, crowding scores suffer from a lack of interpretability, statistical relevance and extensibility to other types of systems. As such, this study introduces a new congestion measure based on the ratio of arrival and departure load given a specific time window to characterize system crowding. A mathematical framework is proposed and used to perform a 3-part evaluation and comparison on the new and traditional measures with their large temporal scale sample path properties, their short temporal scale fluid limit modelled properties and with a case study on a regional Emergency Department to illustrate and confirm the proven properties. The results, in the form of mathematical properties and correlations, show that this new measure is more pertinent to the tracking of crowding from a system perspective, notably service performances on a local scale, compared to other measures. Through this congestion measure and its characterization with a fluid limit model, this study proposes an alternative approach and takes a first step toward a new data-driven paradigm to better monitor, forecast and optimize system crowding.

Keywords: Congestion; Data-driven model; Emergency Department crowding; Fluid model; Little’s law; Queuing theory (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12351-024-00855-4

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