IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v24y2024i4d10.1007_s12351-024-00855-4.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    Université de Reims Champagne Ardenne)

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.

Suggested Citation

  • Adrien Wartelle & Farah Mourad-Chehade & Farouk Yalaoui & David Laplanche & Stéphane Sanchez, 2024. "Changing the perspective of system crowding evaluation using a new congestion measure: application to the Emergency Department," Operational Research, Springer, vol. 24(4), pages 1-35, December.
    • Handle: RePEc:spr:operea:v:24:y:2024:i:4:d:10.1007_s12351-024-00855-4
    DOI: 10.1007/s12351-024-00855-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-024-00855-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-024-00855-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stanley Gershwin & James Schor, 2000. "Efficient algorithms for buffer space allocation," Annals of Operations Research, Springer, vol. 93(1), pages 117-144, January.
    2. Md Asaduzzaman & Thierry Chaussalet & Nicola Robertson, 2010. "A loss network model with overflow for capacity planning of a neonatal unit," Annals of Operations Research, Springer, vol. 178(1), pages 67-76, July.
    3. Diane Naouri & Carlos El Khoury & Christophe Vincent-Cassy & Albert Vuagnat & Jeannot Schmidt & Youri Yordanov & for the French Society of Emergency Medicine Evaluation and Quality Committee, 2018. "The French Emergency National Survey: A description of emergency departments and patients in France," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.
    4. Arnoud Bruin & A. Rossum & M. Visser & G. Koole, 2007. "Modeling the emergency cardiac in-patient flow: an application of queuing theory," Health Care Management Science, Springer, vol. 10(2), pages 125-137, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    2. Hui Zhang & Thomas J. Best & Anton Chivu & David O. Meltzer, 2020. "Simulation-based optimization to improve hospital patient assignment to physicians and clinical units," Health Care Management Science, Springer, vol. 23(1), pages 117-141, March.
    3. Stefan Helber & Katja Schimmelpfeng & Raik Stolletz & Svenja Lagershausen, 2011. "Using linear programming to analyze and optimize stochastic flow lines," Annals of Operations Research, Springer, vol. 182(1), pages 193-211, January.
    4. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    5. Yanting Chen & Jingui Xie & Taozeng Zhu, 2023. "Overflow in systems with two servers: the negative consequences," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 838-863, September.
    6. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    7. Tom van Woensel & Frederico R B Cruz, 2014. "Optimal Routing in General Finite Multi-Server Queueing Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
    8. Juliane Müller & Christine Shoemaker & Robert Piché, 2014. "SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications," Journal of Global Optimization, Springer, vol. 59(4), pages 865-889, August.
    9. Hideaki Takagi & Yuta Kanai & Kazuo Misue, 2017. "Queueing network model for obstetric patient flow in a hospital," Health Care Management Science, Springer, vol. 20(3), pages 433-451, September.
    10. F. R. B. Cruz & A. R. Duarte & G. L. Souza, 2018. "Multi-objective performance improvements of general finite single-server queueing networks," Journal of Heuristics, Springer, vol. 24(5), pages 757-781, October.
    11. Asaduzzaman, Md & Chaussalet, Thierry J., 2014. "Capacity planning of a perinatal network with generalised loss network model with overflow," European Journal of Operational Research, Elsevier, vol. 232(1), pages 178-185.
    12. Jie Bai & Andreas Fügener & Jan Schoenfelder & Jens O. Brunner, 2018. "Operations research in intensive care unit management: a literature review," Health Care Management Science, Springer, vol. 21(1), pages 1-24, March.
    13. Federico Nuñez-Piña & Joselito Medina-Marin & Juan Carlos Seck-Tuoh-Mora & Norberto Hernandez-Romero & Eva Selene Hernandez-Gress, 2018. "Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    14. Guan Wang & Yang Woo Shin & Dug Hee Moon, 2016. "Comparison of three flow line layouts with unreliable machines and profit maximization," Flexible Services and Manufacturing Journal, Springer, vol. 28(4), pages 669-693, December.
    15. Gustavo Ramiro Rodríguez Jáuregui & Ana Karen González Pérez & Salvador Hernández González & Manuel Darío Hernández Ripalda, 2017. "Analysis of the emergency service applying the queueing theory," Contaduría y Administración, Accounting and Management, vol. 62(3), pages 733-745, Julio-Sep.
    16. Colledani, Marcello & Tolio, Tullio, 2009. "Performance evaluation of production systems monitored by statistical process control and off-line inspections," International Journal of Production Economics, Elsevier, vol. 120(2), pages 348-367, August.
    17. Song-Hee Kim & Ward Whitt, 2014. "Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes?," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 464-480, July.
    18. Gregory Dobson & Hsiao-Hui Lee & Edieal Pinker, 2010. "A Model of ICU Bumping," Operations Research, INFORMS, vol. 58(6), pages 1564-1576, December.
    19. Ziwei Lin & Nicla Frigerio & Andrea Matta & Shichang Du, 2021. "Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 223-253, March.
    20. R. Bekker & A. Bruin, 2010. "Time-dependent analysis for refused admissions in clinical wards," Annals of Operations Research, Springer, vol. 178(1), pages 45-65, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:24:y:2024:i:4:d:10.1007_s12351-024-00855-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.