IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p9667-d881326.html
   My bibliography  Save this article

Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study

Author

Listed:
  • Emilien Arnaud

    (Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France
    Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France)

  • Mahmoud Elbattah

    (Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France
    Faculty of Environment and Technology, University of the West of England, Bristol BS16 1QY, UK)

  • Christine Ammirati

    (Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France
    Amiens Picardy University Hospital—SimuSanté, 80000 Amiens, France)

  • Gilles Dequen

    (Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France)

  • Daniel Aiham Ghazali

    (Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France
    INSERM UMR1137, Infection, Antimicrobials, Modelling, Evolution, University of Paris-Diderot, 75018 Paris, France)

Abstract

Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called “Prediction of the Patient Pathway in the Emergency Department” (3P-U) to predict patient outcomes. Materials: Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH’s ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as “likely to be discharged”, 18,815 (17.8%) were flagged as “likely to be admitted”, and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units. Discussion and conclusions: The 3P-U model’s AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.

Suggested Citation

  • Emilien Arnaud & Mahmoud Elbattah & Christine Ammirati & Gilles Dequen & Daniel Aiham Ghazali, 2022. "Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9667-:d:881326
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/9667/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/9667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Woo Suk Hong & Adrian Daniel Haimovich & R Andrew Taylor, 2018. "Predicting hospital admission at emergency department triage using machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    2. Ki Hong Kim & Jeong Ho Park & Young Sun Ro & Ki Jeong Hong & Kyoung Jun Song & Sang Do Shin, 2020. "Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-16, November.
    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. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    2. Douglas Spangler & Thomas Hermansson & David Smekal & Hans Blomberg, 2019. "A validation of machine learning-based risk scores in the prehospital setting," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-18, December.

    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:gam:jijerp:v:19:y:2022:i:15:p:9667-:d:881326. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.