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

Author

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  • 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
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    References listed on IDEAS

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    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.
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