IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04263512.html
   My bibliography  Save this paper

Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions

Author

Listed:
  • Yazan Alnsour

    (UWO - University of Wisconsin Oshkosh)

  • Marina Johnson

    (MSU - Montclair State University [USA])

  • Abdullah Albizri

    (MSU - Montclair State University [USA])

  • Antoine Harfouche

    (CEROS - Centre d'Etudes et de Recherches sur les Organisations et la Stratégie - UPN - Université Paris Nanterre)

Abstract

Artificial intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today's healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research's findings have practical and theoretical implications in AI and HSC management.

Suggested Citation

  • Yazan Alnsour & Marina Johnson & Abdullah Albizri & Antoine Harfouche, 2023. "Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions," Post-Print hal-04263512, HAL.
    • Handle: RePEc:hal:journl:hal-04263512
    DOI: 10.4018/JGIM.323059
    Note: View the original document on HAL open archive server: https://hal.science/hal-04263512
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04263512/document
    Download Restriction: no
    File URL: https://libkey.io/10.4018/JGIM.323059?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
    ---><---

    More about this item

    Keywords

    Artificial Intelligence; Predictive Analytics; Length of Stay; Healthcare Supply Chain; Clinical Decision Support;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:hal-04263512. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.