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

Yazan Alnsour, Marina Johnson, Abdullah Albizri and Antoine Harfouche ()
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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

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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.

Keywords: Artificial Intelligence; Predictive Analytics; Length of Stay; Healthcare Supply Chain; Clinical Decision Support (search for similar items in EconPapers)
Date: 2023-01
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-hea
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Published in Journal of Global Information Management, 2023, 31 (1), pp.1-14. ⟨10.4018/JGIM.323059⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04263512

DOI: 10.4018/JGIM.323059

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