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

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

Yazan Alnsour, Marina Johnson, Abdullah Albizri, Antoine Harfouche Harfouche
Copyright: © 2023 |Pages: 14
DOI: 10.4018/JGIM.323059
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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.
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2. Research Framework

In order to attain our research goal, we employ the design science research paradigm to guide the development of the IT artifact, as it is an overarching framework for constructing IT artifacts (Abbasi et al., 2012; Hevner et al., 2004). The IT artifact is “a thing that has, or can be transformed into, material existence as an artificially made object (e.g., model, instantiation) or process (e.g., method, software)” (Gregor & Hevner, 2013). According to the design science research paradigm, this paper presents an innovative artifact – an AI-based framework utilizing state-of-the-art machine learning algorithms – to predict LOS and help hospitals better manage patient LOS for improved resource capacity planning and attaining efficient discharge plans (see Figure 1).

Figure 1.

AI-based Artifact: Proposed framework

JGIM.323059.f01

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