Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Global Information Management Année : 2023

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

Résumé

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.
Fichier principal
Vignette du fichier
Alnasour et al 2023.pdf (1.07 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04263512 , version 1 (28-10-2023)

Licence

Domaine public

Identifiants

Citer

Yazan Alnsour, Marina Johnson, Abdullah Albizri, Antoine Harfouche. Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions. Journal of Global Information Management, 2023, 31 (1), pp.1-14. ⟨10.4018/JGIM.323059⟩. ⟨hal-04263512⟩
12 Consultations
32 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More