Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions
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- handle: 10670/1.l8u63q
- hal: hal-04263512
- doi: 10.4018/JGIM.323059
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Mots-clés
Artificial Intelligence Predictive Analytics Length of Stay Healthcare Supply Chain Clinical Decision SupportSujets proches
Health care delivery Health services Personal health services Medical services Medical and health care industry Delivery of medical care Health care Healthcare Delivery of health careCiter ce document
Yazan Alnsour et al., « Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions », HAL-SHS : droit et gestion, ID : 10.4018/JGIM.323059
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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.