Sustainable Use of Resources in Hospitals: A Machine Learning-Based Approach to Predict Prolonged Length of Stay at the Time of Admission

Abstract

Length of Stay (LOS) and Prolonged Length of Stay (pLOS) are critical indicators of hospital efficiency. Reducing pLOS, particularly in public healthcare settings, is crucial for optimizing bed allocation, resource utilization, and overall cost containment, while also improving patient safety and autonomy. This study investigates the effectiveness of different machine learning (ML) models in predicting LOS and pLOS.Methods. We conducted a retrospective cohort study analyzing data from over 12,471 discharges from a northern Italian hospital between 2022 and 2023. Sixteen regression and fourteen classification algorithms were compared for predicting LOS as a continuous variable and as multi-class categories ("1-3 days", "4-10 days", "> 10 days"). The same models were evaluated for pLOS prediction, defined as any hospitalization exceeding 8 days. We analyzed two versions of the dataset: one containing only structured data (demographics, clinical information, and hospitalization details) and another incorporating features extracted from free-text diagnoses provided by clinicians. Only data readily available upon admission was utilized.Results. Ensemble models, combining multiple ML algorithms, achieved superior performance in predicting both LOS and pLOS compared to traditional single-algorithm models, particularly when utilizing both structured and unstructured data. Among the most influential features identified were the average LOS for same-service hospitalizations within the previous month, the number of recent transfers across wards, and the need for multidimensional geriatric assessment. Notably, incorporating text-embeddings from diagnoses led to improved results, especially in the presence of comorbidities.Discussion. Our findings provide evidence that integration of ML, particularly ensemble models, offers significant potential for improving LOS prediction and identifying patients at increased risk of pLOS, representing a helpful tool to guide healthcare professionals and bed managers in making informed decisions. Furthermore, this study underscores the importance of incorporating ML into hospital operations to address the challenges of an aging population with chronic diseases, while containing costs and streamlining patient flow, ultimately contributing to a more sustainable healthcare system.

Keywords: LOS, pLOS, Machine Learning, Hospital Admissions, Public Healthcare, Sustainability

DOI: 10.54941/ahfe1005520