Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study - Ricciardi, Carlo; Marino, Marta Rosaria; Trunfio, Teresa Angela; Majolo, Massimo; Romano, Maria; Amato, Francesco; Improta, Giovanni (Frontiers in Digital Health, 2024)
Background. Recently, crowding in Emergency Departments (EDs) has become a recognized critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found as a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care process and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS thorough forecasting tools enabling early improvements. Methods. The purpose of this work is to use a limited set of features impacting ED-LOS, both related to patient characteristics and to ED workflow, to predict it. Different factors were chosen (age, gender, triage level, time of admission, arrival mode) and analysed. Then, machine learning (ML) algorithms were employed to foresee ED-LOS. ML procedures were implemented taking into consideration a dataset of patients obtained from the ED database of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy) and referred to the period 2014-2019. Results. For the years considered, 496172 admissions were evaluated and 143641 of them (28,9%) revealed a prolonged ED-LOS. Considering the total amount of data (48,1% female e 51,9% male), 51,7% patients with prolonged ED-LOS were male and 47,3% were female. Regarding the age groups, the portion of patients that most affected the prolonged ED-LOS was the over 64. The evaluation metrics of Random Forest algorithm (RF) proved to be the best ones; indeed, it achieved the highest accuracy (74.8%), Precision (72.8%) and Recall (74.8%) in predicting ED-LOS. Conclusions. Different variables, referred to patients’ personal and clinical attributes and to the ED process, have a direct impact on the value of ED-LOS. The suggested prediction model has encouraging results; thus, it may be applied to anticipate and manage ED-LOS, preventing crowding and optimizing effectiveness and efficiency of the ED.