Machine Learning Models for Predicting "Left Without Being Seen" in Distributed Emergency Departments: A Comparative Study in Naples, Italy

Emergency departments (EDs) are challenged by overcrowding, which affects the quality of care and increases the rate of patients leaving without being seen (LWBS). The study aims to predict LWBS cases in Local Health Authority No. 3 in Naples, Italy, a distributed ED system, using machine learning (ML) models and compare its performance with single-center EDs. Data from 53,761 patients at LHA No. 3, 83,739 at Hospital H1, and 77,607 at Hospital H2 from the year 2022 were analyzed. The dataset included gender, age, triage score, mode of arrival, and time of admission. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR) were implemented using the KNIME Analytics platform. The primary outcome was LWBS. All ML models showed high accuracy rates above 90%. The RF model showed the highest accuracy (91.2%) and F-measure (95.3%). The study also revealed patterns in patient flow, most notably a peak in arrivals between 6:00 and 12:00. A balanced age distribution was observed, in contrast to the older patient demographics in previous studies. The ML models, particularly RF, were highly effective in predicting LWBS cases in a distributed ED system. This high predictive accuracy can contribute to the efficient allocation of ED resources and improve patient satisfaction, thereby addressing the problem of overcrowding.