Scala Arianna, Trunfio Teresa Angela, Majolo Massimo, Chiacchio Michelangelo, Russo Giuseppe, Montuori Paolo, Improta Giovanni
Department of Public Health, University of Naples "Federico II", Naples, Italy.
Local Health Authority, Napoli 3 Sud, Torre del Greco, Italy.
BMC Emerg Med. 2025 Jul 15;25(1):121. doi: 10.1186/s12873-025-01287-9.
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing LWBS rates and to develop a predictive model using machine learning (ML) techniques. A retrospective analysis was conducted on 80,614 ED visits recorded at Maresca Hospital in Torre del Greco, Italy, between 2019 and 2023. Statistical analyses were performed to examine correlations between patient characteristics, operational variables, and LWBS occurrences. Four ML classification algorithms-Random Forest, Naïve Bayes, Decision Tree, and Logistic Regression-were evaluated for their predictive capabilities. Random Forest demonstrated the highest performance on the minority class, achieving an overall accuracy of 72%. Feature importance analysis highlighted waiting time, triage score, and access mode as significant predictors. These findings suggest that predictive modeling may support hospital resource planning and patient flow management strategies to reduce LWBS rates.
急诊科拥挤已成为医院管理中的一个关键问题，导致患者等待时间增加，未经诊治离开（LWBS）的比率升高。本研究旨在确定影响LWBS比率的关键因素，并使用机器学习（ML）技术开发一个预测模型。对意大利托雷德尔格雷科的马雷斯卡医院2019年至2023年期间记录的80614次急诊科就诊进行了回顾性分析。进行了统计分析，以检查患者特征、运营变量和LWBS发生情况之间的相关性。对四种ML分类算法——随机森林、朴素贝叶斯、决策树和逻辑回归——的预测能力进行了评估。随机森林在少数类上表现出最高的性能，总体准确率达到72%。特征重要性分析突出了等待时间、分诊分数和就诊方式是重要的预测因素。这些发现表明，预测建模可能有助于医院资源规划和患者流程管理策略，以降低LWBS比率。
