Improved understanding of factors affecting prolonged emergency department (ED) length of stay is crucial to improving patient outcomes. Our investigation builds on prior work by considering ED length of stay in operationally distinct time periods and using benchmark and novel machine learning techniques applied only to data that would be available to ED operators in real time. This study was a retrospective review of patient visits over 1 year at 2 urban EDs, including 1 academic and 1 academically affiliated ED, and 2 suburban, community EDs. ED length of stay was partitioned into 3 components: arrival-to-room, room-to-disposition, and admit disposition to departure. Prolonged length of stay for each component was considered beyond 1, 3, and 2 hours, respectively. Classification models (logistic regression, random forest, and XGBoost) were applied, and important features were evaluated. In total, 135,044 unique patient encounters were evaluated for the arrival-to-room, room-to-disposition, and admit disposition-to-departure models, which had accuracy ranges of 84% to 96%, 66% to 77%, and 62% to 72%, respectively. Waiting room and ED volumes were important features in all arrival-to-room models. Room-to-disposition results identified patient characteristics and ED volume as the most important features for prediction. Boarder volume was an important feature of the admit disposition-to-departure models for all sites. Academic site models noted nurse staffing ratios as important, whereas community site models noted hospital capacity and surgical volume as important for admit disposition-to-departure prediction. This study identified granular capacity, flow, and nurse staffing predictors of ED length of stay not previously reported in the literature. Our novel methodology allowed for more accurate and operationally meaningful findings compared to prior modeling methods.

中文翻译：

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急诊科住院时间的详细视图：提高预测能力并提取实时、可操作的见解


更好地了解影响急诊科 (ED) 住院时间延长的因素对于改善患者治疗效果至关重要。我们的调查建立在之前的工作基础上，考虑了急诊科在不同操作时间段内的停留时间，并使用基准和新颖的机器学习技术，仅适用于急诊科操作员实时可用的数据。这项研究是对 2 个城市急诊室（包括 1 个学术急诊室和 1 个学术附属急诊室，以及 2 个郊区社区急诊室）一年多来患者就诊的回顾性分析。 ED 的停留时间分为 3 个部分：到达房间、房间到处置以及允许处置到离开。每个部分的延长停留时间分别被认为超过 1、3 和 2 小时。应用分类模型（逻辑回归、随机森林和 XGBoost），并评估重要特征。总共对 135,044 次独特的患者接触情况进行了评估，包括到达房间、房间到处置以及入院处置到离开模型，其准确度范围为 84% 至 96%、66% 至 77%，以及分别为 62% 至 72%。候诊室和急诊室容量是所有到达房间模型的重要特征。房间到处置结果将患者特征和 ED 体积确定为最重要的预测特征。寄宿生数量是所有站点的准入处置到出发模型的一个重要特征。学术站点模型指出护士人员配置比例很重要，而社区站点模型则指出医院容量和手术量对于入院处置到出院预测很重要。这项研究确定了急诊科住院时间的细粒度容量、流量和护士人员配置预测因素，此前文献中未曾报道过。 与之前的建模方法相比，我们的新颖方法可以得出更准确且具有操作意义的发现。