BACKGROUND Crowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED) arrivals on a short time-horizon incorporating data from prehospital services. METHODS Time of notification and ED arrival was obtained for all 191,939 arrivals at the ED of a Norwegian university hospital from 2010 to 2018. An arrival notification was an automatically captured time stamp which indicated the first time the ED was notified of an arriving patient, typically by a call from an ambulance to the emergency service communication center. A Poisson time-series regression model for forecasting the number of arrivals on a 1-, 2- and 3-h horizon with continuous weekly and yearly cyclic effects was implemented. We incorporated time of arrival notification by modelling time to arrival as a time varying hazard function. We validated the model on the last full year of data. RESULTS In our data, 20% of the arrivals had been notified more than 1 hour prior to arrival. By incorporating time of notification into the forecasting model, we saw a substantial improvement in forecasting accuracy, especially on a one-hour horizon. In terms of mean absolute prediction error, we observed around a six percentage-point decrease compared to a simplified prediction model. The increase in accuracy was particularly large for periods with large inflow. CONCLUSIONS The proposed model shows increased predictability in ED patient inflow when incorporating data on patient notifications. This approach to forecasting arrivals can be a valuable tool for logistic, decision making and ED resource management.

中文翻译：

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使用院前数据实时预测急诊科的到来情况。

背景技术在全球范围内，急诊室（ED）的拥挤是一个挑战。为了应对日常操作中的拥挤情况，需要更好的工具来改善对急诊室中患者流量的监控。这项研究的目的是开发一个持续更新的监控系统，以在短时间内预测急诊科（ED）的到来情况，并结合院前服务的数据。方法从2010年到2018年，获得了191 939名挪威大学医院急诊室的通知和ED到达的时间。到达通知是自动捕获的时间戳，表示时间戳是第一次通知ED，通常是到达患者。通过救护车打给紧急服务通讯中心的电话。实施了Poisson时间序列回归模型，该模型用于预测1小时，2小时和3小时地平线上的到达次数，并具有连续的每周和每年周期效应。我们通过将到达时间建模为时变危害函数，从而将到达时间通知纳入其中。我们根据最近一整年的数据对模型进行了验证。结果在我们的数据中，有20％的抵达者在抵达前1小时以上得到了通知。通过将通知时间纳入预测模型，我们看到了预测准确性的显着提高，尤其是在一小时的时间范围内。在平均绝对预测误差方面，与简化的预测模型相比，我们观察到下降了约六个百分点。对于流入量较大的时间段，精度的提高特别大。结论所提出的模型在合并患者通知中的数据时显示出ED患者入流的可预测性增加。这种预测到达量的方法可能是物流，决策和ED资源管理的宝贵工具。