PREDICTING AMBULANCE TIME OF ARRIVAL TO THE EMERGENCY DEPARTMENT USING GLOBAL POSITIONING SYSTEM AND GOOGLE MAPS
PREDICTING AMBULANCE TIME OF ARRIVAL TO THE EMERGENCY DEPARTMENT USING GLOBAL POSITIONING SYSTEM AND GOOGLE MAPS
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DOI：
10.3109/10903127.2013.811562
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发表时间：
2013-10-01
影响因子：
2.4
通讯作者：
Warden, Craig
中科院分区：
文献类型：
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作者：
Fleischman, Ross J.;Lundquist, Mark;Warden, Craig
目标。推导和验证一个可以准确预测救护车到达时间的模型，该模型可以作为Google Maps Web应用程序实现。方法：研究方法。这是对2008年1月1日至12月31日期间俄勒冈州马尔特诺马县所有现场交通的回顾研究。现场和目的地医院地址被转换为坐标。ArcGIS Network Analyst用于根据街道网络速度限制来估计交通时间。然后，我们创建了一个线性回归模型，以使用天气、患者特征、灯光和警报器的使用、日光和高峰时间间隔来提高这些街道网络估计的准确性。该模型是从50%的样本中得出的，并在其余样本上进行了验证。协变量的显著性由p<；0.05确定，用于模型系数的t检验。准确率以计算机辅助调度记录的实际运输时间5分钟内的估计值的比例来量化。然后，我们构建了一个基于Google Maps的Web应用程序，以演示实际EMS操作中的应用程序。结果。其中包括48,308架运输机。街道网络对交通时间的估计在5分钟内准确，实际交通时间不到16%。实际的交通时间在白天和高峰时段较长，而使用灯光和警报器时则较短。年龄在18岁以下、性别、潮湿天气和创伤系统进入并不是运输时间的显著预测因素。我们的模型预测到达时间在5分钟内的概率为73%。对于灯光和警报器的传输，准确率在5分钟内达到77%。验证数据集中的准确度是相同的。对于8.8分钟以下的交通工具，灯光和警报器平均节省3.1分钟，对于较长时间的交通工具，平均节省5.3分钟。结论。仅根据街道网络对运输时间的估计大大低估了运输时间。一个包含较少变量的简单模型可以很好地预测救护车到达急诊科的时间。这个模型可以链接到全球定位系统数据和一个自动化的谷歌地图网络应用程序，以优化急救部门的资源使用。灯光和警报器的使用对运输时间有很大影响。
Objective. To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application. Methods. This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p < 0.05 for a t-test of the model coefficients. Accuracy was quantified by the proportion of estimates that were within 5 minutes of the actual transport times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations. Results. There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports. Conclusions. An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of arrival to the emergency department with good accuracy. This model could be linked to global positioning system data and an automated Google Maps web application to optimize emergency department resource use. Use of lights and sirens had a significant effect on transport times.