Wallace David J, Kahn Jeremy M, Angus Derek C, Martin-Gill Christian, Callaway Clifton W, Rea Thomas D, Chhatwal Jagpreet, Kurland Kristen, Seymour Christopher W
Clinical Research, Investigation and Systems Modeling of Acute Illness (CRISMA) Center, the Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA; The Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.
Acad Emerg Med. 2014 Jan;21(1):9-16. doi: 10.1111/acem.12289.
Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports.
This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time.
The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (± standard deviation [±SD]) absolute error was 4.8 (±7.3) minutes using linear arc, 3.5 (±5.4) minutes using Google Maps, and 4.4 (±5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p < 0.01). Estimation accuracy was lower for each method among transports more than 20 minutes (mean [±SD] absolute error was 12.7 [±11.7] minutes for linear arc, 9.8 [±10.5] minutes for Google Maps, and 11.6 [±10.9] minutes for ArcGIS). Estimates were within 5 minutes of observed transport time for 79% of linear arc estimates, 86.6% of Google Maps estimates, and 81.3% of ArcGIS estimates. The regression-based approach did not substantially improve estimation. There were large differences in hospital catchment areas estimated by each method.
Route-based transport time estimates demonstrate moderate accuracy. These methods can be valuable for informing a host of decisions related to the system organization and patient access to emergency medical care; however, they should be employed with sensitivity to their limitations.
院外转运时间的估算是急诊医疗系统研究与规划的重要组成部分;然而,这些估算的准确性尚不清楚。作者在一大群院外患者转运中,针对三种估算方法与观察到的转运时间进行了准确性检验。
这是一项验证性研究,分别使用了华盛顿州金县2002年至2006年以及宾夕法尼亚州西南部2005年至2011年的院外记录。转运时间估算采用三种方法生成:直线弧距离法、谷歌地图法和ArcGIS网络分析家法。评估了估算误差(定义为观察到的转运时间与估算的转运时间之间的绝对差值)以及在指定误差阈值范围内的估算时间比例。基于主要结果,使用了一种纳入人口密度、一天中的时间和季节的回归估算方法来评估准确性的提高。最后,使用固定驾车时间,对每种方法估算的医院服务区域进行了比较。
作者分析了29935例送往44家医院的院外转运。直线弧距离法的平均(±标准差[±SD])绝对误差为4.8(±7.3)分钟,谷歌地图法为3.5(±5.4)分钟,ArcGIS法为4.4(±5.7)分钟。所有两两比较均具有统计学意义(p < 0.01)。对于超过20分钟的转运,每种方法的估算准确性都较低(直线弧距离法的平均[±SD]绝对误差为12.7[±11.7]分钟,谷歌地图法为9.8[±10.5]分钟,ArcGIS法为11.6[±10.9]分钟)。直线弧距离法估算的79%、谷歌地图法估算的86.6%以及ArcGIS法估算的81.3%在观察到的转运时间的5分钟范围内。基于回归的方法并未显著提高估算准确性。每种方法估算的医院服务区域存在很大差异。
基于路线的转运时间估算显示出中等准确性。这些方法对于为一系列与系统组织和患者获得紧急医疗护理相关的决策提供信息可能很有价值;然而,在使用时应考虑到其局限性。