摘要
目的依托杭州城市数据大脑交通小脑,探索城市交通拥堵状态下救护车优先快速通行,安全转运危急重症患者,提高抢救成功率。方法运用大数据、云计算、人工智能技术,开发“一键护航”数智系统,应用院前急救场景,保障执行紧急任务的救护车全路程安全、快速通行。收集2019年9月8日0时至2021年9月8日24时杭州市萧山区医疗急救指挥中心120指挥调度系统救护车通行时间,利用高德地图软件测量相同路线的车辆通行时间,统计同时期最近一次未护航相同路线急救事件车辆通行时间,使用秩和检验,比较用时差异;调查分析用户的满意度。结果经过两年的实战应用,救护车在“一键护航”下平均行驶时间比高德地图软件预估时间和相同路线急救事件用时分别节约57.5%和24.3%;交通事故发生率为0。结论救护车在城市数据大脑“一键护航”下全路程绿灯,快速通行,既优化了通行模式,又缩短了行驶时间,且驾驶员使用意愿强烈,呼救者对救护车到达和转运速度满意,为当前智慧急救体系建设拓展了思路。
Objective Based on Hangzhou City Brain system,to explore the priority and fast passing for ambulances under traffic congestion and the safe transfer of critical patients in order to improve the success rate of resuscitation.Methods To develop the“one-click escort”digital intelligence system by using big data,cloud computing and artificial intelligence,and to apply it to pre-hospital emergency scenarios to ensure green lights for the ambulances on emergency missions.Ambulance travel time of 120 command and dispatching system of Hangzhou Xiaoshan Medical Emergency Dispatching Centre from 0 h september 8,2019 to 24 h September 8,2021,the predicted travel time on the same route by using Gaode Map,the time spent on the most recent emergency incident on the same route were compared by using rank sum test,P<0.05 was taken as statistical significance.The satisfaction of the user was investigated and analyzed.Results After two years of application in the field,the average travel time of ambulances under“one-click escort”was reduced by 57.5%and 24.3%compared to software prediction and historical data;the traffic accident rate was 0.Conclusions Ambulances can safely pass with green lights for the entire journey under the“one-click escort”of the City Data Brain,which not only optimizes the passage pattern but also shortens the travelling time.The drivers have strong willingness for utilization and the callers also have higher satisfaction with the speed of ambulance arrival and transfer,which expands the ideas for the ongoing construction of smart emergency system.
作者
孙亚群
章鹏飞
王璐
邬利平
王慧丹
Sun Ya-qun;Zhang Peng-fei;Wang Lu;Wu Li-ping;Wang Hui-dan(Hangzhou Xiaoshan Medical Emergency Dispatching Centre,Hangzhou 311203,China)
出处
《中国急救医学》
CAS
CSCD
2022年第6期545-549,共5页
Chinese Journal of Critical Care Medicine
基金
杭州市萧山区重大投资项目(2017-330109-65-01-073263-000)。
关键词
院前急救
人工智能
优先通行
急救时间
救护车
Prehospital emergency care
Artificial intelligence
Priority passage
Response time
Ambulance
