緊急醫療救護案件區位模型分析

Modelling and Spatial Analysis for Calls of Emergency Medical Service with Ambulances Deployment

黃國平(Kevin P. Hwang) ； 吳青翰(Ching-Han Wu) ； 洪慈佑(Tzu-Yu Hung)

《規劃學報》 32期 (2005/12) Pp. 13-30

https://doi.org/10.6404/JP.200512.0013

摘要

全國歷年緊急醫療救護出勤及送醫人數均不斷增加，緊急醫療救護系統重要性與日劇增。為了減少急重症病患死亡及失能，救護人員能否儘快到達現場，施行急救處置是重要的關鍵。本研究利用變異數分析及變異係數的計算，將發生時間分為周二－周四、周五－周一兩組，每組再細分為六個時段。利用群集分析發現，在12個時段中，最多案件數量之群集中心點皆非常接近；而利用K方程式，分析不同時段之救護案件空間分佈，結果周二～周四，以時段07:00-11:00之群聚現象較明顯；而周五～周一則以時段11:00-15:00較明顯。本研究進一步以集群中心點，配置救護車，計算其無法在反應時間8分鐘內到達案發現場的機率，結果發現，最多案件數量之群集，必須配置二台救護車，始可達到90%之案件，在8分鐘內到達現場的水準。

關鍵字

緊急醫療救護 ； K方程式 ； 群集分析

並列摘要

The numbers of emergency medical service calls are increasing year by year, and the importance of emergency medical service system are getting more and more important. In order to decrease the death rate or disability of critical patients, whether the first-aid team can reach the scenes in time and provide pre-hospital care is the key factor. After ANOVA and computing coefficient of variation, the approach is to break down the week into two parts: Tuesday to Thursday, and Friday to Monday, and 24-hour period of every part is divided further into six equal-length time durations. By cluster analysis, we found that the centroids of clusters involving maximum cases in all 12 durations are very nearly close. Using Ripley's K function to analyze the spatial distribution of case locations in their durations, it appears that 7:00-11:00 in first part and 11:00-15:00 in second part are more clustered. Finally, we relocate the ambulances in centroids of clusters, and calculate the probability of response time over 8 minutes. It shows that we should deploy two ambulances in the cluster of most cases to achieve the desired response time for at least 90% of calls.

並列關鍵字

Emergency Medical Service ； Ripley's K function ； Cluster Analysis

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