緊急救護嚴重創傷跨縣市運送之空間分布與影響因素

Spatial Distribution and Influence Factors of Cross-District Transports among Major Trauma in Emergency Medical Services System

柯昭穎(Chow-In Ko)

指導教授 ： 馬惠明

共同指導教授 ： 賴美淑

國立臺灣大學/公共衛生學院/預防醫學研究所/碩士(2008年)

https://doi.org/10.6342/NTU.2008.03166

摘要

研究目的：將適合的病人，在及時的時間，送達正確的醫院，是緊急醫療區域化（Regionalization）運作最重要的基礎，可以減少嚴重創傷病患（major trauma）的死亡與失能。嚴重創傷緊急救護跨縣市送醫（cross-district emergency medical transport）的情形，最能適切反映「為患者預後更佳，故選擇跨縣市送醫」的需求，可藉以評估區域化運作是否合宜。「區域化」的議題，具有空間的意涵，可視為一種地理資料。因此，研究跨縣市送醫，應結合地理空間分析（spatial analysis）。台灣緊急醫療系統對於區域化運作，尚無系統評估，對於過去跨縣市送醫的需求數量、叢集區域、與影響因素，因缺乏研究，並不明瞭。因此，本研究欲結合地理資訊系統（GIS: Geographic Information System）的應用，針對北台灣嚴重創傷緊急救護，分析系統區域化運作中，跨縣市送醫相關之空間、時間、與人為因素。
研究材料與方法：本研究由行政院衛生署醫事處提供民國九十六年一月份北部五縣市，包括台北市、台北縣、基隆市、宜蘭縣、及桃園縣之「緊急救護記錄清冊電子檔」為研究資料，包含三個主要研究子題：(1)以敘述性統計，分析北部五縣市嚴重創傷救護跨縣市送醫的數量與比率，(2)應用地理資訊系統，定位緊急救護個案的求救地點，分析嚴重創傷救護跨縣市送醫的空間分布，(3)分析時間與人為因素，包括以回歸模式分析送醫運輸時間差、及以卡方檢定分析家屬與救護技術員之人為決定，對嚴重創傷救護選擇跨縣市送醫的影響。
研究結果：本研究包含三項主要結果：(1)民國九十六年一月份北部五縣市的嚴重創傷救護送醫案例共6,235件，包含跨縣市送醫案例389件，其中以台北縣佔326件為最多，其跨縣市送醫比率達12.8%（95% CI：11.6~14.2 %）也最高，其比率與另四個縣市（95% CI：0~3.1%），有明顯差異。(2)以GIS分析此6,235個案例的求救地點，可於地圖上定位該點位置的成功率為75.5%。跨縣市送醫個案叢集的地段，主要分布在台北縣的三重市，蘆洲市，板橋市北側、永和市、中和市、及林口鄉南側。跨縣市送醫比率較高的地段，則分布在台北縣的林口鄉南側、泰山鄉、汐止市西側、深坑鄉、石碇鄉、平溪鄉、坪林鄉、瑞芳鎮西側、八里鄉、與萬里鄉。 (3)以回歸模式分析，送醫運輸時間的快慢，並非選擇跨縣市送醫的影響因素。以卡方檢定分析人為因素，包括「家屬要求」，或是救護技術員現場判定傷患「病情需要」，才是選擇跨縣市送醫的影響因素。尤其在跨縣市送醫運輸時間反而比未跨縣市運輸更慢的地段，人為因素的影響，更為顯見。
結論：(1)台灣緊急醫療系統之嚴重創傷救護，在依縣市行政區劃分區域的運作模式下，確有跨縣市送醫的需求，並且不同縣市的跨縣市送醫比率，有相當明顯的差異。現行以縣市為基礎的區域化運作模式，應予修正。 (2)應用地理資訊系統，可成功地以地圖呈現的方式，瞭解各個縣市哪些地段的緊急救護，較會選擇跨縣市送醫模式。透過空間分析的方法，找出問題區域更確切的分布位置，可提供未來改善緊急救護區域化運作時，優先探討規劃的重點區域。(3)人為因素，而非運輸時間快慢，才是選擇跨縣市送醫的影響因素，此一關聯，尤其顯見於跨縣市送醫運輸反而更慢的地段。因此，倘若無法限制或更改人為就醫習慣，規劃區域化運作時，應提升此等地段跨縣市送醫的效能，或是重新規範此等地段於緊急醫療網責任區域的隸屬。關於跨縣市送醫對此等地段嚴重創傷預後的影響，未於本研究探討，應另繼續深入分析。

關鍵字

緊急醫療區域化 ； 跨縣市送醫 ； 嚴重創傷 ； 地理資訊系統 ； 空間分析

並列摘要

Objective:“The right patient, to the right place, at the right time.” is the most important principle for regionalization of EMSS (Emergency Medical Services System). EMSS regionalization could effectively improve the mortality and morbidity of major trauma patients. Evaluating the cross-district EMS transports of major trauma patients would promptly represent the exact demand of cross-district access to emergency healthcare, and would reflect the performance of regionalization. With character of spatial data, spatial analysis should be utilized in evaluations of cross-district EMS transport. In consideration of the lack of data in Taiwan for EMS regionalization and cross-district EMS transport, this study is to analyze the impacts of spatial, time, and human factors on cross-district EMS transport of major trauma patients in northern Taiwan.
Method: From the electronic data of Taiwan Emergency Medical Services Registry provided by Department of Health, Taiwan, we analyzed the data of January 2007 among the five districts in northern Taiwan including Taipei City, Taipei County, Taoyuan County, Keelung City, and Eland County. Three subjects were studied: (1) the amount of cross- district EMS transports for major trauma patients, and its proportion among district- wide EMS transports, by descriptive analysis of each district respectively; (2) the spatial distribution and characters of cross-district EMS transports for major trauma patients, by positioning the EMS scene exactly through GIS (Geographic Information System) utilization; (3) the correlation between time or human factor and cross-district EMS transport, by regression analysis for the difference of transport time correlating with cross-district transports, and categorical data analysis for the man-made impacts on cross-district transports, inclusive of patient or family’s insistence or EMS providers’ decision on the receiving hospital.
Result: There are three major results in this study: (1) a total of 6,235 EMS transports of major trauma patients occurred in January 2007 among the five districts, inclusive of 389 cross-district ones. Taipei County possesses the most 326 cross-district EMS transports out of the 389 ones, which accounts for 12.8% (95% CI: 11.6~14.2%) of its county-wide EMS transports, significantly higher than the proportions of the other four districts (95%CI: 0~3.1%). (2) Among the 6,235 EMS transports, EMS scenes were successfully positioned by GIS in 75.5% of cases. The cross-district EMS transports were clustered in Sanchong, Lujou, northern Banchiau, Yungho, Junghe, and southern Linkou. The regions with higher proportion of cross-district EMS transport distributed in southern Linkou, Taishan, western Hsichih, Shenkeng, Shrding, Pingshi, Pinglin, western Rueifang, Bali, and Wanli. (3) The difference of transport time is not an influence factor on deciding cross-district transport. Categorical data analyses reveal both human factors- family’s insistence and EMS providers’ decision on the receiving hospital are significantly (p<0.05) correlated with cross-district transports, especially in regions with longer cross-district transport time compared with non-cross-district ones.
Conclusions: Our data indicate that (1) Taiwan EMSS demands cross-district transports for major trauma patients, and the proportion of demands significantly varied across the island. Current EMS regionalization should be enhanced or revised. (2)Utilizing GIS may comprehensively illustrate the spatial characters for these cross-district transports. Spatial analysis may target the exact regions of concern, providing the spatial priority for reconstruction of EMS regionalization. (3) Human factor, instead of time factor, impacts on the decision of cross-district EMS transport, especially among the regions where cross-district transports are significantly time-consuming than the non-cross- district. For obstacles to restrict human factors, EMSS should improve the efficiency of cross-district transports, or to redefine the geographic jurisdiction of regionalization. The impact on patient outcome by cross-district EMS transports may need further in depth studies.

並列關鍵字

Emergency Medical Services Regionalization ； Cross-district Transport ； Major Trauma ； Geographic Information System ； Spatial Analysis

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