大量傷患事故緊急救護策略評估－以八仙樂園事故為例

An Assessment on the Strategy of Emergency Medical Service for Mass Casualty Incidents - A Case Study of the Flash Fire at Formosa Fun Coast Water Park

10.6149/JDM.2017.0601.03

曾偉文(Wei-Wen Tseng)

大傷事故 ； 緊急救護 ； 後送策略 ； 情境推估 ； Mass casualty incident ； Emergency medical service ； Transportation strategy ； Scenario estimation

災害防救科技與管理學刊

6卷1期（2017 / 03 / 01）

49 - 61

繁體中文

背景：依WHO統計2008年有超過一半的人口住在都市，以此趨勢來看，預估在2050年會有70%的人口居住在都市裡，此意味在都會區發生緊急事故或災害時，很難避免重大傷亡的狀況。目的：藉由災例大量傷病患事故不同緊急救護策略，來比較現場等待時間與到院前等待時間；並就個案特殊性，提出未來類似大傷事故應變方式。方法：2015年6月27日八仙樂園災例499名傷患中，127名由新北市消防局救護車後送傷患作為資料分析，依救護車動員能量、現場後送管制方式、派車抵達率與檢傷方式等設定不同情境進行評估，特別是後送管制方式，就有限救護車資源將傷患依檢傷順序編組後送，即利用不同後送編組，搭配救護車抵離現場時間，以及考量現場與各醫院距離，計算不同傷勢到院前等待時間，來比較隨到隨送與檢傷分組兩者後送的差異。結果：動員救護車數量應能因應現場傷患總數，另依情境推估，派車抵達率與傷患現場等待時間息息相關；另外，案例中現場後送站管制，提出之檢傷分組後送方式（SGS），載送救護車總數需求變少，整體平均等待時間卻拉長，雖然無法兼顧，但因平均載送，較先到先送（FIFO）方式，更可落實病情監控。結論：針對本案例大傷緊急救護與現場應變所發掘的問題，提出未來策進的作法外，利用情境推估方法，對於大量傷病患事故後送策略會有新的思維，對於未來搭配醫療存活率的計算，在大量傷患事故應變上，可以有利於緊急醫療資源的調度。

Background: More than half of world's population lived in cities in 2008. It is expected that 70% of world population will live in urban area by 2050 and means mass causality cannot be avoided in near future in case of emergencies. Purpose: To infer whether different strategies on Emergency Medical Service (EMS) might reduce wait time at the scene and, therefore, death rates among casualties arriving at hospital following a mass casualty incident (MCI), and to propose response measures for similar MCIs based on the particularities of this case in the future. Methods: An incident titled ＂Color Play Asia＂ party, which occurred on 27 June, 2015, in Taiwan, causing 499 burn injuries, was used in the case study. Ignoring self-care and buddy escort, the study focused on 127 patients transported by ambulances of New Taipei City as sample data with various scenarios related to mobilization capacity, transportation control, dispatching rate, and triage methods. The subgroup variables for sorting, particularly on transportation control, focused on the number of patient subgroups; the number of patients in each of the subgroups; the deterioration rate of each patient; the interval between ambulance arrivals and departures; the distance between the site and the hospital. Results: The ambulance requirement for a mass casualty incident is highly dependent on the total injuries, and the dispatch rate is closely related to patients' wait time for transportation to hospital based on the scenario simulation. In this case, the demand for ambulances during the response process was reduced and average wait time was increased when adopting SGS instead of FIFO used in this EMS operation. This results were unsatisfactory for both ambulance demands and waiting time simultaneously. More importantly, however, when this subgroup sorting method was employed, patient monitoring became easy during transportation due to the average loading. Conclusions: Some problems regarding EMS operation and field response in practice were identified, and the countermeasures were proposed to cope with through this case study. A system utilizing scenario simulations was developed in order to compare the differences of mobilized ambulances, arrival rate, triage methods and transportation arrangements which are usually applied 'on the ground' in a mass casualty incident. It is anticipated that the precise survival calculations on MCI response can be beneficial to deploy emergency medical resources.

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