大量傷患事件下緊急醫療服務之應變

在大量傷患事件下，緊急醫療服務的成效直接關乎於傷患的生存率。本研究的目的是協助緊急醫療服務在大量傷患情況下建立醫療疏散計畫，以提升傷患的生存率。相較於一般情況下緊急醫療服務的目標為將傷患儘速送至醫院；在大量傷患事件下受限於可以利用的醫療資源，傷患運送醫院的時間不再被視為主要的目標，而須考量到醫院的處理能力、救護車的數量，以避免大量湧入的傷患阻礙醫院的正常功能。因此，在大量傷患的情況之下，傷患從被救援到醫治結束的時間均須列入考量，以提升整體傷患之存活機率。本研究提出一細胞傳輸模型為基礎之非線性模型，並利用拉氏釋限法將模式拆解成二子問題，分別為線性模型及非線性模型。在求解之每一迭代中，對子問題分別求解，其中非線性模型部分利用梯度投影法求解。數值化實驗和結果分析顯示，本研究提出之模型在大量傷患情境下，對於緊急醫療服務的執行具有可能的效益。

關鍵字

緊急醫療服務；細胞傳輸模型；醫院壅塞；交通壅塞；非線性模型；拉氏釋限法；梯度投影法

並列摘要

Emergency Medical Service (EMS) responses are directly related to the survival rate of casualties in Mass Casualty Incidents (MCI). This study aims to assist EMS response actions in MCIs. In general, EMS response actions position the time of casualty arrival at a hospital (TAH) as the top priority. However, restricted by the finite number of ambulances, medical personnel, and other medical resources, the TAH should not be the only performance indicator in MCIs. The performance of hospitals and ambulances should also be taken into consideration. Therefore, the termination time of hospital treatment is a crucial performance indicator to EMS responses in MCIs. This study aims to establish a casualty assignment system with considering the operational condition of hospitals and traffic circumstance features to diminish the casualty rate in MCIs. In this work, a nonlinear model, composed of a cell transmission model and the impedance function representing the potential congestions in hospitals, is proposed. A Lagrangian heuristics is also developed to divide the original problem into two sub-problems: a linear one and the other nonlinear one. The nonlinear sub-problem is solved by gradient projection, optimizing EMS response actions. Numerical experiments and a series of analyses were conducted to verify the computational efficiency of the proposed model.

並列關鍵字

Emergency Medical Service ； Cell Transmission Model ； Hospital Congestion ； Traffic Congestion ； Nonlinear Problem ； Lagrangian Relaxation ； Gradient Projection

參考文獻

Aylwin, C. J., König, T. C., Brennan, N. W., Shirley, P. J., Davies, G., Walsh, M. S., and Brohi, K. (2006). “Reduction in critical mortality in urban mass casualty incidents: analysis of triage, surge, and resource use after the London bombings on July 7, 2005.” The Lancet, 368(9554), 2219–2225.

Bai, Y., Hwang, T., Kang, S., and Ouyang, Y. (2011). “Biofuel refinery location and supply chain planning under traffic congestion.” Transportation Research Part B: Methodological, Elsevier Ltd, 45(1), 162–175.

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大量傷患事件下緊急醫療服務之應變

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