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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 公共衛生碩士學位學程
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91808
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor王彥雯zh_TW
dc.contributor.advisorCharlotte Wangen
dc.contributor.author金冠成zh_TW
dc.contributor.authorKuan-Chen Chinen
dc.date.accessioned2024-02-22T16:49:11Z-
dc.date.available2024-03-22-
dc.date.copyright2024-02-22-
dc.date.issued2024-
dc.date.submitted2024-02-16-
dc.identifier.citation[1] 內政部首度全臺人流統計 臺北白天比晚上多72萬人 n.d. https://www.moi.gov.tw/News_Content.aspx?n=4&sms=9009&s=220940 (accessed October 31, 2023).
[2] 臺北市政府民政局中文網站-統計資料-臺北市各行政區最新月份人口數及戶數 n.d. https://ca.gov.taipei/News_Content.aspx?n=8693DC9620A1AABF&sms=D19E9582624D83CB&s=EE7D5719108F4026 (accessed October 31, 2023).
[3] 臺北市政府全球資訊網-臺北行政區 n.d. https://www.gov.taipei/cp.aspx?n=1F076481DD9E556B (accessed October 31, 2023).
[4] 臺北市政府全球資訊網-常見問答-臺北市面積為多少?臺北市面積最大的區為何?面積最小的區為何?人口最多的區?人口最少的區? n.d. https://www.gov.taipei/News_Content.aspx?n=EEC70A4186D4C828&s=83285DF68B4A3CF2 (accessed October 31, 2023).
[5] 綜合報告統計表 n.d. https://www.stat.gov.tw/News_Content.aspx?Create=1&n=2755&state=1327FD6AD8DCDA52&s=230300&ccms_cs=1&sms=11065 (accessed January 8, 2024).
[6] Byrne JP, Mann NC, Dai M, Mason SA, Karanicolas P, Rizoli S, et al. Association between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States. JAMA Surg 2019;154:286–93. https://doi.org/10.1001/jamasurg.2018.5097.
[7] Nasser AAH, Nederpelt C, El Hechi M, Mendoza A, Saillant N, Fagenholz P, et al. Every minute counts: The impact of pre-hospital response time and scene time on mortality of penetrating trauma patients. The American Journal of Surgery 2020;220:240–4. https://doi.org/10.1016/J.AMJSURG.2019.11.018.
[8] Lee DW, Moon HJ, Heo NH. Association between ambulance response time and neurologic outcome in patients with cardiac arrest. Am J Emerg Med 2019;37:1999–2003. https://doi.org/10.1016/J.AJEM.2019.02.021.
[9] Channouf N, L’Ecuyer P, Ingolfsson A, Avramidis AN. The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta. Health Care Manag Sci 2007;10:25–45. https://doi.org/10.1007/S10729-006-9006-3.
[10] Brown LH, Lerner EB, Larmon B, LeGassick T, Taigman M. Are EMS call volume predictions based on demand pattern analysis accurate? Prehospital Emergency Care 2007;11:199–203. https://doi.org/10.1080/10903120701204797.
[11] Wong HT, Lai PC. Weather factors in the short-term forecasting of daily ambulance calls. Int J Biometeorol 2014;58:669–78. https://doi.org/10.1007/S00484-013-0647-X/FIGURES/4.
[12] Peters J, Brent Hall G. Assessment of ambulance response performance using a geographic information system. Soc Sci Med 1999;49:1551–66. https://doi.org/10.1016/S0277-9536(99)00248-8.
[13] Chen AY, Lu TY, Ma MHM, Sun WZ. Demand Forecast Using Data Analytics for the Preallocation of Ambulances. IEEE J Biomed Health Inform 2016;20:1178–87. https://doi.org/10.1109/JBHI.2015.2443799.
[14] Lu TC, Chen YT, Ko PCI, Lin CH, Shih FY, Yen ZS, et al. The demand for prehospital advanced life support and the appropriateness of dispatch in Taipei. Resuscitation 2006;71:171–9. https://doi.org/10.1016/J.RESUSCITATION.2006.03.016.
[15] Salminen T, Kaartinen K, Roos M, Vaajanen V, Ekstrand A, Setälä P, et al. Differences between the dispatch priority assessments of emergency medical dispatchers and emergency medical services: a prospective register-based study in Finland. Scand J Trauma Resusc Emerg Med 2023;31:1–10. https://doi.org/10.1186/S13049-023-01072-2/TABLES/4.
[16] Ebben RHA, Vloet LCM, Speijers RF, Tönjes NW, Loef J, Pelgrim T, et al. A patient-safety and professional perspective on non-conveyance in ambulance care: A systematic review. Scand J Trauma Resusc Emerg Med 2017;25. https://doi.org/10.1186/s13049-017-0409-6.
[17] Paulin J, Kurola J, Koivisto M, Iirola T. EMS non-conveyance: A safe practice to decrease ED crowding or a threat to patient safety? BMC Emerg Med 2021;21:1–13. https://doi.org/10.1186/S12873-021-00508-1/FIGURES/3.
[18] 內政部消防署全球資訊網專業版 > 最新消息 > 災情訊息 n.d. https://www.nfa.gov.tw/pro/index.php?code=list&ids=114&page=261 (accessed October 31, 2023).
[19] 行政院人事行政總處全球資訊網-DGPA--辦公日曆表 n.d. https://www.dgpa.gov.tw/informationlist?uid=55 (accessed October 31, 2023).
[20] 臺北大眾捷運股份有限公司-路線及班距 n.d. https://www.metro.taipei/cp.aspx?n=EAD981369A065968&s=C58C8C2C6419810F (accessed October 31, 2023).
[21] 臺北市交通管制工程處全球資訊網-交通流量調查資料(PDF下載) n.d. https://www.bote.gov.taipei/cp.aspx?n=E0C93DC334AE8028 (accessed October 31, 2023).
[22] 7張圖用Google Map路況資料分析台北塞車情況 - The News Lens 關鍵評論網 n.d. https://www.thenewslens.com/article/35880 (accessed October 31, 2023).
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91808-
dc.description.abstract緊急醫療系統的建置成本高昂,如何分配有限的院前救護資源並且有效率的運作系統是此領域中不斷被討論的主題。當社區緊急醫療需求遠高於該區域的緊急救護資源時,便容易發生救護車「跨區」出勤至距離較遠的案件,此即為「跨區派遣」。「跨區派遣」的案件數量越多,可能會導致緊急救護系統運作效率降低。本研究目的在找出在什麼時段、什麼區域、什麼條件下,救護資源相對不足而容易發生「跨區派遣」。若能辨認出這些情境的特定條件,那便能以此調整派遣優先順序,兼顧系統效率和病人自主權。
本研究使用2018年臺北市消防局救護派遣資料進行分析,共有548,934趟去個資化之救護車出勤紀錄,內容包括出勤分隊、案件位址、報案原因、出勤各項時間點紀錄、病患生命徵象等。首先,依照救護紀錄表的案件位址計算案件與關鍵分隊的距離,並訂定「跨區派遣』的定義。接著,採用描述統計以及羅吉斯迴歸分析探討影響「跨區派遣」之變因。
本研究發現跨區派遣佔總案件約十分之一。迴歸分析的結果發現跨區派遣較容易發生在分隊勤務繁重時、一般派遣案件、案件附近醫院壅塞情況較嚴重時、上班尖峰時段、火災一小時內等。
在未來人力配置無法彈性配合救護需求的波動以及救護人力無法同步成長的狀況下,如何辨認出系統超載並且在這些時段將有限的救護資源分配給真正需要的民眾乃是緊急救護係統的下一階段挑戰。
zh_TW
dc.description.abstractEstablishing the emergency medical system is costly, and how to allocate limited pre-hospital resources and use the system most efficiently in various scenarios is a continuously discussed topic in this field. When the demand for emergency medical services far exceeds the available resources in an area, it can lead to the dispatch of ambulances to more distant cases, referred to as "cross-district dispatch." A high frequency of "cross-distric dispatch" can potentially reduce the efficiency of the emergency medical system.
This study aims to identify when, where, and under what conditions there is a relative shortage of ambulance resources, leading to a higher likelihood of "cross-district ambulance" dispatch. By pinpointing specific conditions or even predicting the occurrence of such scenarios, ambulance dispatchers and paramedics can adjust their dispatch and rescue principles based on these factors, balancing system efficiency with patient autonomy.
This study analyzed 548,934 de-identified ambulance dispatch records from Taipei City Fire Department dispatch between 2015 and 2018. The data included the responding units, case locations, reasons for the call, timestamps for various dispatch stages, and patient vital signs. The distance between the case location and the key subunits was calculated based on the information provided in the dispatch records. If the responding subunit was not among the nearest three, it was marked as a "cross-distric dispatch." After organizing the variables in the database, factors representing case timing, case location, case density, and other relevant variables were selected as explanatory variables. A logistic regression analysis was then employed to identify the factors influencing "cross-distric dispatch."
The ultimate findings of this research revealed that "cross-distric dispatch" accounted for 10% of all cases. The regression analysis results indicated that "cross-distric dispatch" was more likely to occur during periods of heavy caseloads and in areas where ambulance resources were already in short supply. Specifically, "cross-distric dispatch" tended to occur more frequently when cases happened on typical weekdays during daytime hours, within one hour of a reported fire incident, in more densely populated subunits, when nearby subunits had a higher volume of severe cases, and in situations where nearby hospitals were experiencing congestion.
With the advancement of the emergency medical system, the content of pre-hospital emergency care has become more complex, and the requirements for paramedics'' skills are increasing. In recent years, the demand for emergency medical services has increased. However, increasing the manpower for these services simultaneously in the future is not feasible. In the situation of limited emergency resources, utilizing the findings of this research to identify system overload and allocating the limited emergency resources to those who genuinely need them during these periods will be the next-stage challenge for the emergency medical system.
en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-22T16:49:11Z
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dc.description.tableofcontents論文口試委員審定書 i
致謝 ii
摘要 iv
Abstract v
目次 vii
圖次 ix
表次 xi
第一章 導論 1
第1節 實習單位特色與簡介 1
第2節 研究背景、架構與假設 1
第3節 文獻回顧 5
第1項 反應時間 5
第2項 救護需求預測 5
第3項 救護資源分配 6
第4節 研究目的與研究問題 7
第二章 方法 8
第1節 資料描述 8
第2節 資料處理 10
第3節 跨區派遣定義 17
第4節 尖峰及非尖峰時段 20
第5節 救護分隊 21
第6節 區域案件量 24
第7節 醫院壅塞程度 24
第三章 結果 25
第1節 描述統計(定義一) 25
第2節 描述統計(定義二) 37
第3節 迴歸分析(定義一) 49
第4節 迴歸分析(定義二) 52
第四章 討論 55
第1節 討論 55
第2節 限制 57
第3節 結論 59
參考文獻 60
附錄 64
 
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dc.language.isozh_TW-
dc.title緊急醫療跨區救護派遣原因分析zh_TW
dc.titleAnalysis of Cross-district Dispatch in Emergency Medical Dispatchen
dc.typeThesis-
dc.date.schoolyear112-1-
dc.description.degree碩士-
dc.contributor.coadvisor馬惠明zh_TW
dc.contributor.coadvisorMatthew Huei-Ming Maen
dc.contributor.oralexamcommittee孫仁堂;陳佩君;蕭朱杏zh_TW
dc.contributor.oralexamcommitteeJen-Tang Sun;Pei-Chun Chen;Chuhsing Kate Hsiaoen
dc.subject.keyword緊急救護系統,到院前醫療,緊急醫療派遣,急診醫學,羅吉斯迴歸,zh_TW
dc.subject.keywordemergency medicine,emergency medical dispatch,emergency medical system,logistic regression,pre-hospital medicine,en
dc.relation.page68-
dc.identifier.doi10.6342/NTU202400677-
dc.rights.note未授權-
dc.date.accepted2024-02-16-
dc.contributor.author-college公共衛生學院-
dc.contributor.author-dept公共衛生碩士學位學程-
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