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【論文摘要】到院前預警訊息對於醫院的應用：以單一醫院之經驗為例
【論文摘要】Pre-hospital Warning Messages in Hospital Applications: A Case Study of a Single Hospital
摘要
研究背景：緊急傷病事故具有一些特性：一定會發生，也沒辦法預期，產生影響是多元化，而應變則有時間壓力。在雜亂的現場與多軌的訊息下，我們希望能改善在時間張力下的訊息透明度，進行緊急事件現場「人、訊、物」的資料收集與訊息傳遞動線重整優化，且藉由即時訊息的拋轉，也有助於改善醫院臨床實務端的緊急應變能力。因應2021年7月1日全國救急救難一站通推行計畫，高雄市政府消防局之救護系統改採內政部消防署之版本，到院前資訊轉拋方式也同步改由國家災害防救科技中心所建立之交換平台，以統一全國標準化到院前預警與救護紀錄表資料傳遞流程。研究方法：本院採用國家災害防救科技中心之拋轉軟體，將資訊儲存院內伺服器，我們將收集到的訊息拆解，將必要且緊急的訊息提取出來，透過程式設計與物聯網的連動，將到院前預警訊息觸發急診閃燈並響鈴，此時急診醫療人員可立即做應變反應，此反應包括：急救空間與物品之整備、緊急應變小組的通知召集、抵院前預開立急診醫囑。而也利用階段是拋轉收到的生命徵象，產製視覺化休克指數圖與ICU入住風險預測，以提醒醫療人員該病人之危急狀況。研究結果：從2023年1月至6月統計資料所示，共有204位具時間敏感性四大急症病人：OHCA、重大外傷、疑似AMI、疑似CVA，經由119救護車送入本院急診。預警案件中OHCA最多（86件,42.16%），而重大外傷最少（18件,8.82%）從預警閃燈閃爍與響鈴至人員確認約耗時1.51分鐘；對於OHCA病人，急診醫療人員平均有4.03分鐘的整備時間，是四大急症中最短的時間；有最長的整備時間的是「疑似AMI」，平均有7.03分鐘；整體而言，四大急症平均有5.51分鐘的應變時間。研究結論：藉由科技的進步、流程的優化、善用資源的整合，來避免重工、資源錯置並大幅提高處置時間敏感性急重症病人之時效與正確性，讓到院前救護與醫院端找到更前瞻的資源共享方式，進而建立完整數位照護經驗回收系統。
並列摘要
Background: Emergency trauma incidents have certain characteristics: they are certain to occur, cannot be predicted, have diverse impacts, and require rapid response. In chaotic environments with multiple sources of information, we aim to improve message transparency under time pressure. This involves optimizing data collection and message transmission workflows for "people, information, and resources" at emergency incident scenes. Additionally, the real-time message transfer contributes to enhancing the emergency response capabilities at the hospital's clinical end. In response to the Promising One-stop First Aid Implementation Plan, effective from July 1, 2021, the Kaohsiung City Fire Department's ambulance system switched to the version provided by the Ministry of the Interior's National Fire Agency. The pre-hospital information transfer method was also synchronized with the exchange platform established by the National Science and Technology Center for Disaster Reduction to standardize the national pre-hospital warning and ambulance record data transmission process. Methods: Our hospital utilizes the transfer software provided by the National Science and Technology Center for Disaster Reduction to store information on our internal servers. We dissect the collected information, extract necessary and urgent messages, and use programming and IoT connectivity to trigger emergency department alerts, including flashing lights and alarms. At this point, emergency department medical personnel can immediately initiate response actions, including preparing the emergency space and equipment, notifying and assembling the emergency response team, and pre-issuing emergency department orders. We also utilize the received vital signs data to create visual shock index graphs and predict ICU admission risks, providing medical staff with reminders of the patient's critical condition. Results: Data collected from January to June 2023 show that there were a total of 204 time-sensitive emergency patients in four major categories: OHCA, major trauma, suspected AMI, and suspected CVA, who were transported to our hospital's emergency department by 119 ambulances. Among the pre-alert cases, OHCA was the most common (86 cases, 42.16%), while major trauma was the least common (18 cases, 8.82%). The time from alert flashing lights and ringing to personnel confirmation took an average of 1.51 minutes. For OHCA patients, emergency department medical personnel had an average preparation time of 4.03 minutes, the shortest among the four major emergencies. The longest preparation time was for "suspected AMI," averaging 7.03 minutes. Overall, the four major emergencies had an average response time of 5.51 minutes. Discussion: Through technological advancements, process optimization, and efficient resource utilization, we can avoid redundancy, resource misallocation, and significantly improve the timeliness and accuracy of managing time-sensitive critical patients. This allows pre-hospital emergency services and hospitals to find more forward-looking ways to share resources and establish a complete digital care experience feedback system.