傳染病管理系統

Epidemic Prevention and Control-Infectious Disease Management System

10.6320/FJM.202111_25(6).0011

林慧姬(Hui-Chi Lin)；鄭禮呈(Li-Cheng Zheng)；林佩蓉(Pei-Jung Lin)；郭律成(Lu-Cheng Kuo)；陳宜君(Yee-Chun Chen)；陳信希(Hsin-Hsi Chen)；高嘉宏(Jia-Horng Kao)

新冠肺炎（COVID-19） ； 感染預防和管制 ； 法定傳染病 ； 流程分析 ； 自動化通報 ； COVID-19 ； infection control ； notifiable infectious disease ； process analysis ； automated disease notification

台灣醫學

25卷6期（2021 / 11 / 25）

797 - 805

繁體中文

疾病管制署「防疫雲」子計畫－運用醫療資訊系統進行自動化傳染病通報，介接醫院端醫療資訊系統，帶入疾管署通報系統進行資料交換，大幅改善感染管制人員耗時費力手工作業，進行轉歸通報，藉此健全通報系統，提升整體防疫效能。然而2017年研究報告發現，僅有醫學中心全和少數中小型醫院，建構自動介接通報傳染病。本研究分享某醫學中心運用流程分析，設計系統架構等開發過程，及系統效能。在2021年5月新冠病毒疫情高峰時通報瞬間爆量，單日通報量達639人次，藉由本系統簡單操作、資料交換達成通報、檢體送驗的任務。實測人工作業和使用資料介接自動通報、送驗每一個案所花費時間，在疾管署網頁人工登打「通報」所花費的時間平均17.3±3.6分鐘，程式資料交換3.5±1.0分鐘；人工登打「送驗」花費平均4.7±0.5分鐘；程式資料交換花費1.9±0.6分鐘。開發過程雖然費心耗時，一旦建置成功，不僅省時、精準，省下來的人時讓感染管制人員能發揮專業，在疫情期間避免疫病於院區交叉散播，並持續兼顧推動組合式照顧降低醫療照護相關感染。總結，醫院開發具有資料交換功能、行政管理的傳染病通報系統，有助於防疫，也間接提升了臨床照護品質及病人安全。

＂Electronic Medical Record Auto-reporting Project＂, a sub-project of the National Epidemic Cloud Project sponsored by Taiwan Centers for Disease Control (CDC), bridging the Healthcare Information System of hospitals with CDC Infectious Disease Reporting System, established and upgraded a web-based notification system in order to improve efficiency of surveillance of healthcare-associated, endemic, and epidemic infections. However, only a minor proportion of hospitals established a web-based system for notifiable infectious diseases according to a national survey released in 2017. Thus, this study describes a medical center's experience regarding development of an automatic data exchange system complementary to a web-based notification system. This involved process analysis, data architecture design and performance evaluation. At the peak of the COVID-19 epidemic in May 2021, the number of notifiable cases of COVID-19 at this hospital was up to 639 cases a day. We upgraded this system in order to overcome the overwhelming burden of reporting task, support personnel quickly learn to use this system for data exchange. We measured and compared the time for traditional notifications performed by infection control personnel and those performed through this upgraded system. For notification step it took 17.3±3.6 minutes versus 3.5±1.0 minutes, respectively. For laboratory specimen login step it took 4.7±0.5 minutes versus 1.9±0.6 minutes, respectively. In conclusion, the development process required multidisciplinary collaboration and time. Nevertheless, once successful implementation of the system, it saves person-hours and provides precise timely reporting or data exchange. In addition, infection control personnel may provide their professionalism and more focus on promoting bundle care and other prevention measures against healthcare-associated infections or cross transmission of epidemiologically pathogens. All these efforts contribute to epidemic control, quality of care, patient and occupation safety.

1. 簡文山、徐翊娜、溫信財等：以國際法規趨勢審視台灣電子病歷之法律環境。醫療資訊雜誌 2008;17:31-40。
   連結：
2. 黃興進、呂卓勳、蕭如玲等：影響電子病歷交換效益關鍵因素之研究：醫師觀點。電子商務學報 2009;11:95-118。
   連結：
3. Sendak MP, Ratliff W, Sarro D, et al. Real-world integration of a sepsis deep learning technology into routine clinical care: Implementation study. JMIR Med Inform 2020;8:e15182.
   連結：
4. Chalfine A, Cauet D, Lin WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27:794-801.
   連結：
5. Steinmann J, Knaust A, Moussa A, et al. Implementation of a novel on-ward computer-assisted surveillance system for device-associated infections in an intensive care unit. Int J Hyg Environ Health 2008;211:192-9.
   連結：
6. Hebden JN, Wright MO, Fuss EP, et al. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;36:S7-11.
   連結：
7. Wright MO, Fisher A, John M, et al. The electronic medical record as a tool for infection surveillance: successful automation of device-days. Am J Infect Control 2009;37:364-70.
   連結：
8. Kuo FY, Wen TH. Characterizing Diffusion Dynamics of Disease Clustering: A Modified Space-Time DBSCAN (MST-DBSCAN) Algorithm. Annals of the American Association of Geographers 2018;108:1168-86.
   連結：
9. 黃博強：醫療相關手術傷口感染輔助監測與決策支援系統。臺灣大學生醫電子與資訊學研究所學位論文2014;1-33.
   連結：
10. 林慧姬、張馨心、陳明源等：發展線上醫療照護相關泌尿道感染監測系統。台灣醫學 2020;24:690 - 9.
    連結：
11. 林慧姬、曾意儒、陳明源等：多重抗藥性菌株資訊自動化監測與應用。感染管制雜誌 2013;23;290-9.
    連結：
12. Tseng YJ, Wu JH, Lin HC, et al. Development and evaluation of a web-based, hospital-wide healthcare-associated bloodstream infection surveillance and classification system. JMIR Med Inform 2015; 21:e31.
    連結：
13. Tseng YJ, Wu JH, Lin HC, et al. Rule-based healthcare-associated bloodstream infection classification and surveillance system. Stud Health Technol Inform 2013;186:145-9.
    連結：
14. Tseng YJ, Wu JH, Ping XO, et al. A Web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering. J Med Internet Res 2012;14:e131
    連結：
15. 林慧姬、張慈惠、周家玉等：運用電子病歷監測醫療照護相關感染的成效。台灣醫學2020;24:576-85.
    連結：
16. Chen YC, Sheng WH, Wang JT, et al. Effectiveness and limitations of hand hygiene promotion on decreasing healthcare-associated infections. PLoS One 2011;6:e27163.
    連結：
17. 張育菁、林慧姬、黃筱芳等：運用管制圖分析某醫院11年間醫療照護相關的金黃色葡萄球菌血流感染。感染控制雜誌 2020;30:33-42.
    連結：
18. Lin WP, Chang YC, Un-In Wu, et al. Multimodal interventions for bundle implementation to decrease central line-associated bloodstream infections in adult intensive care units in a teaching hospital in Taiwan, 2009-2013. J Microbiol Immunol Infect 2012;45:370-6.
    連結：
19. Lim KP, Kuo SW, Ko WJ, et al. Efficacy of ventilator-associated pneumonia care bundle for prevention of ventilator-associated pneumonia in the surgical intensive care units of a medical center. J Microbiol Immunol Infect 2015;48: 316-21.
    連結：
20. 張馨心：「降低導尿管相關泌尿道感染專案」對醫療照護相關泌尿道感染之影響。臺灣大學公共衛生碩士學位學程學位論文2011;1-99.
    連結：
21. 石裕川、紀佳芬、林瑞豐等：臺灣人因工程與設計的回顧與發展。管理與系統 2018;25:321-65。
    連結：
22. 潘美連、葉美玲、曹筱玫等：中小型院所資訊發展現況與傳染病自動化通報推展策略調查研究。衛生福利部疾病管制署(MOHW106-CDC-C-114-000707)，2016。 https://www.cdc.gov.tw/Professional/ProgramResult/TGVZbjViMFV3Ri9sZ3ZqUjVyaFQrQT09/ Accessed September 5, 2021.