英文摘要
|
The Incident Command Center for COVID-19 pandemic has established since January 23, 2020 at this 2600-bed teaching hospital in Northern Taiwan. Superintendent chaired daily briefing and multidisciplinary or cross sector policies were made efficiently. This study aimed to develop web-based COVID-19 dashboards which report data visually and dynamically for healthcare system management. This article described the development framework of this information system, the design of the actionable layout for data presentation and evaluation of performance. Data were extracted automatically from web-based health information system and laboratory information system in the midnight at the development stage and later, renewed hourly during the peak of the epidemic. Data included number of incident cases with positive results of SARS CoV-2 RT-PCR and antigen assay, number of confirmed cases diagnosed in this hospital and those referred from other hospitals or community-based quarantine settings, active cases who receive COVID-19-specific isolation precaution, numbers of beds in dedicated wards or ICUs taking care of COVID-19 patients, and number of healthcare personnel with COVID-19. Infection control personnel took 8.4 hours to collect these data and summarize data for the report manually, while it took only 16 minutes by using selection screen in data processing and upload function of this system. Information technology also facilitated collecting mandated indicators for assurance of adequate implementation of SARS CoV-2 testing of this hospital during fall and winter. Indicators were generated efficiently and timely, thus, infection control personnel performed root-cause analysis, provided feedback to physicians, and achieved the target through the Plan-Do-Check-Act (PDCA) cycle. Besides, the notifiable COVID-19 cases were labelled on the web-based dashboard of the ward automatically while they remained in special isolation. Thus, the staff (including cleaning personnel) prepare in advance regarding personal protective equipment and other precaution accordingly before leaving the station and heading to patient unit. In conclusion, information technology facilitates infection prevention and control program to assure patient and occupation safety in the healthcare setting in the context of COVID-19 pandemic.
|
参考文献
|
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
吳婷婷、曾麗荷、陳淑美等:護理指導衛教資訊管理系統應用品質管理循環之成效探討。榮總護理 2019;36:62-71.
連結:
-
張麗銀、張瑛瑛、彭素貞等:護理品質監測系統之精實資訊流策略與成效。榮總護理 2017;34:289-98.
連結:
-
Silva DL, Lima CM, Magalhães VCR, et al. Fungal and bacterial coinfections increase mortality of severely ill COVID-19 patients. J Hosp Infect 2021;113:145-54.
連結:
-
Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507-13.
連結:
-
Ranzani OT, Bastos LS, Gelli JG, et al. Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med 2021;15:S2213-600.
連結:
-
Zhou P, Liu Z, Chen Y, et al. Bacterial and fungal infections in COVID-19 patients: a matter of concern. Infect Control Hosp Epidemiol 2020;41:1124-5.
連結:
-
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.
|