Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System.
Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System.
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DOI：
10.2196/10886
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发表时间：
2018-08-08
影响因子：
7.4
通讯作者：
Yeh YT
中科院分区：
文献类型：
--
作者：
Wang MH;Chen HK;Hsu MH;Wang HC;Yeh YT
近年来爆发了几种严重的传染病。为此，为了减轻公共卫生风险，世界各国致力于建立一个信息系统，对国际疾病暴发进行有效的疾病监测、风险评估和早期预警管理。云计算框架可以有效地提供所需的硬件资源和信息访问与交换，方便地连接传染病相关信息，开发跨系统的传染病监测与控制系统。本研究的目的是基于该框架开发医院实验室自动报告（HALR）系统并评估其有效性。我们收集了6个月的数据，并分析了在此期间由HALR和基于网络的法定疾病报告系统（WebNDR）报告的病例。收集系统评价指标，包括敏感性和特异性评价指标。HALR系统报告了15种病原体和5174例病例，WebNDR系统报告了34例病例。在两种系统的比较中，敏感性为100%，特异性根据所报告的病原体而变化。其中，肺炎链球菌、结核分枝杆菌复合体和丙型肝炎病毒的特异性分别为99.8%、96.6%和97.4%。而对流感病毒和乙型肝炎病毒的特异性分别只有79.9%和47.1%。将报告的数据与患者电子病历（EMRs）中的诊断结果整合后，流感病毒和乙型肝炎病毒的特异性分别提高到89.2%和99.1%。HALR系统可以根据检测结果提供指定病原体的早期报告，从而允许及早发现疫情并提供传染病数据的趋势。本研究结果表明，将HALR系统中报告的数据与emr中病例的临床信息（如诊断结果）相结合，可以提高疾病早期检测的敏感性和特异性，从而加强对传染病的控制和预防。
Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.
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影响因子：
3.3
作者：
Calderwood, Michael S.;Platt, Richard;Klompas, Michael
通讯作者：
Klompas, Michael
影响因子：
4.4
作者：
O'Connor, PJ;Crain, AL;Duncan, JE
通讯作者：
Duncan, JE
DOI：
10.1136/jamia.1999.0060099
发表时间：
1999-03-01
影响因子：
6.4
作者：
Shiffman, RN;Brandt, CA;Corb, GJ
通讯作者：
Corb, GJ
影响因子：
4.5
作者：
Pokorny, L.;Rovira, A.;Vilarasau, J.
通讯作者：
Vilarasau, J.
影响因子：
4.9
作者：
Wright, Marc-Oliver;Fisher, Adrienne;Robicsek, Ari
通讯作者：
Robicsek, Ari