Cloud computing for infectious disease surveillance and control: Development and evaluation of a hospital automated laboratory reporting system

Mei Hua Wang, Han Kun Chen, Min Huei Hsu, Hui Chi Wang, Yu Ting Yeh

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: 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. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: 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. Results: 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. Conclusions: 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.

Original languageEnglish
Article numbere10886
JournalJournal of Medical Internet Research
Volume20
Issue number8
DOIs
Publication statusPublished - Aug 1 2018

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Hospital Laboratories
Communicable Diseases
Disease Outbreaks
Electronic Health Records
Orthomyxoviridae
Hepatitis B virus
Sensitivity and Specificity
Access to Information
Streptococcus pneumoniae
Mycobacterium tuberculosis
Information Systems
Hepacivirus
Early Diagnosis
Public Health
Cloud Computing

Keywords

  • Electronic medical records
  • HALR
  • Laboratory autoreporting system

ASJC Scopus subject areas

  • Health Informatics

Cite this

Cloud computing for infectious disease surveillance and control : Development and evaluation of a hospital automated laboratory reporting system. / Wang, Mei Hua; Chen, Han Kun; Hsu, Min Huei; Wang, Hui Chi; Yeh, Yu Ting.

In: Journal of Medical Internet Research, Vol. 20, No. 8, e10886, 01.08.2018.

Research output: Contribution to journalArticle

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abstract = "Background: 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. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: 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. Results: 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. Conclusions: 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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