Not only in the past five years but over the past few decades, several outbreaks of life-altering and life-threatening infectious diseases have highlighted the importance of prevention, diagnostic and monitoring methods. In particular, timely and accurate reporting is crucial for an effective public health response in the fight against infectious diseases.

In the paper titled Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System, authors Mei-Hua Wang RN, Han-Kun Chen MD, Min-Huei Hsu MD, Hui-Chi Wang MS, and Yu-Ting Yeh RN present the development and assessment of a Hospital Automated Laboratory Reporting (HALR) system. The paper is available here.

Traditional reporting systems are often inefficient due to manual data entry errors, delayed reporting, and lack of integration between the health databases of hospitals and public entities. To address these challenges, the authors’ primary objective was to develop a system that enhances infectious disease monitoring and control by automating the reporting of laboratory-confirmed cases to public health authorities, thereby improving the timeliness and accuracy of surveillance.

The HALR system leverages cloud computing to automatically extract laboratory results from hospital databases, cross-reference them with electronic medical records (EMRs), and transmit confirmed cases to the Taiwan Centers for Disease Control (TCDC). This automation not only reduces the burden on healthcare workers but also ensures that emerging infectious threats are rapidly identified. The image below shows the HALR system and it’s modules, identified by the dotted blue line, as well as it’s communication with the TCDC.

HALR_Image

One of the study’s most compelling findings is the system’s ability to significantly enhace the detection of infectious disease cases. During a six-month evaluation period, the HALR system reported 5,174 cases, while the traditional Web-based Notifiable Disease Reporting (WebNDR) system identified only 34. Additionally, by integrating clinical data with laboratory findings, the HALR system demonstrated high sensitivity (100%) and improved specificity for several diseases, ensuring that reports were both comprehensive and accurate.

Cloud computing plays a pivotal role in the HALR system by providing a scalable, adaptable and efficient framework for data storage, processing, and exchange. The cloud-based architecture ensures that the system can dynamically update reporting criteria and swiftly respond to emerging infectious threats by routinely downloading and incorporating the latest notifiable pathogen definitions from the TCDC.

In conclusion, the HALR system utilizes cloud computing to automate and enhance infectious diseases reporting, leading to more timely and accurate surveillance. By integrating laboratory results with clinical data within a cloud-based framework, the system significantly improves specificity, reinforcing public health responses to infectious disease outbreaks.

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