Abstract
Several healthcare disasters have occurred in the past decade, and their occurrence has become more frequent recently due to one natural catastrophe after another. The medical application requirement for such a disaster management system includes effective, reliable, and coordinated responses to disease and injury, accurate surveillance of area hospitals, and efficient management of clinical and research information. Based on the application requirements, this case study describes a grid-based system in a health information supply chain that monitors and detects national infectious events using geographical information system (GIS), radio-frequency identification (RFID), and grid computing technology. This system is fault-tolerant, highly secure, flexible, and extensible, thus making it capable of operation in case of a national catastrophe. It has a low cost of deployment and is designed for large-scale and quick responses. Owing to the grid-based nature of the network, no central server or data centre needs to be built. To reinforce the responsiveness of the national health information supply chain, this case study proposes a practical, tracking-based, spatially-aware, steady, and flexible architecture, based on GIS and RFID, for developing successful infectious disaster management plans to tackle technical issues. The architecture achieves a common understanding of spatial data and processes. Therefore, the system can efficiently and effectively share, compare, and federate—yet integrate—most local health information providers and results in more informed planning and better outcome.
TopSetting The Stage
Early intervention is a key to stop a massive outbreak of diseases and new technologies can help in this. A mass epidemic outbreak caused by a natural disaster drew much attention recently. In response to such events, bodies related to biomedical, public health, defense, as also intelligence communities, are developing new approaches for real-time disease surveillance in an effort to augment existing public health surveillance systems. The term ‘syndromic surveillance’ refers to methods relying on detection of clinical case features that are discernible before confirmed diagnoses are made (Forslundet al., 2004). In particular, even before a laboratory confirms an infectious disease, sick people may show certain behavioural patterns, symptoms or signs, or there may be certain laboratory findings, that can be tracked through a variety of data sources. New information infrastructure and methods to support timely detection and monitoring, including the discipline of syndromic surveillance, are evolving rapidly (Homer et al., 2004; Hoard et al., 2005).