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Telemedicine embraces telecommunication and information technologies in order to serve health care at remote locations outside of the premises of medical institutions. These systems produce and process dynamically growing amount of data to keep the pace with this exponential process. Moreover, they need to support real time processing in some cases which requires a dynamically scalable and fault tolerant architecture. Based on the large amount of data we propose for these telemedicine systems an extensible architecture which uses distributed and disjoint databases as an anonymized storage in order to support data analytics. Utilizing services, these methods could help us to create a system which could derive new medical information from the collected data that could be used to find possible treatments for illnesses and discover hitherto unknown correlations between different risk factors, thus involving cognitive aspects in our outlined solution. The future of adaptive telemedicine systems should be based on the successful application of distributed data processing with services in order to produce high value-added structured information flows and innovative knowledge base.
Garai et al. (Thu,) studied this question.