Analytics Model in a Telehealth Center Based on Cloud Computing and Local Storage

Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.




References:
[1] Watson, A. R. (2016), Impact of the Digital Age on Transforming Healthcare' Healthcare Information Management Systems', Springer,pp. 219--233.
[2] Gaynor, M. G. (2015), 'Evaluation of Patient to Provider Oriented Telemedicine in Hospitals and Physician Practices', Muskie School Capstones. Paper 103.
[3] Mahmud, S.; Iqbal, R. & Doctor, F. (2015), 'Cloud enabled data analytics and visualization framework for health-shocks prediction', Future Generation Computer Systems.
[4] Ola, O. & Sedig, K. (2014), 'The challenge of big data in public health: an opportunity for visual analytics', Online journal of public health informatics 5(3), 223.
[5] Al-Majeed, S. S.; Al-Mejibli, I. S. & Karam, J. (2015), Home telehealth by Internet of Things (IoT), in 'Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on', pp. 609—613.
[6] Popescu, C. (2015), Autonomous Systems for Telemedicine, in 'Complex, Intelligent, and Software Intensive Systems (CISIS), 2015 Ninth International Conference on', pp. 297--301.
[7] Marks, R. (2012). Introduction to Shannon sampling and interpolation theory. Springer Science & Business Media.