eLEARNING SOLUTION TO SUSTAIN THE DECISION MAKING PROCESS FOR PROVISIONING OPTIMIZED TELEMEDICINE SERVICES
eLEARNING SOLUTION TO SUSTAIN THE DECISION MAKING PROCESS FOR PROVISIONING OPTIMIZED TELEMEDICINE SERVICES

Summary/Abstract: As eLearning is more and more employed nowadays as an alternative to traditional learning scenarios, a multitude of eLearning solutions and providers have emerged on the market. Moreover, this domain has advanced to such lengths that most organizations possess already a standard suite of such applications. This paper introduces an eLearning solution that is focused on exposing specialized and personalized content describing the usage of telemedicine services across widely spread geographical areas. The proposed eLearning solution mainly consists of interactive dashboards that present in a simple, intuitive and highly visual manner the results of some complex CDR (Call Detail Records) analyses. Therefore, the system is fed by an analytical platform, whose architectural design will be further described in this paper, together with an argumentation why the proposed data modelling approach and techniques are optimal and are ready to respond to the pursued areas of interest. Overall, the aim of the eLearning solution is to help decision makers to better perceive their business from a global and analytical point of view and to understand behaviours in terms of telemedicine usage trends. This paper focuses on a line of research that has not been yet pursued, but that could impact the optimization of telemedicine services offering. In a nutshell, exposing the analysis of terabytes of CDRs through specific data-mining techniques in a clear and simplified manner supports learning about business facts and trends. Moreover, the assimilation of this type of knowledge is vital in enabling informed decisions about the optimization of provisioning telemedicine services in conformity with their actual usage. In addition, we envision an analogy to traditional Learning Analytics tools centred on personalized data visualization.

• Details
• Contents