Cloud based patient prioritization as service in public health care

This paper proposes and evaluates the performance of a Cyber-healthcare system which is aimed at providing patient prioritization over the cloud as a public health service for the rural and urban communities of the developing world. The underlying cloud-based Internet-of-Things (IoT-Cloud) infrastructure is aimed to be implemented in the city of Lubumbashi in the republic Democratic Republic of the Congo (DRC) with the objective of setting up a community health care network around a mesh of health kiosks. We propose a deployment model for the proposed Cyber-healthcare system, and describe a patient prioritization process as part of its situation awareness component. The results obtained from an experimental prototype reveal the field readiness of the off-the-shelf bio-sensor technology used by the system, the performance achieved when using a solar powered subsystem, the relative communication capabilities provided by its protocols and the network engineering feasibility of the planned community health care network. The relative efficiency of using supervised machine learning compared to unsupervised machine learning when performing patient prioritization, is also revealed through two popular algorithms: Multivariate linear regression (MLR) and K-means clustering (KMC).