Viability study of a personalized and adaptive knowledge-generation telehealthcare system for nephrology (NEFROTEL)
Several important problems in the majority of countries are challenging the centralized and overburdened current model of healthcare. Telehealthcare is presented as a new paradigm that offers high expectations to solve this picture. In this paper we present the major outcomes of the viability study of a novel personalized telehealthcare system for nephrology (NEFROTEL). The study evaluates the accuracy and quality of the knowledge generated by two key processing layers, namely, sensor layer and patient physiological image (PPI) layer, in an independent way, thanks to its modular design. The first one was defined by a personalized falling detection monitor, on account of the consequences of falls in chronic renal patients. The second one was analyzed by means of a PPI's prototype based on a urea compartmental pharmacokinetic model. The experimental study of the falling detector monitor has been more extensive than the other because the latter has already been addressed in other works. The outcomes show, firstly, the capability of the PPIs to provide integrated and correlated physiological knowledge adapted to each patient, and secondly, demonstrate the reliability of the impact detection function of the adaptive human movement monitor compliant with the NEFROTEL paradigm. The study confirms that NEFROTEL is able to provide knowledge concerning a patient in a manner that cannot be accomplished by the ordinary healthcare model at the present time.
telehealthcare nephrology personalized knowledge generation adaptive falling detection physiological models multi-scale knowledge