Proposal and Definition of a Methodology for Remote Detection and Prevention of Hypoxemic Clinical Cases in Patients Susceptible to Respiratory Diseases

Respiratory diseases are nowadays among the main causes of mortality and disability worldwide, and an intense effort has been made in the last years towards their prevention and early detection. Many of the proposed methods are based on the concept of remote medicine or 'telemedicine', a concept that encompasses a collection of techniques based on the remote care and assistance to patients through the utilization of networks and mobile devices. In this context, the current work presents the proposal and development of a methodology for the remote detection and prevention of potential hypoxemic cases in patients susceptible to respiratory diseases. Such methodology makes use of an expert system capable of generating alerts about the degree of hypoxemia of a patient, from a data set coming from both physical measurements and professional considerations. Protocols are defined for the data collection and its treatment using mobile terminals with sensors connected to them. Additionally, the collected data is stored and processed by fuzzy-logic inference systems as a support to the decision process that is the previous step to the generation of the final alert, named 'Global Hypoxemic Risk'. This new methodology has been tested experimentally and the results have been positive, resulting in a substantial reduction in the time for the detection of the oxygen deficit, and in its further treatment and prevention of physiological consequences.