STATE-OF-THE-ART TECHNOLOGICAL SOLUTIONS FOR CONTINUOUS MONITORING OF HEALTH CONDITIONS, DIRECT AND REAL-TIME SUPPORT FOR DIAGNOSIS AND PERSONALISATION OF TREATMENTS.
The digital transformation in the health sector is opening up unprecedented opportunities and amazing prospects. Innovation in the world of medical diagnosis and treatment relies on a wide range of enabling technologies (AI, cloud computing, etc.) and the increasing ability of systems to detect, store, process and share large amounts of data collected in real time, with the aim of triggering autonomous processes and readily supporting critical decision-making activities.
In this context,e-Health IoT plays a very important role because of its potential impact on the health and quality of life of all individuals. Diagnostics, biometrics, and telemedicine are changing radically thanks to the IoT and, in parallel, the amount of biomedical information available to patients and health professionals is growing disproportionately.
The adoption of IoT, due to the very nature of the health market, has been slower than in other sectors, such as sports. However, over the last decade, the spread of IoMT – Internet of Medical Things (IoMT) devices with different functionalities has grown exponentially.
Based on how the devices are used, a distinction can be made between home-based IoMT systems and wearable IoMT devices.
In the first case, the systems allow patients to transfer data on their health condition from their homes to other physical locations (hospitals, diagnostic centres, doctors, etc.) in order to detect the onset of any complications at an early stage and reduce the level of hospitalisation.
Wearable IoMT systems, on the other hand, are interconnected networks of medical devices wearable by the patient for the remote monitoring of specific parameters useful for diagnosis.
In the design of diagnostic support solutions in the medical field, SCAI Lab focused on creating tools capable of encompassing the entire complex framework of information needs regarding the patient’s history (integrating clinical data, instrumental data, pathological events, diagnoses, treatments, etc.).
The proposed innovation concerns the implementation of potential virtuous circuits that put the patient at the centre of the information system, precisely through the use of IoMT infrastructures. The challenge is to promotemore effective evidence-based care through tools for predictive diagnosis and the personalisation of interventions.
In the case of a platform designed for monitoring diabetes, for instance, data sources from IoMT devices (wearable glucose meters and electronic body fat scales) were integrated to allow continuous monitoring of a number of quantitative blood glucose parameters and biological information on the body composition of patients, useful both for monitoring the progress of the disease itself and in the management of the onset/evolution of comorbid conditions.
Edge computing technologies, IoMT infrastructures, and Big Data processing make it possible to monitor certain events continuously, automatically inform health workers and provide them with actual data to identify problems before they become critical. The doctor can refer to real information to check compliance with treatment recommendations or to follow what happens once the patient leaves the treatment or care centre.
In conclusion, the combination of these technologies can truly revolutionise treatment paradigms and protocols, allowing all stakeholders access to complete, consistent and timely patient information. What’s more, IoMT-based monitoring and diagnostic support systems are also able to foster data-driven governance models, because they can direct investments towards the actual needs of patients, thus reducing the inefficiencies that impact on costs.