Covid-19 has increased the development of telemedicine technologies. These solutions were already available before the Coronavirus but have been recently favoured by the change in the behavior of doctors and patients during the pandemic. Constraints, travel and social distancing measures, for example, have encouraged the adoption of digital applications for health and digital channels for the exchange of information and data. Thanks to the tools for remote monitoring, doctors are able to remotely check the vital signs of patients, verifying their difficulty in breathing, body temperature and oxygen saturation levels in the blood. In a post-COVID-19 scenario, new perspectives are opening up for telemedicine: it can be extended, in fact, to different areas of medicine such as the monitoring of cancer patients, asthmatics, diabetics or patients subject to heart disease. Remote control can also be accompanied by visits via video conference, in order to follow the patient continuously through innovative assistance models.
It is estimated that approximately 35% of outpatient and home visits could become virtual in the next future.
AFE devices (Analog Front-End) allow to interface digital electronics with sensitive sensors used in medical applications such as pulse oximeters and heart rate monitors. This technology allows the monitoring of vital signs, recording heart rate variability, peripheral capillary oxygen saturation, optical blood pressure measurements and many other functions useful for bio-sensing. The timing controls are highly customizable and thanks to non-standard connections and low power consumption, they can be integrated into wearable, compact and battery-powered devices.
Wearable patches are also gaining momentum for temperature measurement in at-risk patients requiring continuous monitoring.
Key drivers in the development of devices incorporating low-power microcontrollers (MCUs) will be wireless connectivity and the exploitation of energy-saving features, such as standby and shutdown, to preserve battery life.
Artificial intelligence can be put at the service of medicine by helping to facilitate the achievement of important information that leads to better clinical decisions:
predictive modelling makes possible to identify patients at risk of infection in the hospital, the use of the early warning score in emergency departments and general care departments leads to identify patients at risk of worsening their health conditions.
Artificial intelligence can also help hospitals use their resources more effectively by proactively suggesting interventions to prevent an unscheduled ICU admission. The long-term opportunity is to combine these methods of care, data and forecasts into a single integrated experience to help doctors treat their patients and improve overall results.
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