Ricerc@Sapienza

The COVID-19 emergency has exposed the fragility of many Health Care Systems around the world. Two major critical factors have been related to the management of critical care accesses and the availability of healthcare operators. COVID-like diseases are generally transmitted by airborne pathogens that grant a high contagion rate and rapidity. Unfortunately, health operators and medical doctors are the designated first victims of such epidemics. Infected operators (symptomatic or not) must be put at rest due to the potential contagion risk for the patients. In this manner, the healthcare systems end up almost depleted of operators. In this proposal we want to: 1) provide a preemptive planning strategy for intensive care units' accesses; 2) cope with the healthcare operators' shortage; 3) enforce middle and long term sustainability in pandemic scenarios; 4) enhance the quality of service for the patients. To achieve goal 1) we will implement machine learning algorithms to predict the bed availability in critical care units, as well as predicting the workload availability due to potential contagions of caregivers. As for goal 2) we will implement robots as acting remote interface for physicians at home in smart-working mode, to perform diagnostic tasks that do not require high precision manual interactions, and therefore decreasing the workload of physically available operators. In this manner we will be able to achieve 3) and also to improve the emergency management of hospital facilities. Finally, as for goal 4) the large scale implementation of robots will spare a large amount of time that is generally wasted by the caregivers in sanitization operations, tampering with number and duration of visits to patients who are normally left alone for a long time, also lowering the quality of service perceived by the patients, as well as harming them also on the psychological side, with significant fallbacks on the recovery speed.