Advanced robotics as a support in healthcare organizational response: A COVID-19 pandemic case study.

The use of robotics is becoming widespread in healthcare. However, little is known about how robotics can affect the relationship with patients during an emergency or how it impacts clinicians in their organization and work. As a hospital responding to the consequences of the COVID-19 pandemic "ASST dei Sette Laghi" (A7L) in Varese, Italy, had to react quickly to protect its staff from infection while coping with high budgetary pressure as prices of Personal Protection Equipment (PPE) increased rapidly. In response, it introduced six semi-autonomous robots to mediate interactions between staff and patients. Thanks to the cooperation of multiple departments, A7L implemented the solution in less than 10 weeks. It reduced risks to staff and outlay for PPE. However, the characteristics of the robots affected staff's perceptions. This case study reviews critical issues faced by A7L in introducing these devices and recommendations for the path forward.

COVID-19 detection from exhaled breath.

The SARS-CoV-2 coronavirus emerged in 2019 causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next 4 years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner.