Technologies and main functionalities of the telemonitoring application reCOVeryaIDShow others and affiliations
2024 (English)In: Frontiers in Big Data, E-ISSN 2624-909X, Vol. 7, article id 1360092Article in journal (Refereed) Published
Abstract [en]
The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors.
Place, publisher, year, edition, pages
Frontiers Media S.A., 2024. Vol. 7, article id 1360092
Keywords [en]
artificial intelligence, coronavirus, COVID-19, eHealth, long-term monitoring, rule-based system, telehealth, telemedicine
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-228577DOI: 10.3389/fdata.2024.1360092ISI: 001283170700001PubMedID: 39104732Scopus ID: 2-s2.0-85200202008OAI: oai:DiVA.org:umu-228577DiVA, id: diva2:1890531
2024-08-202024-08-202024-08-20Bibliographically approved