Search

Objective Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (

Background Telemedical approaches represent a valuable tool for the management of coronavirus disease 2019 patients, allowing daily clinical assessment, monitoring of vital parameters, remote visits, and prescription of treatment or hospitalization in case of clinical worsening. This cross-sectional study aims to evaluate the use, barriers and facilitators of the “Lazio ADVICE” telemedical platform, a regional system for remote assistance for coronavirus disease 2019 patients at home, according to General Practitioners and Family Pediatricians of the Local Health Authority Roma 1, during the coronavirus disease 2019 pandemic. Methods An interview-based survey was performed between December 2020 and January 2021. The survey investigated the demographic information of General Practitioner and Family Pediatricians, the knowledge of the platform, frequency of utilization, usefulness, strengths and weaknesses, and hypothesis of future implementation proposed. Results We interviewed 214 physicians and 89 (41.6%) were classified as users and 125 (58.4%) as non-users. Older age and working in District 1, 14 and 15 (vs. District 13) significantly reduced the probability of using the platform physician. Among the 89 users, 19 (21.3%) used the platform every day or even several times a day, 40 (44.9%) several times a week but less than one access per day, 30 (33.7%) used the platform several times a month up to one entry per week. Most of them (92.3%) consider the platform useful. Barriers were poor integration with software and work routine (76.4%), and usability issues (53.9%). Among the 125 non-users, 14 (11.2%) didn’t know the existence of the platform, 60 (48.0%) never tried it and 51 (40.8%) tried to use it. Reported reasons for the interruption of use were not very user-friendly (45.1%), perceived useless (37.3%), non-optimal functioning (23.5%), and lack of time (19.6%). Conclusion The pandemic accelerated the implementation of telemedicine services around Lazio Region, starting a positive and continuous exchange of experiences, activities and best practices among physicians.