Over the last years, the applicants and research team partners have developed the WARD (Wireless Assessment of Respiratory and circulatory Distress) project, using continuous wireless monitoring of vital signs and artificial intelligence algorithms for data interpretation in high-risk patient admitted to medical and surgical wards. The WARD project combines continuous measurements of 10 different physiological modalities with machine learning to develop the WARD-Clinical Support System (WARD-CSS), based on multiple intelligent algorithms, that automatically monitors, interprets, predicts and alert clinical staff. Through a mobile device with a purpose-built Graphic User Interface (GUI), the WARD-CSS stimulates human-machine interaction to improve the monitoring of high-risk hospitalized patients.~The WARD project has hitherto proven an unmet need for continuous monitoring and the potential for automatic detection and prediction of physiological deterioration events. Specifically, observational pilot studies of both patients with acute exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) and postoperative abdominal cancer surgery patients have shown that episodes of desaturation, tachycardia, tachypnea, and bradypnea are much more frequently detected using continuous vital signs monitoring than with existing Early Warning Score (EWS) systems.~Ongoing investigations will determine the efficacy in two very selected populations of high-risk surgical patients and acutely ill medical patients with severe disease. This study will investigate the WARD-systems' implementation, and effectiveness of use and impact in a cohort of patientparticipants admitted
The primary aim of this study is to test and assess the implementation and effectiveness of continuous wireless vital signs monitoring with real-time alerts on:~The frequency of patients monitored with adequate data quality as adequate clinical user satisfaction in the initial versus the last part of the trial (primary outcome).