Integration of a Robotic Platform for Clinical Monitoring of Patients With Severe Acquired Brain Injury in High-Intensity Care Units

Brief Summary

Frailty is not related exclusively to age, but represents a state of increased biological and clinical vulnerability characterized by reduced functional reserve and greater susceptibility to adverse events, even in response to minor stress factors. In patients with complex conditions, such as severe acquired brain injuries, early detection and prompt treatment of clinical deterioration are essential to reduce the risk of acute readmission, complications, and in-hospital mortality. Many of these events are potentially predictable and preventable, given the presence of appropriate monitoring systems. Advanced care models, including early warning systems, have been developed to support healthcare professionals in identifying patients at risk by detecting significant changes in physiological parameters. In recent years, technological innovations-particularly in the fields of robotics and telemedicine-have created new opportunities to enhance clinical monitoring, enabling more systematic data collection and timely clinical assessment. Social assistive robots are emerging as promising tools in healthcare settings. These systems can support clinicians and nurses in routine monitoring activities, help reduce workload, and facilitate more efficient and personalized care. By integrating robotic assistance with digital health platforms, it may be possible to support patient monitoring and clinical workflow, particularly in high-complexity environments such as neurorehabilitation units. This study focuses on the use of an innovative robotic telemedicine system based on the integration of the MOVER-L prototype with the ROB.IN.CARE platform, owned by Item Oxygen Srl, which purchased the platform from CO-ROBOTICS S.R.L. through the PON SI-ROBOTICS project. The system is designed to support the monitoring of patients' clinical conditions by integrating physiological and clinical data and identifying predefined signs of potential deterioration. It supports healthcare professionals by providing clinical information and automated alerts that prompt healthcare-professional review. The study is a low-intensity, non-randomized controlled pilot feasibility interventional study conducted in a Neurorehabilitation Unit. Patients with varying levels of clinical severity, classified according to the Glasgow Coma Scale, are enrolled and allocated to one of two groups: a control group receiving standard care and an experimental group monitored using the robotic system in addition to usual care. The primary objective of the study is to evaluate the feasibility, usability, safety, and reliability of this robotic telemedicine solution in a real clinical setting. In addition, the study assesses its acceptability among both patients and healthcare professionals. These aspects are evaluated using predefined feasibility and technical measures together with validated assessment tools, including the System Usability Scale (SUS) and the Health and Safety Executive Stress Indicator Tool (HSE). The specific role and interpretation of these measures are defined in the study protocol. The integrated MOVER-L/ROB.IN.CARE system combines robotic assistance with the acquisition and integration of physiological and clinical data through a dedicated software architecture. The platform incorporates algorithms designed to stratify clinical risk and assess patient severity. It utilizes established clinical scoring systems, such as the Modified Early Warning Score (MEWS) for early detection of clinical deterioration, the quick Sequential Organ Failure Assessment (qSOFA) for risk assessment in patients with suspected infection, and the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for the assessment of acute kidney injury. The present pilot study is intended to characterize the feasibility of integrating the MOVER-L/ROB.IN.CARE system into routine neurorehabilitation practice, including its technical performance, usability, safety, acceptability, and integration into the clinical workflow. The findings are intended to identify implementation requirements and sources of variability and to inform the design of subsequent adequately powered studies evaluating the clinical impact of robotic monitoring.