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Remote patient monitoring (RPM) is an innovative technology that provides continuous monitoring of patients' health from a distance. The RPM system is versatile because it can be used in the treatment of acute and chronic conditions, helping doctors keep their patients under control between ward rounds or the absence of personal care. However, the implementation of this system is accompanied by numerous problems: from technical to hospital staff management problems. The object of this study is the technical challenges associated with the use of the RPM system. The problem lies in the imperfection of many elements of RPM, including: data management, sensor accuracy, compatibility and user acceptance of the technology. The research found that these technical challenges arise from insufficient standardization, poor sensor data quality, and interoperability issues between medical platforms. These results are explained by the complexity of the technological infrastructure and the need to improve the qualifications of medical workers. The main goal of the current article is to solve the scientific and technical problem of RPM through the proposal of an effective practical development - an automated system for remote monitoring of patients. Since most processes are moving towards their automation, by using the latest scientific developments, among which we can recall machine learning and neural networks and Internet of Things (IoT) technology, it was decided to consider the factors of their application in remote patient monitoring systems. The results of this study can be used to optimize RPM systems in hospitals, as well as to improve the reliability and efficiency of new automated monitoring systems, which is extremely important in the context of war, which causes a shortage of medical personnel and an increase in the number of potential patients per doctor.
I.O. Shevtsov (Fri,) studied this question.