Hypertension is a complex cardiovascular condition, defined as an abnormally high blood pressure. Such long-term and consistent increase in blood pressure could result in coronary heart disease, stroke, kidney damage and other serious debilitating conditions. Complication rate from hypertension depends on how well you can predict and prevent those complications, considering individual patient’s risks. Several mathematical models and computer algorithms that are currently used for these purposes have relatively low accuracy and prognostic value. Machine learning methods could be a next step in improving outcomes of patients with hypertension in terms of calculating their individual risk of complications and choosing rational therapeutic strategy based on that data. We performed a literature review to cover the topic of machine learning methods in the management of patients with hypertension.

The rationale for use of artificial intelligence (AI) in radiology departments to analyze medical images in real-life clinical practice was studied in a multicenter prospective trial. This was a part of the “Experiment on the use of innovative technologies in the field of computer vision for the analysis of medical images and further use in the healthcare system of Moscow” taking place in 2020. The trial included 18 different AI systems and 538 participating radiologists, all working within Unified Radiological Information Service. We evaluated applicability of AI systems, demand from radiologists, the quality of AI implementation, radiologists adaptability and AI impact on the overall radiologists productivity.

The final analysis included 1 762 949 AI processing results and 15 028 feedbacks from radiologists.

Commitment of radiologists to use AI systems was 22.4%. Also 65% of the tested AI systems didn’t increase maximal timeline set for the image analysis. AI implementation for analyzing prophylactic mammography images accelerated delivery of the results in outpatient and inpatient setting by 15.0% (p = 0.03) and 50.0% (p = 0.05) respectively. Lung CT and low-dose CT image analysis (searching for potential lung cancer) took radiologists longer to perform by 42.0% of their standard time (p =0.04) when using AI systems. Such contradictory results of AI implementation in different radiology sub-specialties need to be further analyzed.

Overall the study results suggest time-saving rationale for using AI systems in radiology departments, including emergency settings. The output of AI image analysis should be verified by radiologist.

Background. Remote follow-up of patients with chronic non-communicable diseases is an increasingly popular and one of the most fast-growing telemedicine branches, mainly due to COVID-19 pandemic. There are certain premises for a wider and more routine use of telemedicine, one of them being coverage of telemedical consultations by the government insurance program.

Aim. To test a remote self-control program for patients with chronic non-infectious diseases who are followed-up by the doctors of the rheumatology department of the Moscow Regional Research Clinical Institute n.a. M.F. Vladimirsky, and to assess patient adherence to this program.

Materials and methods. We enrolled 120 patients with rheumatic diseases. The type of telemedicine technology used was a remote self-control program, where patients keep track of the doctor’s prescriptions and recommendations, how they are feeling, and disease symptoms persistence.

Results. The mean age of patients was 46.8±2.3 years; follow-up time ranged from 1 to 7 months with the mean of 96.2±9.3 days. Mean number of drug prescriptions per patient per day was 7.58 (max — 26 prescriptions, min — 2 prescriptions).

Patients adherence to the studied self-control program was 91.7%. Conclusion. Patients with chronic non-infectious diseases, requiring regular follow-up in outpatient settings, benefit from the use of telemedicine technology allowing for self-control, drug prescriptions tracking and motivating for self-education about the disease.

Here we present the key aspects of the fully automated APACHE II scale calculation development. We reviewed implementation opportunities for modern technological solutions in intensive care units. Successful experience of utilizing the clinical decision support system „PHILIPS Digital Resuscitation” for analysis, prognosis and patient data management is described in details. The digital algorithm for automated calculation of the clinical scale used in anesthesiology and intensive care departments is presented.

In this study we evaluated operability of a multicomponent system consisting of ECG recorders (made by different manufacturers), signal conversion drivers (matching layers) provided by ECG equipment vendors according to a single technical task, an ECG signal processing and visualization program. Such system registers and processes ECG signal by means of the software unit integrated into the regional medical information system (RMIS).

System performance evaluation was carried out to confirm the same reproduction of known waveforms in the original manufacturer’s software and in a unified environment of the RMIS. Test signals and synthetic ECG generated with functional generators were used. Technical specialist used built-in measuring instruments to compare amplitude-time parameters of the signals recorded in proprietary software and RMIS software. This allowed identifying an incorrectly working driver of one of the vendors. The error correction led to the driver processing of the ECG signal adequately, which was confirmed by the check method.

The proposed technical solution makes it possible to perform comparative assessment of the ECG quality registration in the manufacturer’s proprietary software and in the RMIS software, to identify incorrect operation of the matching layers at the stage of preclinical studies, and promptly make changes to the software.

Here we describe the possibilities of joint use of agent-based modeling and location intelligence based on geoinformation technologies for solving epidemiological problems.

This approach has an important advantage allowing close to real-life epidemic progression visualization (hepatitis A) in the “digital twin” of the city. The instrument we developed could be used for both research and practical purposes, as well as for managerial decision-making.

The article discusses implementation of electronic medical records in the Eurasian Economic Union (EEU) countries. Several common and country-specific barriers have been detected. In order to harmonize the legislation of the EEU countries, the authors propose a supranational legal regulation of relations in the form of an agreement between the EEU countries. This could will be the starting point for the further development of supranational relations.