On a global scale, a radical transformation of the healthcare sector is taking place right before our eyes. Health care systems in many countries are changing at an incredible pace under the pressure of innovative digital platforms that have gained popularity under the names of artificial intelligence (AI) and machine learning (ML). AI systems, powered by massive amounts of data collected from everywhere, are able to guarantee optimal decision making for key players in the healthcare industry, from the pharmaceutical industry to the smallest healthcare providers. This article presents the results of the analysis of achievements and prospects for the use of innovative digital technologies and platforms in modern health care. According to international experts in medicine, it is possible to automate 36% of functions, primarily at the levels of data collection and analysis, and the use of AI technologies can significantly increase the gross profit of firms and organizations in relation to medical activities. There is no doubt about this since information has already become the main driver of management across all sectors of the global economy in general and in the health care sector in particular. The main motivations for the penetration of AI into health care are the escalating costs and the urgent need to limit them, the problem of poor-quality diagnostics (up to 30% of ongoing studies turn out to be unreliable or are misinterpreted) and the desire for standardization and automation of routine functions up to the creation of self-managed diagnostic models. However, the biggest challenge for AI in healthcare is not whether these technologies will be useful enough, but how to ensure that they are quickly and efficiently implemented in everyday clinical practice.

Оценка качества подготовки студентов медицинских вузов в период пандемии COVID-19 для будущей работы в практическом здравоохранении

Aim. To conduct a comparative analysis of the relationship between doctors and patients in a regional infectious diseases hospital to combat the complications of COVID-19 during the 1st and 2nd waves of the pandemic. Materials and methods. The basis of the study was the infectious diseases hospital deployed in the City Clinical Hospital No. 5 in Nizhny Novgorod. The hospital treated patients with complications of coronavirus infection with concomitant therapeutic or surgical pathology. The study was conducted taking into account the principles of robust management through questioning, on issues related to the social characteristics of patients and their subjective assessments of relationships with medical personnel. The scope of the study was 129 people. Results. The study did not reveal any changes in the relationship between medical workers and patients; both in the 1st and in the 2nd wave of the pandemic, they were built on mutual respect and empathy: medical workers do their best to support patients, the latter, in turn, understand the difficulties of staff work in an infectious diseases hospital and feel gratitude. The established facts are convincing proof of the high level of readiness of medical organizations and their employees to work in extreme conditions associated with a tense epidemic situation. Conclusion. Thus, the results of this study indicate the formation in the 1st wave of the pandemic of a model of relationships built on the principles of mutual trust, support and avoidance of conflicts between doctors and patients of the regional infectious diseases hospital and the preservation of this model during the 2nd wave of the pandemic.

BACKGROUND: Artificial intelligence, like medicine, is a dynamically developing field that can be considered both a science and an art. This makes it much more difficult to use artificial intelligence compared to other technologies that come with a user manual. Research and start-ups in the field of medical artificial intelligence are rapidly multiplying: the popularity of smart mobile devices, networked applications and remote digital services is growing. However, there are still some problems that complicate the widespread use of artificial intelligence algorithms in everyday clinical practice. The reasons for this are the high cost of operating neural network platforms and the limited qualifications of some medical professionals in the field of computer technology. These are only temporary difficulties, though, which should and will be gradually resolved. CONCLUSION: This article focuses on the most sensitive points that are currently hindering the accelerated progress of machine learning in healthcare.

Demographic changes, the digitalization of society and medicine, and economic crises have created an urgent need to shift from a biomedical healthcare system focused on diseases and medical technologies to new models of healthcare delivery. When discussing new concepts of healthcare development, terms such as patient-centeredness and patient-centered medical care are frequently used, but without clear definition. To analyze the terminology related to the development of patient-centered healthcare at the present stage. The reference sources were reviewed and selected using keywords and their combinations in Russian and foreign electronic library systems and databases. The analysis has shown that patient-centeredness is considered in several contexts: as a conceptual framework for healthcare development, a healthcare delivery model, a principle of healthcare organization, and a defining characteristic of healthcare services, medical institutions, and healthcare professionals. The fundamental characteristics of patient-centeredness include treating patients as individuals, considering their lived experiences, collaborative decision-making, and effective communication between patients and healthcare providers. Patient-centeredness is the basis of many healthcare concepts and models, which have similar ideological foundations. It may be considered as a principle of healthcare provision, which is based on the high responsiveness of the healthcare system and its representatives at all levels to the patient’s wishes regarding the process and results of the provision of healthcare services, as well as advanced healthcare capabilities.

Retaining healthcare personnel through the implementation of medical mentoring aimed at adapting future healthcare professionals during training at a medical school, the development of professional involvement and competencies was the main task for the team of the Privolzhsky Research Medical University. The Medical Mentoring project was developed and is being implemented jointly with the Ministry of Health of the Nizhny Novgorod Oblast. The implementation of the project is aimed at increasing the professional adaptation of medical students and retaining medical personnel in the regional healthcare system, implementing a mentoring program in medical and pharmaceutical facilities in the doctor-mentor — student format. In addition, the integration of medical mentoring system made it possible to improve professional competencies affecting the achievement of indicators necessary for working activities. The project allows the student to adapt to the future team, feel their importance, and also develop not only professional, but also supraprofessional skills, educate and teach the future doctor at point-of-care.