In modern conditions of global epidemiological challenges, a systematic approach to engineering (design) and reengineering (redesign) of treatment and diagnostic processes in hospitals acquires a special role for the state healthcare system.In this case, the focus of special attention to the management of hospitals is solving a task of organizing the treatment of patients with COVID-19 in the absence of proven clinical practice and dynamically modify the corresponding information flow, as well as the need for optimization of resource support and enhance its efficiency in the face of strong growth in the number of new cases and lack of standard solutions for the reorganization of hospitals, especially of non-infectious profile.In the paradigm of the systemic approach, effective management of the treatment and diagnostic process is not possible without a deep analysis of all its elements: from the moment the patient is admitted to the hospital until the completion of the treatment process. The recency of COVID-19 and the lack of clinical practice for the treatment of these patients have predetermined the need to develop comprehensive standards of clinical processes and their automation. It is the way of organizing the process to achieve the target state of the patient that forms the requirements for infrastructure and resource provision.The article presents the experience of the N.V. Sklifosovsky Research Institute for Emergency Medicine in organizational and informational support of the process of diagnosis and treatment of patients with COVID-19.

Aims: The aim of the study is to justify the possibility and effectiveness of using software based on artificial intelligence technology for the first interpretation of digital mammograms while maintaining the practice of the second description of X-ray images by a radiologist. Materials and methods: A data set of 100 digital mammography studies (50 Normal, 50 with signs of malignant neoplasms Not normal) was processed by software based on artificial intelligence technology registered in the Russian Federation as a medical device. ROC analysis was performed. Results: When set to 80.0% sensitivity: AI specificity was 90.0% (95% CI: 81.7-98.3), accuracy 85.0% (95% CI: 78.0-92.0). When set to 100% specificity: AI showed sensitivity of 56.0% (95% CI: 42.2-69.8), accuracy of 78.0% (95% CI: 69.9-86.1). When set to 100% sensitivity: AI specificity was 54.0% (95% CI: 40.2-67.8), accuracy 77.0% (95% CI: 68.8-85.2). Two approaches have been proposed that provide for an autonomous first interpretation of digital mammography using AI. The first approach is to evaluate the X-ray image using AI with a higher sensitivity than that of the mammograms double reading by radiologists, with a comparable level of specificity. The second approach implies that the software based on artificial intelligence technology will determine the mammograms category ("Normal" or "Not normal"), indicating the degree of its "confidence" in the obtained result, depending on the corridor into which the predicted value falls. Conclusions: Both proposed approaches for the use of software based on artificial intelligence technology for the purpose of autonomous first interpretation of digital mammograms are able to provide diagnostic quality that is not inferior to the images double reading by radiologists, and even exceeds it. The economic benefit from the practical implementation of this approach nationwide can range from 0.6 to 5.5 billion rubles annually.

Digital transformation of healthcare is a factor accelerating the change in the healthcare system in response to the challenges introduced by global demographic, technological and socio-economic trends. There are no technological barriers for digital transformation; however, there are organizational, legal and social limitations that need to be dealt with. In this article, digital technologies are presented as the main driver for the change development, ensuring the availability, speed and continuity of medical care provision, automation of routine processes and the processing of objective data for making management decisions. From the viewpoint of the established change management practice the article presents the current state of healthcare informatization and digital transformation experience of Moscow inpatient care by implementing the Unified Medical Information and Analytical System (EMIAS). Authors also present the general approach to change management developed by the Moscow Healthcare Department for all types of medical organizations, which is stipulated in regulatory documents. The article determines the main mechanisms of change management, certain limitations and ways to overcome them. Authors also describe the levels of digital transformation of Moscow’s medical organizations and propose the guiding principles for developing introduction measures for digital technology.