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  • Разработка инструмента оценки и прогнозирования потенциальных рисков при внедрении инновационных медицинских технологий в многопрофильном стационаре
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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Разработка инструмента оценки и прогнозирования потенциальных рисков при внедрении инновационных медицинских технологий в многопрофильном стационаре

Медицинские технологии. Оценка и выбор. 2025. Т. 47. № 4. С. 28–32.
Габриелян А. Р., Александрова О. Ю., Михайлов И. А., Ледовских Ю. А.

STUDY OBJECTIVE

To develop a tool for assessing and predicting potential risks associated with the implementation of innovative medical technologies in a multidisciplinary hospital.

MATERIAL AND METHODS

A systematic analysis of scientific literature and the regulatory framework was conducted. Publications were searched in PubMed and eLibrary, and regulations were searched in the ConsultantPlus and Garant systems using keywords related to the implementation of medical technologies. Risk factors were identified and systematized in 124 selected sources (97 publications and 27 regulations) using content analysis and classification grouping.

RESULTS

A risk classification was developed, comprising four groups: A. Legal (lack of a license, violation of intellectual property rights, insufficient personnel accreditation); B. Organizational (workload redistribution, staffing changes, training needs); C. Financial (lack of quotas and adequate funding in the compulsory health insurance system, staff incentive costs); D. Infrastructural (increased time of use of premises and equipment, need for repairs). Management measures are proposed for each group. A scoring system is presented, in which each risk category is assigned 1 point (the maximum total number of points is 12), which allows for a quantitative ranking of the risk level.

CONCLUSION

The proposed tool provides a structured approach to risk management, enabling the prioritization of organizational and managerial decisions at the planning and implementation stages of innovations in a multidisciplinary hospital. Its use helps improve the validity and effectiveness of the introduction of new medical technologies.

Research target: Medical Technologies
Language: Russian
Full text
DOI
Keywords: управление рискамиинновационные технологии innovative technologiesanalysis and risk managementHealth care organization организация здравоохранения
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