phОбщественное здоровьеPublic Health2782-16762949-1274ФГБУ «ЦНИИОИЗ» Минздрава России10.21045/2782-1676-2026-6-1-28-39ph-365Research ArticleЦИФРОВОЕ ЗДРАВООХРАНЕНИЕDIGITAL HEALTHCAREВлияние ИИ-решений на сокращение административной и операционной нагрузки на медицинский персонал в системе здравоохраненияThe impact of AI solutions on reducing the administrative and operational burden on medical personnel in the healthcare systemhttps://orcid.org/0000-0001-9612-8815КаневА. Ф.KanevA. F.

Канев Александр Федорович – кандидат медицинских наук, аналитик 1 категории отдела аналитики и мониторинга

ул. Добролюбова, д. 11, г. Москва, 127254

Aleksandr F. Kanev – Candidate of sciences in medicine, analyst of the 1st category, analyst at the department of analysis and monitoring

11 Dobrolyubova Street, Moscow, 127206

kanev.af@ssmu.ruhttps://orcid.org/0000-0003-0098-1403КобяковаО. С.KobyakovaО. S.

Кобякова Ольга Сергеевна – доктор медицинских наук, профессор, член-корреспондент РАН, директор

ул. Добролюбова, д. 11, г. Москва, 127254

Оlga S. Kobyakova – Doctor of Sciences in Medicine, Professor, Corresponding Member of the RAS, Director

11 Dobrolyubova Street, Moscow, 127206

kobyakovaos@mednet.ruhttps://orcid.org/0000-0003-1896-6420КураковаН. Г.KurakovaN. G.

Куракова Наталия Глебовна – доктор биологических наук, заведующая отделом аналитики и мониторинга

ул. Добролюбова, д. 11, г. Москва, 127254

Natalya G. Kurakova – Doctor of sciences in biology, head of the department of analysis and monitoring

11 Dobrolyubova Street, Moscow, 127206

idmz@mednet.ruhttps://orcid.org/0009-0006-6567-4235КарминаР. Л.KarminaR. L.

Кармина Раиса Леонидовна – заведующая научно-техническим и редакционным отделом

ул. Добролюбова, д. 11, г. Москва, 127254

Raisa L. Karmina – head of the scientific, technical and editorial department, Russian Research Institute of Health

11 Dobrolyubova Street, Moscow, 127206

karminarl@mednet.ruФГБУ «Центральный научно-исследовательский институт организации и информатизации здравоохранения» Министерства здравоохранения Российской ФедерацииРоссияRussian Research Institute of HealthRussian Federation202629042026612839Copyright © Канев А.Ф., Кобякова О.С., Куракова Н.Г., Кармина Р.Л., 20262026Канев А.Ф., Кобякова О.С., Куракова Н.Г., Кармина Р.Л.Kanev A.F., Kobyakova О.S., Kurakova N.G., Karmina R.L.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ph.elpub.ru/jour/article/view/365

Введение. Технологии искусственного интеллекта становятся стратегическим элементом трансформации экосистемы современного здравоохранения. Искусственный интеллект предлагает потенциал для «масштабирования» человеческого опыта, позволяя меньшему количеству специалистов охватить большее число пациентов без потери качества, поэтому рассматривается как мощный инструмент-ассистент, способный усилить аналитические и диагностические возможности врачей. Данный обзор посвящен анализу кейсов успешных внедрений ИИ-решений, оказавших влияние на ключевые показатели эффективности медицинских организаций.Цель исследования: систематизировать данные, демонстрирующие влияние коммерческих и исследовательских решений на основе искусственного интеллекта на сокращение времени выполнения медицинских и административных процедур в здравоохранении и снижение нагрузки на медицинский персонал.Материалы и методы. Поиск релевантных публикаций проводился в международных библиографических базах данных PubMed и Google Scholar по ключевым словам и их комбинациям: «artificial intelligence», «AI», «healthcare efficiency», «workload reduction», «time savings», «clinical decision support», «diagnostic imaging», «automated documentation», «physician burnout». Дополнительно анализировались официальные отчеты и пресс-релизы компаний, разрабатывающих ИИ-решения для здравоохранения.Результаты. Проанализированные в обзоре публикации свидетельствуют о достаточно высокой эффективности ИИ-решений в целом ряде областей экосистемы современного здравоохранения. Снижение административной и диагностической нагрузки способствует преодолению кадрового дефицита за счет повышения производительности существующего персонала. Оптимизация рабочего процесса и снижение времени ожидания повышают доступность медицинской помощи. Сокращение объема рутинных операций положительно коррелирует с уменьшением риска профессионального выгорания.Заключение. Технологии искусственного интеллекта демонстрируют потенциал для трансформации ключевых процессов в здравоохранении. Вместе с тем выявлен дисбаланс в исследовательском фокусе вошедших в обзор публикаций: преобладают работы, измеряющие временные показатели диагностики, в то время как прямое влияние на нагрузку персонала изучено недостаточно. Для комплексной оценки необходимы дальнейшие исследования, учитывающие не только операционные метрики, но и долгосрочные клинические исходы и экономическую эффективность.

Introduction. Artificial intelligence (AI) technologies are becoming a strategic element in the transformation of the modern healthcare ecosystem. AI offers the potential to «scale» the human experience, allowing fewer specialists to reach more patients without loss of quality, therefore it is considered as a powerful assistant tool capable of enhancing the analytical and diagnostic capabilities of doctors. This review is devoted to the analysis of cases of successful implementations of AI solutions that have influenced key performance indicators of medical organizations. The purpose of the study is to systematize data demonstrating the impact of commercial and research solutions based on artificial intelligence on reducing the time required to perform medical and administrative procedures in healthcare and reducing the burden on medical personnel.Materials and methods. The search for relevant publications was conducted in the international bibliographic databases PubMed and Google Scholar by keywords and their combinations: «artificial intelligence», «AI», «healthcare efficiency», «workload reduction», «time savings», «clinical decision support», «diagnostic imaging», «automated documentation», «physician burnout». Additionally, official reports and press releases from companies developing AI solutions for healthcare were analyzed.Results. The publications analyzed in the review indicate that AI solutions are quite effective in a number of areas of the modern healthcare ecosystem. Reducing the administrative and diagnostic burden helps to overcome the personnel shortage by increasing the productivity of existing staff. Optimizing the workflow and reducing waiting times increase the availability of medical care. Reducing the volume of routine operations positively correlates with reducing the risk of professional burnout.Conclusion. Artificial intelligence technologies demonstrate the potential to transform key processes in healthcare. At the same time, an imbalance has been identified in the research focus of the publications included in the review: works measuring diagnostic time indicators predominate, while the direct impact on staff workload has not been sufficiently studied. For a comprehensive assessment, further studies are needed that take into account not only operational metrics, but also long-term clinical outcomes and cost-effectiveness.

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The authors declare that there are no conflicts of interest present.