Effective decision support systems in clinical practice and prevention: literature review

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Abstract

Clinical decision support systems (CDSS) often outperform human capabilities for processing a large amount of information, dramatically simplifying the work of specialists and avoiding medical errors. The implementation of such systems is a complex task that requires high-tech developments. The annual increase in the development of such systems has a geometric progression. However, it is unclear if most of them will be integrated into clinical practice and recommendations. The use of CDSS to address various disease diagnosis, treatment, and prevention issues is demonstrated, and possible linkages between scientific clinical observations and CDSS are examined. Currently, many data gathering and processing systems use machine learning algorithms and convolutional technologies to create CDSS, resulting in data that exceeds the ability of human thinking to determine the logic of recommended decisions. This study presents the most studied modern CDSS, the possibilities of their application, and the implementation issues.

About the authors

Artem A. Komkov

National Medical Research Center of Therapy and Preventive Medicine; Vorokhobov City Clinical Hospital N 67

Author for correspondence.
Email: artemkomkov@gmail.com
ORCID iD: 0000-0001-7159-1790
SPIN-code: 4292-2364

MD, Cand. Sci. (Med.)

Russian Federation, Moscow; Moscow

Svetlana V. Ryazanova

National Medical Research Center of Therapy and Preventive Medicine

Email: srayzanova@gnicpm.ru
ORCID iD: 0000-0001-6776-0694
SPIN-code: 2487-0500

MD, Cand. Sci. (Med.)

Russian Federation, Moscow

Vladimir P. Mazaev

National Medical Research Center of Therapy and Preventive Medicine

Email: vpmazaev@gnicpm.ru
ORCID iD: 0000-0002-9782-0296
SPIN-code: 5288-7010

MD, Dr. Sci. (Med.), Professor

Russian Federation, Moscow

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