Implementing an Artificial Intelligence System in the Work of General Practitioner in the Yamalo-Nenets Autonomous Okrug: Pilot Cross-sectional Screening Observational Study

• Published in: Kuban Scientific Medical Bulletin
• Main Authors: E. V. Zhdanova, E. V. Rubtsova
• Format: Article in Journal/Newspaper
• Language: Russian
• Published: Ministry of Healthcare of the Russian Federation. “Kuban State Medical University” 2022
• Subjects: risk factors; risk groups; cardiovascular diseases; artificial intelligence in medicine; prevention of cardiovascular diseasese; hfnm; Medicine; R; Gorodskaya; nenets; Nenets Autonomous Okrug; Yamalo Nenets; Yamalo-Nenets Autonomous Okrug
• Online Access: https://doi.org/10.25207/1608-6228-2022-29-4-14-31; https://doaj.org/article/c1e1a17c2a344bbebe37488cc004f5e0

Background. Early identification of risk factors (RF) associated with cardiovascular diseases (CVD) is essential for the prevention of CVDs and their complications. CVD risk factors can be identified using Artificial Intelligence (AI) systems, which are capable of learning, analyzing and drawing conclusions. The advantage of AI systems consists in their capacity to process large amounts of data over a short period of time and produce ready-made information. Objectives. Evaluation of the efficiency of implementing an AI software application by a general practitioner for identifying CVD risk factors.Methods. The study included data from 1778 electronic medical histories of patients aged over 18, assigned to an outpatient and polyclinic department of Muravlenkovskaya Gorodskaya Bolnitsa (Muravlenko municipal hospital), Yamalo-Nenets Autonomous Okrug (Russia). The study was conducted in four stages. The first stage involved a preliminary training of the Artificial Intelligence (AI) system under study using numerous CVD risk assessment scales. The Webiomed predictive analytics and risk management software by K-SkAI, Russia, was selected as a platform for this purpose. The second stage included an analysis of medical data to identify CVD risk factors according to the relative risk scale for patients under 40 and the SCORE scale for patients over 40. At the third stage, a specialist analyzed the previous and new information received about each patient. According to the results of the third stage, four risk groups for CVD (low, medium, high and very high) were formed. At the fourth stage, newly diagnosed patients with a high risk of CVD, who had not been previously subject to regular medical check-up, were directed for additional clinical, laboratory and instrumental follow-up examination and consultations of relevant specialists. Statistical data in absolute terms and as a percentage were obtained. Statistical processing of the results was carried out by a computer program aimed at medical decision support. Content ...