A digital solution for optimizing antimicrobial prophylaxis in surgery

   Relevance. Аntimicrobial prophylaxis in surgery (APS) is an important component of strategic healthcare programs aimed at reducing antibiotic resistance. The use of modern APS techniques also reduces the cost of prevention and treatment of surgical infection. However, the actual clinical practice of APS at the present time often differs from the scientifically based one, there is a suboptimal, excessive prescription of antibacterial agents. The introduction of APS using a Medical Decision Support System (DSS) can increase doctors' commitment to following modern medical technology.

   Objective. To develop and implement a APS protocol for a multidisciplinary emergency hospital. To develop and implement a mobile application, a chatbot for choosing a decision on the mode of perioperative antibiotic prophylaxis.

   Methods. Clinical and economic assessment of the "cost of illness" in the emergency department of surgical departments. Literary — an overview of regulatory documents as the basis of the APS protocol. Python programming method, digital application development. Development taking into account State industry standard R 71671-2024 "Medical decision support systems using artificial intelligence". Administrative — issuing orders and orders for the medical institution on the procedure for implementing the protocol, monitoring compliance with the protocol.

   Results. A local APS protocol has been developed. Based on the APS protocol, standard solutions for choosing the APS method have been compiled. Applications (Telegram chatbots) have been developed to facilitate this choice for the doctor who decides on the APS regime. It has been established that the commitment to the implementation of the PAP protocol continues to grow. This may be due to the absence of an increase in cases of infection of the surgical wound, despite a multiple decrease in the use of antimicrobial agents. The visibility and convenience of using APS mode selection algorithms using digital technology are also revealed.

   Conclusions. The implementation of the local protocol is effectively carried out using digital technologies, a system for supporting medical decision-making using artificial intelligence. The implementation of this APS protocol has made it possible to achieve significant cost reductions while maintaining the effectiveness of antimicrobial prophylaxis.

1. The AMS strategy program (Antimicrobial Stewardship Strategy) in the provision of inpatient medical care: Russian clinical guidelines / Ed. by SV Yakovlev, NI Briko, SV Sidorenko, DN Protsenko. Moscow: Pero Publishing House, 2018. (In Russ.)

2. Strategy for preventing the spread of antimicrobial resistance in the Russian Federation for the period up to 2030 Decree of the Government of the Russian Federation dated 25. 09. 2017 No. 2045-p. (In Russ.)

3. Gomon YuM. Comprehensive clinical and economic assessment of the use of antimicrobials at the inpatient stage of medical care. [dissertation] St. Petersburg; 2020. (In Russ.) Доступно по: https://vmeda.mil.ru/upload/site56/document_file/r43eGlVSJ1.pdf?ysclid=mbkp5mhw3y540991490. Ссылка активна на 16. 05. 2025.

4. Koshechkin KA, Svistunov AA, Lebedev GS, Fartushny EN. Practice of using systems based on artificial intelligence in the field of circulation of medicines. Vestnik Roszdravnadzora. 2022;(3):27-33. (In Russ.) EDN: VDFACA.

5. Koshechkin KA. Regulation of artificial intelligence in medicine. Patient-Oriented Medicine and Pharmacy. 2023;1(1):32-40. (In Russ.) doi: 10.37489/2949-1924-0005.

6. Koshechkin KA, Yavorskiy AN. New possibilities of artificial intelligence application in pharmacology. Experimental and Clinical Pharmacology. 2023;86(11S):85. (In Russ.) doi: 10.30906/ekf-2023-86s-85. EDN: MDLEKJ.

7. Vo C, Geoffrion AV, Mohamed ZA, et al. Impact of individualized feedback letters on adherence to surgical antibiotic prophylaxis guidelines: an interrupted time series study (FEEDBACK-ASAP). Am J Infect Control. 2023 Apr;51(4):440-445. doi: 10.1016/j.ajic.2022.06.015.

8. Implementation of the antimicrobial therapy (AMS strategy) control strategy program in surgical departments of medical organizations of the state healthcare system of the city of Moscow in the provision of inpatient medical care : Methodological recommendations / Ed. by AV Shabunin, MV Zhuravleva. Volume 38. – Moscow : State Budgetary Institution of the city of Moscow "Scientific Research Institute of Healthcare Organization and Medical Management of the Department of Healthcare of the City of Moscow", 2020. (In Russ.) EDN: FOJVHK.

9. Berríos-Torres SI, Umscheid CA, Bratzler DW, et al; Healthcare Infection Control Practices Advisory Committee. Centers for Disease Control and Prevention Guideline for the Prevention of Surgical Site Infection, 2017. JAMA Surg. 2017 Aug 1;152(8):784-791. doi: 10.1001/jamasurg.2017.0904.

10. Matthes Eric. Python Crash Course. A Hands-On, Project-Based Introduction to Programming. St. Petersburg: Peter, 2017. (In Russ.) ISBN 978-5-496-02305-4.

11. Kalinina AM, Kulikova MS, Gomova TA, et al. On the topic of demand and usage of the telemedicine model «doctor-patient» for the prevention and control of chronic diseases: the view of a primary health care doctor. Russian Journal of Preventive Medicine. 2021;24(6):28-36. (In Russ.) doi: 10.17116/profmed20212406128.

12. Yarovoy SK, Voskanian ShL, Tutelyan АV, Gladkova LS. Antibacterial prophylaxis of surgical site infections: an epidemiologist’s view. Epidemiology and Infectious Diseases. Current Items. 2020;10(1). (In Russ.) doi: 10.18565/epidem.2020.10.1.21-9.

13. ГОСТ Р 71671-2024. The national standard of the Russian Federation. Medical decision support systems using artificial intelligence. Basic provisions (approved and put into effect by Rosstandart Order No. 1384-ст dated 07. 10. 2024). (In Russ.)