Development of computer-based methodology for remote advanced training of medical doctors

Aim. To formulate the methodology for developing the interactive virtual computer simulations with a rating assessment of trainees’ decisions and the capability of remote access.

Material and Methods. The methods of knowledge engineering were used to extract and formalize expert knowledge about the structure, significance, and relevance of clinical diagnostic information. The materials for creating virtual computer simulations were based on texts from the archival medical records, laboratory data, and multimedia results of instrumental methods of study. A three-tier network architecture was applied to provide the capabilities of remote access. It was organizationally represented by three components: a client layer, a business logic layer, and a data layer. Data transfer was provided by the Web protocols while microservices represented the infrastructure.

Results. The information was formalized and structured after expert analysis and identification of significant diagnostic and prognostic data. The process included defining the domain model, identifying the aggregates and connections between them, and designing software and user interfaces. Possible solutions for trainees are now presented in the form of interactive reference lists. The artifacts of the user’s work are saved in the storage represented by the module for working with the server file system and the object-relational database management system. Each task module contains static and interactive blocks of information. The purpose of static blocks is to provide trainees with the necessary information for making clinical and diagnostic decisions. The interactive blocks allow selecting one or more solutions from the list. The content and sequence of further information presentation are determined by the trainees’ answers to the questions of an interactive block. Trainees’ decision-making competencies are evaluated using the rating system. The final personal rating is calculated as a multiplication of all coefficients related to the trainees’ decisions. This approach integrates a rating system with the trajectory chosen by the trainee for task completion.

Conclusions. The distance learning technologies, developed for clinical disciplines in this study, are quite new and innovative. The repository of virtual computer simulations is under development to improve the methodological support of remote clinical training.

1. Petrova V.N. Potential of problem based learning technology in high school practice. Siberian Journal of Psychology. 2017;65:112–124 (In Russ.). DOI: 10.17223/17267080/65/9.

2. Ellaway R.H., Poulton T., Jivram T. Decision PBL: A 4-year retrospective case study of the use of virtual patients in problem-based learning. Med. Teach. 2015;37(10):926–934. DOI: 10.3109/0142159X.2014.970627.

3. Bateman J., Allen M., Samani D., Kidd J., Davies D. Virtual patient design: exploring what works and why. A grounded theory study. Med. Educ. 2013;47(6):595–606. DOI: 10.1111/medu.12151.

4. Karas S.I. Virtual patients as a format for simulation learning in the continuing medical education (review article). Bulletin of Siberian Medicine. 2020;19(1):140–149 (In Russ.). DOI: 10.20538/1682-0363-2020-1-140-149.

5. Hege I., Kononowicz A.A., Berman N.B., Lenzer B., Kiesewetter J. Advancing clinical reasoning in virtual patients – development and application of a conceptual framework. GMS J. Med. Educ. 2018;35(1):Doc12. DOI: 10.3205/zma001159.

6. Cook D.A., Erwin P.J., Triola M.M. Computerized Virtual Patients in Health Professions Education: A Systematic Review and Meta-Analysis. Acad. Med. 2010;85(10):1589–1602. DOI: 10.1097/ACM.0b013e3181edfe13.

7. Consorti F., Mancuso R., Nocioni M., Piccolo A. Effi cacy of virtual patients in medical education: A meta-analysis of randomized studies. Computers & Education. 2012;59(3):1001–1008. DOI: 10.1016/j.compedu.2012.04.017.

8. The portal for continuing medical and pharmaceutical education of the Ministry of Нealth (In Russ.). URL: https://edu.rosminzdrav.ru/specialistam/proekty/2/na-nashem-portale-realizovany-novye-interaktivnye-obrazovatelnye-moduli-virtualnyi-pacient-s-ispolzovaniem-sovremennykh-simuljacionnykh-obrazovatelnykh-tekhnologii/#c971 (аvailable from: 02.11.2020).

9. The portal of methodical center for specialists accreditation of First Moscow Medical University (In Russ.). URL: https://selftest.mededtech.ru/ (аvailable from: 02.11.2020).

10. Karas S.I., Arzhanik M.B., Baev A.E., Vaizov V.K., Vasiltseva O.Y., Grakova E.V. et al. Virtual patients with cardiovascular pathology: technology for postgraduate medical education. Cardiovascular Therapy and Prevention. 2019;18(6):51–56 (In Russ.). DOI: 10.15829/1728-8800-2019-6-51-56.

11. Arzhanik M.B., Karas S.I., Grakova E.V., Vasiltseva O.A., Korneeva T.B., Kara-Sal E.E. Methodology in cardiologists’ postgraduate education. Russian Journal of Cardiology. 2019;24(12):104–108 (In Russ.). DOI: 10.15829/1560-4071-2019-12-104-108.

12. Electronic Virtual Patients. URL: https://virtualpatients.eu (available from: 01.11.2020).

13. The Regenstrief EHR Clinical Learning Platform. URL: https://www. regenstrief.org/implementation/clinical-learning (available from: 01.11.2020).