An e-learning platform for clinical reasoning in cardiovascular diseases: a study reporting on learner and tutor satisfaction.

Background: Medical students gain essential skills through hospital training and internships, which complement their theoretical education. However, virtual patient platforms have been shown to effectively promote clinical reasoning and enhance learning outcomes. This study evaluates a web-based platform designed for learning clinical reasoning in cardiovascular diseases, detailing its functionalities and user satisfaction.

Methods: The Virtual Patient platform presents medical students with clinically valid scenarios, encompassing stages such as patient description, anamnesis, objective examination, presumptive diagnosis, health investigations, treatment planning, complications, differential and final diagnoses, and prognosis. Scenarios are generated either automatically or manually by professors, based on labeled and annotated clinical data. The Virtual Patient contains two types of medical cases: simple scenarios describing patients with one pathology, and complex scenarios describing patients with several related pathologies. The platform was evaluated by a total of 210 users: 178 medical students, 7 professors, and 25 engineering students, using questionnaires adjusted for each evaluation round to assess satisfaction and gather feedback. The evaluation by medical students was performed in four rounds, each round corresponding to successive enhancements of the platform functionalities and addition of new cases, with a total number of 1,098 evaluation sessions.

Results: The platform was evaluated at different implementation stages, involving simple and complex scenarios for various heart diseases. The majority of students found the platform very useful (82.58%), with significant appreciation for its features and functionalities, for example the dialogue module supporting natural language interactions in Romanian and English or the feed-back obtained during interaction. Professors highly valued the platform's flexibility in scenario generation, real-time feedback provision, and data management capabilities. They appreciated the possibility to provide feedback and score student performance in real-time or after the session, though some professors suggested improving the explainability of the scores.

Conclusions: The Virtual Patient platform enables medical students to virtually replicate hospital interactions, diagnose patients, and plan treatments in clinically valid scenarios for cardiovascular diseases. User evaluations demonstrated high satisfaction and appreciation for the platform's features. Future work will focus on expanding medical cases, enhancing the dialogue module, improving scenario generation for complex cases, and extending the synthetic data generation component to produce additional types of medical investigations.