Learning Gain During a Fast Self-driven Bronchoscopy Simulation Training: A Preliminary Study Comparing Low and High-fidelity Models

Abstract

Background: Nowadays, there is a growing need for competence in bronchoscopy. However, traditional mentorship-based training presents limitations in learning consistency, subjective evaluation, and patient safety concerns. Simulation-based training has gained attention for its potential to enhance skill acquisition and objective assessment. This study explores the effectiveness of self-driven bronchoscopy simulation training using high-fidelity (HFM) and low-fidelity (LFM) models on novice medical students.

Methods: Medical students without prior bronchoscopy experience were randomly assigned to a self-learn using either HFM or LFM for 4 hours. Pre and posttests were conducted to evaluate the students' knowledge and skill gains with a modified Bronchoscopy Skills and Tasks Assessment Tool. After their test results, students were asked to answer a questionnaire based on a Likert Scale to assess their satisfaction with self-learning.

Results: The results revealed significant progression in both groups, but the HFM group outperformed the LFM group in terms of total score, knowledge, skills, and procedure duration. Participant satisfaction with the educational process was generally high in both groups. However, this study has certain limitations, such as a small sample size, a short training period, and the absence of real evaluation in patients.

Conclusion: This study demonstrates the efficacy of self-driven bronchoscopy simulation training using HFM and LFM of bronchoscopy, with HFM offering superior gain for skill and knowledge. Larger-scale and long-term studies are recommended to further investigate the effectiveness of this self-learning and retention of the bronchoscopy knowledge.