Evaluation reveals that a novel tracheostomy model enhances skills and confidence in general practitioners, suggesting effective training solutions.
Introduction Basic tracheostomy management is an essential skill for primary care practitioners, yet there is often limited training in formal medical education. This study aimed to develop and evaluate a low-cost, 3D-printed tracheostomy model to enhance simulation-based training and improve proficiency and confidence in airway management among general practitioners (GPs) in Rwanda, where prior research has identified gaps in training. Materials and Methods This two-phase study included expert validation by otolaryngologists followed by pilot testing of the model. Five local otolaryngologists evaluated the model’s anatomical accuracy, clinical relevance, and training utility. Subsequently, 40 GPs from across Rwanda participated in a hands-on simulation workshop in Rwanda. Training focused on suctioning, tube replacement, neck tie placement, and managing accidental decannulation. Pre- and post-training surveys assessed changes in knowledge, confidence, skill, and perceived relevance. Procedural skills were independently evaluated through standardized video and rubric assessments. Results Otolaryngologists rated the model highly for accuracy, relevance, and cost-effectiveness. Each reusable 3D-printed tracheostomy simulation model was produced at a cost of $32.22 USD, offering a sustainable and affordable alternative to commercial simulation tools in low-resource settings. During the training workshop, significant improvements were observed across all domains. Knowledge scores increased by 15% (p<0.001), and confidence scores improved by +2.11 to +2.59 points (p<0.001) on a 5-point scale. Objective skills ratings showed marked gains in tracheostomy technique (+2.55, p<0.001) and post-care management (+2.90, p<0.001). Conclusion This affordable, 3D-printed tracheostomy model effectively enhanced GP confidence, skills, and knowledge. The project highlights the value of context-appropriate simulation tools in strengthening clinical capacity in low-resource settings.
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Patel et al. (2026) studied this question.
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