Cutting Through Barriers: Laceration Showcase and Virtual Procedure Lab for Emergency Medicine Trainees in India

突破障碍:印度急诊医学培训生的撕裂伤处理展示和虚拟手术实验室

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Abstract

Procedural training for Emergency Medicine (EM) trainees is a critical component of physician development. Building on previous virtual pilot projects, this project aimed to assess the feasibility and engagement of a remote procedural training program for Indian postgraduate EM trainees.The primary goal of this project was to design and implement a competitive remote procedural skill showcase followed by a virtual procedure lab for advanced skill development. To further enhance the learner experience and foster intrinsic motivation, gamification strategies were leveraged through a visible leaderboard and peer-voted recognition, providing both formative feedback and peer-based validation. Participants submitted photographs of laceration repairs that were evaluated by two blinded reviewers. The top scorers presented to their peers, and a winner was chosen by peer vote. The showcase was followed by a virtual suture workshop led by a wound care expert and supported by multiple facilitators, allowing large group demonstration and individual practice with directed feedback in small breakout rooms.51 participants from PGY1-3 levels submitted laceration repairs; each PGY class chose a winner, with secondary awards for most dramatic laceration repair and best pediatric laceration repair. This was followed by an interactive virtual suture lab to practice advanced repair techniques. 160 trainees attended the virtual suture lab. This technical report highlights the project's feasibility in delivering engaging training in a remote environment. The combination of structured evaluation, blinded review, and facilitated procedure practice with individualized feedback likely increased engagement compared to the previous pilot project. The project received informal positive feedback and demonstrated the feasibility and acceptability of remote procedural teaching, paving the way for future scalable and cost-effective models.

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