Survey-based analysis of fundamental tasks for effective use of electrosurgical instruments

Surgical endoscopy(2013)

引用 4|浏览11
暂无评分
摘要
Background Despite widespread use of electrosurgical instruments, there are no widely accepted tasks for training and evaluation of technical skills. The purpose of this study is to propose a set of tasks and report experts’ evaluations of the proposed tasks for validity, technical skills versus knowledge requirements, and utility for future privileging curricula. Methods A set of seven hands-on tasks involving electrical energy were identified based on the fundamental use of surgical energy (FUSE) curriculum of the Society of American Gastrointestinal and Endoscopic Surgeons. A web-based survey was developed based on the seven identified tasks, with seven questions asked for each task. These questions rated candidate tasks on their suitability for validation studies, the role of manual skills, and the appropriateness of including the task in privileging curricula. Members of the FUSE committee were sent the web-based survey, and responses were recorded. Results Of the 27 members of the FUSE committee, 16 responded to the survey. A total of 775 Likert-style responses were recorded and quantified to a 1–5 range. Overall, responses within task–question pairs had a mean standard deviation of 0.83, suggesting general agreement. Tasks requiring bi-manual dexterity scored higher than single-handed tasks on a combined, four-question Likert-scale index for task validation ( p < 0.0001), and a two-question index for manual skills ( p < 0.0001). Conclusions Survey responses indicated general agreement that the identified tasks represent important technical skills and are consistent with actions in the operating room. Bimanual tasks were favored for validation purposes over single-handed tasks. The traction and monopolar dissection and monopolar coaptation tasks had the highest agreement with validation-oriented questions (97 and 87 %, respectively).
更多
查看译文
关键词
Surgical training,FUSE,Electrosurgery
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要