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利用自我选择和基于网络的反馈工具的住院医师机器人课程。

A resident robotic curriculum utilizing self-selection and a web-based feedback tool.

机构信息

Division of Urogynecology and Pelvic Surgery, Department of Obstetrics and Gynecology, Atrium Health, Charlotte, NC, USA.

Division of Colorectal Surgery, Department of Surgery, Atrium Health, Charlotte, NC, USA.

出版信息

J Robot Surg. 2023 Apr;17(2):383-392. doi: 10.1007/s11701-022-01428-3. Epub 2022 Jun 13.

Abstract

To describe an obstetrics and gynecology residency robotic curriculum, facilitated by a web-based feedback and case-tracking tool, allowing for self-selection into advanced training. Phase I (Basic) was required for all residents and included online training modules, online assessment, and robotic bedside assistant dry lab. Phase II (Advanced) was elective console training. Before live surgery, 10 simulation drills completed to proficiency were required. A web-based tool was used for surgical feedback and case-tracking. Online assessments, drill reports, objective GEARS assessments, subjective feedback, and case-logs were reviewed (7/2018-6/2019). A satisfaction survey was reviewed. Twenty four residents completed Phase I training and 10 completed Phase II. To reach simulation proficiency, residents spent a median of 4.1 h performing required simulation drills (median of 10 (3, 26) attempts per drill) before live surgery. 128 post-surgical feedback entries were completed after performance as bedside assistant (75%, n = 96) and console surgeon (5.5%, n = 7). The most common procedure was hysterectomy 111/193 (58%). Resident console surgeons performed portions of 32 cases with a mean console time of 34.6 ± 19.5 min. Mean GEARS score 20.6 ± 3.7 (n = 28). Mean non-technical feedback results: communication (4.2 ± 0.8, n = 61), workload management (3.9 ± 0.9, n = 54), team skills (4.3 ± 0.8, n = 60). Residents completing > 50% of case assessed as "apprentice" 38.5% or "competent" 23% (n = 13). After curriculum change, 100% of surveyed attendings considered residents prepared for live surgical training, vs 17% (n = 6) prior to curriculum change [survey response rate 27/44 (61%)]. Attendings and residents were satisfied with curriculum; 95% and recommended continued use 90% (n = 19).This two-phase robotic curriculum allows residents to self-select into advanced training, alleviating many challenges of graduated robotic training.

摘要

描述一个妇产科住院医师机器人课程,由一个基于网络的反馈和案例跟踪工具提供便利,允许自我选择高级培训。第一阶段(基础)是所有住院医师的必修课,包括在线培训模块、在线评估和机器人床边助手干实验室。第二阶段(高级)是选修控制台培训。在进行活体手术之前,需要完成 10 次模拟训练达到熟练程度。使用基于网络的工具进行手术反馈和案例跟踪。在线评估、训练报告、客观 GEARS 评估、主观反馈和案例记录被审查(2018 年 7 月至 2019 年 6 月)。审查了一份满意度调查。24 名住院医师完成了第一阶段的培训,10 名住院医师完成了第二阶段的培训。为了达到模拟熟练程度,住院医师在活体手术前中位数花费了 4.1 小时完成规定的模拟训练(中位数 10 次(3 次,26 次)每次训练的尝试)。作为床边助手(75%,n=96)和控制台外科医生(5.5%,n=7)完成后,完成了 128 次手术后的反馈。最常见的手术是子宫切除术 111/193(58%)。住院医师控制台外科医生完成了 32 例手术的部分手术,控制台时间平均为 34.6±19.5 分钟。平均 GEARS 得分为 20.6±3.7(n=28)。平均非技术反馈结果:沟通(4.2±0.8,n=61),工作量管理(3.9±0.9,n=54),团队技能(4.3±0.8,n=60)。完成>50%病例评估的住院医师被评估为“学徒”的占 38.5%,“胜任”的占 23%(n=13)。课程变更后,100%的调查主治医生认为住院医师为活体手术培训做好了准备,而课程变更前为 17%(n=6)[调查回复率 27/44(61%)]。主治医生和住院医师对课程感到满意;95%和 90%(n=19)推荐继续使用。这个两阶段的机器人课程允许住院医师自我选择高级培训,缓解了机器人培训逐渐增加的许多挑战。

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