Department of Surgery, Section of Minimally Invasive Surgery, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8109, St. Louis, MO , 63110, USA.
Surg Endosc. 2024 Oct;38(10):6033-6036. doi: 10.1007/s00464-024-11134-w. Epub 2024 Aug 7.
Surgical autonomy for trainees has remained elusive to quantify. Proportion of active control time (ACT) of a trainee during a robotic case can be used as a broad measure of autonomy. However, this metric lacks in the granular detail of quantifying at what specific steps trainees were actively participating. We aim to quantify trainee involvement during robotic-assisted hiatal hernia repair at a task-specific level.
We performed a retrospective review of surgical performance data from robotic-assisted hiatal hernia repairs performed. These cases were segmented into 5 tasks by AI-assisted annotation with human review. The segmented tasks included: hiatal dissection, gastric fundus mobilization, mediastinal dissection, cruroplasty and fundoplication. Tasks were excluded if video segmentation of tasks was incorrect. During each task, ACT was recorded for resident, fellow and attending. Resident and fellow ACT per task was compared using the Mann-Whitney U test.
Residents had the highest %ACT in the hiatal dissection (53%), gastric fundus mobilization (84%) and fundoplication (57%) tasks. Fellows had greater than 80% ACT in all 5 tasks, with the highest %ACT in the gastric fundus mobilization (100%) and hiatal dissection (88%). There was a significant difference between resident and fellow ACT during mediastinal dissection and cruroplasty.
This study demonstrates how objective performance metrics and automated case segmentation can quantify trainee participation at a task-specific level. By utilizing data afforded by a robotic surgery platform, we are able to provide an objective and automated form of assessment with minimal impact on the workflow of attendings and residents. Our findings can serve to inform residents on what steps they can expect to be involved in during the procedure, appropriate to their PGY level. With this task-level data, we can provide a roadmap for trainee progression to achieve full surgical autonomy.
培训生的手术自主性一直难以量化。学员在机器人手术过程中的主动控制时间（ACT）比例可以作为自主性的大致衡量标准。然而，该指标缺乏量化学员在哪些具体步骤中积极参与的详细信息。我们旨在以特定任务的方式量化学员在机器人辅助食管裂孔疝修复中的参与度。
我们对机器人辅助食管裂孔疝修复手术的手术表现数据进行了回顾性研究。这些病例通过人工智能辅助注释并进行人工审核进行了 5 个任务的分割。分割的任务包括：裂孔解剖、胃底游离、纵隔解剖、补片成形和胃底折叠。如果任务的视频分割不正确，则排除该任务。在每个任务中，记录住院医师、研究员和主治医生的 ACT。使用 Mann-Whitney U 检验比较住院医师和研究员在每个任务中的 ACT。
住院医师在裂孔解剖（53%）、胃底游离（84%）和胃底折叠（57%）任务中的 ACT 最高。研究员在所有 5 个任务中的 ACT 均超过 80%，其中胃底游离（100%）和裂孔解剖（88%）的 ACT 最高。在纵隔解剖和补片成形术方面，住院医师和研究员的 ACT 之间存在显著差异。
本研究展示了客观绩效指标和自动化病例分割如何以特定任务的方式量化学员的参与度。通过利用机器人手术平台提供的数据，我们能够以最小影响主治医生和住院医师工作流程的方式提供客观和自动化的评估形式。我们的发现可以为住院医师提供他们在手术过程中预计会参与哪些步骤的信息，这与他们的 PGY 水平相对应。有了这个任务级别的数据，我们可以为学员提供实现完全手术自主性的路线图。
