Bitner Daniel P, Kutana Saratu, Carsky Katherine, Addison Poppy, DeChario Samuel P, Antonacci Anthony, Mikhail David, Yatco Edward, Chung Paul J, Filicori Filippo
Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, New York, New York, USA.
Institute for Spine and Scoliosis, Lawrenceville, New Jersey, USA.
J Laparoendosc Adv Surg Tech A. 2023 May;33(5):471-479. doi: 10.1089/lap.2022.0439. Epub 2023 Jan 20.
Prior studies on technical skills use small collections of videos for assessment. However, there is likely heterogeneity of performance among surgeons and likely improvement after training. If technical skill explains these differences, then it should vary among practicing surgeons and improve over time. Sleeve gastrectomy cases ( = 162) between July 2018 and January 2021 at one health system were included. Global evaluative assessment of robotic skills (GEARS) scores were assigned by crowdsourced evaluators. Videos were manually annotated. Analysis of variance was used to compare continuous variables between surgeons. Tamhane's test was used to define differences between surgeons with the eta-squared value for effect size. Linear regression was used for temporal changes. A value <.05 was considered significant. Variations in operative time discriminated between individuals (e.g., between 2 surgeons, means were 91 and 112 minutes, Tamhane's = 0.001). Overall, GEARS scores did not vary significantly (e.g., between those 2 surgeons, means were 20.32 and 20.6, Tamhane's = 0.151). Operative time and total GEARS score did not change over time ( = 0.0001-0.096). Subcomponent scores showed idiosyncratic temporal changes, although force sensitivity increased among all ( = 0.172-0.243). For a novice surgeon, phase-adjusted operative time ( = 0.24), but not overall GEARS scores ( = 0.04), improved over time. GEARS scores showed less variability and did not improve with time for a novice surgeon. Improved technical skill does not explain the learning curve of a novice surgeon or variation among surgeons. More work could define valid surrogate metrics for performance analysis.
先前关于技术技能的研究使用少量视频集进行评估。然而，外科医生之间的表现可能存在异质性，并且训练后可能会有所提高。如果技术技能能够解释这些差异，那么它应该在执业外科医生之间有所不同，并且会随着时间的推移而提高。纳入了某一医疗系统在2018年7月至2021年1月期间的袖状胃切除术病例（n = 162）。由众包评估人员分配机器人技能的全球评估评估（GEARS）分数。视频进行了人工标注。方差分析用于比较外科医生之间的连续变量。使用塔姆哈尼检验来定义外科医生之间的差异，并计算效应大小的eta平方值。线性回归用于分析时间变化。P值<0.05被认为具有统计学意义。手术时间的差异能够区分个体（例如，两名外科医生之间，平均值分别为91分钟和112分钟，塔姆哈尼检验P = 0.001）。总体而言，GEARS分数没有显著差异（例如，这两名外科医生之间，平均值分别为20.32和20.6，塔姆哈尼检验P = 0.151）。手术时间和GEARS总评分没有随时间变化（P = 0.0001 - 0.096）。子组件评分显示出特殊的时间变化，尽管所有医生的力敏感性都有所增加（P = 0.172 - 0.243）。对于一名新手外科医生，经阶段调整的手术时间有所改善（P = 0.24），但GEARS总评分没有改善（P = 0.04）。对于新手外科医生，GEARS分数的变异性较小，且没有随时间提高。技术技能的提高并不能解释新手外科医生的学习曲线或外科医生之间的差异。更多的工作可以定义有效的替代指标用于性能分析。
