Cha Jackie S, Athanasiadis Dimitrios I, Peng Yuhao, Wu David, Anton Nicholas E, Stefanidis Dimitrios, Yu Denny
Department of Industrial Engineering, Clemson University, SC, USA.
School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Hum Factors. 2024 Mar;66(3):729-743. doi: 10.1177/00187208221101292. Epub 2022 May 24.
The purpose of this study was to identify objective measures that predict surgeon nontechnical skills (NTS) during surgery.
NTS are cognitive and social skills that impact operative performance and patient outcomes. Current methods for NTS assessment in surgery rely on observation-based tools to rate intraoperative behavior. These tools are resource intensive (e.g., time for observation or manual labeling) to perform; therefore, more efficient approaches are needed.
Thirty-four robotic-assisted surgeries were observed. Proximity sensors were placed on the surgical team and voice recorders were placed on the surgeon. Surgeon NTS was assessed by trained observers using the NonTechnical Skills for Surgeons (NOTSS) tool. NTS behavior metrics from the sensors included communication, speech, and proximity features. The metrics were used to develop mixed effect models to predict NOTSS score and in machine learning classifiers to distinguish between exemplar NTS scores (highest NOTSS score) and non-exemplar scores.
NTS metrics were collected from 16 nurses, 12 assistants, 11 anesthesiologists, and four surgeons. Nineteen behavior features and overall NOTSS score were significantly correlated (12 communication features, two speech features, five proximity features). The random forest classifier achieved the highest accuracy of 70% (80% F1 score) to predict exemplar NTS score.
Sensor-based measures of communication, speech, and proximity can potentially predict NOTSS scores of surgeons during robotic-assisted surgery. These sensing-based approaches can be utilized for further reducing resource costs of NTS and team performance assessment in surgical environments.
Sensor-based assessment of operative teams' behaviors can lead to objective, real-time NTS measurement.
本研究旨在确定可预测手术过程中外科医生非技术技能（NTS）的客观指标。
非技术技能是影响手术操作表现和患者预后的认知与社交技能。当前手术中评估非技术技能的方法依赖基于观察的工具来对外科医生术中行为进行评分。这些工具执行起来资源消耗大（如观察时间或人工标注时间）；因此，需要更高效的方法。
观察了34例机器人辅助手术。在手术团队成员身上放置了接近传感器，在外科医生身上放置了语音记录器。由经过培训的观察者使用外科医生非技术技能（NOTSS）工具评估外科医生的非技术技能。来自传感器的非技术技能行为指标包括沟通、语音和接近度特征。这些指标用于建立混合效应模型以预测NOTSS评分，并用于机器学习分类器以区分典型非技术技能评分（最高NOTSS评分）和非典型评分。
收集了16名护士、12名助手、11名麻醉医生和4名外科医生的非技术技能指标。19种行为特征与总体NOTSS评分显著相关（12种沟通特征、2种语音特征、5种接近度特征）。随机森林分类器预测典型非技术技能评分的准确率最高，为70%（F1分数为80%）。
基于传感器的沟通、语音和接近度测量可能预测机器人辅助手术期间外科医生的NOTSS评分。这些基于传感的方法可用于进一步降低手术环境中非技术技能和团队表现评估的资源成本。
基于传感器对外科手术团队行为进行评估可实现客观、实时的非技术技能测量。
