Dru Christopher J, Anger Jennifer T, Souders Colby P, Bresee Catherine, Weigl Matthias, Hallett Elyse, Catchpole Ken
Division of Urology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Can J Urol. 2017 Jun;24(3):8814-8821.
We sought to apply the principles of human factors research to robotic-assisted radical prostatectomy to understand where training and integration challenges lead to suboptimal and inefficient care.
Thirty-four robotic-assisted radical prostatectomy and bilateral pelvic lymph node dissections over a 20 week period were observed for flow disruptions (FD) - deviations from optimal care that can compromise safety or efficiency. Other variables - physician experience, trainee involvement, robot model (S versus Si), age, body mass index (BMI), and American Society of Anesthesiologists (ASA) physical status - were used to stratify the data and understand the effect of context. Effects were studied across four operative phases - entry to insufflations, robot docking, surgical intervention, and undocking. FDs were classified into one of nine categories.
An average of 9.2 (SD = 3.7) FD/hr were recorded, with the highest rates during robot docking (14.7 [SD = 4.3] FDs/hr). The three most common flow disruptions were disruptions of communication, coordination, and equipment. Physicians with more robotic experience were faster during docking (p < 0.003). Training cases had a greater FD rate (8.5 versus 10.6, p < 0.001), as did the Si model robot (8.2 versus 9.8, p = 0.002). Patient BMI and ASA classification yielded no difference in operative duration, but had phase-specific differences in FD.
Our data reflects the demands placed on the OR team by the patient, equipment, environment and context of a robotic surgical intervention, and suggests opportunities to enhance safety, quality, efficiency, and learning in robotic surgery.
我们试图将人为因素研究的原则应用于机器人辅助根治性前列腺切除术,以了解培训和整合方面的挑战在何处导致护理效果欠佳和效率低下。
在20周的时间内,对34例机器人辅助根治性前列腺切除术及双侧盆腔淋巴结清扫术进行观察,以寻找流程中断(FD)情况,即偏离最佳护理流程,可能会影响安全性或效率的情况。使用其他变量——医生经验、实习生参与情况、机器人型号(S型与Si型)、年龄、体重指数(BMI)以及美国麻醉医师协会(ASA)身体状况分级——对数据进行分层,并了解环境因素的影响。在四个手术阶段——气腹建立、机器人对接、手术干预和对接解除——对影响进行研究。FD情况被分为九类中的一类。
平均每小时记录到9.2次(标准差 = 3.7)FD情况,其中机器人对接阶段发生率最高(每小时14.7次[标准差 = 4.3])。三种最常见的流程中断情况是沟通中断、协调中断和设备问题。机器人操作经验更丰富的医生在对接过程中速度更快(p < 0.003)。培训案例的FD发生率更高(8.5次与10.6次,p < 0.001),Si型机器人也是如此(8.2次与9.8次,p = 0.002)。患者BMI和ASA分级在手术时长方面没有差异,但在FD方面存在阶段特异性差异。
我们的数据反映了机器人手术干预中患者、设备、环境和背景对手术室团队的要求,并提示了提高机器人手术安全性、质量、效率和学习效果的机会。