Open Access

ARTICLE

Surgical flow disruptions during robotic-assisted radical prostatectomy

Christopher J. Dru¹, Jennifer T. Anger¹, Colby P. Souders¹, Catherine Bresee², Matthias Weigl³, Elyse Hallett⁴, Ken Catchpole⁵

1 Division of Urology, Cedars-Sinai Medical Center, Los Angeles, California, USA
2 Cedars-Sinai Biostatistics & Bioinformatics Research Center, Los Angeles, California, USA
3 Institute for Occupational, Social, and Environmental Medicine, Ludwig-Maximilians University, Munich, Germany
4 Pacific Science and Engineering Group, San Diego, California, USA
5 Department of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
Address correspondence to Dr. Christopher J. Dru, 8635 West 3^rd Street, Suite 1070, Los Angeles, CA 90048 USA

Canadian Journal of Urology 2017, 24(3), 8814-8821.

Abstract

Introduction: 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.
Materials and methods: 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.
Results: 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.
Conclusions: 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.

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