Improving Teamwork: Evaluating Workload of Surgical Team During Robot-assisted Surgery

Urology ◽  
2017 ◽  
Vol 107 ◽  
pp. 120-125 ◽  
Author(s):  
Lora A. Cavuoto ◽  
Ahmed A. Hussein ◽  
Vivek Vasan ◽  
Youssef Ahmed ◽  
Ayesha Durrani ◽  
...  
2017 ◽  
Vol 27 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Kevin Sexton ◽  
Amanda Johnson ◽  
Amanda Gotsch ◽  
Ahmed A Hussein ◽  
Lora Cavuoto ◽  
...  

IntroductionRobot-assisted surgery (RAS) has changed the traditional operating room (OR), occupying more space with equipment and isolating console surgeons away from the patients and their team. We aimed to evaluate how anticipation of surgical steps and familiarity between team members impacted efficiency.MethodsWe analysed recordings (video and audio) of 12 robot-assisted radical prostatectomies. Any requests between surgeon and the team members were documented and classified by personnel, equipment type, mode of communication, level of inconvenience in fulfilling the request and anticipation. Surgical team members completed questionnaires assessing team familiarity and cognitive load (National Aeronautics and Space Administration – Task Load Index). Predictors of team efficiency were assessed using Pearson correlation and stepwise linear regression.Results1330 requests were documented, of which 413 (31%) were anticipated. Anticipation correlated negatively with operative time, resulting in overall 8% reduction of OR time. Team familiarity negatively correlated with inconveniences. Anticipation ratio, per cent of requests that were non-verbal and total request duration were significantly correlated with the console surgeons’ cognitive load (r=0.77, p=0.006; r=0.63, p=0.04; and r=0.70, p=0.02, respectively).ConclusionsAnticipation and active engagement by the surgical team resulted in shorter operative time, and higher familiarity scores were associated with fewer inconveniences. Less anticipation and non-verbal requests were also associated with lower cognitive load for the console surgeon. Training efforts to increase anticipation and team familiarity can improve team efficiency during RAS.


2020 ◽  
Author(s):  
Joan Torrent-Sellens ◽  
Ana Jiménez-Zarco ◽  
Francesc Saigí-Rubió

BACKGROUND Increasingly intelligent and autonomous robots are destined to have a huge impact on our society. Their adoption, however, represents a major change to the healthcare sector’s traditional practices, which, in turn, poses certain challenges. To what extent is it possible to foresee a near-future scenario in which minor routine surgery is directed by robots? And what are the patients’ or general public’s perceptions of having surgical procedures performed on them by robots, be it totally or partially? A patient’s trust in robots and AI may facilitate the spread and use of such technologies. OBJECTIVE The goal of our study was to establish the factors that influence how people feel about having a medical operation performed on them by a robot. METHODS We used data from a 2017 Flash Eurobarometer (number 460) of European Commission with 27,901 citizens aged 15 years and over in the 28 countries of the European Union. The research designs and tests a technology acceptance model (TAM). Logistic regression (odds ratios, OR) to model the predictors of trust in robot-assisted surgery was calculated through motivational factors, robots using experience and sociodemographic independent variables. RESULTS The negative relationship between most of the predictors of ease of use, expected benefits and attitude towards robots, and confidence in robot-assisted surgery was contrasted. The only non-sociodemographic predictor variable that has a positive relationship with trust in robots participating in a surgical intervention is previous experience in the use of robots. In this context, we analyze the confidence predictors for three different levels of robot use experience (zero use, average use, and high use). The results obtained indicate that, as the experience of using robots increases, the predictive coefficients related to information, attitude and perception of robots become more negative. Research results also determined that variables of a sociodemographic nature played an important predictive role. It was confirmed that the effect of experience on trust in robots for surgical interventions was greater among men, people between 40 and 54 years old, and those with higher educational levels. CONCLUSIONS Despite the considerable benefits for the patient that the use of robots can bring in a surgical intervention, the results obtained show that trust in robots goes beyond rational decision-making. By contrasting the reasons that generate trust and mistrust in robots, especially by highlighting the experience of use as a key element, the research makes a new contribution to the state of the art and draws practical implications of the use of robots for health policy and practice.


2020 ◽  
Vol 6 (3) ◽  
pp. 127-130
Author(s):  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Nico Lösch ◽  
Peter P. Pott

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.


Urology ◽  
2015 ◽  
Vol 86 (4) ◽  
pp. 751-757 ◽  
Author(s):  
Khurshid A. Guru ◽  
Somayeh B. Shafiei ◽  
Atif Khan ◽  
Ahmed A. Hussein ◽  
Mohamed Sharif ◽  
...  

2021 ◽  
Vol 24 ◽  
pp. S226
Author(s):  
M. Shin ◽  
J. Lavin ◽  
U. Kreaden ◽  
R. Tominaga ◽  
D. D'Attilio

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