MP10-07 THE RELATIONSHIP OF TECHNICAL SKILLS AND COGNITIVE WORKLOAD TO ERRORS DURING ROBOTIC SURGICAL EXERCISES

Abstract

You have accessJournal of UrologyCME1 May 2022MP10-07 THE RELATIONSHIP OF TECHNICAL SKILLS AND COGNITIVE WORKLOAD TO ERRORS DURING ROBOTIC SURGICAL EXERCISES Sidney I. Roberts, Steven Cen, Jessica H. Nguyen, Laura C. Perez, Luis G. Medina, Runzhuo Ma, Sandra P. Marshall, Rafal Kocielnik, Anima Anandkumar, and Andrew J. Hung Sidney I. RobertsSidney I. Roberts More articles by this author , Steven CenSteven Cen More articles by this author , Jessica H. NguyenJessica H. Nguyen More articles by this author , Laura C. PerezLaura C. Perez More articles by this author , Luis G. MedinaLuis G. Medina More articles by this author , Runzhuo MaRunzhuo Ma More articles by this author , Sandra P. MarshallSandra P. Marshall More articles by this author , Rafal KocielnikRafal Kocielnik More articles by this author , Anima AnandkumarAnima Anandkumar More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002532.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Surgical performance and skill in robotic surgery has been shown to impact patient outcomes and complication rates. Cognitive workload, or mental strain in the working memory, has been shown to differ between surgeon experience levels. Herein, we attempt to understand the relationship between surgeon technical skills, cognitive workload, and discrete errors committed during a simulated robotic dissection task. METHODS: Participant surgeons performed a robotic surgery dissection exercise (peeling a clementine, removing a single wedge). Participants were grouped based on surgical experience: novice (no prior surgical experience), intermediate (<100 robotic cases), and expert (≥100 cases). Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The task was also evaluated for errors during active dissection or passive retraction. Cognitive workload was quantified as an Index of Cognitive Activity (ICA), derived from Task-Evoked-Pupillary-Response metrics; ICA ranged 0-1, with 1 representing maximum ICA. Generalized Estimating Equation (GEE) was used for all modelings to establish relationships between surgeon technical skills, cognitive workload and errors. RESULTS: Overall there were 22 patients: 7 novices, 9 intermediates, and 6 experts. We found a strong association between technical skills, as measured by multiple GEARS domains (depth perception, force sensitivity and robotic control), and passive errors - with higher GEARS scores associated with a lower relative risk of errors (all p <0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores) novices and experts had a similar range of ICA scores (ICA 0.47 and 0.42, respectively) (Figure 1). CONCLUSIONS: This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skills were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical performance and reduce surgical errors. Source of Funding: This study was supported in part by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number K23EB026493 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e148 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sidney I. Roberts More articles by this author Steven Cen More articles by this author Jessica H. Nguyen More articles by this author Laura C. Perez More articles by this author Luis G. Medina More articles by this author Runzhuo Ma More articles by this author Sandra P. Marshall More articles by this author Rafal Kocielnik More articles by this author Anima Anandkumar More articles by this author Andrew J. Hung More articles by this author Expand All Advertisement PDF DownloadLoading ...