Purpose:Previously, we identified 8 objective suturing performance metrics highly predictive of urinary continence recovery after robotic-assisted radical prostatectomy. Here, we aimed to test the feasibility of providing tailored feedback based upon these clinically relevant metrics and explore the impact on the acquisition of robotic suturing skills.Materials and Methods:Training surgeons were recruited and randomized to a feedback group or a control group. Both groups completed a baseline, midterm and final dry laboratory vesicourethral anastomosis (VUA) and underwent 4 intervening training sessions each, consisting of 3 suturing exercises. Eight performance metrics were recorded during each exercise: 4 automated performance metrics (derived from kinematic and system events data of the da Vinci® Robotic System) representing efficiency and console manipulation competency, and 4 suturing technical skill scores. The feedback group received tailored feedback (a visual diagram+verbal instructions+video examples) based on these metrics after each session. Generalized linear mixed model was used to compare metric improvement (Δ) from baseline to the midterm and final VUA.Results:Twenty-three participants were randomized to the feedback group (11) or the control group (12). Demographic data and baseline VUA metrics were comparable between groups. The feedback group showed greater improvement than the control group in aggregate suturing scores at midterm (mean Δ feedback group 4.5 vs Δ control group 1.1) and final VUA (Δ feedback group 5.3 vs Δ control group 4.9). The feedback group also showed greater improvement in the majority of the included metrics at midterm and final VUA.Conclusions:Tailored feedback based on specific, clinically relevant performance metrics is feasible and may expedite the acquisition of robotic suturing skills.

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Introduction:We created a suturing skills assessment tool that comprehensively defines criteria around relevant subskills of suturing and confirmed its validity.Methods: Five expert surgeons and an educational psychologist participated in a cognitive task analysis to deconstruct robotic suturing into an exhaustive list of technical skill domains and subskill descriptions. Using the Delphi methodology, each cognitive task analysis element was systematically reviewed by a multi-institutional panel of 16 surgical educators and implemented in the final product when content validity index reached !0.80. In the subsequent validation phase, 3 blinded reviewers independently scored 8 training videos and 39 vesicourethral anastomoses using EASE (End-to-End Assessment of Suturing Expertise); 10 vesicourethral anastomoses were also scored using RACE (Robotic Anastomosis Competency Evaluation), a previously validated but simplified suturing assessment tool. Inter-rater reliability was measured with intra-class correlation for normally distributed values and prevalence-adjusted bias-adjusted Kappa for skewed distributions. Expert (!100 prior robotic cases) and trainee (<100 cases) EASE scores from the non-training cases were compared using a generalized linear mixed model.

INTRODUCTION AND OBJECTIVE:The role of interactive online learning tools in medical education has expanded in multiple disciplines but is still absent in urology. We sought to develop and evaluate an interactive online urology curriculum for medical students, to be used at a multi-institutional level.METHODS: We created an online case-based urology module using the Canvas platform (Instructure, Salt Lake City, UT) with sections on prostate cancer, urinary incontinence, and erectile dysfunction. In August-September 2021, 21 students at 4 institutions completed the module as a component of their urology elective. Students answered questions on a discussion board and engaged in asynchronous dialogue with physicians at their institution. Students completed anonymous pre-module and post-module surveys assessing their confidence in 4 domains of learning for each disease process: patient evaluation, pathophysiology, literature appraisal, and patient counseling. Outcomes in each domain were scored on a 5-point Likert scale, and scores were averaged across the 3 disease processes (prostate cancer, urinary incontinence, and erectile dysfunction). Outcomes were compared using Wilcoxon signedrank tests.RESULTS: Nine students (43% response rate) completed both the pre-module and post-module surveys. Median confidence scores increased across all 4 learning domains (Figure ): patient evaluation (4.0 vs. 2.7, p[0.007), pathophysiology (4.0 vs. 2.7, p[0.007), literature appraisal (4.0 vs. 2.0, p[0.007), and patient counseling (4.0 vs. 2.3, p[0.008). Overall, 7/9 students rated the module "excellent" or "very good." At least 7/9 students "strongly agree" or "somewhat agree" that the module was effective in learning foundational knowledge, improving critical thinking, facilitating performance assessment, and facilitating individualized feedback from the instructors; 6/9 students felt that online modules should be incorporated into future urology electives.CONCLUSIONS: In a multi-institutional pilot, an interactive online curriculum for medical students improved confidence across learning domains for multiple disease processes. We plan to expand this program to more institutions and additional disease processes in order to improve medical student urologic education.

INTRODUCTION AND OBJECTIVE: Surgical feedback is usually provided by supervising surgeons in the operating room. In a previous study, we identified eight objective suturing performance metrics most predictive of urinary continence recovery after robotic radical prostatectomy. In this study, we aim to use a systematic way to provide customized feedback based on these metrics to expedite the acquisition of robotic suturing skills.METHODS: Training surgeons (prior robotic surgery caseload 15) were recruited and randomized to either the feedback group (FG) or the control group (CG). Both groups completed 3 dry-lab vesicourethral anastomoses (VUAs) and four suturing training sessions with 3 exercises (Figure 1a). Performance metrics were recorded and water-tight test was performed after VUA. The FG received formative feedback on metrics they failed to pass, benchmarked by the performance of 8 experienced robotic surgeons (prior robotic surgery caseload >100) (Figure 1b). Automated performance metrics (APMs) were derived from the kinematics and system events data of the daVinci robotic system, representing efficiency and console manipulation competency. Robotic anastomosis competency evaluation (RACE) scores assess suturing technical skills. RACE scores were evaluated independently by !2 trained-assessors blinded to participants' identity. Averaged scores were used for analysis. The intra-class coefficients (ICCs) were measured to monitor the inter-rater reliability. Metric improvement from baseline was compared between the FG and the CG by generalized linear mixed model.RESULTS: 23 participants were randomized to the FG (N[11) and the CG (N[12). The experience levels and baseline VUA metrics were comparable between groups. The ICC was moderately high for all RACE skill domains (ICCs >0.58, p <0.05). Overall, most metrics of the FG improved more than the CG (Figure 1c). Specifically in VUA tests, the total RACE scores of FG improved more than the CG at the midterm VUA (mean, DFG 4.5 vs DCG 1.1, p[0.05). This difference remained but non-statistically significant in the final VUA (DFG 5.3 vs DCG 4.9, p[0.78).CONCLUSIONS: Customized feedback based on select APMs and RACE scores may expedite the acquisition of robotic suturing skills. This pilot study paves the way to larger randomized-controlled trials on the efficacy of feedback.