Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial

Stacey C Carter, Alexander Chiang, Galaxy Shah, Lorna Kwan, Jeffrey S Montgomery, Amer Karam, Christopher Tarnay, Khurshid A Guru, Jim C Hu
Annals of Surgery 2015, 261 (5): 870-5

OBJECTIVE: To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition.

BACKGROUND: The acquisition of surgical skills may be challenging for novel techniques and/or those with prolonged learning curves.

METHODS: Randomized controlled trial involving 41 resident physicians performing the Tubes (Da Vinci Intuitive Surgical, Sunnyvale, CA) simulator exercise with versus without peer feedback of video-recorded performance through a social networking Web page. Data collected included simulator exercise score, time to completion, and comfort and satisfaction with robotic surgery simulation.

RESULTS: There were no baseline differences between the intervention group (n = 20) and controls (n = 21). The intervention group showed improvement in mean scores from session 1 to sessions 2 and 3 (60.7 vs 75.5, P < 0.001, and 60.7 vs 80.1, P < 0.001, respectively). The intervention group scored significantly higher than controls at sessions 2 and 3 (75.5 vs 59.6, P = 0.009, and 80.1 vs 65.9, P = 0.019, respectively). The mean time (seconds) to complete the task was shorter for the intervention group than for controls during sessions 2 and 3 (217.4 vs 279.0, P = 0.004, and 201.4 vs 261.9, P = 0.006, respectively). At the study conclusion, feedback subjects were more comfortable with robotic surgery than controls (90% vs 62%, P = 0.021) and expressed greater satisfaction with the learning experience (100% vs 67%, P = 0.014). Of the intervention subjects, 85% found that peer feedback was useful and 100% found it effective.

CONCLUSIONS: Video-based peer feedback through social networking appears to be an effective paradigm for surgical education and accelerates the robotic surgery learning curve during simulation.

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