Ning Zhang1, Baran D Sumer. 1. Department of Otolaryngology-Head and Neck Surgery, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas.
Abstract
IMPORTANCE: Simulation-based standardized training is important for the clinical training of physicians practicing robotic surgery. OBJECTIVE: To train robotic surgery-naïve student volunteers using the da Vinci Skills Simulator (dVSS) for transoral robotic surgery (TORS). DESIGN: Prospective inception cohort in 2012. SETTING: Academic referral center. PARTICIPANTS: Sixteen medical student volunteers lacking experience in robotic surgery. INTERVENTIONS: Participants trained with the dVSS in 12 exercises until competent, defined as an overall score of at least 91%. After a 1-, 3-, 5-, or 7-week postinitial training hiatus (n = 4 per group), participants reachieved competence on follow-up. MAIN OUTCOMES AND MEASURES: Total training time (TTT) to achieve competency, total follow-up time (TFT) to reachieve competency, and performance metrics. RESULTS: All participants became competent. The TTT distribution was normal based on the Anderson-Darling normality test (P > .50), but our sample was divided into a short training time (STT) group (n = 10 [63%]) and long training time (LTT) group (n = 6 [37%]). The mean (SD) TTT was 2.4 (0.6) hours for the STT group and 4.7 (0.5) hours for the LTT group. All participants reachieved competence with a mean TFT that was significantly shorter than TTT. There was no significant difference between STT and LTT in mean TFT at 1 and 3 weeks (P = .79), but the LTT group had a longer TFT at 5 and 7 weeks (P = .04) but with no difference in final follow-up scores (P = .12). CONCLUSIONS AND RELEVANCE: Physicians in training can acquire robotic surgery competency. Participants who acquire skills faster regain robotic skills faster after a training hiatus, but, on retraining, all participants can regain equivalent competence. This information provides a benchmark for a simulator training program.
IMPORTANCE: Simulation-based standardized training is important for the clinical training of physicians practicing robotic surgery. OBJECTIVE: To train robotic surgery-naïve student volunteers using the da Vinci Skills Simulator (dVSS) for transoral robotic surgery (TORS). DESIGN: Prospective inception cohort in 2012. SETTING: Academic referral center. PARTICIPANTS: Sixteen medical student volunteers lacking experience in robotic surgery. INTERVENTIONS:Participants trained with the dVSS in 12 exercises until competent, defined as an overall score of at least 91%. After a 1-, 3-, 5-, or 7-week postinitial training hiatus (n = 4 per group), participants reachieved competence on follow-up. MAIN OUTCOMES AND MEASURES: Total training time (TTT) to achieve competency, total follow-up time (TFT) to reachieve competency, and performance metrics. RESULTS: All participants became competent. The TTT distribution was normal based on the Anderson-Darling normality test (P > .50), but our sample was divided into a short training time (STT) group (n = 10 [63%]) and long training time (LTT) group (n = 6 [37%]). The mean (SD) TTT was 2.4 (0.6) hours for the STT group and 4.7 (0.5) hours for the LTT group. All participants reachieved competence with a mean TFT that was significantly shorter than TTT. There was no significant difference between STT and LTT in mean TFT at 1 and 3 weeks (P = .79), but the LTT group had a longer TFT at 5 and 7 weeks (P = .04) but with no difference in final follow-up scores (P = .12). CONCLUSIONS AND RELEVANCE: Physicians in training can acquire robotic surgery competency. Participants who acquire skills faster regain robotic skills faster after a training hiatus, but, on retraining, all participants can regain equivalent competence. This information provides a benchmark for a simulator training program.
Authors: Neil D Gross; F Christopher Holsinger; J Scott Magnuson; Umamaheswar Duvvuri; Eric M Genden; Tamer Ah Ghanem; Kathleen L Yaremchuk; David Goldenberg; Matthew C Miller; Eric J Moore; Luc Gt Morris; James Netterville; Gregory S Weinstein; Jeremy Richmon Journal: Head Neck Date: 2016-03-07 Impact factor: 3.147
Authors: Babak Givi; Michael G Moore; Arnaud F Bewley; Charles S Coffey; Marc A Cohen; Amy C Hessel; Scharukh Jalisi; Steven Kang; Jason G Newman; Liana Puscas; Maisie Shindo; Andrew Shuman; Punam Thakkar; Donald T Weed; Ara Chalian Journal: Head Neck Date: 2020-05-08 Impact factor: 3.147