Stefano Puliatti1,2,3, Elio Mazzone4,5, Marco Amato6,7,8, Ruben De Groote1,2, Alexandre Mottrie1,2, Anthony G Gallagher1,9,10. 1. ORSI Academy, Melle, Belgium. 2. Department of Urology, OLV, Aalst, Belgium. 3. Department of Urology, University of Modena and Reggio Emilia, Modena, Italy. 4. Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy. 5. Vita-Salute San Raffaele University, Milan, Italy. 6. ORSI Academy, Melle, Belgium. marcohz92@gmail.com. 7. Department of Urology, OLV, Aalst, Belgium. marcohz92@gmail.com. 8. Department of Urology, University of Modena and Reggio Emilia, Modena, Italy. marcohz92@gmail.com. 9. Faculty of Life and Health Sciences, Ulster University, Derry, Northern Ireland, UK. 10. Faculty of Medicine, KU Leuven, Leuven, Belgium.
Abstract
BACKGROUND: To improve patient safety, there is an imperative to develop objective performance metrics for basic surgical skills training in robotic surgery. OBJECTIVE: To develop and validate (face, content, and construct) the performance metrics for robotic suturing and knot tying, using a chicken anastomotic model. DESIGN, SETTING AND PARTICIPANTS: Study 1: In a procedure characterization, we developed the performance metrics (i.e., procedure steps, errors, and critical errors) for robotic suturing and knot tying, using a chicken anastomotic model. In a modified Delphi panel of 13 experts from four EU countries, we achieved 100% consensus on the five steps, 18 errors and four critical errors (CE) of the task. Study 2: Ten experienced surgeons and nine novice urology surgeons performed the robotic suturing and knot tying chicken anastomotic task. The mean inter-rater reliability for the assessments by two experienced robotic surgeons was 0.92 (95% CI, 0.9-0.95). Novices took 18.5 min to complete the task and experts took 8.2 min. (p = 0.00001) and made 74% more objectively assessed performance errors than the experts (p = 0.000343). CONCLUSIONS: We demonstrated face, content, and construct validity for a standard and replicable basic anastomotic robotic suturing and knot tying task on a chicken model. Validated, objective, and transparent performance metrics of a robotic surgical suturing and knot tying tasks are imperative for effective and quality assured surgical training.
BACKGROUND: To improve patient safety, there is an imperative to develop objective performance metrics for basic surgical skills training in robotic surgery. OBJECTIVE: To develop and validate (face, content, and construct) the performance metrics for robotic suturing and knot tying, using a chicken anastomotic model. DESIGN, SETTING AND PARTICIPANTS: Study 1: In a procedure characterization, we developed the performance metrics (i.e., procedure steps, errors, and critical errors) for robotic suturing and knot tying, using a chicken anastomotic model. In a modified Delphi panel of 13 experts from four EU countries, we achieved 100% consensus on the five steps, 18 errors and four critical errors (CE) of the task. Study 2: Ten experienced surgeons and nine novice urology surgeons performed the robotic suturing and knot tying chicken anastomotic task. The mean inter-rater reliability for the assessments by two experienced robotic surgeons was 0.92 (95% CI, 0.9-0.95). Novices took 18.5 min to complete the task and experts took 8.2 min. (p = 0.00001) and made 74% more objectively assessed performance errors than the experts (p = 0.000343). CONCLUSIONS: We demonstrated face, content, and construct validity for a standard and replicable basic anastomotic robotic suturing and knot tying task on a chicken model. Validated, objective, and transparent performance metrics of a robotic surgical suturing and knot tying tasks are imperative for effective and quality assured surgical training.
Entities:
Keywords:
Face; Proficiency-based metrics; Surgical training; content and construct validation
Authors: Aude E Vanlander; Elio Mazzone; Justin W Collins; Alexandre M Mottrie; Xavier M Rogiers; Henk G van der Poel; Isabelle Van Herzeele; Richard M Satava; Anthony G Gallagher Journal: Eur Urol Date: 2020-02-21 Impact factor: 20.096
Authors: Stefano Puliatti; Marco Amato; Elio Mazzone; Giuseppe Rosiello; Ruben De Groote; Pietro Piazza; Luca Sarchi; Rui Farinha; Alexandre Mottrie; Anthony G Gallagher Journal: J Robot Surg Date: 2021-08-12
Authors: Stefano Puliatti; Marco Amato; Rui Farinha; Artur Paludo; Giuseppe Rosiello; Ruben De Groote; Andrea Mari; Lorenzo Bianchi; Pietro Piazza; Ben Van Cleynenbreugel; Elio Mazzone; Filippo Migliorini; Saverio Forte; Bernardo Rocco; Patrick Kiely; Alexandre Mottrie; Anthony G Gallagher Journal: Int J Comput Assist Radiol Surg Date: 2022-01-07 Impact factor: 2.924
Authors: Alexandre Mottrie; Elio Mazzone; Peter Wiklund; Markus Graefen; Justin W Collins; Ruben De Groote; Paolo Dell'Oglio; Stefano Puliatti; Anthony G Gallagher Journal: BJU Int Date: 2020-12-20 Impact factor: 5.588