BACKGROUND: Use of robot-assisted radical prostatectomy (RARP) for prostate cancer is increasing. Structured surgical training and objective assessment are critical for outcomes. OBJECTIVE: To develop and validate a modular training and assessment pathway via Healthcare Failure Mode and Effect Analysis (HFMEA) for trainees undertaking RARP and evaluate learning curves (LCs) for procedural steps. DESIGN, SETTING, AND PARTICIPANTS: This multi-institutional (Europe, Australia, and United States) observational prospective study used HFMEA to identify the high-risk steps of RARP. A specialist focus group enabled validation. Fifteen trainees who underwent European Association of Urology robotic surgery curriculum training performed RARP and were assessed by mentors using the tool developed. Results produced LCs for each step. A plateau above score 4 indicated competence. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used a modular training and assessment tool (RARP Assessment Score) to evaluate technical skills. LCs were constructed. Multivariable Kruskal-Wallis, Mann-Whitney U, and κ coefficient analyses were used. RESULTS AND LIMITATIONS: Five surgeons were observed for 42 console hours to map steps of RARP. HFMEA identified 84 failure modes and 46 potential causes with a hazard score ≥8. Content validation created the RARP Assessment Score: 17 stages and 41 steps. The RARP Assessment Score was acceptable (56.67%), feasible (96.67%), and had educational impact (100%). Fifteen robotic surgery trainees were assessed for 8 mo. In 426 RARP cases (range: 4-79), all procedural steps were attempted by trainees. Trainees were assessed with the RARP Assessment Score by their expert mentors, and LCs for individual steps were plotted. LCs demonstrated plateaus for anterior bladder neck transection (16 cases), posterior bladder neck transection (18 cases), posterior dissection (9 cases), dissection of prostatic pedicle and seminal vesicles (15 cases), and anastomosis (17 cases). Other steps did not plateau during data collection. CONCLUSIONS: The RARP Assessment Score based on HFMEA methodology identified critical steps for focused RARP training and assessed surgeons. LCs demonstrate the experience necessary to reach a level of competence in technical skills to protect patients. PATIENT SUMMARY: We developed a safety and assessment tool to gauge the technical skills of surgeons performing robot-assisted radical prostatectomy. Improvement was monitored, and measures of progress can be used in future to guide mentors when training surgeons to operate safely.
BACKGROUND: Use of robot-assisted radical prostatectomy (RARP) for prostate cancer is increasing. Structured surgical training and objective assessment are critical for outcomes. OBJECTIVE: To develop and validate a modular training and assessment pathway via Healthcare Failure Mode and Effect Analysis (HFMEA) for trainees undertaking RARP and evaluate learning curves (LCs) for procedural steps. DESIGN, SETTING, AND PARTICIPANTS: This multi-institutional (Europe, Australia, and United States) observational prospective study used HFMEA to identify the high-risk steps of RARP. A specialist focus group enabled validation. Fifteen trainees who underwent European Association of Urology robotic surgery curriculum training performed RARP and were assessed by mentors using the tool developed. Results produced LCs for each step. A plateau above score 4 indicated competence. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used a modular training and assessment tool (RARP Assessment Score) to evaluate technical skills. LCs were constructed. Multivariable Kruskal-Wallis, Mann-Whitney U, and κ coefficient analyses were used. RESULTS AND LIMITATIONS: Five surgeons were observed for 42 console hours to map steps of RARP. HFMEA identified 84 failure modes and 46 potential causes with a hazard score ≥8. Content validation created the RARP Assessment Score: 17 stages and 41 steps. The RARP Assessment Score was acceptable (56.67%), feasible (96.67%), and had educational impact (100%). Fifteen robotic surgery trainees were assessed for 8 mo. In 426 RARP cases (range: 4-79), all procedural steps were attempted by trainees. Trainees were assessed with the RARP Assessment Score by their expert mentors, and LCs for individual steps were plotted. LCs demonstrated plateaus for anterior bladder neck transection (16 cases), posterior bladder neck transection (18 cases), posterior dissection (9 cases), dissection of prostatic pedicle and seminal vesicles (15 cases), and anastomosis (17 cases). Other steps did not plateau during data collection. CONCLUSIONS: The RARP Assessment Score based on HFMEA methodology identified critical steps for focused RARP training and assessed surgeons. LCs demonstrate the experience necessary to reach a level of competence in technical skills to protect patients. PATIENT SUMMARY: We developed a safety and assessment tool to gauge the technical skills of surgeons performing robot-assisted radical prostatectomy. Improvement was monitored, and measures of progress can be used in future to guide mentors when training surgeons to operate safely.
Authors: Beiqun Zhao; Hannah M Hollandsworth; Arielle M Lee; Jenny Lam; Nicole E Lopez; Benjamin Abbadessa; Samuel Eisenstein; Bard C Cosman; Sonia L Ramamoorthy; Lisa A Parry Journal: J Surg Educ Date: 2019-09-23 Impact factor: 2.891
Authors: Anthony F Adili; Julia Di Giovanni; Emma Kolesar; Nathan C Wong; Jen Hoogenes; Shawn Dason; Bobby Shayegan Journal: Can Urol Assoc J Date: 2017-11 Impact factor: 1.862
Authors: Beiqun Zhao; Jenny Lam; Hannah M Hollandsworth; Arielle M Lee; Nicole E Lopez; Benjamin Abbadessa; Samuel Eisenstein; Bard C Cosman; Sonia L Ramamoorthy; Lisa A Parry Journal: Surg Endosc Date: 2019-07-08 Impact factor: 4.584