A Novel Resident Robotic Curriculum Using Self-Selection for Advanced Training and a Single Web-Based Tool for Feedback and Case-Tracking

Abstract

Study Objective Describe a novel two-phase robotic curriculum facilitated by a web-based feedback and case-tracking tool, which allows residents to self-select into advanced training. Design Descriptive. Setting Academic Hospital. Patients or Participants OBGYN residents and faculty. Interventions Phase I (Basic), required of all residents, includes online training modules, assessment, and robotic dry lab. Phase II (Advanced) console training is elective. Residents complete 10 SIM drills to proficiency before performing live surgery. A web-based tool is used for surgical feedback. Resident online assessments, SIM lab reports, feedback, and case-logs were reviewed (7/2018-6/2019). Attendings and residents completed a beta-tested satisfaction survey. Measurements and Main Results All 24 residents completed Phase I training. Phase II was initiated by 12 residents and 10/12(83.3%) completed all 10 SIM drills. A median of 10(3,26) attempts were required to complete each drill with a mean time of 5.1hrs to complete all drills. 128 web-based post-surgical feedback entries were completed. Residents performed as bedside assistants (75%,n=96), console surgeon (5.5%,n=7), or both (19.5%,n=25). Most common tracked procedure was hysterectomy 111/193(57.5%). Console surgeons performed 32 cases with a mean console time of 34.6±19.5min. Most common procedure steps included: colpotomy/cuff closure 31.1%, uterine artery 75% and utero-ovarian vessel seal/transection 84.4%(n=32). 28/32(87.5%) Global Evaluative Assessment of Robotic Skills (GEARS) assessments were completed. Mean GEARS score 20.6±3.7(n=28), communication (4.2±0.8,n=61), workload management (3.9±0.9,n=54), and team skills (4.3±0.8,n=60). Residents completing >50% of case were assessed as “apprentice” 38.5% or “competent” 23.1%(n=13). 27/44(61.4%), satisfaction survey response rate. After curriculum change, attendings considered residents prepared as bedside assistants and console surgeons (100%), whereas only 16.7% were prepared before (n=6). Respondents were satisfied with the curriculum (95.0%), found the web-based tool helpful to give/receive feedback (90.0%), and recommended continued use (90.0%) (n=19). Conclusion This novel robotic curriculum allows residents to self-select into advanced training. A web-based tool improves individualized feedback and case-tracking alleviates many challenges of graduated robotic training.