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A resident robotic curriculum utilizing self-selection and a web-based feedback tool

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Abstract

To describe an obstetrics and gynecology residency robotic curriculum, facilitated by a web-based feedback and case-tracking tool, allowing for self-selection into advanced training. Phase I (Basic) was required for all residents and included online training modules, online assessment, and robotic bedside assistant dry lab. Phase II (Advanced) was elective console training. Before live surgery, 10 simulation drills completed to proficiency were required. A web-based tool was used for surgical feedback and case-tracking. Online assessments, drill reports, objective GEARS assessments, subjective feedback, and case-logs were reviewed (7/2018-6/2019). A satisfaction survey was reviewed. Twenty four residents completed Phase I training and 10 completed Phase II. To reach simulation proficiency, residents spent a median of 4.1 h performing required simulation drills (median of 10 (3, 26) attempts per drill) before live surgery. 128 post-surgical feedback entries were completed after performance as bedside assistant (75%, n = 96) and console surgeon (5.5%, n = 7). The most common procedure was hysterectomy 111/193 (58%). Resident console surgeons performed portions of 32 cases with a mean console time of 34.6 ± 19.5 min. Mean GEARS score 20.6 ± 3.7 (n = 28). Mean non-technical feedback results: communication (4.2 ± 0.8, n = 61), workload management (3.9 ± 0.9, n = 54), team skills (4.3 ± 0.8, n = 60). Residents completing > 50% of case assessed as “apprentice” 38.5% or “competent” 23% (n = 13). After curriculum change, 100% of surveyed attendings considered residents prepared for live surgical training, vs 17% (n = 6) prior to curriculum change [survey response rate 27/44 (61%)]. Attendings and residents were satisfied with curriculum; 95% and recommended continued use 90% (n = 19).This two-phase robotic curriculum allows residents to self-select into advanced training, alleviating many challenges of graduated robotic training.

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Acknowledgements

The authors thank the Carolinas Simulation Center and Sharlene Wright for their support of this project. Presented at the American Urogynecologic Society’s 41st Scientific Meeting, October 8–10, 2020, Virtual; and at the American Association of Gynecologic Laparoscopists’ 49th Scientific Meeting, November 6–14, 2020, Virtual; North Carolina Obstetrical and Gynecological Society Annual Meeting, March 19–20, Virtual

Funding

No external funds were received for this study.

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Authors and Affiliations

  1. Division of Urogynecology and Pelvic Surgery, Department of Obstetrics and Gynecology, Atrium Health, Charlotte, NC, USA

    Amanda L. Merriman, Megan E. Tarr & Erinn M. Myers

  2. Division of Colorectal Surgery, Department of Surgery, Atrium Health, Charlotte, NC, USA

    Kevin R. Kasten

Authors
  1. Amanda L. Merriman
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  2. Megan E. Tarr
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  3. Kevin R. Kasten
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  4. Erinn M. Myers
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Contributions

All four authors took part in the study design, data collection, analysis, or interpretation of the data, as well as drafting and critical revisions of this manuscript and approved of the final version.

Corresponding author

Correspondence to Amanda L. Merriman.

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Conflict of interest

A.L. Merriman: nothing to disclose; M.E. Tarr: nothing to disclose; K.R. Kasten: nothing to disclose; E.M. Myers: nothing to disclose.

Ethical approval

Carolinas HealthCare System institutional review board deemed that this study met criteria for exempt status, IRB study #03-20-24E. SQUIRE-EDU (Standards for Quality Improvement Reporting Excellence in Education): Publication Guidelines for Educational Improvement) extension of the SQUIRE 2.0 were used to report data.

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Merriman, A.L., Tarr, M.E., Kasten, K.R. et al. A resident robotic curriculum utilizing self-selection and a web-based feedback tool. J Robotic Surg 17, 383–392 (2023). https://doi.org/10.1007/s11701-022-01428-3

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  • DOI: https://doi.org/10.1007/s11701-022-01428-3

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