Employing the Aviation Model to Reduce Errors in Robotic Gynecological Surgery: A Narrative Review
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author | Title | Focus | Type of Training | No. of Training Session | No. of Participants | Results | Conclusions |
---|---|---|---|---|---|---|---|
F. Zattoni et al. [20] | Development of a surgical safety training program and checklist for conversion during robotic partial nephrectomies | Training | Inanimate simulation | 20 | - | No. of errors (median of 2 groups):
| Open conversion simulations might improve teamwork and facilitate timely conversions to open surgery |
R. Bertolo et al. [21] | Single session of robotic human cadaver training: the immediate impact on urology residents in a teaching hospital | Training | Cadaveric training | 27 | 27 | Post-training improvement:
| Human cadaver robotic training allowed for immediate improvement in robotic skills |
P. Ramos et al. [22] | Face, content, construct, and concurrent validity of dry laboratory exercises for robotic training using a global assessment tool | Training | Dry lab training | - | 26 | Dry lab training rating (0–10):
| These results demonstrate the usefulness of dry lab training |
C.Y. Ro et al. [23] | A novel drill set for the enhancement and assessment of robotic surgical performance | Training | VR simulator | 147 | 21 | Post-training improvement:
| The robotic learning curve for novices reflected an improvement in scores (p < 0.05) |
G. Dulan et al. [24] | Proficiency-based training for robotic surgery: construct validity, workload, and expert levels for nine inanimate exercises | Training | Dry lab training | 432 | 12 | Experts vs. novices (composite score): 932 ± 67 vs. 618 ± 111 (p < 0.001) | Using objective performance metrics, all exercises demonstrated construct validity |
N. Raison et al. [25] | Virtually competent: a comparative analysis of virtual reality and dry-lab robotic simulation training | Training | VR simulator vs. dry lab training | 129 | 43 | Improvement in technical proficiency dry lab vs. VR session (mean GEARS score):
| Dry lab training showed significantly greater improvements than VR simulation after 3 training sessions |
L.K. Newcomb et al. [26] | Correlation of virtual reality simulation and dry lab robotic technical skills | Training | VR simulator vs. Dry lab training | 300 | 30 | Spearman’s correlation coefficients between corresponding VR and dry lab drills (overall score): 0.87 (p < 0.010) | VR drills were found to have a statistically significant correlation with the corresponding dry lab drills |
S.S. Sheth, et al. [27] | Virtual reality robotic surgical simulation: an analysis of gynecology trainees | Training | VR simulator | 1360 | 34 | Post-training improvement:
| Virtual reality robotic simulation enhances skills at all training levels |
P. Culligan et al. [28] | Predictive validity of a training protocol using a robotic surgery simulator | Training | VR simulator | - | 19 | Surgery results (surgeons after training vs. control surgeons):
| Completing the simulator training seems to have reduced the learning curve for beginner robotic surgeons |
M.G. Leon, et al. [29] | Impact of robotic single and dual console systems in the training of minimally invasive gynecology surgery (MIGS) fellows | Training | Dual consoles | 126 | 8 | Dual console vs. single console training:
| Dual console robotic training provides fellows the opportunity for longer console time, a higher number of surgical steps performed, and added interaction with the attending surgeon |
M.L. McCarroll et al. [30] | Development and implementation results of an interactive computerized surgical checklist for robotic-assisted gynaecologic surgery | Checklist | - | - | - | Thirty-day readmissions:
| Integrating a specific checklist for gynecologic robotic-assisted surgery resulted in a significant reduction in readmissions |
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Restaino, S.; Paparcura, F.; Arcieri, M.; Pellecchia, G.; Poli, A.; Gallotta, V.; Gueli Alletti, S.; Cianci, S.; Capozzi, V.A.; Bogani, G.; et al. Employing the Aviation Model to Reduce Errors in Robotic Gynecological Surgery: A Narrative Review. Healthcare 2024, 12, 1614. https://doi.org/10.3390/healthcare12161614
Restaino S, Paparcura F, Arcieri M, Pellecchia G, Poli A, Gallotta V, Gueli Alletti S, Cianci S, Capozzi VA, Bogani G, et al. Employing the Aviation Model to Reduce Errors in Robotic Gynecological Surgery: A Narrative Review. Healthcare. 2024; 12(16):1614. https://doi.org/10.3390/healthcare12161614
Chicago/Turabian StyleRestaino, Stefano, Federico Paparcura, Martina Arcieri, Giulia Pellecchia, Alice Poli, Valerio Gallotta, Salvatore Gueli Alletti, Stefano Cianci, Vito Andrea Capozzi, Giorgio Bogani, and et al. 2024. "Employing the Aviation Model to Reduce Errors in Robotic Gynecological Surgery: A Narrative Review" Healthcare 12, no. 16: 1614. https://doi.org/10.3390/healthcare12161614