TY - JOUR
T1 - International expert consensus-driven surgical process model for robot-assisted hysterectomy
T2 - Delphi study results
AU - Nyangoh Timoh, Krystel
AU - Galuret, Soline
AU - Hébert, Thomas
AU - Azaïs, Henri
AU - Barahona, Marc
AU - Becker, Sven
AU - Bolze, Pierre Adrien
AU - Boisramé, Thomas
AU - Borghese, Bruno
AU - Carbonnel, Marie
AU - Cela, Vito
AU - Chauleur, Céline
AU - Chalhoub, Tony
AU - Cheung, Tak Hong
AU - Closon, François
AU - Crochet, Patrice
AU - Dabi, Yohann
AU - De Landsheere, Laurent
AU - Faller, Emilie
AU - Fanfani, Francesco
AU - Gauthier, Tristan
AU - Gotlieb, Walter
AU - Ianieri, Manuel Maria
AU - Ind, Thomas
AU - Kim, Tae Joong
AU - Koskas, Martin
AU - Ng, Joseph
AU - Merlot, Benjamin
AU - Paek, Jiheum
AU - Philip, Charles André
AU - Raimondo, Diego
AU - Roman, Horace
AU - Rosendal, Maria
AU - Seracchioli, Renato
AU - Simoncini, Tommaso
AU - Tran, Phuong Lien
AU - Tourette, Claire Marie
AU - Huaulmé, Arnaud
AU - Jannin, Pierre
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
PY - 2026/1
Y1 - 2026/1
N2 - Background: Despite the widespread use of robot-assisted total laparoscopic hysterectomy (rTLH), there is still significant variability in how the procedure is performed, leading to inconsistencies in surgical outcomes and challenges in training. While existing curricula focus on technical skills, they lack formal models that capture procedural logic and variability. An expert-validated, standardized SPM is essential for improving reproducibility, enhancing surgical education, and enabling integration with artificial intelligence (AI)-driven systems. We sought to develop the first consensus-based surgical process model (SPM) for standard rTLH SPM (e.g., normal BMI, non-enlarged uterus) using a Delphi methodology involving international experts. Methods: A five-round Delphi study was conducted from November 2023 to October 2024 with 35 expert robotic gynecologic surgeons from 10 countries. Panelists iteratively reviewed, refined, and reached consensus on the phases, steps, and SPM paths of rTLH. Consensus was defined as ≥ 75% agreement on Likert scale responses. Results: The final SPM comprises 7 phases and 34 surgical steps, each precisely defined through expert consensus. Seven validated SPM paths were identified, reflecting real-world procedural variability while preserving a common surgical practice centered on uterine pedicle dissection. Conclusions: This study provides the first internationally validated SPM for rTLH, offering a formal, adaptable representation of the procedure. The model supports improved training and objective performance assessment, and serves as a foundational tool for surgical data science and AI applications in robotic gynecologic surgery.
AB - Background: Despite the widespread use of robot-assisted total laparoscopic hysterectomy (rTLH), there is still significant variability in how the procedure is performed, leading to inconsistencies in surgical outcomes and challenges in training. While existing curricula focus on technical skills, they lack formal models that capture procedural logic and variability. An expert-validated, standardized SPM is essential for improving reproducibility, enhancing surgical education, and enabling integration with artificial intelligence (AI)-driven systems. We sought to develop the first consensus-based surgical process model (SPM) for standard rTLH SPM (e.g., normal BMI, non-enlarged uterus) using a Delphi methodology involving international experts. Methods: A five-round Delphi study was conducted from November 2023 to October 2024 with 35 expert robotic gynecologic surgeons from 10 countries. Panelists iteratively reviewed, refined, and reached consensus on the phases, steps, and SPM paths of rTLH. Consensus was defined as ≥ 75% agreement on Likert scale responses. Results: The final SPM comprises 7 phases and 34 surgical steps, each precisely defined through expert consensus. Seven validated SPM paths were identified, reflecting real-world procedural variability while preserving a common surgical practice centered on uterine pedicle dissection. Conclusions: This study provides the first internationally validated SPM for rTLH, offering a formal, adaptable representation of the procedure. The model supports improved training and objective performance assessment, and serves as a foundational tool for surgical data science and AI applications in robotic gynecologic surgery.
KW - Delphi
KW - Expert consensus
KW - Hysterectomy
KW - Laparoscopy
KW - Robot-assisted surgery
KW - Surgical process model
UR - https://www.scopus.com/pages/publications/105020095498
U2 - 10.1007/s00464-025-12339-3
DO - 10.1007/s00464-025-12339-3
M3 - Article
C2 - 41152527
AN - SCOPUS:105020095498
SN - 0930-2794
VL - 40
SP - 541
EP - 552
JO - Surgical Endoscopy
JF - Surgical Endoscopy
IS - 1
ER -