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.
International expert consensus-driven surgical process model for robot-assisted hysterectomy: Delphi study results
Ianieri M. M.;
2025-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
