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International expert consensus on metric-based characterization of robot-assisted total laparoscopic hysterectomy
Margarita Afonina1,2,3, Arnold Advincula4, Martin Martino5
1Orsi Academy, Melle, Belgium. afoninamargarita@hotmail.com.
This study developed and validated performance metrics for robot-assisted total laparoscopic hysterectomy (RATLH) using expert consensus. These metrics standardize procedure steps, errors, and critical errors for improved surgical assessment.
Area of Science:
- Surgical innovation and medical device development.
- Robotic surgery and minimally invasive procedures.
- Gynaecological surgery and patient outcomes.
Background:
- Robot-assisted total laparoscopic hysterectomy (RATLH) is increasingly utilized, necessitating standardized performance evaluation.
- Current assessment methods for RATLH lack comprehensive, validated procedural metrics.
- Establishing objective metrics is crucial for training, quality improvement, and surgical standardization.
Purpose of the Study:
- To develop standardized procedural performance metrics for RATLH.
- To establish face and content validity of these metrics through expert consensus.
- To create a foundation for future reliability and construct validity assessments.
Main Methods:
- A core metrics team conducted a detailed task deconstruction of the RATLH procedure.
- Two modified Delphi consensus meetings (face-to-face and online) were employed.
- Twenty-eight international RATLH experts participated in the consensus process.
Main Results:
- Initial metrics included 20 Phases, 110 Steps, 119 Errors, and 54 Critical Errors.
- Post-Delphi consensus resulted in 20 Phases, 116 Steps, 134 Errors, and 56 Critical Errors.
- 100% consensus was achieved on 41 general edits, validating the metrics.
Conclusions:
- This study provides the first comprehensive metric-based characterization of RATLH.
- Expert consensus validated a structured methodology for assessing Steps, Errors, and Critical Errors.
- Future research will focus on evaluating the reliability and construct validity of the developed metrics.
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