# Determining the metrics of competence in robotic hysterectomy: a systematic review

## TL;DR

This paper reviews existing metrics for assessing skill in robotic hysterectomy and finds a lack of standardized, validated tools.

## Contribution

The study identifies gaps in current robotic hysterectomy training metrics and highlights the need for new assessment tools.

## Key findings

- Only six articles were found proposing metrics for robotic hysterectomy competence.
- Predictive validity was demonstrated in two studies, but simulator and intraoperative metrics showed limited correlation.
- Current tools cannot reliably differentiate skill levels in robotic surgery.

## Abstract

With the rapidly increasing use of robotic-assisted surgery in gynecology, there is a clear need for a structured robotic hysterectomy curriculum. To develop an effective training program, valid performance metrics that reliably assess skill level is required. As part of robotic curriculum development with IMRA using Kern’s framework, this systematic review aims to identify proposed metrics of competence and assess their validity within the context of robotic hysterectomy training. A systematic literature search of OVID MEDLINE and EMBASE was conducted following the PRISMA guidelines, with keywords related to ‘hysterectomy’, ‘robot-assisted’, and ‘metric’. The study aims, methods, outcomes, description of metrics, measurements of metrics, and metrics validity were extracted and analyzed. The initial search yielded 531 articles, of which 3 were included. Three additional articles were identified through citation and website searching, resulting in a total of six articles being included in this review. Development and identification of both simulator and intraoperative metrics greatly varied between the studies. Several studies utilized an expert consensus-based methodology, such as a modified Delphi methodology, to develop performance metrics. All metrics were assessed for content, construct, and predictive validity. Two studies were able to demonstrate predictive validity; however, there was limited correlation between virtual reality simulator metrics and intraoperative scores. This review highlights the lack of standardized and validated metrics specific to robotic hysterectomy, as well as the inability of established assessment tools to differentiate between robotic surgical skill level. This forms the context for ongoing work at IMRA to develop a granular assessment tool to assess skill acquisition as part of a robotic hysterectomy curriculum.