Measures of performance and proficiency in robotic assisted surgery: a systematic review

Charlotte El-Sayed*, A Yiu, P G Vaughan-Shaw, J Todd, P Lin, Z Kasmani, C Munsch, L Rooshenas, M Campbell, S P Bach, J Burke

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Robotic assisted surgery (RAS) has seen a global rise in adoption. Despite this, there is not a standardised training curricula nor a standardised measure of performance. We performed a systematic review across the surgical specialties in RAS and evaluated tools used to assess surgeons’ technical performance. Using the PRISMA 2020 guidelines, Pubmed, Embase and the Cochrane Library were searched systematically for full texts published on or after January 2020–January 2022. Observational studies and RCTs were included; review articles and systematic reviews were excluded. The papers’ quality and bias score were assessed using the Newcastle Ottawa Score for the observational studies and Cochrane Risk Tool for the RCTs. The initial search yielded 1189 papers of which 72 fit the eligibility criteria. 27 unique performance metrics were identified. Global assessments were the most common tool of assessment (n = 13); the most used was GEARS (Global Evaluative Assessment of Robotic Skills). 11 metrics (42%) were objective tools of performance. Automated performance metrics (APMs) were the most widely used objective metrics whilst the remaining (n = 15, 58%) were subjective. The results demonstrate variation in tools used to assess technical performance in RAS. A large proportion of the metrics are subjective measures which increases the risk of bias amongst users. A standardised objective metric which measures all domains of technical performance from global to cognitive is required. The metric should be applicable to all RAS procedures and easily implementable. Automated performance metrics (APMs) have demonstrated promise in their wide use of accurate measures.
Original languageEnglish
Article number16
Number of pages19
JournalJournal of Robotic Surgery
Volume18
DOIs
Publication statusPublished - 13 Jan 2024

Bibliographical note

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© Springer-Verlag London Ltd., part of Springer Nature 2024.

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