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Course: Task-Based Objective Performance Indicators in Robotic Lobectomy Offer a Novel Avenue for Case Assessments

CME Credits: 1.00

Released: 2023-07-12

Adoption of surgical robotics in minimally invasive thoracic surgery in the US has expanded over 20% since 2016. Beyond case load and peer assessment, there are limited actionable insights providing consistently reliable evaluations of thoracic surgical performance. Moreover, the establishment of objective benchmarks during procedural tasks to determine thoracic surgical competency remains poorly defined. We propose a novel, task-based assessment of thoracic performance during robotic lobectomies (RLs) that obviates the inherent limitations or bias that persist in existing approaches. This methodology uses a standardized task-based annotation card and a proprietary data recorder that captures robotic video, instrument and hand movements, and system events, like energy use, during RLs. These measurements can be used to establish machine learning–enabled objective performance indicators (OPIs) for each specific task throughout the procedure, like surgeon wrist articulation and movement smoothness. These OPIs possess unbridled opportunity to identify critical indicators of surgeon assessment that form competency benchmarks. On further study, these may be used to provide actionable, data-driven insights supporting personalized training plans and further correlated with clinical outcomes to provide recommended interventions.


To identify the key insights or developments described in this article


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