Saratu Kutana1, Daniel P Bitner2, Poppy Addison1, Paul J Chung1,3, Mark A Talamini3, Filippo Filicori1,3. 1. Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA. 2. Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA. DBitner@northwell.edu. 3. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
Abstract
BACKGROUND: Evaluation of robotic surgical skill has become increasingly important as robotic approaches to common surgeries become more widely utilized. However, evaluation of these currently lacks standardization. In this paper, we aimed to review the literature on robotic surgical skill evaluation. METHODS: A review of literature on robotic surgical skill evaluation was performed and representative literature presented over the past ten years. RESULTS: The study of reliability and validity in robotic surgical evaluation shows two main assessment categories: manual and automatic. Manual assessments have been shown to be valid but typically are time consuming and costly. Automatic evaluation and simulation are similarly valid and simpler to implement. Initial reports on evaluation of skill using artificial intelligence platforms show validity. Few data on evaluation methods of surgical skill connect directly to patient outcomes. CONCLUSION: As evaluation in surgery begins to incorporate robotic skills, a simultaneous shift from manual to automatic evaluation may occur given the ease of implementation of these technologies. Robotic platforms offer the unique benefit of providing more objective data streams including kinematic data which allows for precise instrument tracking in the operative field. Such data streams will likely incrementally be implemented in performance evaluations. Similarly, with advances in artificial intelligence, machine evaluation of human technical skill will likely form the next wave of surgical evaluation.
BACKGROUND: Evaluation of robotic surgical skill has become increasingly important as robotic approaches to common surgeries become more widely utilized. However, evaluation of these currently lacks standardization. In this paper, we aimed to review the literature on robotic surgical skill evaluation. METHODS: A review of literature on robotic surgical skill evaluation was performed and representative literature presented over the past ten years. RESULTS: The study of reliability and validity in robotic surgical evaluation shows two main assessment categories: manual and automatic. Manual assessments have been shown to be valid but typically are time consuming and costly. Automatic evaluation and simulation are similarly valid and simpler to implement. Initial reports on evaluation of skill using artificial intelligence platforms show validity. Few data on evaluation methods of surgical skill connect directly to patient outcomes. CONCLUSION: As evaluation in surgery begins to incorporate robotic skills, a simultaneous shift from manual to automatic evaluation may occur given the ease of implementation of these technologies. Robotic platforms offer the unique benefit of providing more objective data streams including kinematic data which allows for precise instrument tracking in the operative field. Such data streams will likely incrementally be implemented in performance evaluations. Similarly, with advances in artificial intelligence, machine evaluation of human technical skill will likely form the next wave of surgical evaluation.
Authors: Nazema Y Siddiqui; Megan E Tarr; Elizabeth J Geller; Arnold P Advincula; Michael L Galloway; Isabel C Green; Hye-Chun Hur; Michael C Pitter; Emily E Burke; Martin A Martino Journal: J Minim Invasive Gynecol Date: 2016-03-21 Impact factor: 4.137
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Authors: Nazema Y Siddiqui; Michael L Galloway; Elizabeth J Geller; Isabel C Green; Hye-Chun Hur; Kyle Langston; Michael C Pitter; Megan E Tarr; Martin A Martino Journal: Obstet Gynecol Date: 2014-06 Impact factor: 7.661
Authors: Poppy Addison; Daniel Bitner; Katie Carsky; Saratu Kutana; Samuel Dechario; Anthony Antonacci; David Mikhail; Samuel Pettit; Paul J Chung; Filippo Filicori Journal: Surg Endosc Date: 2022-08-04 Impact factor: 3.453