Browsing by Author "Martino, Martin A."

Now showing 1 - 4 of 4
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    Expert Consensus Recommendations for Robotic Surgery Credentialing
    (Wolters Kluwer, 2020-11) Stefanidis, Dimitrios; Huffman, Elizabeth M.; Collins, Justin W.; Martino, Martin A.; Satava, Richard M.; Levy, Jeffrey S.; Surgery, School of Medicine
    Objective: To define criteria for robotic credentialing using expert consensus. Background: A recent review of institutional robotic credentialing policies identified significant variability and determined current policies are largely inadequate to ensure surgeon proficiency and may threaten patient safety. Methods: 28 national robotic surgery experts were invited to participate in a consensus conference. After review of available institutional policies and discussion, the group developed a 91 proposed criteria. Using a modified Delphi process the experts were asked to indicate their agreement with the proposed criteria in three electronic survey rounds after the conference. Criteria that achieved 80% or more in agreement (consensus) in all rounds were included in the final list. Results: All experts agreed that there is a need for standardized robotic surgery credentialing criteria across institutions that promote surgeon proficiency. 49 items reached consensus in the first round, 19 in the second, and 8 in the third for a total of 76 final items. Experts agreed that privileges should be granted based on video review of surgical performance and attainment of clearly defined objective proficiency benchmarks. Parameters for ongoing outcome monitoring were determined and recommendations for technical skills training, proctoring, and performance assessment were defined. Conclusions: Using a systematic approach, detailed credentialing criteria for robotic surgery were defined. Implementation of these criteria uniformly across institutions will promote proficiency of robotic surgeons and has the potential to positively impact patient outcomes.
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    How Wearable Technology Can Facilitate AI Analysis of Surgical Videos
    (Wolters Kluwer, 2020-10-05) Pugh, Carla M.; Ghazi, Ahmed; Stefanidis, Dimitrios; Schwaitzberg, Steven D.; Martino, Martin A.; Levy, Jeffrey S.; Surgery, School of Medicine
    Operative video has great potential to enable instant replays of critical surgical decisions for training and quality review. Recently, artificial intelligence (AI) has shown early promise as a method of enabling efficient video review, analysis, and segmentation. Despite the progress with AI analysis of surgical videos, more work needs to be done to improve the accuracy and efficiency of AI-driven video analysis. At a recent consensus conference held on July 10–11, 2020, 8 research teams shared their work using AI for surgical video analysis. Four of the teams showcased the utility of wearable technology in providing objective surgical metrics. Data from these technologies were shown to pinpoint important cognitive and motor actions during operative tasks and procedures. The results support the utility of wearable technology to facilitate efficient and accurate video analysis and segmentation.
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    Proving the Effectiveness of the Fundamentals of Robotic Surgery (FRS) Skills Curriculum: A Single-blinded, Multispecialty, Multi-institutional Randomized Control Trial
    (Lippincott, 2020-08) Satava, Richard M.; Stefanidis, Dimitrios; Levy, Jeffrey S.; Smith, Roger; Martin, John R.; Monfared, Sara; Timsina, Lava R.; Wardkes Darzi, Ara; Moglia, Andrea; Brand, Timothy C.; Dorin, Ryan P.; Dumon, Kristoffel R.; Francone, Todd D.; Georgiou, Evangelos; Goh, Alvin C.; Marcet, Jorge E.; Martino, Martin A.; Sudan, Ranjan; Vale, Justin; Gallagher, Anthony G.; Surgery, School of Medicine
    Objective: To demonstrate the noninferiority of the fundamentals of robotic surgery (FRS) skills curriculum over current training paradigms and identify an ideal training platform. Summary Background Data: There is currently no validated, uniformly accepted curriculum for training in robotic surgery skills. Methods: Single-blinded parallel-group randomized trial at 12 international American College of Surgeons (ACS) Accredited Education Institutes (AEI). Thirty-three robotic surgery experts and 123 inexperienced surgical trainees were enrolled between April 2015 and November 2016. Benchmarks (proficiency levels) on the 7 FRS Dome tasks were established based on expert performance. Participants were then randomly assigned to 4 training groups: Dome (n = 29), dV-Trainer (n = 30), and DVSS (n = 32) that trained to benchmarks and control (n = 32) that trained using locally available robotic skills curricula. The primary outcome was participant performance after training based on task errors and duration on 5 basic robotic tasks (knot tying, continuous suturing, cutting, dissection, and vessel coagulation) using an avian tissue model (transfer-test). Secondary outcomes included cognitive test scores, GEARS ratings, and robot familiarity checklist scores. Results: All groups demonstrated significant performance improvement after skills training (P < 0.01). Participating residents and fellows performed tasks faster (DOME and DVSS groups) and with fewer errors than controls (DOME group; P < 0.01). Inter-rater reliability was high for the checklist scores (0.82–0.97) but moderate for GEARS ratings (0.40–0.67). Conclusions: We provide evidence of effectiveness for the FRS curriculum by demonstrating better performance of those trained following FRS compared with controls on a transfer test. We therefore argue for its implementation across training programs before surgeons apply these skills clinically.
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    Response to “Proving the Effectiveness of the Fundamentals of Robotic Surgery (FRS) Skills Curriculum A Single-blinded, Multispecialty, Multi-institutional Randomized Control Trial” Not only surgeon's manual skills...”
    (Wolters Kluwer, 2020-12) Satava, Richard M.; Stefanidis, Dimitrios; Levy, Jeffrey S.; Smith, Roger; Martin, John R.; Monfared, Sara; Timsina, Lava R.; Wardkes Darzi, Ara; Moglia, Andrea; Brand, Timothy C.; Dorin, Ryan P.; Dumon, Kristoffel R.; Francone, Todd D.; Georgiou, Evangelos; Goh, Alvin C.; Marcet, Jorge E.; Martino, Martin A.; Sudan, Ranjan; Vale, Justin; Gallagher, Anthony G.; Surgery, School of Medicine