UP-14 Surgical decision-making rules and pattern recognition for error avoidance: task analysis of a robotic prostatectomy
Thursday June 27, 2019 from
TBD
Presenter

Avril J. Lusty, Canada

Urological Oncology Fellow

Urology

University of Ottawa

Abstract

Surgical decision-making rules and pattern recognition for error avoidance: Task analysis of a robotic prostatectomy

Avril J. Lusty1, Rodney Breau1.

1Urology , The Ottawa Hospital, Ottawa, ON, Canada

Introduction: Robotic surgery is at the forefront of surgical innovation and a robotic prostatectomy presents novel challenges for both postgraduate learners and seasoned specialists alike. At this time, robotic curricula have yet to be formalized, and as such we aimed to determine the surgical decision-making rules and patterns used by experienced urologic oncologists to complete a robotic prostatectomy.

Methods: A cognitive task analysis (CTA) method was used to perform a series of semi-structured interviews in which incident-probing questions allowed urologic oncologists to describe visual cues and pattern recognition, and the surgical decision-making processes used during a robotic prostatectomy. Four urologic oncologists from The Ottawa Hospital experienced in robotic prostatectomy underwent five CTA interviews, each lasting between one to two hours. Each interview was transcribed, reviewed by two authors, and subsequent thematic analysis and coding grids were performed for the 20 interviews. A single CTA grid was then formulated.

Results: The final CTA grid describes a map of a robotic prostatectomy including the steps and goals of the procedure, landmarks for steps of the procedure, key visual cues for each step, complications or difficulties that could be encountered for each step and complication prevention and management. Specific content not yet described in the literature also includes how the lack of haptic feedback is compensated by the expert robotic surgeons.

Conclusion: The CTA of a robotic prostatectomy documented the surgical decision-making rules, patterns and visual cues urologic oncologists use to avoid errors, compensate for difficult patient anatomy and/or disease and to manage intraoperative surgical complications. This data can be used to produce robust robotic educational curricula.

 

 


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