MP51-13 SURGICAL SKILL QUALITY IMPROVEMENT: UTILIZING A PEER VIDEO REVIEW WORKSHOP FOR SURGEONS PERFORMING ROBOTIC PROSTATECTOMY

Background: Cardiac arrests (CA) are a leading global cause of mortality. The American Heart Association (AHA) promotes several important strategies associated with improved cardiac arrest outcomes, including decreasing pulse check time and maintaining a chest compression fraction (CCF) > 0.80. Video review is a potential tool to improve skills and analyze deficiencies in various situations, however its use in improving medical resuscitation remains poorly studied in the emergency department (ED). We implemented a quality improvement initiative, which utilized video review of cardiac arrest resuscitations in an effort to improve compliance with such AHA quality metrics. Methods: A cardiopulmonary resuscitation Video Review Team (CoVeRT) of emergency medicine residents were assembled to analyze CA resuscitations in our urban academic ED. Videos were reviewed by two residents, one of whom was a senior resident (PGY-3 or -4), and analyzed for numerous quality improvement metrics, including pulse check time, CCF, time to intravenous access, and time to patient attached to monitor. Results: We collected data on 94 cardiac arrest resuscitations between July 2017 and June 2020. Average pulse check time was 13.09 (SD ±5.97) seconds, and 38% of pulse checks were less than 10 seconds. After the implementation of the video review process, there was a significant decrease in average pulse check time (p=0.01) and a significant increase in CCF (p=0.01) throughout the study period. Conclusions: Our study suggests that the video review and feedback process was significantly associated with improvements in AHA quality metrics for resuscitation in CA among patients presented to the ED.

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Feasibility of expert and crowd-sourced review of intraoperative video for quality improvement of intracorporeal urinary diversion during robotic radical cystectomy

Canadian Urological Association Journal ◽

10.5489/cuaj.4442 ◽

2017 ◽

Vol 11 (10) ◽

pp. 331-6 ◽

Cited By ~ 3

Author(s):

Mitchell G. Goldenberg ◽

Jamal Nabhani ◽

Christopher J.D. Wallis ◽

Sameer Chopra ◽

Andrew J. Hung ◽

...

Keyword(s):

Quality Improvement ◽

Radical Cystectomy ◽

Perceived Risk ◽

High Volume ◽

Specific Content ◽

Logistic Regression Models ◽

Video Review ◽

Ileal Stricture ◽

Robotic Radical Cystectomy ◽

Clinically Significant

Introduction: Development of uretero-ileal stricture (UIS) after robotic-assisted radical cystectomy (RARC) may be dependent on surgical technique. Video review of intraoperative technique is an emerging paradigm for surgical quality improvement. We examined whether surgeon-perceived risk of UIS or crowd-sourced assessment of robotic skill are associated with the development of UIS.Methods: We conducted a case-control study comparing the operative technique of uretero-ileal anastomoses resulting in clinically significant UIS with the contralateral anastomosis for the same patient. De-identified videos were analyzed by 1) five high-volume surgeons; and 2) crowd workers (Crowd-Sourced Assessment of Technical Skill, C-SATS) to determine Global Evaluative Assessment of Robotic Skill (GEARS) score. Mantel-Haenszel common odds ratio (OR) estimates were calculated to assess the association between surgeon performance and the development of UIS. Logistic regression models were used to examine the association between GEARS scores and the development of UIS.Results: A total of 10 UIS videos were compared to eight control videos by five surgeons and 2142 crowd workers. Expert surgeons systematically evaluated intraoperative footage, however, no association between the expert mode response and UIS (OR 0.42; 95% confidence interval [CI] 0.05‒3.45; p=0.91) was identified. Crowd-sourced assessment was not predictive of UIS (p=0.62).Conclusions: We used video review to systematically analyze procedure-specific content and technique. The inability of surgeons to predict UIS may reflect the questionnaire, uncontrolled patient factors, or a lack of power. Crowd-sourced GEARS score was unsuccessful in predicting UIS after RARC.

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