Development of a high fidelity, multidisciplinary, crisis simulation model for robotic surgical teams

Siddhant Patki,Arjun Nathan, Craig Lyness,Premala Nadarajah, Stefan Sevastru, Ahmed Mahrous, Pedro De-Silva, Angeline Shoniwa,Shabnam Undre,Prasad Patki

Journal of robotic surgery(2023)

Cited 0|Views7
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Abstract
Immediate access to the patient in crisis situations, such as cardiac arrest during robotic surgery, can be challenging. We aimed to present a full immersion simulation module to train robotic surgical teams to manage a crisis scenario, enhance teamwork, establish clear lines of communication, improve coordination and speed of response. Start time of cardiopulmonary resuscitation (CPR), first defibrillator shock and robotic de-docking time from the first ‘cardiac arrest call’ were recorded. Observational Teamwork Assessment for Surgery (OTAS) scores were used in control and test simulations to assess performance along with a participant survey. Repeat scenarios and assessment were conducted at a 6-month interval for the same team to validate knowledge retention and an additional scenario was run with a new anaesthetic team to validate modular design. OTAS scores improved across all specialty teams after training with emergency algorithm and at retention validity re-test ( p = 0.0181; p = 0.0063). There was an overall reduction in time to CPR (101–48 s), first defibrillator shock (> 302 s to 86 s) and robot de dock time (86–25 s) Improvement remained constant at retention validity re-test. Replacing the anaesthetic team showed improvement in time to CPR, first shock and robotic de-dock times and did not affect OTAS scores ( p = 0.1588). The module was rated highly for realism and crisis training by all teams. This high-fidelity simulation training module is realistic and feasible to deliver. Its modular design allows for efficient assessment and feedback, optimising staff training time and making it a valuable addition to robotic team training.
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Key words
robotic surgical teams,crisis simulation model,simulation model
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