• Stephen W Cone, Azhar Rafiq, and Ronald C Merrell.
• Department of Surgery, Virginia Commonwealth University, Richmond, VA, USA. scone2@mcvh-vcu.edu
• Simul Healthc. 2008 Jan 1;3(2):111-5.

IntroductionResuscitation science is a dynamic part of healthcare training, with an expanding role for simulation. Historically, performance measurement and documentation relied upon the presence of an instructor, an expensive and potentially inaccurate assessment tradition that tied performance testing to a fixed facility. We hypothesize that an automated system might be developed and validated to document performance in airway management for self assessment in the absence of a human trainer. The system would also store and transmit data to a central registry to document skill acquisition and maintenance.MethodsMultiple video and pressure inputs captured and documented resuscitation task performance on a readily available standard practice manikin. Bag-valve-mask ventilation (BVM), endotracheal intubation, and ventilation via endotracheal tube (ETT) were studied for accuracy, adequacy, and time. The 12 participants performed each task for 5 repetitions, resulting in 60 total attempts for each skill.ResultsTwelve untrained participants performed 3 critical tasks in airway management. Review with the system informed the participant of his/her performance and desired outcome. The system also documented skill performance objectively relative to a standard, recording both successes and failures. Compressed and abstracted performance records populated an average 14-megabyte file size (excluding full motion video).DiscussionThis system was successfully used to document student performance of BVM, orotracheal intubation, and ventilation via ETT. The system easily integrates documentation, including text reports, airway pressure readings, still images and videos of task performance. Such digital documentation could guide skill acquisition and quantitatively certify performance with minimal reliance upon an instructor and evaluator.

24 keywords...

hide…