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J Anesth ; 30(1): 12-9, 2016 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-26493397

RESUMO

PURPOSE: Generally, novices are taught fiberoptic intubation on patients by attending anesthesiologists; however, this approach raises patient safety concerns. Patient safety should improve if novice learners are trained for basic skills on simulators. In this educational study, we assessed the time and number of attempts required to train novices in fiberoptic bronchoscopy and fiberoptic intubation on simulators. Because decay in skills is inevitable, we also assessed fiberoptic bronchoscopy and fiberoptic intubation skill decay and the amount of effort required to regain fiberoptic bronchoscopy skill. METHODS: First, we established attempt- and duration-based quantitative norms for reaching skill proficiency for fiberoptic bronchoscopy and fiberoptic intubation by experienced anesthesiologists (n = 8) and prepared an 11-step checklist and a 5-point global rating scale for assessment. Novice learners (n = 15) were trained to reach the established skill proficiency in a Virtual Reality simulator for fiberoptic bronchoscopy skills and a Human Airway Anatomy Simulator for fiberoptic intubation skills. Two months later, novices were reassessed to determine decay in learned skills and the required time to retrain them to fiberoptic bronchoscopy proficiency level. RESULTS: Proficiency in fiberoptic bronchoscopy skill level was achieved with 11 ± 5 attempts and after 658 ± 351 s. After 2 months without practice, the time taken by the novices to successful fiberoptic bronchoscopy on the Virtual Reality simulator increased from 41 ± 8 to 68 ± 31 s (P = 0.0138). Time and attempts required to retrain them were 424 ± 230 s and 9.1 ± 4.6 attempts, respectively. CONCLUSION: Novices were successfully trained to proficiency skill level. Although fiberoptic bronchoscopy skills started to decay within 2 months, the re-training time was shorter.


Assuntos
Broncoscopia/educação , Tecnologia de Fibra Óptica/educação , Intubação Intratraqueal/métodos , Simulação por Computador , Humanos , Aprendizagem
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