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Comparison of a Multimedia Simulator to a Human Model for Teaching FAST Exam Image Interpretation and Image Acquisition
ACADEMIC EMERGENCY MEDICINE 2011; 18:413–419 © 2011 by the Society for Academic Emergency Medicine Abstract Objectives: This study compared the effectiveness of a multimedia ultrasound (US) simulator to normal human models during the practical portion of a course designed to teach the skills of both image acquisition and image interpretation for the Focused Assessment with Sonography for Trauma (FAST) exam. Methods: This was a prospective, blinded, controlled education study using medical students as an US-naïve population. After a standardized didactic lecture on the FAST exam, trainees were separated into two groups to practice image acquisition on either a multimedia simulator or a normal human model. Four outcome measures were then assessed: image interpretation of prerecorded FAST exams, adequacy of image acquisition on a standardized normal patient, perceived confidence of image adequacy, and time to image acquisition. Results: Ninety-two students were enrolled and separated into two groups, a multimedia simulator group ( n = 44), and a human model group ( n = 48). Bonferroni adjustment factor determined the level of significance to be p = 0.0125. There was no difference between those trained on the multimedia simulator and those trained on a human model in image interpretation (median 80 of 100 points, interquartile range [IQR] 71–87, vs. median 78, IQR 62–86; p = 0.16), image acquisition (median 18 of 24 points, IQR 12–18 points, vs. median 16, IQR 14–20; p = 0.95), trainee’s confidence in obtaining images on a 1–10 visual analog scale (median 5, IQR 4.1–6.5, vs. median 5, IQR 3.7–6.0; p = 0.36), or time to acquire images (median 3.8 minutes, IQR 2.7–5.4 minutes, vs. median = 4.5 minutes, IQR = 3.4–5.9 minutes; p = 0.044). Conclusions: There was no difference in teaching the skills of image acquisition and interpretation to novice FAST examiners using the multimedia simulator or normal human models. These data suggest that practical image acquisition skills learned during simulated training can be directly applied to human models.
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