Brenda G Fahy1, Jean E Cibula2, W Travis Johnson3, Lou Ann Cooper4, David Lizdas3, Nikolaus Gravenstein3, Samsun Lampotang3. 1. Department of Anesthesiology, University of Florida, PO Box 100254, Gainesville, FL, 32610-0254, USA. bfahy@anest.ufl.edu. 2. Department of Neurology, University of Florida, Gainesville, FL, USA. 3. Department of Anesthesiology, University of Florida, PO Box 100254, Gainesville, FL, 32610-0254, USA. 4. Office for Educational Affairs, University of Florida College of Medicine, Gainesville, FL, USA.
Abstract
OBJECTIVE: Develop and pilot test a simulator that presents ten commonly encountered representative clinical contexts for trainees to learn basic electroencephalogram (EEG) interpretation skills. METHODS: We created an interactive web-based training simulator that allows self-paced, asynchronous learning and assessment of basic EEG interpretation skills. The simulator uses the information retrieval process via a free-response text box to enhance learning. Ten scenarios were created that present dynamic (scrolling) EEG tracings resembling the clinical setting, followed by questions with free-text answers. The answer was checked against an accepted word/phrase list. The simulator has been used by 76 trainees in total. We report pilot study results from the University of Florida's neurology residents (N = 24). Total percent correct for each scenario and average percent correct for all scenarios were calculated and correlated with most recent In-training Examination (ITE) and United States Medical License Examination (USMLE) scores. RESULTS: Neurology residents' mean percent correct scenario scores ranged from 27.1-86.0% with an average scenario score of 61.2% ± 7.7. We showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. CONCLUSION: We developed an online interactive EEG interpretation simulator to review basic EEG content and assess interpretation skills using an active retrieval approach. The pilot study showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. Since the ITE is a measure of clinical practice, this is evidence that the simulator can provide self-directed instruction and shows promise as a tool for assessment of EEG knowledge.
OBJECTIVE: Develop and pilot test a simulator that presents ten commonly encountered representative clinical contexts for trainees to learn basic electroencephalogram (EEG) interpretation skills. METHODS: We created an interactive web-based training simulator that allows self-paced, asynchronous learning and assessment of basic EEG interpretation skills. The simulator uses the information retrieval process via a free-response text box to enhance learning. Ten scenarios were created that present dynamic (scrolling) EEG tracings resembling the clinical setting, followed by questions with free-text answers. The answer was checked against an accepted word/phrase list. The simulator has been used by 76 trainees in total. We report pilot study results from the University of Florida's neurology residents (N = 24). Total percent correct for each scenario and average percent correct for all scenarios were calculated and correlated with most recent In-training Examination (ITE) and United States Medical License Examination (USMLE) scores. RESULTS: Neurology residents' mean percent correct scenario scores ranged from 27.1-86.0% with an average scenario score of 61.2% ± 7.7. We showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. CONCLUSION: We developed an online interactive EEG interpretation simulator to review basic EEG content and assess interpretation skills using an active retrieval approach. The pilot study showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. Since the ITE is a measure of clinical practice, this is evidence that the simulator can provide self-directed instruction and shows promise as a tool for assessment of EEG knowledge.
Entities:
Keywords:
Active retrieval; Education/methods; Electroencephalography; Graduate Medical Education; Simulation
Authors: Brenda G Fahy; Samsun Lampotang; Jean E Cibula; W Travis Johnson; Lou Ann Cooper; David Lizdas; Nikolaus Gravenstein; Terrie Vasilopoulos Journal: Cureus Date: 2022-04-24