A randomized controlled educational pilot trial of interictal epileptiform discharge identification for neurology residents.

OBJECTIVE: To assess the effectiveness of an educational program leveraging technology-enhanced learning and retrieval practice to teach trainees how to correctly identify interictal epileptiform discharges (IEDs). METHODS: This was a bi-institutional prospective randomized controlled educational trial involving junior neurology residents. The intervention consisted of three video tutorials focused on the six IFCN criteria for IED identification and rating 500 candidate IEDs with instant feedback either on a web browser (intervention 1) or an iOS app (intervention 2). The control group underwent no educational intervention ("inactive control"). All residents completed a survey and a test at the onset and offset of the study. Performance metrics were calculated for each participant. RESULTS: Twenty-one residents completed the study: control (n = 8); intervention 1 (n = 6); intervention 2 (n = 7). All but two had no prior EEG experience. Intervention 1 residents improved from baseline (mean) in multiple metrics including AUC (.74; .85; p < .05), sensitivity (.53; .75; p < .05), and level of confidence (LOC) in identifying IEDs/committing patients to therapy (1.33; 2.33; p < .05). Intervention 2 residents improved in multiple metrics including AUC (.81; .86; p < .05) and LOC in identifying IEDs (2.00; 3.14; p < .05) and spike-wave discharges (2.00; 3.14; p < .05). Controls had no significant improvements in any measure. SIGNIFICANCE: This program led to significant subjective and objective improvements in IED identification. Rating candidate IEDs with instant feedback on a web browser (intervention 1) generated greater objective improvement in comparison to rating candidate IEDs on an iOS app (intervention 2). This program can complement trainee education concerning IED identification.

On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges.

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.