On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal
Jaden D Barfuss1, Fábio A Nascimento1, Erik Duhaime2
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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.
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