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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Clinical Neurophysiology Practice |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467981X23000240 |
| _version_ | 1797387863459889152 |
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| author | Jaden D. Barfuss Fábio A. Nascimento Erik Duhaime Srishti Kapur Ioannis Karakis Marcus Ng Aline Herlopian Alice Lam Douglas Maus Jonathan J. Halford Sydney Cash M. Brandon Westover Jin Jing |
| author_facet | Jaden D. Barfuss Fábio A. Nascimento Erik Duhaime Srishti Kapur Ioannis Karakis Marcus Ng Aline Herlopian Alice Lam Douglas Maus Jonathan J. Halford Sydney Cash M. Brandon Westover Jin Jing |
| author_sort | Jaden D. Barfuss |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T22:31:15Z |
| format | Article |
| id | doaj.art-514659037f9c4f3bbe82186d8f9a3c04 |
| institution | Directory Open Access Journal |
| issn | 2467-981X |
| language | English |
| last_indexed | 2024-03-08T22:31:15Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Clinical Neurophysiology Practice |
| spelling | doaj.art-514659037f9c4f3bbe82186d8f9a3c042023-12-18T04:24:35ZengElsevierClinical Neurophysiology Practice2467-981X2023-01-018177186On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform dischargesJaden D. Barfuss0Fábio A. Nascimento1Erik Duhaime2Srishti Kapur3Ioannis Karakis4Marcus Ng5Aline Herlopian6Alice Lam7Douglas Maus8Jonathan J. Halford9Sydney Cash10M. Brandon Westover11Jin Jing12Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Corresponding author at: 50 Staniford St., Suite 401, Boston, MA 02114, USA.Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USACentaur Labs, Boston, MA, USACentaur Labs, Boston, MA, USADepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USASection of Neurology, Department of Internal Medicine, Health Sciences Centre, University of Manitoba, Winnipeg, MB, CanadaDivision of Epilepsy, Department of Neurology, Yale University, New Haven, CT, USADepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USADepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAMedical University of South Carolina, Charlestown, SC, USADepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USADepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USADepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAObjective: 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.http://www.sciencedirect.com/science/article/pii/S2467981X23000240EEGEpilepsyEducationInterictal epileptiform discharges |
| spellingShingle | Jaden D. Barfuss Fábio A. Nascimento Erik Duhaime Srishti Kapur Ioannis Karakis Marcus Ng Aline Herlopian Alice Lam Douglas Maus Jonathan J. Halford Sydney Cash M. Brandon Westover Jin Jing On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges Clinical Neurophysiology Practice EEG Epilepsy Education Interictal epileptiform discharges |
| title | On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges |
| title_full | On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges |
| title_fullStr | On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges |
| title_full_unstemmed | On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges |
| title_short | On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges |
| title_sort | on demand eeg education through competition a novel app based approach to learning to identify interictal epileptiform discharges |
| topic | EEG Epilepsy Education Interictal epileptiform discharges |
| url | http://www.sciencedirect.com/science/article/pii/S2467981X23000240 |
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