Authors :
Presenting Author: Hannah Kostan, BS – Baylor College of Medicine
Atul Maheshwari, MD – Baylor College of Medicine
Lu Lin, MD, PhD – Baylor College of Medicine
Alica Goldman, MD, PhD – Baylor University
Vaishnav Krishnan, MD, PhD – Baylor College of Medicine
Gabriela Tantillo, MD – Baylor College of Medicine
Kareem Gadelmola, MD – Baylor College of Medicine
Rationale: Formative assessment of student performance is essential for students to learn how to approach EEG interpretation. Workplace-based assessments (WBAs) offer a unique opportunity to provide real-time, skill-specific feedback in authentic clinical contexts (Norcini and Burch 2007). This pilot study addresses a critical gap by introducing and evaluating the use of a novel EEG WBA in a medical student elective setting. By examining feasibility, perceived utility, and degree of help needed over time, this work aims to inform best practices for competency-based education for advanced medical students.
Methods: This prospective pilot study was conducted as part of a two-week EEG elective at Baylor College of Medicine (Oct 2024–May 2025). Medical students were asked to complete two workplace-based assessments (WBAs) per week, during which faculty observed their EEG interpretation and provided real-time feedback. In cases where the assigned attending was unavailable, a designated “WBA attending” completed the assessment. After the observation, students documented EEG features that were identified and summarized feedback provided by faculty. The faculty in turn assessed students using a standard 5-point entrustment scale (describing the degree that help was needed), which did not contribute directly to the student’s grade. Students and faculty were also asked to complete an anonymous survey assessing the feasibility and educational value of the WBA using Likert scales and free-text responses.
Results: A total of 12 students completed 47 EEG WBAs with 6 faculty across the study period. The proportion of students where faculty reported “I helped a lot” decreased significantly from 41.7% to 13.6% from Week 1 to Week 2 on the elective (Chi-square test,
p=0.035). Among the 47 WBAs, the most commonly identified EEG features were myogenic artifacts (77%), eye blinks (72%), posterior dominant rhythm (55%), and sleep spindles (49%). The most common abnormal finding was focal epileptiform discharges (30%). Less common normal variants (e.g., mu rhythm, wickets) were rarely reviewed (< 10%), suggesting faculty chose to review more prominent features over subtle variants. 15 participants (10 students and 5 faculty) completed a survey to evaluate the feasibility and educational value of the EEG WBA. Altogether, the majority agreed or strongly agreed that the WBA tool was easy to use (93.3%), helpful for learning EEG interpretation (93.3%), there was sufficient time to complete the EEG WBA (80.0%), and they would want to participate even if it wasn’t required (80.0%). There were no statistically significant differences between faculty and student survey responses (
p>0.05, Fisher’s exact test).
Conclusions: Collectively, these findings indicate that the EEG WBA is a feasible, well-received, and valid approach for formative assessment of EEG interpretation skills in a medical student clinical elective.
References
Norcini, John, and Vanessa Burch. 2007. “Workplace-Based Assessment as an Educational Tool: AMEE Guide No. 31.” Medical Teacher 29 (9): 855–71. https://doi.org/10.1080/01421590701775453.
Funding: There was no external funding for this research.