Utilizing natural language processing to analyze student narrative reflections for medical curriculum improvement

Medical curricula improvement is an ongoing process to keep material relevant and improve the student's learning experience to better prepare them for patient care. Many programs utilize end-of-year evaluations, but these frequently have low response rates and lack actionable feedback. We hypothesized that student reflections written during a fourth year Sub-Internship could be used retrospectively to mine additional information as feedback for future curriculum adjustments. However, reflections contain a large amount of narrative content that would require a cumbersome and essentially infeasible manual review process for busy medical education faculty.