Reflective writing is used by medical educators to identify challenges and promote inter-professional skills. These non-medical skills are central to leadership and career development, and are clinically relevant and vital to a trainees success as a practicing physician. However, identification of actionable feedback from reflective writings can be chal- lenging. In this work, we utilize a Natural Language Processing pipeline that incorporates a seeded Term Frequency- Inverse Document Frequency matrix along with sentence-level summarization, sentiment analysis, and clustering to organize sentences into groups, which can aid educators in assessing common challenges experienced by Acting In- terns. Automated analysis of reflective writing is difficult due to its subjective nature; however, our method is able to identify known and new challenges such as issues accessing the electronic health system and adjusting to specialty differences. Medical educators can utilize these topics to identify areas needing attention in the medical curriculum and help students through this transitional time.
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