1. Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy
- Author
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Christopher C. Stahl, Azita G. Hamedani, Alexandra A. Rosser, Rebecca M. Minter, Aaron S. Kraut, Benjamin H. Schnapp, Mary Westergaard, Sarah A. Jung, and Jacob A. Greenberg
- Subjects
Models, Educational ,Faculty, Medical ,Formative Feedback ,media_common.quotation_subject ,computer.software_genre ,Machine learning ,01 natural sciences ,Latent Dirichlet allocation ,Professional activity ,Expert committee ,Article ,Specialties, Surgical ,Machine Learning ,010104 statistics & probability ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Medicine ,Humans ,Narrative ,Professional Autonomy ,030212 general & internal medicine ,0101 mathematics ,Single institution ,Interpretability ,media_common ,Natural Language Processing ,Surgeons ,business.industry ,Data Science ,Internship and Residency ,General Medicine ,Competency-Based Education ,Actual practice ,symbols ,Feasibility Studies ,Surgery ,Artificial intelligence ,Clinical Competence ,business ,computer ,Natural language processing ,Autonomy - Abstract
Background Entrustable Professional Activities (EPAs) contain narrative ‘entrustment roadmaps’ designed to describe specific behaviors associated with different entrustment levels. However, these roadmaps were created using expert committee consensus, with little data available for guidance. Analysis of actual EPA assessment narrative comments using natural language processing may enhance our understanding of resident entrustment in actual practice. Methods All text comments associated with EPA microassessments at a single institution were combined. EPA—entrustment level pairs (e.g. Gallbladder Disease—Level 1) were identified as documents. Latent Dirichlet Allocation (LDA), a common machine learning algorithm, was used to identify latent topics in the documents associated with a single EPA. These topics were then reviewed for interpretability by human raters. Results Over 18 months, 1015 faculty EPA microassessments were collected from 64 faculty for 80 residents. LDA analysis identified topics that mapped 1:1 to EPA entrustment levels (Gammas >0.99). These LDA topics appeared to trend coherently with entrustment levels (words demonstrating high entrustment were consistently found in high entrustment topics, word demonstrating low entrustment were found in low entrustment topics). Conclusions LDA is capable of identifying topics relevant to progressive surgical entrustment and autonomy in EPA comments. These topics provide insight into key behaviors that drive different level of resident autonomy and may allow for data-driven revision of EPA entrustment maps.
- Published
- 2020