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Using Natural Language Processing to Visualize Narrative Feedback in a Medical Student Performance Dashboard.
Natural language processing (NLP) dashboards for medical student feedback did not significantly improve competency review efficiency. However, reviewers found predictive elements useful and felt more confident in competence decisions.
Area of Science:
- Medical Education
- Natural Language Processing
- Health Informatics
Background:
- Clinical competency committees use narrative feedback for learner assessment.
- Reviewing extensive narrative feedback is time-consuming.
- Natural language processing (NLP) offers potential for efficient feedback analysis.
Purpose of the Study:
- To explore the efficiency of NLP-generated visual dashboards for reviewing narrative feedback for preclerkship medical students.
- To assess the utility of visual summaries and predictive analytics in competency reviews.
Main Methods:
- Utilized preclerkship competency review data (2014-2021) from Northwestern University Feinberg School of Medicine.
- Conducted a user needs analysis with experienced reviewers to inform dashboard design.
- Developed visual dashboards displaying outcome predictions and comparative narrative visualizations.
- Surveyed six faculty reviewers on dashboard utility and impact on efficiency.
Main Results:
- The predictive component of the dashboard was deemed most useful by reviewers.
- Only 17% of reviewers agreed the dashboard improved process efficiency.
- 50% of reviewers felt more confident in competence decisions due to the visuals.
- 50% of reviewers indicated willingness to use visual summaries in future reviews.
Conclusions:
- Visualizing and summarizing narrative feedback presents limitations within comprehensive assessment systems.
- Future work will focus on optimizing dashboards to meet reviewer needs.
- Advancements in large language models and external collaboration are key for future development.

