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Published on: September 20, 2018
Comparing content coverage in medical curriculum to trainee-authored clinical notes.
Joshua C Denny1, Peter Speltz, Raquel Maddox
1Department of Biomedical Informatics, Vanderbilt University, Nashville, TN;
This study introduces natural language processing (NLP) to evaluate medical curriculum content against trainee clinical notes. NLP analysis identified curriculum gaps, particularly in outpatient care, aiding future medical education revisions.
Area of Science:
- Medical Education
- Natural Language Processing
- Curriculum Evaluation
Background:
- Traditional medical curriculum evaluation relies on manual keyword entry and trainee logs, which can be subjective and incomplete.
- There is a need for objective and comprehensive methods to assess the alignment between medical curricula and actual clinical experiences.
- Evaluating clinical content coverage is crucial for ensuring trainees gain exposure to essential medical conditions.
Purpose of the Study:
- To employ natural language processing (NLP) to compare the clinical content of a medical curriculum with electronic medical record (EMR) notes.
- To assess the coverage of 25 core clinical problems (CCPs) and seven infectious disease categories within the curriculum and trainee notes.
- To identify discrepancies and areas of low content coverage in the medical curriculum.
Main Methods:
- Utilized natural language processing (NLP) techniques to analyze text from a four-year medical curriculum and trainee-generated EMR notes.
- Compared the frequency of 25 core clinical problems (CCPs) and seven infectious disease categories across both data sources.
- Quantified content coverage and identified specific areas of divergence between the curriculum and clinical practice.
Main Results:
- Most core clinical problems (CCPs) were represented in both the curriculum and trainee EMR notes.
- Lecture-based curricula demonstrated higher coverage of rare clinical conditions compared to trainee notes.
- Identified significant gaps in content coverage, predominantly concerning outpatient complaints and management.
Conclusions:
- Natural language processing (NLP) offers a robust method for evaluating medical curriculum content and trainee clinical exposure.
- The findings highlight specific areas within the medical curriculum that require revision, particularly for outpatient settings.
- This NLP-driven approach can inform future medical curriculum development and enhance educational program effectiveness.
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