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Development and evaluation of a clinical note section header terminology
Joshua C Denny1, Randolph A Miller, Kevin B Johnson
1Vanderbilt University School of Medicine, Nashville, TN, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
Summary
A new hierarchical section header terminology improves clinical note understanding by capturing 99.9% of sections. This advancement aids interoperability and natural language processing for electronic health records.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Clinical Documentation
Background:
- Clinical documentation is primarily in natural language text.
- Healthcare providers commonly organize notes into sections (e.g., History of Present Illness, Physical Examination).
- Standardized section headers are lacking, hindering data extraction and analysis.
Purpose of the Study:
- To develop a comprehensive hierarchical section header terminology for clinical notes.
- To evaluate the performance of this new terminology against existing standards like LOINC and the Evaluation and Management billing schema.
- To assess the potential for improved clinical note understanding and interoperability.
Main Methods:
- Developed a hierarchical section header terminology with 1109 concepts and 4332 synonyms.
- Mapped the terminology to LOINC and other vocabularies.
- Physicians evaluated the terminology using a corpus of history and physical notes, comparing it to LOINC and the E/M billing schema.
Main Results:
- The developed terminology covered 99.9% of clinical sections in the evaluated notes.
- LOINC matched 77% of section header concepts and 20% of section header strings.
- Evaluated documents contained a median of 54 sections, with 27 major sections.
Conclusions:
- The hierarchical section header terminology significantly enhances the ability to understand and process clinical notes.
- This terminology offers improved interoperability compared to current standards like LOINC for section identification.
- Further development and integration into natural language processing systems are recommended for broader application.
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