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An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic
John Novoa-Laurentiev1, Mica Bowen1, Avery Pullman1
1Department of Medicine, Brigham & Women's Hospital, 75 Francis Street, Boston, MA, 02115, United States, 1 8572824088.
JMIR Medical Informatics
|August 26, 2025
Summary
A new natural language processing (NLP) tool, VTExt, accurately extracts venous thromboembolism (VTE) symptoms from clinical notes. This enables better quality measurement and timely VTE diagnosis in primary care.
Area of Science:
- Natural Language Processing (NLP)
- Clinical Informatics
- Health Quality Measurement
Background:
- Delayed diagnosis of venous thromboembolism (VTE) increases patient morbidity and mortality.
- Clinical notes contain crucial information for timely VTE diagnosis and quality measurement, but extraction from unstructured text is challenging.
- Current electronic clinical quality measures (eCQMs) lack NLP for data extraction, limiting accuracy and efficiency.
Purpose of the Study:
- To develop an NLP tool (VTExt) for extracting VTE symptoms from primary care clinical notes.
- To integrate this tool into an eCQM to quantify delayed VTE diagnoses.
Main Methods:
- Iterative development of a rule-based NLP tool (VTExt) using an internal dataset.
- Creation and physician-guided optimization of a VTE symptom lexicon.
- External validation across two independent healthcare organizations with different EHR systems.
- Rigorous performance evaluation using metrics like AUC, PPV, NPV, sensitivity, and specificity.
Main Results:
- VTExt demonstrated near-perfect performance in extracting VTE symptoms from primary care notes.
- The tool achieved promising results in external validation across different institutions and EHR systems.
- VTExt's performance was comparable or superior to deep learning and machine learning models.
Conclusions:
- The study presents a generalizable NLP approach for extracting clinical information from notes across various EHR systems.
- VTExt is the first NLP application to be incorporated into a nationally endorsed eCQM.
- This tool enhances the potential for accurate VTE detection and quality measurement in primary care.
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