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Development of a Method for Extracting Structured Dose Information from Free-Text Electronic Prescriptions
Man Qing Liang1,2, Vivek Gidla3, Aman Verma4
1Faculty of Pharmacy, Université de Montréal, Montréal, Canada.
Studies in Health Technology and Informatics
|August 24, 2019
Summary
This study created an automatic parser for electronic prescriptions to define daily doses. The tool successfully identified 90% of doses and 86% of frequencies in medication instructions.
Area of Science:
- Health Informatics
- Natural Language Processing
Background:
- Electronic prescriptions contain unstructured free text, making medication data extraction challenging.
- Accurate interpretation of dosage instructions is crucial for patient safety and effective treatment.
Purpose of the Study:
- To develop an automated tool for parsing unstructured electronic prescriptions.
- To specifically focus on accurately defining the daily dose and frequency of medications.
Main Methods:
- Manual coding of a dataset of electronic discharge prescriptions.
- Development and implementation of a named-entity recognition (NER) parser.
- Rule-based system for structuring medication data.
Main Results:
- The NER parser achieved 90% accuracy in identifying medication doses.
- The parser demonstrated 86% accuracy in identifying medication frequencies.
- Successfully processed 255 distinct dosage instructions.
Conclusions:
- An automated parser tool can effectively structure medication data from free-text electronic prescriptions.
- The developed NER tool significantly improves the accuracy of extracting dose and frequency information.
- This approach offers a scalable solution for analyzing medication data in clinical settings.
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