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PASTE: patient-centered SMS text tagging in a medication management system.
Shane P Stenner1, Kevin B Johnson, Joshua C Denny
1Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee, USA. shane.stenner@vanderbilt.edu
Journal of the American Medical Informatics Association : JAMIA
|October 11, 2011
Summary
This study shows a new system can accurately extract medication details and actions from patient text messages. This technology aids in patient-centered medication management through natural language processing.
Area of Science:
- Health Informatics
- Natural Language Processing
- Mobile Health
Background:
- Mobile technologies offer platforms for electronic patient-centered medication management.
- MyMediHealth (MMH) is a system for medication management, including scheduling, records, and reminders via text messages.
- Extending MMH for two-way interaction via SMS presents natural language processing challenges.
Purpose of the Study:
- To evaluate the performance of a system designed to extract medication information and administration actions from patient-submitted SMS messages.
- To develop and assess the Patient-centered Automated SMS Tagging Engine (PASTE) for processing natural language patient messages.
Main Methods:
- The Patient-centered Automated SMS Tagging Engine (PASTE) was developed using natural language processing, custom lexicons, and knowledge sources.
- A pilot evaluation involved 130 medication messages from 16 volunteers, with system output compared to manual tagging.
Main Results:
- Verified medication names achieved an F-measure of 91.3%, medication terms 94.7%, and action terms 90.4%.
- The overall F-measure for medication names was 79.8%, and for medication action terms was 90%.
Conclusions:
- Previous studies show success in extracting medication information from clinical documents.
- This evaluation confirms the feasibility of extracting medication information from patient-generated SMS messages.
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