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Natural language generation in health care
A J Cawsey1, B L Webber, R B Jones
1Department of Computing and Electrical Engineering, Heriot-Watt University, Edinburgh, Scotland. alison@cee.hw.ac.uk
Journal of the American Medical Informatics Association : JAMIA
|December 10, 1997
Summary
Generating well-written documents from structured health data using natural language generation improves communication. This technology enhances understanding and engagement for both healthcare professionals and patients.
Area of Science:
- Health Informatics
- Natural Language Processing
Background:
- Effective communication is crucial in healthcare settings.
- Structured health data can be challenging for direct comprehension.
- Automated document generation offers a potential solution.
Purpose of the Study:
- To explore the application of natural language generation (NLG) in healthcare.
- To demonstrate how NLG can transform structured data into understandable documents.
Main Methods:
- Utilizing natural language generation techniques.
- Dynamically selecting content, organization, and language based on audience and context.
Main Results:
- Automatically generated documents from structured data are more comprehensible and convincing.
- NLG has been successfully applied to create health education materials, decision support critiques, and medical reports.
Conclusions:
- Natural language generation is a valuable tool for improving healthcare communication.
- Automated document creation enhances the clarity and impact of health information.