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Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical
Stephanie Grim1, Anne Fuhlbrigge1, John F Thomas1
1School of Medicine, Peer Mentored Care Collaborative, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Text mining of electronic consultations (eConsults) reveals common clinical questions and specialist recommendations across medical subspecialties. Natural language processing insights can inform telehealth technology use and administration.
Area of Science:
- Health Informatics
- Medical Telecommunications
- Natural Language Processing
Background:
- Electronic consultations (eConsults) are a novel telehealth technology.
- Free text fields in eConsult orders contain valuable descriptive information.
- Text mining and language processing can extract key insights from eConsult data.
Purpose of the Study:
- To analyze the content of eConsult orders using text mining.
- To identify common clinical questions and care recommendations across subspecialties.
- To assess the utility of natural language processing (NLP) for understanding eConsult use cases.
Main Methods:
- Extracted text data from 18,609 eConsult orders at an academic medical center.
- Utilized n-gram frequencies to analyze clinical questions and specialist responses.
- Examined eConsult data across 28 subspecialties.
Main Results:
- eConsult volumes varied significantly by subspecialty.
- Top bigram frequencies differed greatly by specialty (e.g., "thyroid nodule" in Endocrinology, "shoulder pain" in Orthopedics).
- Specialist response text showed high frequency for terms like "ref range" (Endocrinology) and "surgical oncology" (Medical Oncology).
Conclusions:
- Natural language processing can offer insights into common eConsult use cases.
- Pre-processing of text data is necessary for meaningful analysis.
- Understanding eConsult content can inform providers and administrators about telehealth utilization.
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