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Text Mining and Automation for Processing of Patient Referrals
James Todd1, Brent Richards2, Bruce James Vanstone1
1Bond Business School, Bond University, Gold Coast, Queensland, Australia.
Applied Clinical Informatics
|March 29, 2018
Summary
Automating the assignment of referral reasons using text mining and natural language processing (NLP) shows promise. This pilot study successfully demonstrated potential for saving healthcare personnel time on manual tasks.
Area of Science:
- Health Informatics
- Computational Linguistics
Background:
- Healthcare processes often involve time-consuming, repetitive tasks.
- Assigning clinical urgency to patient referrals is a manual process that could be automated.
- Text mining and natural language processing (NLP) offer potential solutions for automating such tasks.
Purpose of the Study:
- To trial and evaluate a pilot study for determining reasons for patient referrals.
- To assess the feasibility of automating a component of clinical urgency assignment.
Main Methods:
- Extracting text from scanned patient referrals.
- Processing text to remove irrelevant symbols and identify key information.
- Comparing processed referral data against a list of conditions using similarity scores.
Main Results:
- The pilot study was successful, indicating potential for automated referral reason assignment.
- Identified challenges and solutions for future research in developing sophisticated classification models.
- Demonstrated the value of text mining and NLP in automating manual hospital tasks.
Conclusions:
- A pilot study successfully demonstrated the potential for automating the assignment of referral reasons.
- This work provides a foundation for further research and development in healthcare automation.
- Automating manual tasks with text mining and NLP can optimize human resource allocation in hospitals.
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