ArticleApplied clinical informatics2018
Text Mining and Automation for Processing of Patient Referrals.
Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies.International journal of environmental research and public health · 2022Pooled it
- Observational
- Multistage machine learning model for automated referral triage in pain medicine.Future healthcare journal · 2026Article
- A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.Scientific reports · 2024Article
- AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.Scientific reports · 2024Article
- Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.Frontiers in digital health · 2023Article
- Deep learning-based NLP data pipeline for EHR-scanned document information extraction.JAMIA open · 2022Article
- Article
- Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients.Journal of healthcare informatics research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVarious tasks within health care processes are repetitive and time-consuming, requiring personnel who could be better utilized elsewhere. The task of assigning clinical urgency categories to internal patient referrals is one such case of a time-consuming process, which may be amenable to automation through the application of text mining and natural language processing (NLP) techniques.
objectiveThis article aims to trial and evaluate a pilot study for the first component of the task-determining reasons for referrals.
methodsText is extracted from scanned patient referrals before being processed to remove nonsensical symbols and identify key information. The processed data are compared against a list of conditions that represent possible reasons for referral. Similarity scores are used as a measure of overlap in terms used in the processed data and the condition list.
resultsThis pilot study was successful, and results indicate that it would be valuable for future research to develop a more sophisticated classification model for determining reasons for referrals. Issues encountered in the pilot study and methods of addressing them were outlined and should be of use to researchers working on similar problems.
conclusionThis pilot study successfully demonstrated that there is potential for automating the assignment of reasons for referrals and provides a foundation for further work to build on. This study also outlined a potential application of text mining and NLP to automating a manual task in hospitals to save time of human resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.