By a News Reporter-Staff News Editor at Clinical Trials Week -- Fresh data on Clinical Research - Clinical Trials and Studies are presented in a new report. According to news reporting out of Gold Coast, Australia, by NewsRx editors, research stated, “Various 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.”
Our news journalists obtained a quote from the research from Bond University, “This article aims to trial and evaluate a pilot study for the first component of the task-determining reasons for referrals. Text 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. This 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. This 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.”
According to the news editors, the research concluded: “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.”
For more information on this research see: Text Mining and Automation for Processing of Patient Referrals. Applied Clinical Informatics , 2018;9(1):232-237.
Our news journalists report that additional information may be obtained by contacting J. Todd, Bond Business School, Bond University, Gold Coast, Queensland, Australia. Additional authors for this research include B. Richards, B.J. Vanstone and A. Gepp.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1055/s-0038-1639482. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-16), Reports Outline Clinical Trials and Studies Findings from Bond University (Text Mining and Automation for Processing of Patient Referrals), Clinical Trials Week, 830, ISSN: 1543-6764, BUTTER® ID: 015514592
From the newsletter Clinical Trials Week.
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