[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120650-en":3,"doc-seo-120650-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120650,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning","Effective and rapid triaging from primary care into secondary care is essential for timely treatment and for managing increasing healthcare demand. Manual referral review across multiple sources is labor-intensive and can prolong referral-to-treatment times, while vague symptoms reduce accuracy. This work presents a heterogeneous data-driven hybrid machine learning model that integrates natural language processing for explainable risk stratification at the triage point. It reports strong performance and real-world pilot results, with accuracy improvements over clinicians and an estimated clinician time saving of 8 hours per week.","Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning  \nArticle  \nPublished Version  \nCreative Commons: Attribution 4.0 (CC-BY)  \nOpen Access  \nWang, Bing, Li, Weizi ORCID logoORCID: [https://orcid.org/0000-0003-2878-3185](https://orcid.org/0000-0003-2878-3185) , Bradlow, Anthony, Bazuaye, Eghosa and Chan, Antoni T. Y. (2023) Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning. Decision Support Systems, 166. 113899. ISSN 0167-9236 doi: [https://doi.org/10.1016/j.dss.2022.113899 Available](https://doi.org/10.1016/j.dss.2022.113899 Available) at  \n[https://centaur. reading.ac. uk/108770/](https://centaur. reading.ac. uk/108770/)  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1016/j.dss.2022.113899](http://dx.doi.org/10.1016/j.dss.2022.113899)  \nPublisher: Elsevier  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nDecision Support Systems 166 (2023) 113899  \nContents lists available at ScienceDirect  \nDecision Support Systems  \njournal [homepage: www.elsevier.com/locate/dss](homepage: www.elsevier.com/locate/dss)  \n| Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Bing Wang a, Weizi Lia, *, Anthony Bradlow b, Eghosa Bazuayec, Antoni T.Y. Chan b\u003Cbr>a Informatics Research Centre, Henley Business School, University of Reading, Reading RG6 6UD, UK b Rheumatology Department, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK c Informatics Department, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>Primary to secondary care triage Ensemble method for heterogeneous data Prediction explanation\u003Cbr>NLP |  | Effective and rapid triaging from primary care into secondary care plays a pivotal role in providing patients with timely treatment and managing increasing demands for healthcare resources. Existing triaging methods from primary care to secondary care are labor-intensive processes that involve manually reviewing referral data from multiple sources and can cause long referral to treatment time. There has been no research using machine learning methods that automatically analyzes heterogeneous data including referral letters to recognize regularities to support the primary to secondary care triage. In this paper, we propose a heterogeneous data-driven hybrid machine learning model including Natural Language Processing (NLP) to improve hospital triage efficiency at the point of triage. The proposed model achieved a precision of 0.83, recall of 0.82, F1-Score of 0.83, accuracy of 0.82, AUC of 0.90 in identifying patients with non-inflammatory conditions (NIC) and inflammatory arthritis (IA) at the point of triage with explainable risk stratifications. Our model is piloted in a real-world trial in a large secondary care hospital in the UK to compare referral accuracy and time saved between our model and clinicians, and evaluate its acceptability by users. Our model achieved precision and recall of 0.83 and 0.81, compared with the precision and recall of 0.80 and 0.78 by clinicians. The research also shows that our model enabled decision support can save clinicians 8 h per week in assessing the referral assessment. This paper is the first study to streamline hospital triage from primary care to secondary care using machine learning","cbCailhtyT3HagBL","https://ap.wps.com/l/cbCailhtyT3HagBL","pdf",6984020,1,15,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is triaging from primary care to secondary care important?\",\"answer\":\"Rapid triage and referral assessment enable timely medical intervention and help prevent death and disability, while also managing increasing healthcare resource demands.\"},{\"question\":\"What problem does the proposed model address?\",\"answer\":\"It targets the labor-intensive manual process and the reduced referral accuracy caused by vague symptoms, by automatically analyzing heterogeneous referral data using a hybrid machine learning approach with NLP.\"},{\"question\":\"How does the model perform and what practical impact does it have?\",\"answer\":\"The model achieves reported classification performance for non-inflammatory conditions versus inflammatory arthritis and provides explainable risk stratifications. In a real-world pilot, it showed improved referral accuracy and reduced assessment time compared with clinicians, with an estimated 8 hours saved per week.\"}]","Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning | PDF",1785731166,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improving-triaging-from-primary-care-into-secondary-care-using-heterogeneous-data-driven-hybrid-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-triaging-from-primary-care-into-secondary-care-using-heterogeneous-data-driven-hybrid-machine-learning/120650/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is triaging from primary care to secondary care important?","Question",{"text":75,"@type":76},"Rapid triage and referral assessment enable timely medical intervention and help prevent death and disability, while also managing increasing healthcare resource demands.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the proposed model address?",{"text":80,"@type":76},"It targets the labor-intensive manual process and the reduced referral accuracy caused by vague symptoms, by automatically analyzing heterogeneous referral data using a hybrid machine learning approach with NLP.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the model perform and what practical impact does it have?",{"text":84,"@type":76},"The model achieves reported classification performance for non-inflammatory conditions versus inflammatory arthritis and provides explainable risk stratifications. 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