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It addresses the growing use of AI for clinical decision support while noting limited research focused on reducing time-consuming administrative workload. Searches across major health care and engineering databases were completed between April and June 2022. From 1,439 records, 12 studies were included, showing emphasis on supervised scheduling tasks with limited GP involvement. Findings highlight strong potential, constrained by scarce open-source data and a preference for diagnostic-centered work.","Aalborg Universitet  \nMachine Learning in General Practice: Scoping Review of Administrative Task Support and Automation  \nSørensen, Natasha Lee; Bemman, Brian; Jensen, Martin Bach; Moeslund, Thomas B. ; Laust Thomsen, Janus  \nPublished in:  \nBMC primary care  \nDOI (link to publication from Publisher):  \n10.1186/s12875-023-01969-y  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nSørensen, N. L. , Bemman, B. , Jensen, M. B. , Moeslund, T. B. , & Laust Thomsen, J. (2023) . Machine Learning in General Practice: Scoping Review of Administrative Task Support and Automation. BMC primary care, 24(1),[14] . [https://doi.org/10.1186/s12875-023-01969-y](https://doi.org/10.1186/s12875-023-01969-y)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nSørensen et al. BMC Primary Care (2023) 24:14 [https://doi.org/10.1186/s12875-023-01969-y](https://doi.org/10.1186/s12875-023-01969-y)  \nBMC Primary Care  \n RESEARCH Open Access  \nMachine learning in general practice:  \nscoping review of administrative task support and automation  \nNatasha Lee Sørensen1 , Brian Bemman 1,2* , Martin Bach Jensen1 , Thomas B. Moeslund2 and Janus Laust Thomsen1  \nAbstract  \nBackground Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEEXplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability.  \nConclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement","cbCaieAwz7LsvtCJ","https://ap.wps.com/l/cbCaieAwz7LsvtCJ","pdf",1408052,1,15,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the purpose of this scoping review?\",\"answer\":\"To summarize research on machine learning methods used for support and automation of administrative tasks in general practice, focusing on evidence addressing time-consuming non-clinical workload.\"},{\"question\":\"Which databases and tools were used to conduct the review?\",\"answer\":\"PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEEXplore were searched, and Covidence was used for screening eligible studies and extracting data.\"},{\"question\":\"What did the review find about the types of administrative tasks and methods used?\",\"answer\":\"Most studies focused on scheduling tasks using supervised machine learning with relatively low GP involvement, while four studies used the latest methods and varied notably in data setting, type, and availability.\"},{\"question\":\"Why is additional research needed, and what improvements are recommended?\",\"answer\":\"The review indicates a limited research field due to issues such as lack of open-source data and prioritization of diagnostic-based tasks; future work should leverage open data, cutting-edge methods, and clearly stated GP involvement to improve replicability.\"}]","Machine Learning in General Practice - Scoping Review of Administrative Task Support and Automation | PDF",1785719912,38,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-in-general-practice-scoping-review-of-administrative-task-support-and-automation","",{"@graph":36,"@context":89},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-general-practice-scoping-review-of-administrative-task-support-and-automation/118721/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of this scoping review?","Question",{"text":75,"@type":76},"To summarize research on machine learning methods used for support and automation of administrative tasks in general practice, focusing on evidence addressing time-consuming non-clinical workload.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which databases and tools were used to conduct the review?",{"text":80,"@type":76},"PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEEXplore were searched, and Covidence was used for screening eligible studies and extracting data.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the review find about the types of administrative tasks and methods used?",{"text":84,"@type":76},"Most studies focused on scheduling tasks using supervised machine learning with relatively low GP involvement, while four studies used the latest methods and varied notably in data setting, type, and availability.",{"name":86,"@type":73,"acceptedAnswer":87},"Why is additional research needed, and what improvements are recommended?",{"text":88,"@type":76},"The review indicates a limited research field due to issues such as lack of open-source data and prioritization of diagnostic-based tasks; 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