Machine learning in general practice: scoping review of administrative task support and automation

Sørensen, Natasha Lee; Bemman, Brian; Jensen, Martin Bach; Moeslund, Thomas B.; Thomsen, Janus Laust

Abstract

Background
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 IEEE Xplore) 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.

Conclusion
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, so that improved and replicable scientific research can be done.

Citation

Sørensen, N. L., Bemman, B., Jensen, M. B., Moeslund, T. B., & Thomsen, J. L. (2023). Machine learning in general practice: scoping review of administrative task support and automation. BMC Primary Care, 24, Article 14. https://doi.org/10.1186/s12875-023-01969-y

• Journal Article Type: Article
• Acceptance Date: Jan 4, 2023
• Online Publication Date: Jan 14, 2023
• Publication Date: Jan 14, 2023
• Deposit Date: Jan 21, 2025
• Journal: BMC Primary Care
• Electronic ISSN: 2731-4553
• Publisher: BioMed Central
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Article Number: 14
• DOI: https://doi.org/10.1186/s12875-023-01969-y
• Public URL: https://durham-repository.worktribe.com/output/3344443
• Additional Information: Available open access via DOI