Dataset Information

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

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.

SUBMITTER: Sorensen NL

PROVIDER: S-EPMC9840326 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Sørensen Natasha Lee NL Bemman Brian B Jensen Martin Bach MB Moeslund Thomas B TB Thomsen Janus Laust JL

BMC primary care 20230114 1

<h4>Background</h4>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.<h4>Methods</h4>Databases covering the f ...[more]

PMID: 36641467

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Project description:ObjectiveWe conducted a scoping review with the aim of comprehensively investigating what tools or methods have been examined in general practice research that capture a wide range of psychosocial problems (PSPs) and serve to identify patients and highlight their characteristics.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and the Joanna Briggs Institute Reviewer's Manual on scoping reviews. A systematic search was conducted in four electronic databases (Medline [Ovid], Web of Science Core Collection, PsycInfo, Cochrane Library) for quantitative and qualitative studies in English, Spanish, French, and German with no time limit. The protocol was registered with Open Science Framework and published in BMJ Open.ResultsOf the 839 articles identified, 66 met the criteria for study eligibility, from which 61 instruments were identified. The publications were from 18 different countries, with most studies employing an observational design and including mostly adult patients. Among all instruments, 22 were reported as validated, which we present in this paper. Overall, quality criteria were reported differently, with studies generally providing little detail. Most of the instruments were used as paper and pencil questionnaires. We found considerable heterogeneity in the theoretical conceptualisation, definition, and measurement of PSPs, ranging from psychiatric case findings to specific social problems.Discussion and conclusionThis review presents a number of tools and methods that have been studied and used in general practice research. Adapted and tailored to local circumstances, practice populations, and needs, they could be useful for identifying patients with PSPs in daily GP practice; however, this requires further research. Given the heterogeneity of studies and instruments, future research efforts should include both a more structured evaluation of instruments and the incorporation of consensus methods to move forward from instrument research to actual use in daily practice.

Project description:OBJECTIVE:To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. DESIGN:A scoping review. DATA SOURCES:MEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018. ELIGIBILITY CRITERIA:We included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context. DATA EXTRACTION AND SYNTHESIS:We summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items. RESULTS:Of 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16?543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%). CONCLUSIONS:Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health. REGISTRATION NUMBER:Registered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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