Development of Primary Care Research in North America, Europe, and Australia From 1974 to 2017

Hajjar, Florence; Saint-Lary, Olivier; Cadwallader, Jean-Sébastien; Chauvin, Pierre; Boutet, Alexandre; Steinecker, Magali; Robert, Sarah; Ibáñez, Gladys

doi:10.1370/afm.2328

Cited by 33 publications

(32 citation statements)

References 8 publications

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“…The United States is the single dominant country in the field, which is unsurprising given its population, wealth, and research resources and output. [64][65][66][67] The high standing of the United Kingdom and Netherlands despite smaller populations may be attributable to primary care data availability, 68,69 facilitated by high adoption rates of EHRs, 70 and strong information technology academics and industries. 71,72 Investments in data generation, quality, and access will increase future possibilities for AI to be used to strengthen primary care in the corresponding region.…”

Section: Key Findingsmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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PURPOSE Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODSWe performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in English. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTSOf 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%).CONCLUSIONS Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed. Ann Fam Med 2020;18:250-258. https://doi.

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Section: Key Findingsmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…An underlying driver of AI research, and by extension maturation, is data availability, particularly after the shift toward data-driven machine learning methods. The United States is the single dominant country in the field, which is not surprising given its population, wealth, and research resources and output (58)(59)(60)(61). The high standing of the United Kingdom and Netherlands despite smaller populations may be attributable to PC data availability (62,63), facilitated by high adoption rates of EMRs (64), and strong information technology academics and industries (65,66).…”

Section: Discussionmentioning

confidence: 99%

Research on Artificial Intelligence and Primary Care: A Scoping Review

Kueper

Terry

Zwarenstein

et al. 2019

Preprint

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Objective: The purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care. Methods:We performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI.Results: Of 5,515 non-duplicate documents, 405 met our eligibility criteria. The body of literature is primarily focused on creating novel AI methods or modifying existing AI methods to support physician diagnostic or treatment recommendations, for chronic conditions, using data from higher income countries. Meaningfully more studies had at least one author with a technology, engineering, or math appointment than with a primary care appointment (57 (14%) compared to 217 (54%)). Predominant AI subfields were supervised machine learning and expert systems. Discussion:Overall, AI research associated with primary care is at an early stage of maturity with respect to widespread implementation in practice settings. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.

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“…In the past few decades, there has been a proliferation of primary care research studies in many countries and with publications in the eld increasing by about 75% and major primary care research databases approximately tripling the amount of information stored between 2004 and 2013 in just the UK (1,2).…”

Section: Introductionmentioning

confidence: 99%

Bayesian factor analysis in primary care: a systematic review protocol

Zhang

Schuster

2020

Preprint

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Background: Bayesian factor analysis allows for efficient use of preliminary data and information that corresponds to the increasing needs of questionnaire construct validation in primary care research. This systematic review will summarise evidence on the current use of Bayesian factor analysis in primary care.Methods: A comprehensive search strategy will be adopted to identify relevant literature (research studies in primary care) indexed in Medline, Scopus, EMBASE, CINAHL and Cochrane Library. The search strategy includes terms and synonyms for Bayesian statistics, factor analysis and primary care. Forward and backward searches will be conducted manually on the references of articles that meet the inclusion and exclusion criteria to further identify eligible studies. Multiple reviewers will conduct data extraction independently. The analyses will include descriptive synthesis summarizing features about the use and reporting of the respective Bayesian factor analysis approach in primary care. Discussion: This systematic review will provide a comprehensive overview of the current use of Bayesian factor analysis in primary care and provide recommendations for its proper future use in primary care and beyond.Systematic review registration: PROSPERO registration number: CRD42018114978

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Development of Primary Care Research in North America, Europe, and Australia From 1974 to 2017

Cited by 33 publications

References 8 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Research on Artificial Intelligence and Primary Care: A Scoping Review

Bayesian factor analysis in primary care: a systematic review protocol

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