How does Canada stack up? A bibliometric analysis of the primary
healthcare electronic medical record literature

Terry, Amanda; Stewart, Moira; Fortin, Martin; Wong, Sabrina T.; Kennedy, Maureen Shawn; Birtwhistle, Richard; Grava-Gubins, Inese; Webster, Greg; Thind, Amardeep

doi:10.14236/jhi.v20i4.2

Cited by 5 publications

(3 citation statements)

References 9 publications

(12 reference statements)

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“…[1][2][3][4][5][6][7] This application includes primary care, [8][9][10] defined by Barbara Starfield as "The level of a health service system that provides entry into the system for all new needs and problems, provides person-focused (as opposed to disease-oriented) care over time, provides care for all but very uncommon or unusual conditions, and coordinates or integrates care provided elsewhere or by others." 11(pp8-9) Given the recent surge in uptake of electronic health records (EHRs) and thus availability of data, 12,13 there is potential for AI to benefit both primary care practice and research, especially in light of the breadth of practice and rapidly increasing amounts of information that humans cannot meaningfully condense and comprehend. 1,2,[4][5][6][7][8][9][10][11][14][15][16][17][18][19][20] AI's immediate usefulness is not guaranteed, however: EHRs were predicted to transform primary care for the better, but led to unanticipated outcomes and encountered barriers to adoption.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…The use of electronic medical records (EMRs) in primary health care has recently improved in Canada with estimates of physician uptake increasing from 37% in 2009 to 75% in 2015 1 , 2. Using primary care EMRs to measure, track and evaluate childhood obesity have been proposed for multiple reasons.…”

Section: Introductionmentioning

confidence: 99%

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Carsley

Birken

Parkin

et al. 2018

BMJ Health Care Inform

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Background Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However, the use of EMRs in research has been impeded by lack of standardisation of EMRs systems, data access and concerns about the quality of the data. Objectives The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes. Methods A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Record Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the World Health Organization Growth Standards and Reference. Results A total of 54,964 children were identified from EMRALD. Overall, 93% had at least one complete set of growth measurements to calculate a body mass index (BMI) z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.

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“…AI research began in the 1950s, but the recognition of its potential for adoption in health care settings is widespread and ongoing (7)(8)(9). Applications of AI to the foundational sector of health care, Primary Care (PC), are particularly compelling and promoted by advances in technology and the availability of data such as through increased use of Electronic Medical Records (EMRs) (10)(11)(12).…”

Section: Introductionmentioning

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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How does Canada stack up? A bibliometric analysis of the primary healthcare electronic medical record literature

Cited by 5 publications

References 9 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Research on Artificial Intelligence and Primary Care: A Scoping Review

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