Measuring Electronic Health Record Use in Primary Care: A Scoping Review

Huang, Michael Z.; Gibson, Candace J.; Terry, Amanda

doi:10.1055/s-0037-1615807

Cited by 39 publications

(27 citation statements)

References 44 publications

(64 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%

“…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. 12,[21][22][23] AI could also harm, for example, by exaggerating racial, class, or sex biases if models are built with biased data or used with new populations for whom performance may be poor. Liability, trust, and disrupted workflow are further concerns.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…(13)(14)(15) Confirmando a constataçãoda implantação predominante do sistema de software CDS, o preenchimento de seus instrumentos de coleta foi reconhecido pelos participantes como uma das principais ações do processo de implantação e com efeitos significativos sobre o trabalho das equipes, tais como a sobrecarga, a redução do tempo para o cuidado, as imposições e resistências. Tais efeitos são advindos do preenchimento das fichas e configuram a coexistência entre papel e informatização.…”

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Informatização Da Atenção Básica a Saúde: Avanços E Desafios

et al. 2018

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, Valéria Conceição de Oliveira 6 RESUMO: Objetivo: analisar a implantação da estratégia e-SUS Atenção Básica na Região Oeste de Minas Gerais, Brasil. Método: estudo de caso, descritivo, com dados de 62 responsáveis pela implantação em 54 municípios, de maio a junho de 2015, além de dois entrevistados e análise dos documentos: "Implantação do e-SUS AB na SRS Divinópolis" e "Municípios por Situação de Implantação do e-SUS AB na SRS Divinópolis". Os dados foram analisados porestatística descritiva e Análise de Conteúdo, com suporte do software Atlas TI, versão 7.0. Resultados: em 44 (81,5%) dos municípios foi implantado apenas o cadastro de dados simplificado e 22 (40,7%) dos municípios estão no cenário três de implantação, 34 (54,8%) dos responsáveis pela implantaçãonão são servidores públicos efetivos. Conclusão: a implantação apresenta fragilidades relacionadas à infraestrutura, deficiências nas capacitações e resistência dos profissionais. É necessário rever o planejamento da implantação considerando sua complexidade. DESCRITORES: Sistema de informação em saúde; Tecnologia da informação; Informática em saúde; Informática em enfermagem; Atenção primária à saúde. COMPUTERIZATION OF PRIMARY HEALTH CARE INFORMATION SYSTEMS: ADVANCES AND CHALLENGESABSTRACT: Objective: to analyze the implantation of the e-SUS Primary Health Care strategy in the West Region of Minas Gerais, Brazil. Method: descriptive case study, with data from 62 people responsible for implementation in 54 municipalities, between May and June 2015, as well as two interviewees and analysis of the following documents: "Implantation of the e-SUS PHC in SRS Divinópolis" (Implantação do e-SUS AB na SRS Divinópolis) and "Municipalities, by Implantation of the e-SUS PHC in SRS Divinópolis" (Municípios por Situação de Implantação do e-SUS AB na SRS Divinópolis). The data were analyzed by descriptive statistics and Content Analysis, using the Atlas ti software, version 7.0. Results: in 44 (81.5%) of the municipalities, only the simplified data register was implemented, and 22 (40.7%) of the municipalities are in scenario three of implantation, 34 (54.8%) of those responsible for implantation are not permanently contracted staff. Conclusion: The implantation has weaknesses related to infrastructure and shortcomings in the professionals' training and resistance. It is necessary to review the planning of the implantation, taking its complexity into account. DESCRIPTORS: Health Information Systems; Information Technology; Health Informatics; Nursing Informatics; Primary Health Care. INFORMATIZACIÓN DE LA ATENCIÓN PRIMARIA DE SALUD: AVANCES Y DESAFÍOSRESUMEN: bjetivo: Analizar la implantación de la estrategia del e-SUS Atención Primaria en la Región Oeste de Minas Gerais, Brasil. Método: Estudio de caso, descriptivo, con datos de 62 responsables de la implantación en 54 municipios, entre mayo y junio de 2015, además de dos entrevistados y análisis de documentos: "Implantação do e-SUS AB na SRS Divinópolis" y "Municípios por Situação de Implantação do e-SUS AB na ...

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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%

“…AI's immediate usefulness is not guaranteed: EMRs were predicted to transform PC for the better, but led to unanticipated outcomes and barriers to adoption (11,(28)(29)(30). Similarly, AI could benefit PC but also do harm.…”

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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Measuring Electronic Health Record Use in Primary Care: A Scoping Review

Abstract: There is a need to create standardized frameworks for assessing EHR use.

Cited by 39 publications

References 44 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Informatização Da Atenção Básica a Saúde: Avanços E Desafios

Research on Artificial Intelligence and Primary Care: A Scoping Review

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