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Ten Ways Artificial Intelligence Will Transform Primary Care.
Lin, Steven Y; Mahoney, Megan R; Sinsky, Christine A.
Afiliação
  • Lin SY; Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. stevenlin@stanford.edu.
  • Mahoney MR; Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Sinsky CA; American Medical Association, Chicago, IL, USA.
J Gen Intern Med ; 34(8): 1626-1630, 2019 08.
Article em En | MEDLINE | ID: mdl-31090027
ABSTRACT
Artificial intelligence (AI) is poised as a transformational force in healthcare. This paper presents a current environmental scan, through the eyes of primary care physicians, of the top ten ways AI will impact primary care and its key stakeholders. We discuss ten distinct problem spaces and the most promising AI innovations in each, estimating potential market sizes and the Quadruple Aims that are most likely to be affected. Primary care is where the power, opportunity, and future of AI are most likely to be realized in the broadest and most ambitious scale. We propose how these AI-powered innovations must augment, not subvert, the patient-physician relationship for physicians and patients to accept them. AI implemented poorly risks pushing humanity to the margins; done wisely, AI can free up physicians' cognitive and emotional space for patients, and shift the focus away from transactional tasks to personalized care. The challenge will be for humans to have the wisdom and willingness to discern AI's optimal role in twenty-first century healthcare, and to determine when it strengthens and when it undermines human healing. Ongoing research will determine the impact of AI technologies in achieving better care, better health, lower costs, and improved well-being of the workforce.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Atenção Primária à Saúde / Inteligência Artificial Tipo de estudo: Etiology_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Gen Intern Med Assunto da revista: MEDICINA INTERNA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Atenção Primária à Saúde / Inteligência Artificial Tipo de estudo: Etiology_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Gen Intern Med Assunto da revista: MEDICINA INTERNA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos