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Review
. 2020 Nov 30:2020:10.31478/202011f.
doi: 10.31478/202011f. eCollection 2020.

Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic

Nakul Aggarwal  1 , Mahnoor Ahmed  2 , Sanjay Basu  3 , John J Curtin  1 , Barbara J Evans  4 , Michael E Matheny  5 , Shantanu Nundy  6 , Mark P Sendak  7 , Carmel Shachar  3 , Rashmee U Shah  8 , Sonoo Thadaney-Israni  9
Affiliations
Review

Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic

Nakul Aggarwal et al. NAM Perspect. .
No abstract available

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Conflict of interest statement

Conflict-of-Interest Disclosures: Mark Sendak discloses that his employer holds a license agreement with Cohere Med, Inc. Sanjay Basu discloses that he receives grants from the National Institutes of Health and the Centers for Disease Control and Prevention; that he receives personal fees from the New England Journal of Medicine, PLoS Medicine, Collective Health, and HealthRight360; and that he has a pending patent relative to this work. Shantanu Nundy discloses that he is employed by Accolade, Inc., which provides personalized advocacy and population health services.

Figures

Figure 1
Figure 1. Artificial Intelligence in Health Settings Outside the Hospital and Clinic
NOTE: Represented in the orange third are the typical hospital and clinic settings. Represented in the blue two-thirds are the settings in which most health-related events and human experiences unfold, including the home, work, and community environments. Health-relevant data captured in these settings, for example via smartphone and wearable technology, can inform personalized and timely interventions, as well as public and environmental health assessments.
Figure 2
Figure 2. Advancing the Quintuple Aim
SOURCE: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine.
Figure 3
Figure 3. Translational Path for AI into Clinical Care
NOTE: Depicted here are the key steps towards successful implementation of AI applications in HSOHC into clinical workflows, including engagement of diverse stakeholders, thoughtful application design and development, evaluation and validation, and diffusion and scaling of technologies.
See this image and copyright information in PMC

References

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    1. American Medical Association. Medicare’s major new primary care pay model: Know the facts. 2019. [October 26, 2020]. https://www.ama-assn.org/practicemanagement/payment-delivery-models/medi... .
    1. American Association of Family Physicians. Chronic Care Management. 2020. [October 26, 2020]. https://www.aafp.org/family-physician/practice-and-career/getting-paid/c... .
    1. National Science and Technology Council, Committee on Technology, Executive Office of the President. Preparing for the Future of Artificial Intelligence. 2016. [October 26, 2020]. https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_file... .
    1. Matheny M, Thadaney Israni S, Ahmed M, and Whicher D, , editors. NAM Special Publication. Washington, DC: National Academy of Medicine; 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril.

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