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Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Author

Listed:
  • Taridzo Chomutare

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Miguel Tejedor

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Therese Olsen Svenning

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Luis Marco-Ruiz

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Maryam Tayefi

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Karianne Lind

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Fred Godtliebsen

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
    Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway)

  • Anne Moen

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
    Institute for Health and Society, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway)

  • Leila Ismail

    (Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
    National Water and Energy Center, United Arab Emirates University, Al Ain 15551, United Arab Emirates
    School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia)

  • Alexandra Makhlysheva

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

  • Phuong Dinh Ngo

    (Norwegian Centre for E-Health Research, 9019 Tromsø, Norway)

Abstract

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

Suggested Citation

  • Taridzo Chomutare & Miguel Tejedor & Therese Olsen Svenning & Luis Marco-Ruiz & Maryam Tayefi & Karianne Lind & Fred Godtliebsen & Anne Moen & Leila Ismail & Alexandra Makhlysheva & Phuong Dinh Ngo, 2022. "Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators," IJERPH, MDPI, vol. 19(23), pages 1-18, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16359-:d:995355
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    References listed on IDEAS

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    1. Eva K. Lee & Hany Y. Atallah & Michael D. Wright & Eleanor T. Post & Calvin Thomas & Daniel T. Wu & Leon L. Haley, 2015. "Transforming Hospital Emergency Department Workflow and Patient Care," Interfaces, INFORMS, vol. 45(1), pages 58-82, February.
    2. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
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