IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p16359-d995355.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/16359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/23/16359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael R. Miller & Robert J. Alexander & Vincent A. Arbige & Robert F. Dell & Steven R. Kremer & Brian P. McClune & Jane E. Oppenlander & Joshua P. Tomlin, 2017. "Optimal Allocation of Students to Naval Nuclear-Power Training Units," Interfaces, INFORMS, vol. 47(4), pages 320-335, August.
    2. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    3. Eva K. Lee & Helder I. Nakaya & Fan Yuan & Troy D. Querec & Greg Burel & Ferdinand H. Pietz & Bernard A. Benecke & Bali Pulendran, 2016. "Machine Learning for Predicting Vaccine Immunogenicity," Interfaces, INFORMS, vol. 46(5), pages 368-390, October.
    4. Diego Tlapa & Ignacio Franco-Alucano & Jorge Limon-Romero & Yolanda Baez-Lopez & Guilherme Tortorella, 2022. "Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    5. Diego Tlapa & Guilherme Tortorella & Flavio Fogliatto & Maneesh Kumar & Alejandro Mac Cawley & Roberto Vassolo & Luis Enberg & Yolanda Baez-Lopez, 2022. "Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review," IJERPH, MDPI, vol. 19(15), pages 1-23, July.
    6. Miguel Angel Ortíz-Barrios & Juan-José Alfaro-Saíz, 2020. "Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-41, April.
    7. Yasar A. Ozcan & Elena Tànfani & Angela Testi, 2017. "Improving the performance of surgery-based clinical pathways: a simulation-optimization approach," Health Care Management Science, Springer, vol. 20(1), pages 1-15, March.
    8. Hui Zhang & Thomas J. Best & Anton Chivu & David O. Meltzer, 2020. "Simulation-based optimization to improve hospital patient assignment to physicians and clinical units," Health Care Management Science, Springer, vol. 23(1), pages 117-141, March.
    9. Jakob Heins & Jan Schoenfelder & Steffen Heider & Axel R. Heller & Jens O. Brunner, 2022. "A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy," Interfaces, INFORMS, vol. 52(6), pages 508-523, November.
    10. 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.
    11. Sanjay L. Ahire, 2023. "McLeod Health Optimizes Staffing for Patient Room Cleaning," Interfaces, INFORMS, vol. 53(2), pages 155-168, March.
    12. A. J. Thomas Schneider & P. Luuk Besselink & Maartje E. Zonderland & Richard J. Boucherie & Wilbert B. van den Hout & Job Kievit & Paul Bilars & A. Jaap Fogteloo & Ton J. Rabelink, 2018. "Allocating Emergency Beds Improves the Emergency Admission Flow," Interfaces, INFORMS, vol. 48(4), pages 384-394, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16359-:d:995355. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.