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

Taridzo Chomutare (), Miguel Tejedor, Therese Olsen Svenning, Luis Marco-Ruiz, Maryam Tayefi, Karianne Lind, Fred Godtliebsen, Anne Moen, Leila Ismail, Alexandra Makhlysheva and Phuong Dinh Ngo
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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
Anne Moen: Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
Leila Ismail: Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Alexandra Makhlysheva: Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
Phuong Dinh Ngo: Norwegian Centre for E-Health Research, 9019 Tromsø, Norway

IJERPH, 2022, vol. 19, issue 23, 1-18

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.

Keywords: artificial intelligence; machine learning; CFIR; AI implementation; eHealth; healthcare; deep learning; diagnosis; prognosis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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