Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 19, 2021
Date Accepted: Dec 27, 2021
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Implementation frameworks for AI translation into healthcare practice: A Scoping Review
ABSTRACT
Background:
Significant efforts have been made to develop Artificial Intelligence (AI) solutions for healthcare improvements. Despite the enthusiasm, healthcare professionals still struggle to implement AI in their daily practices.
Objective:
This paper aims to identify what implementation frameworks have been used to understand AI's application in healthcare practice.
Methods:
A scoping review was carried out following PRISMA guidelines using the Cochrane, EBM Reviews, Embase, Medline(R), and PsychInfo databases to identify publications that reported frameworks, models, and theories concerning AI implementation in healthcare. The review focused on studies published in English and investigating AI implementation in healthcare since 2000. A total of 2,541 unique publications were retrieved from the databases and screened on titles and abstracts by two independent reviewers. Selected articles were thematically analysed against Nilsen’s taxonomy of implementation frameworks, and Greenhalgh’s framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of healthcare technologies.
Results:
Seven articles met all eligibility criteria for inclusion in the review. Two articles included formal frameworks that directly addressed AI implementation, and the other articles provided limited descriptions of elements influencing implementation. Collectively the seven articles identified elements that aligned with all of the NASSS domains, but no single article demonstrated comprehensive consideration of factors known to influence technology implementation. New domains were identified including dependency on data input and existing processes, shared decision making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.
Conclusions:
This literature review demonstrates that understanding how to implement AI technology in healthcare practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from implementation science.
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Copyright
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