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Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review
Halmstad University, School of Business, Innovation and Sustainability. School of Administration and Economic Science, Santa Catarina State University, Florianópolis, Brazil.ORCID iD: 0000-0003-1390-1820
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0002-3727-6153
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0002-3576-2393
Centre for Health Economics and Policy Innovation, Imperial College Business School, London, United Kingdom.ORCID iD: 0000-0003-4984-0126
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2022 (English)In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 24, no 1, article id e32215Article in journal (Refereed) Published
Abstract [en]

Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.

Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.

Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.

Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the 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 in health care 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 the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg. 

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications , 2022. Vol. 24, no 1, article id e32215
Keywords [en]
implementation framework, artificial intelligence, scoping review, service innovation
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, IDC
Identifiers
URN: urn:nbn:se:hh:diva-46282DOI: 10.2196/32215ISI: 000766779500002PubMedID: 35084349Scopus ID: 2-s2.0-85123814747OAI: oai:DiVA.org:hh-46282DiVA, id: diva2:1634627
Projects
Vinnova 2019-04526Knowledge Foundation 20200208 01H
Funder
Vinnova, 2019-04526Knowledge Foundation, 20200208 01H
Note

This research is included in the CAISR Health research profile.

Available from: 2022-02-03 Created: 2022-02-03 Last updated: 2024-12-03Bibliographically approved

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Gama, FábioTyskbo, DanielNygren, Jens M.Reed, JulieSvedberg, Petra

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