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

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.

SUBMITTER: Chomutare T

PROVIDER: S-EPMC9738234 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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

Chomutare Taridzo T Tejedor Miguel M Svenning Therese Olsen TO Marco-Ruiz Luis L Tayefi Maryam M Lind Karianne K Godtliebsen Fred F Moen Anne A Ismail Leila L Makhlysheva Alexandra A Ngo Phuong Dinh PD

International journal of environmental research and public health 20221206 23

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 the ...[more]

PMID: 36498432

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Project description:ObjectiveAlthough artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging.MethodA literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients.ResultsA total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5).ConclusionThis scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare.Clinical relevance statementThe identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice.Key points• Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

Project description:BackgroundSignificant 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.ObjectiveThis paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.MethodsA 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.ResultsIn 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.ConclusionsThis 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.

Project description:Background:Inactivity and sedentary lifestyle have led experts to recommend an increase in structured, workplace-based physical activity (PA) initiatives. Previous studies on workplace-based PA have only shown moderate and short-term effects. This has been attributed to the lack of clear implementation strategies and understanding of factors that may hinder or enable uptake of PA. To ensure long-term, sustainable outcomes, there is a need for a better understanding of implementation strategies, and barriers and facilitators to workplace-based PA. Method:A scoping review of studies investigating implementation approaches and factors affecting uptake of workplace-based PA was conducted. Qualitative and quantitative articles published in MEDLINE, Embase, Scopus, or PsycINFO between 2008 and 2018 evaluating the implementation of PA were included. Data on study characteristics, evaluation, and implementation methods applied were systematically extracted. Two reviewers extracted, coded, and organised factors affecting uptake using the Theoretical Domains Framework (TDF). Results:After dual, blinded screening of titles and abstracts, 16 articles reporting on eight studies were included in the review. Several different methods of implementation were applied, including information meeting, kick-off events, and "change agents" as the most common. A total of 109 factors influencing implementation were identified, consisting of 57 barriers and 52 facilitators. Barriers most often related to the TDF domains Environmental Context and Resources (n?=?34, 36.2%), Social influences (n?=?13, 13.8%), and Social/Professional Role and Identity (n?=?8, 8.5%). Likewise, facilitators most often related to the TDF domains Social influences (n?=?17, 19.5%), Environmental Context and Resources (n?=?16, 18.4%), and Social/Professional Role and Identity (n?=?9, 10.3%). Conclusion:Our review has highlighted the multilevel factors affecting the uptake of workplace-based PA and underpins the complexities in implementation of such initiatives. The published literature predominantly provides details from the employees' perspectives on factors that need to be addressed and a lack of attention to these factors will cause them to hamper uptake of PA. The analysis of barriers and facilitators provides a theoretical foundation to guide future intervention design. However, further research is needed to fully understand the success or failure of implementation processes.

Project description:IntroductionGuideline-adherent physiotherapy can improve patient outcomes and reduce costs in the healthcare system. However, although there are numerous guidelines for physiotherapy practice, services are not consistently based on clinical practice guidelines. While various systematic and scoping reviews have highlighted barriers, facilitators and implementation strategies for guideline-adherent practice in other health professions, this scoping review aims to explore the barriers and facilitators for guideline-adherent physiotherapy and summarises the strategies used to implement such practice.Methods and analysisThis scoping review will be based on Arksey and O'Malley's scoping review methodology and the methodological guidance for conducting scoping reviews published by Joanna Briggs Institute. Relevant publications will be first searched from the beginning of June 2023 on the MEDLINE and CINAHL databases before we expand the search to other databases such as EMBASE, the Cochrane Library and PEDro at the end of June 2023. Two reviewers will independently screen the titles and abstracts of all retrieved citations for inclusion against the eligibility criteria before conducting an independent full-text screening. The criteria will be tested on a sample of abstracts before beginning the abstract review to ensure that they are robust enough to capture any articles that may relate. The extracted data will finally be collated and charted to summarise key findings regarding our research question.Ethics and disseminationThis scoping review will provide an extensive overview of the barriers, facilitators and implementation strategies for guideline-adherent physiotherapy. As scoping reviews are a form of secondary data analysis, ethical review is not required. Results will be disseminated through a peer-reviewed publication and stakeholder meetings.Trial registration numberThis scoping review has been registered on 3 April 2023 on the Open Science Framework under https://doi.org/10.17605/OSF.IO/SEUW6.

Dataset Information

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

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

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