Evidence map›Paper›PMID 36498432›Full record

ArticleInternational journal of environmental research and public health2022

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 1 more

Abstract readScoping Review
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

73 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Taridzo ChomutareNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.ORCID 0000-0003-3603-175X
Miguel TejedorNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.
Therese Olsen SvenningNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.
Luis Marco-RuizNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.ORCID 0000-0001-6349-3162
Maryam TayefiNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.
Karianne LindNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.
Fred GodtliebsenNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.
Anne MoenNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.ORCID 0000-0002-3825-9355
Leila IsmailDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0000-0003-0946-1818
Alexandra MakhlyshevaNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.ORCID 0000-0002-0106-1951
Phuong Dinh NgoNorwegian Centre for E-Health Research, 9019 Tromsø, Norway.ORCID 0000-0002-0057-8801

Funding

Norwegian Centre for E-health Research AI Implementation Report
6 · The paper itself

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.

Indexed as

Artificial IntelligenceDelivery of Health CareHealth FacilitiesAI implementationartificial intelligenceCFIRdeep learningdiagnosiseHealthhealthcaremachine learningprognosis

Identifiers

PMID36498432
PMCPMC9738234

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.