Evidence map›Paper›PMID 39695581›Full record

ArticleBMC nursing2024

Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives.

Osama Mohamed Elsayed Ramadan, Majed Mowanes Alruwaili, Abeer Nuwayfi Alruwaili, Mohamed Gamal Elsehrawy, Sulaiman Alanazi

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 3 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

69 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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9 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

5 authors.

Osama Mohamed Elsayed RamadanCollege of Nursing, Department of Maternity and Pediatric Health Nursing, Jouf University, Sakaka, 72388, Saudi Arabia. omramadan@ju.edu.sa.ORCID http://orcid.org/0000-0002-9616-8590
Majed Mowanes AlruwailiCollege of Nursing, Nursing Administration and Education Department, Jouf University, Sakaka, 72388, Saudi Arabia. majed@ju.edu.sa.
Abeer Nuwayfi AlruwailiCollege of Nursing, Nursing Administration and Education Department, Jouf University, Sakaka, 72388, Saudi Arabia.
Mohamed Gamal ElsehrawyNursing Administration and Education Department, College of Nursing, Kingdom of Saudi Arabia, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Kingdom of Saudi Arabia.
Sulaiman AlanaziCollege of Nursing, Jouf University, Sakaka, 72388, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntegrating Artificial Intelligence (AI) in nursing practice is revolutionising healthcare by enhancing clinical decision-making and patient care. However, the adoption of AI by registered nurses, especially in varied healthcare settings such as Saudi Arabia, remains underexplored. Understanding the facilitators and barriers from the perspective of frontline nurses is crucial for successful AI implementation.

aimThis study aimed to explore registered nurses' perspectives on the facilitators and barriers to AI adoption in nursing practice in Saudi Arabia and to propose an extended Technology Acceptance Model for AI in Nursing (TAM-AIN).

methodsA qualitative study utilising focus group discussions was conducted with 48 registered nurses from four major healthcare facilities in Al-Kharj, Saudi Arabia. Thematic analysis, guided by the Technology Acceptance Model framework, was employed to analyse the data.

resultsKey facilitators of AI adoption included perceived benefits to patient care (85%), strong organisational support (70%), and comprehensive training programs (75%). Primary barriers involved technical challenges (60%), ethical concerns regarding patient privacy (55%), and fears of job displacement (45%). These findings led to the development of TAM-AIN, an extended model that incorporates additional constructs such as ethical alignment, organisational readiness, and perceived threats to professional autonomy.

conclusionsAI adoption in nursing practice requires a holistic approach that addresses technical, educational, ethical, and organisational challenges. The proposed TAM-AIN offers a comprehensive framework for optimising AI integration into nursing practice, emphasising the importance of nurse-centred implementation strategies. This model provides healthcare institutions and policymakers with a robust tool to facilitate successful AI adoption and enhance patient outcomes.

Indexed as

Artificial IntelligenceHealthcare InnovationNursing InformaticsQualitative ResearchSaudi ArabiaTechnology Acceptance Model

Identifiers

PMID39695581
PMCPMC11654280

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.