Evidence map›Paper›PMID 39819624›Full record

ArticleBMC nursing2025

Empowering nurse leaders: readiness for AI integration and the perceived benefits of predictive analytics.

Mohamed Hashem Kotp, Hossam Ali Ismail, Hassan Ahmed Awad Basyouny, Mohamed Ahmed Aly, Abdelaziz Hendy, Abdulqadir J Nashwan, Ahmed Hendy, Aliaa Ezz Eldin Abd Elmoaty

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Technology's Impact on Nurse Manager Practices.Nursing administration quarterly
    Article
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

8 authors.

Mohamed Hashem KotpNursing Administration, Faculty of Nursing, Helwan University, Cairo, Egypt. mohamed_hashem@nursing.helwan.edu.eg.
Hossam Ali IsmailNursing Administration, Faculty of Nursing, Helwan University, Cairo, Egypt.
Hassan Ahmed Awad BasyounyNursing Administration, Faculty of Nursing, Helwan University, Cairo, Egypt.
Mohamed Ahmed AlyNursing Administration, Faculty of Nursing, Helwan University, Cairo, Egypt.
Abdelaziz HendyPediatric Nursing, Faculty Nursing, Ain Shams University, Cairo, Egypt. Abdelaziz.hendy@nursing.asu.edu.eg.
Abdulqadir J NashwanNursing & Midwifery Research Department (NMRD), Hamad Medical Corporation, Doha, Qatar.
Ahmed HendyDepartment of Computational Mathematics and Computer Science, Institute of Natural Sciences and Mathematics, Ural Federal University, Yekaterinburg, 620002, Russian Federation.
Aliaa Ezz Eldin Abd ElmoatyNursing Administration, Faculty of Nursing, Helwan University, Cairo, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial Intelligence (AI) is increasingly being integrated into healthcare, particularly through predictive analytics that can enhance patient care and operational efficiency. Nursing leaders play a crucial role in the successful adoption of these technologies.

aimThis study aims to assess the readiness of nursing leaders for AI integration and evaluate their perceptions of the benefits of AI-driven predictive analytics in healthcare.

methodsA descriptive cross-sectional study was conducted among 187 nurse leaders across nine private hospitals in Cairo. The sample was selected using a combination of simple random sampling and non-probability convenience sampling methods to ensure a diverse representation of nursing leadership. Data collection took place from March to May 2024, utilizing a structured questionnaire specifically designed to assess nurse leaders' readiness for AI integration and their perceptions of AI-driven predictive analytics The data were analyzed using IBM SPSS Statistics, version 26.0. Exploratory Factor Analysis (EFA) was employed to identify underlying factors related to readiness and perceived benefits. Confirmatory Factor Analysis (CFA) was subsequently performed to validate the factor structure. Multiple linear regression analysis was conducted to identify significant predictors of AI readiness and perceived benefits.

resultsThe study revealed that over one-third of nurse leaders exhibited high readiness for AI integration. Significant predictors of readiness included age, educational attainment, and employment status. Positive correlations were found between readiness and perceived benefits of AI, particularly in areas such as care planning and decision-making.

conclusionThe findings suggest that nursing leaders are generally prepared to integrate AI into their workflows, especially those with advanced education and experience. However, further training and policy development are necessary to fully realize the benefits of AI in nursing practice.

Indexed as

Artificial intelligenceDecision support systemsNursing informaticsNursing leadershipPatient care managementPredictive analytics

Identifiers

PMID39819624
PMCPMC11737245

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.