Evidence map›Paper›PMID 40652598›Full record

ArticleNursing outlook

Education, empowerment, and elevating nursing voices: Nursing informatics leaders' perspectives on the path forward with artificial intelligence in nursing.

Meghan Reading Turchioe, Christianna Pepingco, Charlene Ronquillo, Stephen A Ferrara, Max Topaz, Robin Austin, Kay Lytle

Abstract read
In one paragraph

Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Meghan Reading TurchioeColumbia University School of Nursing, New York, NY. Electronic address: mr3554@cumc.columbia.edu.
Christianna PepingcoColumbia University School of Nursing, New York, NY.
Charlene RonquilloUniversity of British Columbia Okanagan, Kelowna, British Columbia, Canada.
Stephen A FerraraColumbia University School of Nursing, New York, NY.
Max TopazColumbia University School of Nursing, New York, NY.
Robin AustinUniversity of Minnesota School of Nursing, Minneapolis, MN.
Kay LytleDuke University Health System, Durham, NC.

Funding

Data-driven shared decision-making to reduce symptom burden in atrial fibrillationR00NR019124 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TURCHIOE, MEGHAN READING · 2022 to 2024
$732k
NINR NIH HHS R00 NR019124
6 · The paper itself

Abstract

backgroundAs artificial intelligence (AI) is rapidly transforming healthcare, there is a critical need to understand how AI can be thoughtfully integrated into nursing practice to support clinicians, reduce burden, and enhance patient care. PURPOSE: We aimed to understand nursing informatics leaders' perspectives on AI's current and future state in nursing.

methodsWe conducted a qualitative study with 20 national experts in nursing informatics. The Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework guided data collection and analysis using directed content analysis.

findingsParticipants highlighted the unclear value of AI to nurses and of nurses to technology vendors and AI developers. They identified nursing-specific (burnout, education, and attitudes) and organization-specific (leadership and culture) factors that influence AI's integration in nursing. They highlighted how education, establishing ideal nurse-AI partnerships, and leveraging AI, may alleviate burnout. DISCUSSION: Education, empowerment, and elevating nursing voices ("three e's") can guide the nursing profession to thrive in the era of AI.

Indexed as

Artificial IntelligenceEmpowermentLeadershipNursing InformaticsAdultAttitude of Health PersonnelFemaleHumansMaleMiddle AgedQualitative ResearchArtificial intelligenceHealth information technologyNursingNursing informatics

Identifiers

PMID40652598
PMCPMC12337132

What OpenQuestion holds

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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.