Evidence map›Paper›PMID 38898636›Full record

ArticleJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2024

Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.

Ann M Wieben, Bader G Alreshidi, Brian J Douthit, Marisa Sileo, Pankaj Vyas, Linsey Steege, Andrea Gilmore-Bykovskyi

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Article in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Observational
  3. Article
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  8. Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2024
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  9. 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.

Ann M WiebenUniversity of Wisconsin-Madison School of Nursing, Madison, Wisconsin, USA.ORCID https://orcid.org/0000-0001-9275-0439
Bader G AlreshidiDepartment of Medical Surgical Nursing, University of Hail College of Nursing, Hail, Saudi Arabia.
Brian J DouthitUnited States Department of Veterans Affairs, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
Marisa SileoBoston Children's Hospital, Boston, Massachusetts, USA.
Pankaj VyasUniversity of Arizona, Tucson, Arizona, USA.
Linsey SteegeUniversity of Wisconsin-Madison School of Nursing, Madison, Wisconsin, USA.
Andrea Gilmore-BykovskyiBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine & Public Health, Madison, Wisconsin, USA.

Funding

University of Wisconsin-Madison School of Nursing
6 · The paper itself

Abstract

introductionThe purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption.

designQualitative descriptive study.

methodsNurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke.

resultsFour major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools.

conclusionThis study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy. CLINICAL RELEVANCE: This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.

Indexed as

artificial intelligenceclinical decision supportmachine learningnurses

Identifiers

PMID38898636
PMCPMC11771625

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.