Evidence map›Paper›PMID 40132137›Full record

ArticleJournal of primary care & community health

Augmenting Community Nursing Practice With Generative AI: A Formative Study of Diagnostic Synergies Using Simulation-Based Clinical Cases.

Odelyah Saad, Mor Saban, Erika Kerner, Chedva Levin

Abstract read
In one paragraph

Article in Journal of primary care & community health. 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.

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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

4 authors.

Odelyah SaadJerusalem College of Technology, Jerusalem, Israel.ORCID 0000-0002-7291-9142
Mor SabanTel Aviv University, Tel Aviv, Israel.ORCID 0000-0001-6869-0907
Erika KernerTel Aviv University, Tel Aviv, Israel.
Chedva LevinJerusalem College of Technology, Jerusalem, Israel.ORCID 0000-0002-3336-4279

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare the diagnostic accuracy and clinical decision-making of experienced community nurses versus state-of-the-art generative AI (GenAI) systems for simulated patient case scenarios.

methodsIn the months of 5 to 6/2024, 114 community Israeli nurses completed a questionnaire including 4 medical case studies. Responses were also collected from 3 GenAI models (ChatGPT-4, Claude 3.0, and Gemini 1.5), analyzed both without word limits and with a 10-word constraint. Responses were scored on accuracy, speed, and comprehensiveness.

resultsNurses scored higher on average compared to the shortened GenAI responses. GenAI responses were faster but more verbose, and contained unnecessary information. Gemini (full version) and Claude (full version) achieved the highest accuracy among the GenAI models.

conclusionsWhile GenAI shows potential to support aspects of nursing practice, human clinicians currently exhibit advantages in holistic clinical reasoning abilities, a skill requiring experience, contextual knowledge, and ability to bring concise and practical responses. Further research is needed before GenAI can adequately substitute nursing expertise.

Indexed as

Artificial IntelligenceClinical Decision-MakingCommunity Health NursingPatient SimulationAdultClinical CompetenceFemaleHumansIsraelMaleMiddle AgedSurveys and Questionnairesartificial intelligenceChatGPTClaudeclinical reasoningcommunity service

Identifiers

PMID40132137
PMCPMC11938853

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.