Evidence map›Paper›PMID 41310693›Full record

ArticleBMC nursing2025

Readability, reliability, and quality of nursing care plan texts generated by ChatGPT.

Mucahide Gokcen Gokalp, Sebnem Cinar Yucel, Zehra Cakir, Rüveyda Kargi

Abstract read
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. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mucahide Gokcen GokalpDepartment of Nursing, Faculty of Health Sciences, Amasya University, Amasya, 05100, Turkey. mucahide.gokcen@amasya.edu.tr.ORCID https://orcid.org/0000-0002-3790-7875
Sebnem Cinar YucelDepartment of Fundamentals of Nursing, Faculty of Nursing, Ege University, Izmir, 35100, Turkey.ORCID https://orcid.org/0000-0002-8904-7395
Zehra CakirMerzifon Kara Mustafa Paşa State Hospıtal, Amasya, 35100, Turkey.ORCID https://orcid.org/0000-0001-8217-1986
Rüveyda KargiSabuncuoğlu Şerefeddin Traınıng and Research Hospıtal, Amasya, 35100, Turkey.ORCID https://orcid.org/0009-0007-1949-9728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNursing care plans require clinical reasoning, prioritization, and patient-centered decision-making, which distinguishes them from more general AI-generated educational texts. As large language models such as ChatGPT are increasingly used to support nursing education and care planning, it is essential to evaluate the readability, reliability, and quality of the nursing care plans they produce. PURPOSE: This study aims to evaluate the readability, reliability, and quality of nursing care plan texts generated by ChatGPT.

methodsThe study sample consisted of 50 texts generated by ChatGPT (version 4.0) based on selected nursing diagnoses from NANDA 2021–2023. These texts were evaluated using a descriptive criteria form, the DISCERN tool, and readability indices including the Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index, and Flesch-Kincaid Grade Level (FKGL).

resultsThe analysis demonstrated that the nursing care plans generated by ChatGPT showed a moderate level of quality and reliability. However, the readability levels were generally higher than what is desirable for clinical and educational use, indicating that the texts may be difficult for some users to understand without adaptation. The findings also suggest that the presence of verifiable references contributes positively to the overall quality and reliability of the generated care plans.

conclusionEvaluating the readability, reliability, and quality of AI-generated nursing care plans is essential for ensuring their safe and meaningful use in nursing education and clinical practice. These findings highlight the importance of guiding and refining AI-supported care planning to better align with professional standards and patient-centered care needs.

Indexed as

Artificial intelligenceChatbotChatGPTNursing careNursing care planNursing education

Identifiers

PMID41310693
PMCPMC12751850

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.