Evidence map›Paper›PMID 38746816›Full record

ArticleInternational journal of nursing studies advances2024

Comparing ChatGPT and clinical nurses' performances on tracheostomy care: A cross-sectional study.

Tongyao Wang, Juan Mu, Jialing Chen, Chia-Chin Lin

Abstract read
In one paragraph

Article in International journal of nursing studies advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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.

Tongyao WangSchool of Nursing, Li Kai Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
Juan MuSchool of Nursing, Peking University, Beijing, China.
Jialing ChenSchool of Nursing, Li Kai Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
Chia-Chin LinSchool of Nursing, Li Kai Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The release of ChatGPT for general use in 2023 by OpenAI has significantly expanded the possible applications of generative artificial intelligence in the healthcare sector, particularly in terms of information retrieval by patients, medical and nursing students, and healthcare personnel. Objective: To compare the performance of ChatGPT-3.5 and ChatGPT-4.0 to clinical nurses on answering questions about tracheostomy care, as well as to determine whether using different prompts to pre-define the scope of the ChatGPT affects the accuracy of their responses. Design: Cross-sectional study. Setting: The data collected from the ChatGPT was collected using the ChatGPT-3.5 and 4.0 using access provided by the University of Hong Kong. The data from the clinical nurses working in mainland China was collected using the Qualtrics survey program. Participants: No participants were needed for collecting the ChatGPT responses. A total of 272 clinical nurses, with 98.5 % of them working in tertiary care hospitals in mainland China, were recruited using a snowball sampling approach. Method: We used 43 tracheostomy care-related questions in a multiple-choice format to evaluate the performance of ChatGPT-3.5, ChatGPT-4.0, and clinical nurses. ChatGPT-3.5 and GPT-4.0 were both queried three times with the same questions by different prompts: no prompt, patient-friendly prompt, and act-as-nurse prompt. All responses were independently graded by two qualified otorhinolaryngology nurses on a 3-point accuracy scale (correct, partially correct, and incorrect). The Chi-squared test and Fisher exact test with post-hoc Bonferroni adjustment were used to assess the differences in performance between the three groups, as well as the differences in accuracy between different prompts. Results: ChatGPT-4.0 showed significantly higher accuracy, with 64.3 % of responses rated as 'correct', compared to 60.5 % in ChatGPT-3.5 and 36.7 % in clinical nurses ( Conclusion: ChatGPT may serve as a complementary medical information tool for patients and physicians to improve knowledge in tracheostomy care. Tweetable abstract: ChatGPT-4.0 can answer tracheostomy care questions better than most clinical nurses. There is no reason nurses should not be using it.

Indexed as

ChatGPTEducationGenerative artificial intelligenceNursingTracheostomy

Identifiers

PMID38746816
PMCPMC11080343

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.