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The ChatGPT effect and transforming nursing education with generative AI: Discussion paper.
Gosak, Lucija; Pruinelli, Lisiane; Topaz, Maxim; Stiglic, Gregor.
Affiliation
  • Gosak L; Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia. Electronic address: lucija.gosak2@um.si.
  • Pruinelli L; College of Nursing and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address: lisianepruinelli@ufl.edu.
  • Topaz M; Columbia University School of Nursing, New York City, NY, USA. Electronic address: mt3315@cumc.columbia.edu.
  • Stiglic G; Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor 2000, Slovenia; Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK. Electronic address: gregor.stiglic@um.si.
Nurse Educ Pract ; 75: 103888, 2024 Feb.
Article in En | MEDLINE | ID: mdl-38219503
ABSTRACT

AIM:

The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process.

BACKGROUND:

The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation.

DESIGN:

Discussion paper.

METHODS:

We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results.

RESULTS:

In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases.

CONCLUSIONS:

Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Education, Nursing Type of study: Qualitative_research Limits: Humans Language: En Journal: Nurse Educ Pract / Nurse educ. pract / Nurse education in practice Journal subject: EDUCACAO / ENFERMAGEM Year: 2024 Document type: Article Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Education, Nursing Type of study: Qualitative_research Limits: Humans Language: En Journal: Nurse Educ Pract / Nurse educ. pract / Nurse education in practice Journal subject: EDUCACAO / ENFERMAGEM Year: 2024 Document type: Article Country of publication: United kingdom