Croatian Nursing Journal, Vol. 8 No. 1, 2024.
Professional paper
https://doi.org/10.24141/2/8/1/3
The Future of Triage: The Analysis of Traditional Methods Compared to ChatGPT
Helena Mayerhoffer
orcid.org/0009-0004-8778-7316
; University of Applied Health Sciences, Zagreb, Croatia
*
* Corresponding author.
Abstract
Introduction. Triage is the assessment of the patient’s condition in order to determine the urgency of treatment. It is usually performed by a nurse, often using a five-level protocol.
Aim. To conduct a comparative analysis of the accuracy of categorization and diagnosis between ChatGPT (a chatbot which uses machine learning algorithms) and traditional medical triage, as well as to provide recommendations on how artificial intelligence can improve the work of medical professionals in patient triage.
Methods. The literature selected for comparison is “Emergency Nursing: 5-Tier Triage Protocols”. The most common diagnoses for which patients present to the emergency department were selected for research. Then, triage categories were selected and case presentations were created. These cases were presented to ChatGPT, and its responses were compared
with the literature.
Results. ChatGPT correctly categorizes triage cases in 43.33% of cases, with an average category difference of 0.7. Although it made mistakes in 1 or 2 categories in some cases, it assigned diagnoses to a higher category for patient safety.
Discussion. Comparison with other studies shows that errors occur in up to 40% of nurse decisions due to various factors such as inexperience, speed of work, and a large number of patients, which could be reduced by additional artificial intelligence assistance. It is necessary to take into account factors that artificial intelligence cannot take over and that it can only be a help, not a substitute for medical personnel.
Conclusion. ChatGPT has potential for usage in medical triage, but with improvements in specialized training of models on medical data and terminology to improve the accuracy and reliability of the model.
Keywords
Hrčak ID:
317730
URI
Publication date:
6.6.2024.
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