Triage Performance Across Large Language Models, ChatGPT, and Untrained Doctors in Emergency Medicine: Comparative Study Article Swipe
Lars Masanneck
,
Linea Schmidt
,
Antonia Seifert
,
Tristan Kölsche
,
Niklas Huntemann
,
Robin Jansen
,
Mohammed Mehsin
,
Michael Bernhard
,
Sven G. Meuth
,
Lennert Böhm
,
Marc Pawlitzki
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/53297
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/53297
Background Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. Objective This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models’ responses can enhance the triage proficiency of untrained personnel. Methods A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters’ MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. Results GPT-4–based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5–based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4–based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. Conclusions While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models’ triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors’ triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/53297
- https://www.jmir.org/2024/1/e53297/PDF
- OA Status
- gold
- Cited By
- 51
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399667220
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399667220Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/53297Digital Object Identifier
- Title
-
Triage Performance Across Large Language Models, ChatGPT, and Untrained Doctors in Emergency Medicine: Comparative StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-14Full publication date if available
- Authors
-
Lars Masanneck, Linea Schmidt, Antonia Seifert, Tristan Kölsche, Niklas Huntemann, Robin Jansen, Mohammed Mehsin, Michael Bernhard, Sven G. Meuth, Lennert Böhm, Marc PawlitzkiList of authors in order
- Landing page
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https://doi.org/10.2196/53297Publisher landing page
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https://www.jmir.org/2024/1/e53297/PDFDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.jmir.org/2024/1/e53297/PDFDirect OA link when available
- Concepts
-
Triage, Medical emergency, Medicine, Chatbot, Disaster medicine, Emergency department, Human factors and ergonomics, Poison control, Nursing, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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-
51Total citation count in OpenAlex
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2025: 39, 2024: 12Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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