Does one size fit all? Developing an evaluation strategy to assess large language models for patient safety event report analysis
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View abstract on PubMed
Summary
This summary is machine-generated.Evaluating large language models (LLMs) for patient safety event (PSE) report analysis is crucial. Different LLMs excel with clinical or general language, suggesting tailored approaches are needed for effective PSE report analysis.
Area Of Science
- Medical Informatics
- Natural Language Processing
- Patient Safety
Background
- Analyzing patient safety event (PSE) reports is vital for improving healthcare safety.
- Traditional analysis methods face challenges with the hybrid nature of PSE report language.
- Large language models (LLMs) offer potential for enhancing PSE report analysis.
Purpose Of The Study
- To develop and apply a data-driven strategy for evaluating the suitability of LLMs for analyzing PSE reports.
- To assess the comprehension capabilities of different LLMs using specific metrics.
Main Methods
- A novel evaluation strategy was proposed, focusing on target tokens and sentences within PSE reports.
- Perplexity was used as a key metric to quantitatively assess LLM performance.
- Four distinct LLMs were evaluated on their understanding of PSE report content.
Main Results
- Significant differences in perplexity scores were observed across six of seven event categories among the evaluated LLMs.
- Clinical LLMs demonstrated superior performance with clinical narratives, commonly found in nurse and physician reports.
- General-purpose LLMs showed better performance with colloquial language and communication-centric themes in reports.
Conclusions
- The effectiveness of LLMs in PSE report analysis depends on the alignment between the model's training data and the report's linguistic characteristics.
- A one-size-fits-all LLM approach may not be optimal; specialized or hybrid strategies might be more beneficial for comprehensive PSE report analysis.
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