A comprehensive qualitative analysis of patient dialogue summarization using large language models applied to noisy, informal, non-English real-world data
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View abstract on PubMed
Summary
This summary is machine-generated.Large Language Models (LLMs) effectively summarize noisy, informal patient-healthcare dialogues in Portuguese. LLaMA3 slightly outperformed Qwen2, showing potential for improving digital health communication efficiency and patient satisfaction.
Area Of Science
- Natural Language Processing
- Digital Health Communication
- Artificial Intelligence in Healthcare
Background
- Healthcare teams require efficient access to patient information for personalized communication.
- Digital health platforms often involve informal, real-world communication data.
- Summarizing patient-provider dialogues can enhance response times and accuracy.
Purpose Of The Study
- To evaluate Large Language Models (LLMs) for summarizing patient-healthcare team dialogues.
- To assess LLM performance on noisy, informal, and under-represented language data (Portuguese).
- To determine the feasibility of using LLM-generated summaries in a real-world e-health setting.
Main Methods
- Collected and anonymized a dataset of Portuguese WhatsApp messages between patients and a healthcare team.
- Assessed dialogue quality (size, readability, correctness).
- Generated summaries using LLaMA3 and Qwen2 with specific prompts, evaluated by volunteers on coverage, relevance, redundancy, and veracity.
Main Results
- LLMs can produce effective summaries of patient-healthcare dialogues, even with low-quality, under-represented language data.
- LLaMA3 showed a slight advantage over Qwen2 in summary coverage and veracity.
- Volunteer evaluations indicated successful summarization despite challenging data characteristics.
Conclusions
- LLMs show significant potential for practical application in assisting healthcare professionals with patient message responses.
- This approach can enhance communication efficiency, clarity, and patient satisfaction in digital healthcare.
- The findings are particularly relevant for resource-constrained settings with limited primary care access.
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