Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study

Taken together, our results suggest that state-of-the-art pretrained language models are a valuable tool to provide qualitative insights about the patient experience to better understand unmet needs, patient priorities, and how a disease impacts daily functioning and quality of life to inform processes aimed at improving health care delivery and ensure that drug development focuses more on the actual priorities and unmet needs of patients. The key limitations of our work are the small data sample as well as the fact that the manual summaries were created by 1 annotator only. Furthermore, the results hold only for the examined models and prompting strategies, potentially not generalizing to other models and strategies.

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Publication Details

Journal
JMIR Medical Informatics
Published
2025-04-14
DOI
https://doi.org/10.2196/62909
Citations
3
Primary Topic
Social Media in Health Education
Type
article
Field-Weighted Citation Impact
6.50
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article

Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study

Susana Veríssimo, Philipp Heinisch, David Schmidt, Philipp Cimiano et al.
3 citations
JMIR Medical Informatics
Social Media in Health Education
6.50
article

Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study

Susana Veríssimo, Philipp Heinisch, David Schmidt, Philipp Cimiano, Janik Jaskolski, Matthias Härtung, Cornelius Starke-Knäusel, Rakhi Asokkumar Subjagouri Nair
article en
3 citations

Abstract

Taken together, our results suggest that state-of-the-art pretrained language models are a valuable tool to provide qualitative insights about the patient experience to better understand unmet needs, patient priorities, and how a disease impacts daily functioning and quality of life to inform processes aimed at improving health care delivery and ensure that drug development focuses more on the actual priorities and unmet needs of patients. The key limitations of our work are the small data sample as well as the fact that the manual summaries were created by 1 annotator only. Furthermore, the results hold only for the examined models and prompting strategies, potentially not generalizing to other models and strategies.

JMIR Medical InformaticsVol. 13
Bielefeld University (DE), Hochschule Bielefeld (DE)
Openalex Percentile: Top 4%
Social Media in Health Education
6.50
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Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study — Susana Veríssimo, Philipp Heinisch, et al. · JMIR Medical Informatics (2025) | TGRS Research Map | TGRS