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.
Authors
- Susana Veríssimo
- Philipp Heinisch (ORCID: https://orcid.org/0009-0002-8079-5570)
- David Schmidt (ORCID: https://orcid.org/0000-0001-7728-2884)
- Philipp Cimiano (ORCID: https://orcid.org/0000-0002-4771-441X)
- Janik Jaskolski
- Matthias Härtung (ORCID: https://orcid.org/0000-0001-6549-066X)
- Cornelius Starke-Knäusel
- Rakhi Asokkumar Subjagouri Nair
Institutions
- Bielefeld University (DE)
- Hochschule Bielefeld (DE)
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