Faculty, Staff and Student Publications
Language
English
Publication Date
1-1-2025
Journal
AMIA Summits on Translational Science Proceedings
PMID
40502247
PMCID
PMC12150723
PubMedCentral® Posted Date
6-10-2025
PubMedCentral® Full Text Version
Post-print
Abstract
The publication and sharing of clinical notes are crucial for healthcare research and innovation. However, privacy regulations such as HIPAA and GDPR pose significant challenges. While de-identification techniques aim to remove protected health information, they often fall short of achieving complete privacy protection. Similarly, the current state of synthetic clinical note generation can lack nuance and content coverage. To address these limitations, we propose an approach that combines de-identification, filtration, and synthetic clinical note generation. Variations of this approach currently retain 36%-61% of the original note's content and fill the remaining gaps using an LLM, ensuring high information coverage. We also evaluated the de-identification performance of the hybrid notes, demonstrating that they surpass or at least match the standalone de-identification methods. Our results show that hybrid notes can maintain patient privacy while preserving the richness of clinical data. This approach offers a promising solution for safe and effective data sharing, encouraging further research.
Published Open-Access
yes
Recommended Citation
Rahman Sarkar, Atiquer; Chuang, Yao-Shun; Jiang, Xiaoqian; et al., "Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing" (2025). Faculty, Staff and Student Publications. 943.
https://digitalcommons.library.tmc.edu/uthshis_docs/943