Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.

ABSTRACT:

Background

The digitization of health care, facilitated by the adoption of electronic health records systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in health care efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey toward protecting patient data deidentification saw the transition from rule-based systems to more mixed approaches including machine learning for deidentifying patient data. Subsequently, the emergence of large language models has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive deidentification. However, despite large languag

SUBMITTER: Doremus O 

PROVIDER: S-EPMC12223680 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature