Unveiling Endoscopic Procedures in MAUDE: An ETL-LLM Method

Authors

• Yuheng Shi
• Eric Yang
• Yang Gong

Language

English

Publication Date

6-26-2025

Journal

Studies in Health Technology and Informatics

DOI

10.3233/SHTI250680

PMID

40588888

Abstract

The increased dependence on patient safety studies using the MAUDE database underscores the critical need to define and standardize the methods for extracting and analyzing event reports. The lack of reproducible methods leads to an inconsistent understanding of reported events and diminishes their effectiveness in informing clinicians. Thus, an ETL pipeline combined with LLMs was proposed to standardize the identification and interpretation of the reports. Using endoscopic clip reports as an example, the ETL-LLM method demonstrates the effectiveness of extracting and analyzing categorical and narrative reports, helping uncover insights related to patient complications, surgical procedures, and device uses. This innovative and transparent method of examining MAUDE underscores its potential to inform clinicians promptly and encourages more research on patient safety through open-access databases.

Keywords

Endoscopy, Humans, Patient Safety, Data Mining, Databases, Factual, MAUDE, endoscopic procedures, large language model, patient safety

Published Open-Access

yes

Recommended Citation

Yuheng Shi, Eric Yang, and Yang Gong, "Unveiling Endoscopic Procedures in MAUDE: An ETL-LLM Method" (2025). Faculty, Staff and Student Publications. 855.
https://digitalcommons.library.tmc.edu/uthshis_docs/855