Creating a database for health IT events via a hybrid deep learning model.

Journal: Journal of biomedical informatics
Published Date:

Abstract

OBJECTIVE: The use of poorly designed and improperly implemented health information technology (HIT) may compound risks because it can disrupt established work patterns and encourage workarounds. Analyzing and learning from HIT events could reduce the risks and improve safety but are limited by accessible HIT event reports. In this study, we propose a hybrid deep learning model to identify HIT event reports from the FDA resource and thus establish the first publicly accessible database for HIT event reports.

Authors

  • Hong Kang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Texas, USA.
  • Yang Gong
    School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA.