Abstract: Falls are among the most common adverse events in hospitals, with an associated direct medical cost of $50 billion dollars annually. Automated or semi-automated text mining methods have shown promise to address healthcare acquired conditions such as falls. However, several challenges remain, including lack of interpretability and clinical translatability of findings. In this study of factors associated with falls, we engaged clinical nurses in the text mining process in an effort to enhance clinical translatability.

Describe the new knowledge and additional skills the participant will gain after attending your presentation.: After attending this session, the attendee will be able to 1) recall one important challenge of clinical text mining, 2) summarize the benefits of including clinical nurses in the process of clinical text mining, and 3) apply a participatory process to engage clinical nurses in text mining

Authors:

Ragnhildur Bjarnadottir (Presenter)
University of Florida

Robert Lucero, University of Florida
Laurence Solberg, North Florida/South Georgia Veterans Health System
Yonghui Wu, University of Florida
Kimberly Martinez, UF Health Shands Hospital
Summer Bolin, UF Health Shands Hospital
Shannon Dwarica, UF Health Shands Hospital
Jaime Thomas, UF Health Shands Hospital