Automatically Identifying Event Reports of Workplace Violence and Communication Failures using Large Language Models.

Abstract

Safety event reporting forms a cornerstone of identifying and mitigating risks to patient and staff safety. However, variabilities in reporting and limited resources to analyze and classify event reports delay healthcare organizations' ability to rapidly identify safety event trends and to improve workplace safety. We demonstrated how large language models can classify safety event report narratives as workplace violence (F1: 0.80 for physical violence; F1: 0.94 for verbal abuse) and communication failures (F1: 0.94) as a first step toward enabling automated labeling of safety event reports and ultimately improving workplace safety.