Identifying Type II workplace violence from clinical notes using natural language processing

Background: Type II workplace violence in health care, perpetrated by patients/clients toward home healthcare nurses, is a serious health and safety issue. A significant portion of violent incidents are not officially reported. Natural language processing can detect these “hidden cases” from clinical notes. In this study, we computed the 12-month prevalence of Type II workplace violence from home healthcare nurses’ clinical notes by developing and utilizing a natural language processing system. Methods: Nearly 600,000 clinical visit notes from two large U.S.-based home healthcare agencies were analyzed. The notes were recorded from January 1, 2019 to December 31, 2019. Rule- and machine-learning-based natural language processing algorithms were applied to identify clinical notes containing workplace violence descriptions. Results: The natural language processing algorithms identified 236 clinical notes that included Type II workplace violence toward home healthcare nurses. The prevalence of physical violence was 0.067 incidents per 10,000 home visits. The prevalence of nonphysical violence was 3.76 incidents per 10,000 home visits. The prevalence of any violence was four incidents per 10,000 home visits. In comparison, no Type II workplace violence incidents were recorded in the official incident report systems of the two agencies in this same time period. Conclusions and Application to Practice: Natural language processing can be an effective tool to augment formal reporting by capturing violence incidents from daily, ongoing, large volumes of clinical notes. It can enable managers and clinicians to stay informed of potential violence risks and keep their practice environment safe.