Originally Aired - Thursday, March 14 • 11:30 AM - 12:30 PM Eastern Time (US & Canada)
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Location: W204A
Type: General Education
Session ID: 193
Title: Workplace Violence: Predicting Potential Violent Patients in Inpatient Healthcare Settings
Description: Occupational Safety and Health Administration (OSHA) defines workplace violence (WPV) as any act or threat of physical violence, harassment, intimidation or other threatening disruptive behavior occurring at work. Healthcare and social service workers are five times more likely to be injured than other workers and WPV rates continue to rise. Due in part to poor reporting systems, and the common misperception that violent events should be expected while working in healthcare, prevention measures rarely match the issue’s severity and often go unreported. This session will focus on efforts, in a large safety-net hospital, to address an important gap impacting WPV prevention efforts through the development of a predictive model to more accurately identify―in an inpatient healthcare setting―potentially violent patients, thus enabling healthcare workers to mitigate risks of impending WPV incidents.
Level: Intermediate
Format: 60-Minute Lecture
Learning Objective #1: Describe the magnitude of workplace violence and its impact on healthcare professionals in an inpatient hospital setting
Learning Objective #2: Describe strategies to analyze violent and nonviolent groups of patients based on the patient history, which includes but is not limited to reason for visit, past diagnoses or medication history (if any), and patient characteristics like age group, marital status, and past violent hospital encounters to build a predictive model
Learning Objective #3: Review and evaluate the predictive model performance using performance metrics including balanced accuracy, sensitivity and specificity on a held-out test set
Learning Objective #4: Develop a plan to employ this predictive model in the inpatient setting—including, for example, the most useful metric to provide (such as risk quotient versus predicative probability)—and how best to commutate which features are most predictive for a given patient
