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Predicting Workplace Violence in the Emergency Department Based on Electronic Health Record Data.

Hyungbok Lee, Heeje Yun, Minjin Choi

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    Machine learning models can predict workplace violence against emergency nurses using electronic health record data. Patient dissatisfaction and visit factors significantly increase risk, enabling proactive safety measures.

    Keywords:
    Electronic health recordEmergency departmentMachine learningPredictive modelingWorkplace aggression

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    Area of Science:

    • Healthcare Informatics
    • Nursing Safety
    • Machine Learning in Healthcare

    Background:

    • Emergency departments (EDs) face high risks of workplace violence (WPV).
    • Emergency nurses are particularly vulnerable to WPV.
    • Existing prediction methods for WPV are limited.

    Purpose of the Study:

    • To develop and evaluate machine learning models for predicting WPV in EDs.
    • To identify key factors contributing to WPV risk using electronic health record (EHR) data.

    Main Methods:

    • Utilized EHR data from January 2016 to December 2021.
    • Identified WPV cases through nursing record analysis.
    • Employed machine learning algorithms, including Random Forest, to predict WPV based on ED visit and stay factors.

    Main Results:

    • Random Forest achieved the highest prediction accuracy (0.90).
    • Predicting WPV was more accurate using both ED visit and stay factors compared to ED visit factors alone.
    • Key predictors included patient dissatisfaction, long ED stays, high patient volume, and psychiatric symptoms.

    Conclusions:

    • WPV in EDs can be predicted using historical EHR data.
    • Early prediction and intervention can enhance emergency nurse safety and care quality.
    • Continuous monitoring of risk factors from admission to discharge is crucial for WPV prevention.