Information system use antecedents of nursing employee turnover in a hospital setting

Abstract

Background: Voluntary turnover (VTO) of nursing employees is expensive for hospital systems and is often associated with lower levels of patient satisfaction, as well as adverse patient outcomes such as falls and medication errors. Purpose: The aim of this study was to establish nurses' electronic medical record (EMR) use patterns and test if they can be used to predict VTO. Methodology/Approach: The study followed 1,836 hospital nurses via the collection of EMR metadata through two 1-month time periods that were 1 year apart. Machine learning algorithms were then used to derive patterns of EMR utilization using VTO as a key variable for classification. Post hoc analysis of the most predictive variables was conducted.Results: The predictive model was effective in identifying which nurses would turnover 73.4% of the time and which nurses would not turnover 84.1% of the time. Practice Applications: The ability to accurately predict nurses' intentions to leave is critical to reducing turnover. Early identification can lead to specific interventions to mitigate factors that are adversely impacting the nursing experience. Post hoc analysis and the key informant interviews indicated that many nurses do not appear to have good EMR navigation skills and spend significant effort in search of patient information.

An app for predicting nurse intention to quit the job using artificial neural networks (ANNs) in Microsoft Excel

“…There has not been a predictive model built for analyzing nurses’ intention to leave with an online app. Although the authors developed models using the naïve Bayes classifier and support vector machine for predicting the intention to leave using nurse EMR utilization [ 35 ] and working motivation, job satisfaction, and stress levels [ 38 ] as predictors, no such app was demonstrated for readers to predict the intention to leave online as we did in this study.…”

“…Although the authors developed models using the naïve Bayes classifier and support vector machine for predicting the intention to leave using nurse EMR utilization [35] and working motivation, job satisfaction, and stress levels [38] as predictors, no such app was demonstrated for readers to predict the intention to leave online as we did in this study. More than half of nurses were considering quitting the job, [4,5,9,12,13] similar to our finding: 51.7% and 38.3% for intention to leave and nonintention, respectively.…”

“…Machine learning algorithms have been applied to predict nurses’ intention to quit the job, such as patterns of nurses’ electronic medical record (EMR) utilization using the Naïve Bayes classifier on the variable of voluntary turnover (VTO) for classification, [ 35 ] multilevel logistic regression modeling to predict the intention to leave the nursing profession in eight countries (Germany, Italy, France, The Netherlands, Belgium, Poland, Slovakia, and China), [ 36 ] the Multifactor Leadership Questionnaire (MLQ-5X) to assess nurses’ perceptions of their nurse managers’ leadership styles when the Anticipated Turnover Scale applied to assess nurses’ intention to leave the job for 280 nurses in 3 public sector hospitals and 1 university-affiliated (teaching) hospital in northern Jordan, [ 37 ] and the Support Vector Machine applied to predict nurses’ intention to quit the job using working motivation and job satisfaction. [ 38 ] Nonetheless, no studies of nurses’ intention to quit their jobs were found in the PubMed library using machine learning (or ANN).…”

Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI

“…The Gaussian NB classifier yielded the most favorable outcomes for this dataset, demonstrating the highest recall rate (0.54) and maintaining a low false negative rate (4.5%) across the observation set. Similarly, the authors in [10] focused on nurses' utilization of electronic medical records and their potential relationship to voluntary turnover. They employed the NB algorithm, which achieved an accuracy of 73.4% in predicting nurse turnover and 84.1% in predicting nonturnover instances.…”

“…However, most existing research has utilized single ML models, such as SVMs, logistic regression (LR), naïve Bayesian (NB), decision trees (DT), and random forests (RF). Most studies have focused primarily on using basic features and ML techniques to predict employee turnover with reasonable accuracy [7]- [10]. However, these approaches struggle to extract additional useful features that can help us understand complex data structures and reflect the connections among employee features.…”