Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

Sadaaki Fukui; Wei Wu

doi:10.1176/appi.ps.20250364

January 30, 2026Psychiatric Services

Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

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Key Points

The aim is to develop a machine learning method to predict employee turnover based on HR data and job well-being indicators.
Applied machine learning models to historical HR data.
Integrated additional survey data on job well-being.
Focused on identifying high-risk employees for turnover.
The machine learning models effectively predicted turnover risk.
Augmenting predictions with job well-being indicators improved accuracy.
The approach reduces labor intensity compared to traditional methods.

Implication

Demonstrates a method to predict turnover in employees using HR data and job well-being surveys, suggesting improved retention strategies.

Abstract

This study provides a viable and less labor-intensive method for identifying employees at high risk for turnover by applying ML models trained on historical HR data and identifies additional survey data that could augment predictions.

Discussion

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Cite This Study

Fukui et al. (2026) studied this question.

synapsesocial.com/papers/6980ff37c1c9540dea8120c7 https://doi.org/10.1176/appi.ps.20250364

View Full Paper

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Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

Key Points

Implication

Abstract

Discussion

Cite This Study

Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

Key Points

Implication

Abstract

Discussion

Cite This Study

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