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Fukui S, Wu W, Greenfield J, Salyers MP, Morse G, Garabrant J, Bass E, Kyere E, Dell N. Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees. The journal of mental health policy and economics. 2023 Jun 1; 26(2):63-76.
Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects. BACKGROUND: Human resources (HR) departments collect extensive employee data that can be useful for predicting turnover. Yet, these data are not often used to address turnover due to the complex nature of recorded data forms. AIMS OF THE STUDY: The goal of the current study was to predict community mental health center employees' turnover by applying machine learning (ML) methods to HR data and to evaluate the feasibility of the ML approaches. METHODS: Historical HR data were obtained from two community mental health centers, and ML approaches with random forest and lasso regression as training models were applied. RESULTS: The results suggested a good level of predictive accuracy for turnover, particularly with the random forest model (e.g., Area Under the Curve was above .8) compared to the lasso regression model overall. The study also found that the ML methods could identify several important predictors (e.g., past work years, wage, work hours, age, job position, training hours, and marital status) for turnover using historical HR data. The HR data extraction processes for ML applications were also evaluated as feasible. DISCUSSION: The current study confirmed the feasibility of ML approaches for predicting individual employees' turnover probabilities by using HR data the organizations had already collected in their routine organizational management practice. The developed approaches can be used to identify employees who are at high risk for turnover. Because our primary purpose was to apply ML methods to estimate an individual employee's turnover probability given their available HR data (rather than determining generalizable predictors at the wider population level), our findings are limited or restricted to the specific organizations under the study. As ML applications are accumulated across organizations, it may be expected that some findings might be more generalizable across different organizations while others may be more organization-specific (idiographic). IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The organization-specific findings can be useful for the organization's HR and leadership to evaluate and address turnover in their specific organizational contexts. Preventing extensive turnover has been a significant priority for many mental health organizations to maintain the quality of services for clients. IMPLICATIONS FOR HEALTH POLICIES: The generalizable findings may contribute to broader policy and workforce development efforts. IMPLICATIONS FOR FURTHER RESEARCH: As our continuing research effort, it is important to study how the ML methods and outputs can be meaningfully utilized in routine management and leadership practice settings in mental health (including how to develop organization-tailored intervention strategies to support and retain employees) beyond identifying high turnover risk individuals. Such organization-based intervention strategies with ML applications can be accumulated and shared by organizations, which will facilitate the evidence-based learning communities to address turnover. This, in turn, may enhance the quality of care we can offer to clients. The continuing efforts will provide new insights and avenues to address data-driven, evidence-based turnover prediction and prevention strategies using HR data that are often under-utilized.
