# Developing a Data-Driven Management System Using Machine Learning and Mixed-Methods Research to Predict Job Turnover Among MentalHealth Professionals

> **NIH NIH R34** · INDIANA UNIVERSITY INDIANAPOLIS · 2020 · $244,635

## Abstract

Project Summary/Abstract
The main goal of this study is to build a data-driven, evidence-based organizational management system that
can inform effective recruitment and retention strategies to prevent excessive turnover. High turnover rates
(estimated 25-60% annually) are devastating for mental health care systems, affecting organizations (e.g.,
cost), employees (e.g., work well-being), and most critically, the quality of care. Human resource departments
collect extensive employee data that can be useful predictors for turnover, but these data are not often
analyzed to address turnover issues in mental health organizations. Computational methods have greatly
evolved and can now access and analyze large and complex data. This pilot study will achieve three specific
aims: Aim 1: build and test turnover prediction models by developing and applying machine learning
algorithms to existing human resource data; Aim 2: generate critical questions to enhance turnover prediction
through qualitative methods; and Aim 3: test the enhanced model in predicting turnover at 12 months. In Aim
1, using past human resource data and service encounters from [two mental health organizations (rural and
urban locations)], we will develop machine learning algorithms to predict turnover. The algorithms will address
turnover questions simultaneously (e.g., Who are the most likely to leave? What factors predict turnover at
varying time points in employment?). In Aim 2, we will interview key informants: “leavers” (employees who
voluntarily terminate employment during the study); “stayers” (employees with extreme longevity in the
organization); and “predictees” (identified as likely to leave, based on our algorithms). The findings will be
discussed in two focus groups in order to generate, refine, and validate 5-10 critical questions to enhance
prediction of turnover. In Aim 3, we will conduct an on-line survey of all current employees to assess the 5-10
critical questions and link survey data with data from human resources and services to examine the improved
precision between the theory-based model (predictors in the survey) and the data-driven model (machine
learning algorithms) in predicting actual turnover 12 months later. Machine learning can model complex and
dynamic variable relationships (e.g., handling a large number of variables, accounting for heterogeneity) and
overcome limitations in traditional turnover research that often relies on small, cross-sectional, and
convenience samples. Successful completion of this study will promote data-driven, evidence-based
organizational management practices to address turnover, which is aligned with NIMH priorities of capitalizing
on existing data structures and using technologies to improve mental health service quality. This study will be a
critical step in developing highly adaptable machine learning algorithms to predict turnover; ultimately, we
envision that this system will be partnered with future clinical interventions...

## Key facts

- **NIH application ID:** 9895943
- **Project number:** 1R34MH119411-01A1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Sadaaki Fukui
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $244,635
- **Award type:** 1
- **Project period:** 2019-12-18 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895943

## Citation

> US National Institutes of Health, RePORTER application 9895943, Developing a Data-Driven Management System Using Machine Learning and Mixed-Methods Research to Predict Job Turnover Among MentalHealth Professionals (1R34MH119411-01A1). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/9895943. Licensed CC0.

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