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

使用机器学习和混合方法研究开发数据驱动的管理系统来预测心理健康专业人员的工作流动率

• 批准号: 10461312
• 负责人: Sadaaki Fukui
• 金额: $ 10.7万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-18 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10461312
• 关键词: Accounting; Address; Affect; Algorithms; Caring; Client; Clinical; Clinical Trials; Community Mental Health Centers; Complex; Computer Systems; Computing Methodologies; Data; Data Sources; Effectiveness; Employee; Employment; Evaluation; Financial compensation; Focus Groups; Future; Goals; Health Professional; Health Services Research; Healthcare Systems; Heterogeneity; Human Resources; Intervention; Interview; Investments; Job Satisfaction; Lead; Learning; Light; Link; Longevity; Machine Learning; Mental Health; Mental Health Services; Modeling; National Institute of Mental Health; Occupations; Performance; Personal Satisfaction; Personnel Turnover; Pilot Projects; Practice Management; Predictive Factor; Productivity; Professional Burnout; Qualitative Methods; Quality of Care; Recording of previous events; Research; Research Methodology; Risk; Sampling; Services; Structure; Surveys; System; Technology; Testing; Time; Training; Work; base; burnout; complex data; cost; data resource; data-driven model; effective therapy; evidence base; heterogenous data; human data; improved; informant; innovation; machine learning algorithm; mental health organization; predictive modeling; prevent; prospective; recruit; retention rate; rural area; theories; treatment services; urban area

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 to reduce turnover in mental health.

项目总结/文摘

项目成果

Structural Racism, Workforce Diversity, and Mental Health Disparities: A Critical Review.

• DOI: 10.1007/s40615-022-01380-w
• 发表时间: 2023-08
• 期刊: JOURNAL OF RACIAL AND ETHNIC HEALTH DISPARITIES
• 影响因子: 3.9
• 作者: Kyere, Eric;Fukui, Sadaaki
• 通讯作者: Fukui, Sadaaki

Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees.

利用人力资源数据进行机器学习：预测社区心理健康中心员工的流动率。

• 发表时间: 2023
• 期刊: The journal of mental health policy and economics
• 作者: Fukui,Sadaaki;Wu,Wei;Greenfield,Jaime;Salyers,MichelleP;Morse,Gary;Garabrant,Jennifer;Bass,Emily;Kyere,Eric;Dell,Nathaniel
• 通讯作者: Dell,Nathaniel