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: 10321230
• 负责人: Sadaaki Fukui
• 金额: $ 22.35万
• 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/10321230
• 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.

项目摘要/摘要 这项研究的主要目标是建立一个以数据为基础的循证组织管理系统 可以告知有效的招聘和保留策略，以防止过度离职。高离职率 （估计每年25-60％）对精神卫生保健系统有毁灭性的影响，影响组织（例如， 费用），员工（例如，工作福祉），以及最批判地的护理质量。人力资源部门 收集广泛的员工数据，这些数据可能是营业额有用的预测指标，但是这些数据并不常见 分析以解决精神卫生组织中的流动问题。计算方法有很大 进化，现在可以访问和分析大而复杂的数据。该试点研究将达到三个特定的 目标：目标1：通过开发和应用机器学习来构建和测试周转预测模型 现有人力资源数据算法；目标2：产生关键问题以增强离职预测 通过定性方法； AIM 3：测试增强模型在预测12个月的营业额中。目标 1，使用[两个心理健康组织（农村和农村和 ]，我们将开发机器学习算法以预测营业额。算法将解决 流动问题简单（例如，谁最有可能离开的人？ 雇用时间有所不同？）。在AIM 2中，我们将采访关键信息：“ Leavers”（员工 在研究期间自愿终止员工； “逗留者”（在 组织）;和“预测者”（根据我们的算法确定可能离开）。发现将是 在两个焦点小组中讨论，以产生，完善和验证5-10个关键问题以增强 周转的预测。在AIM 3中，我们将对所有当前员工进行在线调查，以评估5-10 关键问题并将调查数据与人力资源和服务的数据联系起来，以检查改进的 基于理论的模型（调查中的预测因子）和数据驱动模型（机器）之间的精度 学习算法）在预测12个月后实际周转率时。机器学习可以建模复杂，并且 动态变量关系（例如，处理大量变量，考虑异质性）和 克服通常依赖小型，横截面和 便利样本。这项研究的成功完成将促进数据驱动的，基于证据的 解决流动率的组织管理实践，与NIMH的优先级相符 在现有的数据结构并使用技术来提高心理健康服务质量。这项研究将是 开发高度适应的机器学习算法以预测营业额的关键步骤；最终，我们 设想该系统将与未来的临床干预措施合作，以减少心理健康的营业额。