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

项目摘要/摘要 这项研究的主要目标是建立一个数据驱动的、基于证据的组织管理系统， 能够为有效的招聘和留住战略提供参考，以防止人员过度流失。高流动率 (估计每年25%-60%)对精神卫生保健系统是毁灭性的，影响到组织(例如， 成本)、员工(例如，工作幸福感)，以及最关键的是护理质量。人力资源部 收集大量的员工数据，这些数据可以作为离职的有用预测指标，但这些数据并不常见 分析以解决心理健康组织中的离职问题。计算方法有很大的优势 经过发展，现在可以访问和分析大型且复杂的数据。这项试点研究将实现三个具体目标 目标：目标1：通过开发和应用机器学习来建立和测试营业额预测模型 现有人力资源数据的算法；目标2：生成关键问题以增强人员流动预测 通过定性的方法；以及目标3：检验改进的模型在预测12个月的营业额中的作用。在AIM 1.使用[两个精神卫生组织(农村和农村)]过去的人力资源数据和服务接触 城市位置)]，我们将开发机器学习算法来预测营业额。这些算法将解决 同时提出离职问题(例如，谁最有可能离职？哪些因素预测营业额 不同的就业时点？)在目标2中，我们将采访关键的告密者：“离职者”(员工 在研究期间自愿终止雇用)；“留下来的人”(在 组织)；和“被预测者”(根据我们的算法，被确定为可能离开)。调查结果将是 在两个焦点小组中进行讨论，以生成、改进和验证5-10个要增强的关键问题 营业额预测。在目标3中，我们将对所有现有员工进行在线调查，以评估5-10 提出关键问题并将调查数据与人力资源和服务部门的数据联系起来，以检查改进后的 基于理论的模型(调查中的预测者)和数据驱动的模型(机器)之间的精度 学习算法)来预测12个月后的实际营业额。机器学习可以对复杂和 动态变量关系(例如，处理大量变量，考虑异质性)和 克服传统离职研究的局限性，这些研究往往依赖于小规模、横截面和 方便的样品。这项研究的成功完成将促进数据驱动、循证发展 解决人员更替问题的组织管理做法，这与NIMH资本化的优先事项保持一致 关于现有的数据结构，并使用技术来提高心理健康服务质量。这项研究将是一项 开发高度适应性的机器学习算法以预测营业额的关键一步；最终，我们 设想这一系统将与未来的临床干预措施相结合，以减少心理健康方面的更替。

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.