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
基本信息
- 批准号:10461312
- 负责人:
- 金额:$ 10.7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccountingAddressAffectAlgorithmsCaringClientClinicalClinical TrialsCommunity Mental Health CentersComplexComputer SystemsComputing MethodologiesDataData SourcesEffectivenessEmployeeEmploymentEvaluationFinancial compensationFocus GroupsFutureGoalsHealth ProfessionalHealth Services ResearchHealthcare SystemsHeterogeneityHuman ResourcesInterventionInterviewInvestmentsJob SatisfactionLeadLearningLightLinkLongevityMachine LearningMental HealthMental Health ServicesModelingNational Institute of Mental HealthOccupationsPerformancePersonal SatisfactionPersonnel TurnoverPilot ProjectsPractice ManagementPredictive FactorProductivityProfessional BurnoutQualitative MethodsQuality of CareRecording of previous eventsResearchResearch MethodologyRiskSamplingServicesStructureSurveysSystemTechnologyTestingTimeTrainingWorkbaseburnoutcomplex datacostdata resourcedata-driven modeleffective therapyevidence baseheterogenous datahuman dataimprovedinformantinnovationmachine learning algorithmmental health organizationpredictive modelingpreventprospectiverecruitretention raterural areatheoriestreatment servicesurban area
项目摘要
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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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。
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 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
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Sadaaki Fukui其他文献
Sadaaki Fukui的其他文献
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{{ truncateString('Sadaaki Fukui', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 10.7万 - 项目类别:
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:
9895943 - 财政年份:2019
- 资助金额:
$ 10.7万 - 项目类别:
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:
10375772 - 财政年份:2019
- 资助金额:
$ 10.7万 - 项目类别:
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