A Data-driven Dashboard Framework for Employee Mental Health and Organisational Decision-making
Daniel Kweku Assumang
*
Department of Project Management, College of Professional Studies, Northeastern University, USA.
Elizabeth Umah
Department of Information Systems and Business Analytics, Florida International University. USA
Martin Msughter Vincent
Policy Studies, National Graduate Institute for Policy Studies, Japan.
Bolatito I. Abolade
Iowa State University, USA
Bukola Elizabeth Shasere
Department of Clinical Core Lab, Mayo Clinic, USA.
*Author to whom correspondence should be addressed.
Abstract
Due to rising mental health issues in the workplace, organisations need to take preemptive, data-driven measures to foster employee well-being. In this review article, a complete, data-driven framework for constructing Mental Health Index Dashboards specific to organisational environments is introduced. This paper utilised a desk review approach. This involves a comprehensive review of scholarly journals. Credible journals and reliable online materials were used, and findings were presented thematically. Through the integration of quantitative and qualitative data sources such as employee surveys, productivity metrics, absenteeism records, and sentiment analysis, the proposed framework is able to produce real-time and actionable insights. Findings show that using visualization of data and machine learning, the dashboard allows human resource professionals and decision-makers to track changes in mental health over time, identify high-risk groups, and assess changes in the effectiveness of interventions over time. The review further presents the study of ethical data handling, employee privacy, and transparency of the organisation, and presents best practices for such implementation. Case studies and deployment of prototype dashboards show how such dashboards can create a healthier workplace, reallocate resources, and find long-term mental health strategies.
Essentially, the dashboard framework creates an enabling atmosphere in which mental health is emphasised and discussed. At the same time, it enhances the productivity of employees, as possible cases of stress are identified at an early stage, each employee is given personalised assistance, and resources are distributed based on data. The framework triggers greater engagement, more employees who are less subject to burnout, and a healthier, more resilient workforce through enforcing mental health in the strategy decision-making process. The review recommends that longitudinal impact studies can be considered to examine the effect of the framework on employees’ well-being and organisational decision-making in the long run.
Keywords: Mental health index, data-driven, decision making, employee well-being, dashboard, predictive analytics