Training on Data Analytics for Occupational Health and Mental Health Outcomes

Organizations increasingly rely on data to design effective workplace health and wellbeing strategies. Without accurate measurement and analysis, it is difficult to identify health risks, track employee wellbeing trends, or evaluate the impact of workplace interventions.

This 10-module course equips HR professionals, occupational health practitioners, data analysts, and organizational leaders with the tools and methodologies needed to apply data analytics in occupational health and mental wellbeing programs. Participants learn how to collect, analyze, and interpret employee health data to support evidence-based decision making and improve workplace health outcomes.

The training explores practical approaches to data-driven occupational health and safety (OHS) management, including HR analytics for mental health trends, analysis of absenteeism and productivity indicators, and monitoring of employee wellbeing metrics. Participants will also learn to design dashboards and reports that help leadership understand workforce health risks and evaluate the effectiveness of wellness initiatives.

In addition, the course covers ethical considerations, data privacy in health analytics, and best practices for integrating analytics into occupational health and corporate wellness strategies. Through hands-on exercises, real-world datasets, and case studies, participants gain practical experience applying analytics to measure and improve workplace health programs.

By the end of the training, participants will be able to build data-driven health strategies, monitor mental health outcomes, and optimize workplace wellbeing programs using actionable insights.

Duration

10 Days

Who Should Attend

• HR Analysts and Data Professionals
• Occupational Health and Safety Officers
• Organizational Psychologists
• Corporate Wellness Managers
• Compliance and Risk Officers

Organizational Impact

• The organization can proactively manage health risks by identifying trends and risk factors before they lead to a crisis or a costly incident.

• By using data to justify investments, the company can improve the ROI of its health and wellness programs and ensure resources are allocated effectively.

• Data-driven insights lead to more targeted and effective interventions, resulting in a healthier, more engaged, and more productive workforce.

• This advanced capability strengthens the organization's strategic approach to human capital, enhancing its reputation as a data-driven and employee-centric employer.

Personal Impact

• The participant will gain a highly valuable and specialized skill set that combines data analytics with the crucial fields of occupational health and human resources.

• This expertise is a crucial skill for career progression into senior strategic roles, such as HR analytics manager or director of health and wellness.

• The individual will be able to contribute directly to the organization's long-term success by providing compelling, data-backed evidence for investing in its people.

• The training provides the professional credibility and authority to lead conversations on employee well-being with data, not just intuition.

By the end of this course, participants will:

• Apply analytics to improve occupational health and mental wellbeing outcomes

• Implement methods for measuring impact of wellbeing programs

• Use HR analytics for mental health trends in strategic planning

• Perform data-driven OHS decision making for risk prevention

• Analyze absenteeism and presenteeism data to identify root causes

• Improve reporting on employee health metrics to internal and external stakeholders

Module 1: Introduction to Data Analytics in Occupational Health

• Role of data in workplace health strategy

• Overview of analytics frameworks and tools

• Identifying key OHS and mental health indicators

Module 2: Data Collection and Management

• Sources of employee health data

• Ensuring data accuracy, privacy, and security

• Integrating HR, EHS, and wellness program data

Module 3: HR Analytics for Mental Health Trends

• Recognizing trends in stress, burnout, and engagement

• Dashboards and alerts for early detection

• Using predictive analytics for mental health risk modeling

Module 4: Measuring Impact of Wellbeing Programs

• Designing KPIs for health initiatives

• Tracking utilization and outcomes of wellness programs

• Evaluation methods for “measuring impact of wellbeing programs”

Module 5: Absenteeism and Presenteeism Analysis

• Tools for “analyzing absenteeism and presenteeism data”

• Identifying patterns linked to health or workplace stress

• Linking insights to workforce planning and policies

Module 6: Advanced Data Visualization and Communication

• Creating meaningful dashboards and infographics

• Customizing reports for various audiences

• Storytelling with data for wellness advocacy

Module 7: Reporting on Employee Health Metrics

• Key elements of “reporting on employee health metrics”

• Internal and external reporting standards

• Case Study: Transparent health data reporting for ESG and compliance

Module 8: Data-Driven Decision Making in OHS

• Practical models for “data-driven OHS decision making”

• Scenario analysis and decision trees

• Embedding analytics in HSE risk management

Module 9: Ethics, Bias, and Responsible Use of Data

• Avoiding bias in health-related data interpretation

• Ethical data use and consent

• Ensuring inclusivity and equity in analysis

Module 10: Developing a Health Analytics Action Plan

• Building a roadmap for analytics in occupational health

• Integrating mental health and OHS insights into HR strategy

• Final Project: Designing a data dashboard for wellness and OHS outcomes