International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Data Driven Strategies for Preventing Workplace Injuries and Improving Employee Health Protection Outcomes

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Volume: 1 | Issue: 5 | Pages: 523-536

Subject: Public Health

Country: Nigeria

DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.523-536

License: CC BY 4.0

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Abstract

Workplace injuries and occupational health challenges remain persistent concerns across public and private sectors, imposing significant human, social, and economic costs. Advances in data availability, analytics, and digital technologies have created new opportunities to transform traditional reactive safety management into proactive, prevention-oriented systems. This abstract examines data-driven strategies for preventing workplace injuries and improving employee health protection outcomes, with emphasis on the integration of real-time data, predictive analytics, and evidence-based decision-making. The approach synthesizes insights from occupational health and safety, public health surveillance, and organizational analytics to demonstrate how diverse data sources can be systematically leveraged. These sources include incident and near-miss reports, wearable sensor data, ergonomics assessments, health records, environmental monitoring, and workforce demographics. Advanced analytical techniques such as machine learning, trend analysis, and risk modeling enable early identification of hazardous patterns, vulnerable worker groups, and high-risk tasks before severe incidents occur. Data-driven dashboards and risk indicators further support timely interventions, continuous monitoring, and accountability across management levels. The abstract also highlights the role of governance, data quality, and ethical considerations, including privacy protection, transparency, and responsible data use, as critical enablers of effective implementation. By embedding analytics into safety policies, training programs, and operational planning, organizations can move beyond compliance-focused approaches toward adaptive systems that continuously learn and improve. Evidence from emerging practices suggests that data-driven injury prevention strategies contribute to measurable reductions in accident rates, severity of injuries, absenteeism, and associated costs, while enhancing employee wellbeing and organizational resilience. Importantly, these strategies align occupational health objectives with broader public health goals by promoting safer work environments, early health risk detection, and sustainable workforce participation. The abstract concludes that data-driven strategies represent a scalable and policy-relevant pathway for strengthening employee health protection, supporting regulatory oversight, and fostering a culture of prevention in modern workplaces. Future research should focus on sector-specific models, capacity building, interoperability standards, and longitudinal evaluation to ensure equitable adoption, robust causal inference, and sustained impact, particularly in resource-constrained settings where injury burdens remain high and data infrastructures are uneven across global supply chains, informal economies, and rapidly digitizing workplaces worldwide with strong stakeholder engagement mechanisms.

How to Cite This Article

Sandra C Anioke, Michael Efetobore Atima (2020). Data Driven Strategies for Preventing Workplace Injuries and Improving Employee Health Protection Outcomes . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 1(5), 523-536. DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.523-536

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Publication Information

Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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