Predictive Analytics in Occupational Health: Transitioning from Lagging to Leading Indicators in Enterprise Risk Management

Kimberly Long Holt

doi:10.55927/fjst.v5i4.50

Authors

Kimberly Long Holt Health and Safety Concepts – Environmental Health & Safety, united state

DOI:

https://doi.org/10.55927/fjst.v5i4.50

Keywords:

Predictive Analytics, Occupational Health and Safety (OHS), Leading Indicators, Total Cost of Risk (TCOR), Internet of the Worker (IoW).

Abstract

The discipline of Occupational Health and Safety (OHS) is undergoing a paradigm shift driven by advances in wearable technology and machine learning. Lagging indicators, such as the Total Recordable Incident Rate (TRIR), have been historically considered the most common risk management metrics for gauging the safety performance of risk managers. Such measures merely describe what occurs once one has been defeated, but not the option of preventing the initial loss. The paper endorses the proposal to change to leading indicators, controlled by predictive analytics and the Internet of the Worker (IoW). By studying real-time biometric and environmental information, organizations would be able to identify pre-incident behavior patterns and prevent losses. This paper will examine the technically based infrastructure of the IoW, validate the financial case, using the Total Cost of Risk (TCOR) analysis, and a holistic ROI model, the underwriting implications of the Experience Modification rate (EMR) optimization and Schedule Rating and the ethical remit of data governance and just culture that must accompany the implementation of these technologies, based on the frames of the Associate in Risk Management (ARM) system

References

ASSP (American Society of Safety Professionals). (2025). Leading Indicators: The New Standard for OHS Management. ASSP Press.

Domino, D. (2024). Predictive Analytics for the Modern Safety Professional. Wiley & Sons.

Heinrich, H. W. (1931). Industrial Accident Prevention: A Scientific Approach. McGraw-Hill.

Hollnagel, E. (2023). Safety-II in Practice: Developing the Resilience Potentials. Routledge.

International Risk Management Institute (IRMI). (2026). The Impact of AI on Workers’ Compensation Cost Structures. IRMI Research Reports.

ISO 31000:2018. Risk Management – Guidelines—International Organization for Standardization.

Kaplan, R. S., & Mikes, A. (2025). Managing Risks: A New Framework. Harvard Business Review.

National Safety Council (NSC). (2025). Workforce Biometrics and the Ethics of Surveillance. NSC Safety Whitepaper.

Petersen, D. (2024). Human Error Reduction and Safety Management (4th ed.). Aloray.

Reason, J. (1990). Human Error. Cambridge University Press.

Shorrock, S., & Williams, C. (Eds.). (2023). Human Factors and Ergonomics in Practice: Improving System Performance and Human Well-Being in the Real World. CRC Press.

Weil, D. (2024). The Fissured Workplace: Why Work Became So Bad for So Many and What Can Be Done to Improve It (Updated ed.). Harvard University Press.