INTEGRATING MACHINE LEARNING WITH OCCUPATIONAL INCIDENT ANALYTICS: EMERGING FRAMEWORKS FOR NATIONWIDE WORKPLACE SAFETY ENHANCEMENT

Abstract

The integration of predictive analytics into United States occupational safety and health practice promises to shift workplace risk management from post-incident recordkeeping toward prospective injury prevention. Synthesizing U.S.-focused research published between 2021 and 2026, this narrative review critically evaluates machine learning applications across severe incident classification, narrative text processing, real-time computer vision, return-to-work outcome forecasting, and emerging federal oversight models. While algorithmic capabilities have reached high computational performance, including production-scale transformer deployments for administrative coding and high-accuracy ensembles for accident narrative parsing, the literature remains dominated by retrospective offline experiments. Crucially, empirical evaluations demonstrate a profound gap between model precision and tangible worker safety, as virtually no published studies measure prospective reductions in workplace injury or illness rates. This lack of demonstrated field impact is further complicated by severe systemic data fragmentation across federal enforcement registries, statistical surveys, sector-specific databases, and state-bounded workers' compensation claims. To bridge this divide, this paper articulates an integrated nationwide framework featuring federated data interoperability, risk-calibrated algorithmic hygiene standards, mandatory prospective evaluation protocols, and a phased evolution toward binding administrative regulation. Aligning computational innovation with measurable workplace hazard reduction, rather than further optimizing classification accuracy on historical datasets, represents the essential mandate for the future of occupational safety analytics.