Description:

Mining safety systems are highly disciplined at measuring incidents, exposures, and corrective actions. Yet one major contributor to prolonged disability and preventable operational disruption is often not measured as a safety-system risk: diagnostic delay. In remote mining workforces, delayed MRI/CT access, repeat or low-quality imaging, fragmented follow-up after abnormal findings, and medical travel burden can prolong uncertainty for injured workers, delay return-to-work planning, and obscure opportunities for prevention.

This presentation introduces a practical Diagnostic Bottleneck Audit framework for remote mining operations. The framework is designed to help mine operators, occupational health teams, worker representatives, and clinical partners map where diagnostic pathways fail after injury or occupational health referral. It focuses on aggregate, prevention-oriented measures: time from injury to definitive imaging, repeat-imaging frequency, unavailable prior imaging, incomplete follow-up of abnormal findings, travel episodes related to diagnostic access, and quality issues that affect clinical decision-making.

The framework also addresses governance requirements that are essential for trust. Individual clinical information remains under clinical governance; employers receive only aggregate, cell-suppressed reporting; worker participation in any individual review is voluntary; and implementation should involve worker or union health and safety representatives and, where applicable, Indigenous data-governance processes.

The session will show how a site can begin with a low-burden baseline audit, identify the highest-value bottlenecks, and prioritize interventions such as imaging quality improvement, referral pathway redesign, regional diagnostic partnerships, tele-radiology review, or mobile diagnostic services where justified. The goal is not claims adjudication or employer access to personal health information. The goal is to convert fragmented diagnostic delays into measurable prevention intelligence that supports safer, faster, and more worker-centred decisions.

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Dr. Dan Gill

Biography