Conference Proceedings
International Mine Health and Safety Conference Proceedings 2024
Conference Proceedings
International Mine Health and Safety Conference Proceedings 2024
Stop investigating everything! How to classify events to maximise organisational learning and reduce systemic risk
Organisations’ incident classification processes are plagued by the overclassification of events, often leading to excessive workloads and suboptimal outcomes. This presentation addresses the pressing need to rethink how incidents are classified to maximise organisational learning and minimise systemic risk.
A major issue in the current incident classification landscape is the tendency to classify events at high levels, overwhelming organisations with investigations they cannot efficiently handle. This results in inadequate risk reduction and learning opportunities. The link between injury classification and investigation methods further compounds these problems, creating unnecessary administrative clutter.
Challenging the misconception that every event, no matter how minor, requires a comprehensive investigation, this presentation advocates for a shift in perspective. The key recommendation is to decouple injury classification and risk matrices from the decision-making process regarding what and how to investigate. Instead, the emphasis should be placed on identifying opportunities for organisational learning and risk reduction.
Organisations can streamline their incident management processes by adopting a more pragmatic approach that aligns incident classification with the organisation’s investigation capacity. This transformation focuses on achieving meaningful outcomes, enhancing safety and improving performance.
This presentation provides an innovative process for organisations to revamp their incident classification practices, enabling them to harness the full potential of incident data for learning and risk mitigation. It highlights the importance of aligning incident classification with the opportunity for organisational learning, ultimately leading to more effective incident management and enhanced safety outcomes.
A major issue in the current incident classification landscape is the tendency to classify events at high levels, overwhelming organisations with investigations they cannot efficiently handle. This results in inadequate risk reduction and learning opportunities. The link between injury classification and investigation methods further compounds these problems, creating unnecessary administrative clutter.
Challenging the misconception that every event, no matter how minor, requires a comprehensive investigation, this presentation advocates for a shift in perspective. The key recommendation is to decouple injury classification and risk matrices from the decision-making process regarding what and how to investigate. Instead, the emphasis should be placed on identifying opportunities for organisational learning and risk reduction.
Organisations can streamline their incident management processes by adopting a more pragmatic approach that aligns incident classification with the organisation’s investigation capacity. This transformation focuses on achieving meaningful outcomes, enhancing safety and improving performance.
This presentation provides an innovative process for organisations to revamp their incident classification practices, enabling them to harness the full potential of incident data for learning and risk mitigation. It highlights the importance of aligning incident classification with the opportunity for organisational learning, ultimately leading to more effective incident management and enhanced safety outcomes.
Contributor(s):
M Alston
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Stop investigating everything! How to classify events to maximise organisational learning and reduce systemic riskPDFThis product is exclusive to Digital library subscription
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Stop investigating everything! How to classify events to maximise organisational learning and reduce systemic riskPDFNormal price $22.00Member price from $0.00
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- Published: 2024
- Pages: 4
- PDF Size: 0.878 Mb.
- Unique ID: P-03460-H4R6N2
- ISBN no: 978-1-922395-26-9