Leading indicators of health and safety failures phase one outputs

Steven Naylor

HSE

Leading indicators
Safety failures
Data
Insight

The Discovering Safety Programme’s leading indicator industry use case is looking to support organisations in the collection and analysis of health and safety lead indicators intelligence as part of drives to promote more proactive health and safety practice across their organisations. Health and safety leading indicators of organisational performance are measurements linked to key functions and processes of an organisation’s safety management system; they are typically used by organisations to help assess the adequacy of their health and safety management arrangements, identify areas for improvement, target the allocation of health and safety resources, and formulate specific proactive actions to prevent future accidents occurring.

Aims and objectives

The primary deliverable for the leading indicators use case is a practical framework to help organisations maximise the potential to convert their routine operational data into effective leading indicator intelligence; it aims to identify specific areas of operations that should provide the focus of intelligence gathering,  identify specific areas where existing organisational data could be put to better use, and identify gaps in intelligence where new opportunities for generating routine data might be beneficial. 

Ultimately, it is hoped that the work undertaken as part of this project will lay important foundations for organisations having available the right sort of operational datasets enabling the adoption of a much more proactive, preventative, targeted form of health and safety practice and risk management, ultimately resulting in reduced numbers of workplace accidents and lives saved. 

Key findings

Major hazards  

Summary of existing HSE evidence base across HSE’s own BAU regulatory intelligence activities with respect to offshore and onshore chemical, creation of a single good guidance framework for leading indicators, mapping of HSE regulatory intelligence against framework, creation of a unified intelligence base across multiple sources, establishment of extent to which published guidance is supported by learning from HSE regulatory intelligence, identification of areas that are particularly well aligned, mapping of UofM learning for construction sector against major hazards framework, identification of potential areas for cross sector learning. 

Construction 

Systematic review of academic literature, thematic review of CD investigation and prosecution reports (underlying causes of accidents), identification of key precursors of serious failures in health and safety and resulting accidents, canvassing of specialist industry opinion through three industry workshops, including in one, a formal Delphi process to capture collective industry opinion on leading indicators of safety performance that have most value to safety decision-making, creation of a broad framework for consideration of leading indicators of safety performance in construction contexts, consideration of how best to assign values to indicators to support routine industry use. 

Recommendations

Major hazards  

Work is currently considering how we might take key outputs from the work and package for potential practical use.  

Early ideas include: 

  • Creation of UK COMAH site composite index of health and safety performance, to support HSE regulatory inspection targeting  

  • Creation of new major hazard incident taxonomies to support better indexing of major incident databases, such as the electronic Major Accident Reporting System (eMARS)  

 

Construction 

Work is currently considering how we might take key outputs from the work and package for potential practical use.  

Early ideas include: 

  • Creation of targeted search terms around key indicators to enable intelligent search and retrieve of industry accident, inspection and investigation databases to support identification of early warning signs of future serious accidents    

  • Integration of learning within apps to support collection of data during safety walkabouts, inspections, audits, integration with performance dashboards to support operational reporting activities  

  • Integration of learning with predictive analytic capabilities to enable prediction of health and safety failures, non-compliances, accidents, to support inspection targeting and intervention design.