The rate of recordable injuries in the electric power sector has declined steadily over the past decade; however, the rate of serious injuries and fatalities (SIFs) has plateaued. Unfortunately, studying SIFs is a paradox. On one hand, SIFs are incredibly important and deserve significant resources for investigation. On the other, learning from these events and detecting causal patterns are challenging because SIFs are relatively rare. To vastly increase the number of learning opportunities and to better characterize safety performance, organizations are beginning to investigate incidents with the potential to cause serious injuries or fatalities (PSIF). PSIFs also offer an opportunity for shared learning, which is necessary to advance toward SIF elimination.
Unfortunately, existing methods of identifying and tracking PSIFs are unscientific, heavily biased, and yield inconsistent understanding of whether an incident is a PSIF or not.
An EEI working group of 20 safety leaders and a technical advisor was assembled to create a method for consistently classifying safety incidents and observations that enables shared learning. The EEI safety classification and learning (SCL) model leverages the latest scientific knowledge and the best features of existing methods. The model was tested and refined by the team using actual safety cases.
The resulting tool defines safety incidents and observations based upon the answers to the following yes or no questions:
