Serious Injuries and Fatalities (SIFs) are broadly defined as a fatality, life-altering or life-threatening injury. “Life-threatening” is often determined by the need for external emergency-response personnel to provide life-sustaining support. Life-altering injuries are disabling events that result in permanent or long-term impairment or loss of use of an internal organ, body function or body part. The identification and analysis of potential SIF events are used to develop prevention strategies. In recent years, SIF incidents have been used to create severity metrics and industry- benchmarking initiatives.
In 2011, Behavioral Science Technologies (BST) and ORC Mercer Worldwide published a groundbreaking study that addressed the cause and prevention of serious injuries and fatalities. Seven major corporations participated: Archer Daniels Midland, BHP Billiton, Cargill, Exxon, Maersk, Potash and Shell. The study was prompted by the disturbing statistical trend that recordable and lost time accidents were decreasing while serious and fatal accidents were leveling or increasing. The participating organizations recognized that the prevention strategies they had in place were not reducing their most severe injuries and fatalities.
Prior to the BST/Mercer study, most accident prevention strategies were based on the traditional Heinrich’s Triangle (See Figure 1). Using the triangle, safety professionals believed that reducing the number of less serious accidents at the bottom of the triangle would result in a reduction of serious accidents at the top of the triangle. In other words, reducing the number of recordable cases would reduce the number of serious cases. Data from this study as well as other supporting data demonstrated that the Heinrich model could not be applied to SIFs. As a result, safety professionals were seeking a new statistical model and prevention strategy to specifically address serious injuries and fatalities. New metrics would also be needed to effectively gauge success.
To better understand the paradigm shift from Heinrich’s Triangle to more progressive strategies, we should know how the Heinrich model was created. Herbert William Heinrich was an assistant superintendent for a large insurance company. In the early 20th century, he conducted a study of more than 75,000 accident reports. The data showed that, for every major-injury accident, there were 29 minor-injury accidents and 300 no-injury accidents. Heinrich concluded that reducing the number of minor accidents would result in a reduction of the number of major accidents.
The BST/Mercer Study demonstrated that Heinrich’s triangle was not an effective model for predicting serious injuries and fatalities because only about 21 percent of accidents have SIF potential. In fact, today’s progressive safety professionals are using new models and metrics that focus on SIF potential to predict and prevent serious injuries in the workplace.
Determining SIF potential
The two common classification systems used to determine SIF potential are the narrative review and event-based decision chart. The narrative review is judgment-based system using trained specialists called raters to classify incidents. Raters are trained using established criteria and calibration exercises. A group of raters reviews the accident narrative to understand the context and circumstances of the event and then reach consensus on the incident’s classification. Once calibrated, each rater can independently and reliably classify incidents. To maintain reliability, the calibration exercises must be periodically performed.
A more popular method is the event-based decision chart. In this system, established criteria are used to develop a flow chart that determines the incident’s classification. For example, the Edison Electric Institute (EEI) recently introduced a classification and learning model based on the presence of high energy, controls applications and the incident’s outcome. This model intended to help ensure uniform incident classification industry-wide and for direct learning.
Most flow charts are simple decision trees that allow users to easily classify an event by answering a short series of questions. Identifying incidents that have SIF potential can help organizations better focus their resources on preventing disabling injuries and fatalities. By understanding the precursors of all potentially serious accidents, effective predictive tools and prevention plans can be developed and implemented.
Precursors and prevention
SIFs have different causes and correlations than more frequent and less severe accidents. The causes of SIF events are most often related to an organization’s cardinal or live-saving rules. These rules are put in place to address high-risk situations. High-risk situations give rise to precursors, which, if not mitigated, result in serious injuries. To prevent SIFs, it is necessary to identify and mitigate precursors.
Precursors are conditions, events or actions that, if not mitigated, can result in a serious accident. Precursors serve as warnings. Predictive models have been developed through precursor analysis. A project conducted by the Construction Industry Institute developed a valid and reliable method for predicting severe or fatal injuries in the construction industry.
Larry Simmons is senior director, serious injury and fatality prevention, at Potash Corp., Saskatoon, Canada.
“A SIF precursor generally has three key aspects: it is a high-risk situation where management controls are absent, ineffective or not complied with and will result in a serious injury or fatality if allowed to continue. For a situational example of precursors with the potential for SIF, we can look at the utility industry and working on power lines. This work is inherently high-risk because it takes place at height around high-voltage lines, but may involve SIF precursors if a worker is not wearing or is not provided the appropriate PPE or if the training on how to properly operate the lift has lapsed. These breakdowns in management controls, if allowed to continue, could result in a serious injury or fatality for this particular work,” he said.
EEI conducted a similar research project that identified the 13 strongest predictors of SIF incidents in the electric power generation and delivery industry. The project also delivered a weighted SIF Precursor Scorecard that can be used in the field to determine the potential for a serious injury or fatality. The ability to predict SIF events provides an opportunity to prevent these accidents from occurring.
Other prevention strategies
Other innovative SIF prevention strategies are being introduced throughout the electrical construction industry. Quanta Services recently introduced its STKY (S**t That Kills You) program to workers in the field. The objective is to identify high-energy hazards workers are exposed to each day and ensure adequate controls are in place so that even if failure occurs, workers will have the capacity to fail safely. The program, implemented using informal chats with crews, provides them with an effective tool to prevent disabling and fatal injuries. STKY discussions are not compliance-based.
As progressive organizations implement new SIF prevention strategies, they must also develop or adopt metrics to determine the success of their programs. SIF events, both potential and actual, are used to create severity metrics that can be used within an organization or as benchmarking tools across an industry. Traditional severity metrics such OSHA DART Rate, Lost Work Days Case Rate and calculations based on lost and/or restricted days do not assess the severity exposure as well as SIF potential. Identifying the SIF potential of all OSHA recordable incidents and calculating a rate based on these cases will provide a more comprehensive assessment of severity. Benchmarking opportunities will be created among companies that use the same criteria and metrics. This should also lead to the development of effective practices that can prevent serious and fatal injuries from occurring.
Number of cases/Employee hours x 200,000 = Potential SIF rate
Metrics have also been developed to identify and measure actual SIF events. Since 2012, EEI has published criteria to identify actual SIF events and has collected this data in its annual safety survey. These criteria have also been adopted by members of OSHA’s Electrical Transmission & Distribution (ET&D) Partnership.
Actual SIF cases are determined using the published criteria to classify serious injuries into 14 categories. This data is effectively used to establish industry benchmarks for actual SIF events. Arming yourself and your employees with the tools to identify potential SIF events before they happen is the first step toward preventing them.