There is a strong, positively correlated relationship between behavior and results. This is particularly true when it comes to risk management in tree care. Specifically, some behaviors observed in tree care operations can have disastrous consequences.

We need only look at TCIA’s weekly “Fatality & Near Miss Report” (subscribe at tcia.org, under the Education & Events tab/Accident Briefs) in order to make this connection. Every Monday morning, I see the evidence of poor decision making displayed before me. I look at the briefs, try to identify the at-risk behavior associated with the incident in question and think about how it could all be avoided. Arborists are dying, mostly because of poor decision making and the associated at-risk behaviors.

One thing I have learned throughout my experience as a risk manager is the importance of measuring both leading and lagging indicators. Lagging indicators, such as incident rates, measure the effectiveness of a safety program after incidents have occurred. Leading indicators, such as qualification rate and participation in training, measure a program’s effectiveness before incidents occur.

Although many safety programs adequately measure lagging indicators, addressing deficiencies with leading indicators as well helps program managers decrease the frequency and severity of injuries. Understanding leading indicators and incorporating them into a safety program balances a program and improves overall results by focusing on the behavior that leads to successful outcomes.

Those responsible for managing risk, including CTSPs, are well aware of Heinrich’s pyramid of accident causation (image). The pyramid graphically illustrates the correlation between an increase in performing at-risk behaviors and an increase in minor to catastrophic injuries. In this way, the top of the pyramid represents the pinnacle of severe outcomes that are possible based on the decisions people make. Each time someone chooses to engage in a risky behavior, they increase the probability of having a much more serious consequence.

Another way to view this dynamic is to view each day as a series of choices. With this viewpoint comes a realization about what each individual has control over. None of us has control over random events, but we do have control over our own behavior. It is my opinion that each individual makes a choice to indulge in various types of at-risk behaviors, such as one-handed chain-saw use. There could be a myriad of different explanations as to why someone chooses to operate a chain saw incorrectly, including a lack of skill or perspective. This choice also could arise from an individual exceeding the limits of their own capability.

Regardless of where at-risk behaviors come from, it is my belief that we can always choose to put ourselves in the position to do the right thing, to do things correctly. When we recognize our days as a series of choices, we also recognize that the best way to stay off of Heinrich’s pyramid is to choose safe behaviors over at-risk behaviors. The insights we gain from tracking leading and lagging indicators help us do that.

To understand the difference between leading and lagging indicators, as well as the relationship between results and behaviors, picture yourself driving on a desert road. Suddenly, you see a large explosion off in the distance as you glance at the rear-view mirror. You might breathe a sigh of relief given that it is behind you. Despite this, you can’t help but imagine how much damage the explosion caused. As you return your eyes to the horizon in front of you, about a mile ahead you see another large explosion. What do you do? Do you keep driving forward or do you stop, look around and chart another course based on what you know?

This dramatic example illustrates that lagging indicators are represented by what we see in the rearview mirror, the results. Leading indicators are signals that something dangerous could happen in the future if you do not change course. Accordingly, we also can view lagging indicators as reactive and based on hindsight, while leading indicators are proactive and based on foresight. Based on this analogy, it is easy to conceptualize the importance of tracking leading and lagging indicators as part of a well-rounded risk-management program.

To fully comprehend the importance of measuring leading and lagging indicators, let’s return to the one-handed chain-saw-use example. Incident data based on injuries caused by one-handed use of a chain saw is well known and understood within the arboricultural industry. In fact, our industry even has safety standards prohibiting this practice. This is because we have linked a behavior to a potential outcome based on what we have learned as an industry. Based on the results, we know that one-handing a chain saw can result in an injury. For this reason, we know the behavior of one-handing increases the potential for an injury.

The good news is that we also know what to do to avoid injuries caused by one-handing a saw – stop doing it. Additionally, there are a variety of other behaviors we can choose from to avoid this practice and possible outcome. For example, we can focus on proper positioning or alternate tool selection. Do we need to use a chain saw, or will a handsaw get the job done? Why reach out with one hand when you can get in the proper position and make the correct cut with both hands?

Ultimately, these decisions are the responsibility of each individual. Before risk managers can consolidate these concepts into an incident-avoidance program, it is necessary to discuss why it is important to measure behavior in order to get better results. Once you understand the concepts, you can design metrics to learn from leading and lagging indicators and achieve successful outcomes based on these metrics. The reality is that there are financial, legal and moral reasons for creating a safety program based on leading and lagging indicators.

Incidents cost money. This is perhaps the most obvious reason why a business would want to create an incident-avoidance program based on leading and lagging indicators. Accordingly, the responsible manager would consider what can be done to reduce these costs. There are also legal reasons for running an effective program.

The risk manager knows the specific requirements of effective safety programs as laid out by enforcement agencies such as OSHA. The risk manager knows the industry standards and how they relate to each local jurisdiction at the branch level, and they are familiar with the potential consequences of running a non-compliant program. For compliance reasons, many companies hold safety meetings to discuss and learn from incidents, close calls and at-risk behaviors. Often this is a legal requirement, but there are also moral reasons for designing a program with these elements.

There is a human side to the metrics as well. For example, a recent TCIA accident brief described a man who fell from a tree because he was not properly secured aloft. Although this is useful information in terms of isolating the at-risk behavior and generating countermeasures, it sheds no light on the human toll of this incident. I have personally visited hospitals, sat with my colleagues at clinics and even attended a funeral as a result of tragic incidents. All these events illustrate the human side behind the metrics. Lives are negatively impacted when accidents occur. Sometimes the impact is felt at an individual level, while others have an impact across relationships and even generations.

Fortunately, a risk manager can use leading and lagging indicators to create a custom program to address their specific needs and goals. This is achieved through a process of continually questioning. What do we know? What are the requirements? What do we learn from our mistakes? And perhaps most important, what are we doing to help people avoid these types of situations in the future based on what we have learned?

Risk managers can create their own incident-avoidance program based on leading and lagging indicators by determining what their goals for the program are. These goals should be based on their results. For example, a risk manager might discover that over the course of three years, incident data reveals an uptick in lacerations to the left hand. These results suggest a trend in behavior. Through thoughtful analysis of this trend, we can isolate a target behavior that will be managed proactively by tracking leading indicators.

In this case, the target behavior to track would be one-handed chain-saw use. Based on this example, you can create a tracking system to measure results, such as incident data, while also measuring the behaviors related to improving the results. This defines the essence of a comprehensive incident-avoidance program based on leading and lagging indicators.

In many instances, the ways in which a company measures and communicates safety results are dictated by OSHA. In this way, defining lagging indicators based on results is a relatively straightforward process. It also provides insight into potential risk-management goals as defined by these results. If a risk manager bases their program purely on reducing the frequency of incidents, they would be off to a good start. Further analyzing the incident in detail would provide insight into specific goals for proactive measures reflected by leading indicators.

Leading indicators lend themselves to customization based on each risk manager’s specific program goals. There could be a variety of leading indicators based on the goal of reducing injuries associated with one-handed chain-saw use. For example, a behavior-based leading indicator could track the number of at-risk vs. safe behaviors related to chain-saw usage. In this case, measurement would involve tracking how many times one-handed operation is observed compared to proper, two-handed chain-saw use. Another viable leading indicator related to this goal could be systematically measuring and appropriately increasing the amount of chain-saw training. Lastly, the risk manager could develop leading indicators related to specific hazard identification, other preventative and corrective actions and communication efforts designed to increase the likelihood of an individual choosing a safe behavior over an at-risk behavior. The end result in designing an incident-avoidance program based on leading and lagging indicators is a customized system for risk management based on each risk manager’s specific goals and needs.

In my organization, I created a “Safety Measure Dashboard”. The dashboard is a graphic depiction of performance in both leading and lagging indicators. The top of the dashboard contains incident frequency and severity data. Each category is noted as either on target, of concern or below target. This way, I have a snapshot of the results and areas in need of improvement. The same is true for leading indicators, with each custom measure reflected in a similar manner.

Although the dashboard is useful, I am always mindful of the human element behind the data. In my case, the most important aspect of managing risk through a comprehensive incident-avoidance program based on leading and lagging indicators is how it helps me help other people. I bring the data to life by helping others make thoughtful decisions, prioritizing safe behaviors so they can stay off of Heinrich’s pyramid and have the greatest chance of success while working in the field.

Bill Owen, CTSP and qualified crew leader (QCL), is director of safety and fleet for Arborwell, an accredited, 24-year TCIA member company based in Hayward, California, a SavATree company.