How to Evolve Safety from Protection to Prevention to Prediction

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Courtesy of Intelex Technologies Inc.

I’m in the market to buy a new car. My shopping experience thus far has led me to be absolutely amazed by how much has changed in the automobile landscape in just a few years. The vehicle I’m replacing is only 6 years old, but in terms of technology and capability it already feels like an antique. One observation that stands out is the amazing pace of change in car safety. Automobile safety has gone from protection, to prevention, to prediction in what feels like a matter of years.

The emergence of automobile safety initiatives in the 1960s and 1970s was driven (no pun intended) by the research of people like Ralph Nader. This research and advocacy led to car manufacturers to make features such as seat belts standard in cars, which soon evolved into air bag technology and structural engineering to account for crumple zones. These innovations transformed vehicles which were once considered “unsafe at any speed” to vehicles that had built-in protective equipment and capabilities. Today, it’s estimated that seat belts save up to 15,000 lives per year in the United States alone.

As the next phase of safety emerged, cars began to leverage advances in technology to complement that initial protection with prevention technologies. These days it’s not just seat belts and airbags protecting drivers and passengers; as you walk a car showroom, you’d be hard-pressed to find even a basic model that lacks a traction control system or anti-lock brakes. These two technologies are designed to augment a driver’s skills by preventing the skids and wheel lock-up that can lead to accidents.

As the computing power underlying those preventative technologies has become cheaper and more powerful, car safety has again evolved from preventative to predictive capabilities. As I browse my local car showrooms, I’m struck by predictive safety features that augment my driving skill like lane departure systems, adaptive cruise control, and collision detection. Features like intelligent steering and braking assist go far beyond simple warning systems when a driver loses focus, helping the driver by predicting and avoiding hazards before they even occur.

The secret behind these features is the vast amount of data being collected on an ongoing basis in every modern vehicle. Today’s cars are as much moving computers as they are modes of transportation, and have systems and models crunching that data at an incredible rate in order to use it in real-time. As this evolution leads us to the promise of self-driving cars, it’s estimated that tomorrow’s cars will be creating over 1GB of data per second.

Tomorrow’s self-driving cars provide all of us consumers a glimpse of the potential impact big data can have on safety. But workplaces today are seeing a similar evolution in safety – from protection, to prevention, to prediction.

The first phase of workplace safety, much like the emergence of automobile safety, was focused on protection via the use of personal protective equipment (PPE). Birthed out of the increasingly dangerous workplaces of the Industrial Revolution, PPE such as hardhats, gloves, and steel toe boots allowed workers to engage in potentially dangerous jobs with a degree of physical protection.

As enterprise software tools became more prevalent in the workplace and as regulatory pressures from organizations, such as OSHA in the U.S., increased, safety programs evolved from the exclusive domain of PPE initiatives to far more robust systems of reporting and data capture. From cumbersome spreadsheets to today’s EHS management systems, health and safety in the workplace now includes the capture and reporting of metrics such as injury and accident frequency and time lost metrics. Those lagging indicators have also given rise to the pursuit of some leading indicators—metrics that can potentially foreshadow safety incidents—as well. Examples of leading indicators include safety-driven risk analyses such as job safety analysis, and the emerging focus on ergonomics programs. All of this goes towards the objective of preventing accidents from occurring.

The underlying secret behind these programs (much like in modern automobiles) is a vast amount of data being collected and managed.

When we take a peek into the day-to-day activity occurring on the Intelex platform we can see all of the 4 V attributes of big data. EHS systems like Intelex are able to collect a staggering volume, veracity, variety, and velocity of data. In fact, as of the writing of this article we can see in our global hosted environment over 5 million task-based records existing, with a velocity of over 14,000 new records being created per day and nearly 15 million transactions happening each month.

That’s a lot of data and a lot of opportunity. That data along with the emergence of data science is now allowing us to take the world of health and safety programs from today’s preventative world, to tomorrow’s predictive one.

Our early data modelling has unlocked some tantalizing nuggets of data-based predictive findings. Like self-driving cars, these insights can allow an EHS system to not only identify hazards but specifically prescribe what to do about them. An example of this is modeling out benchmark organizational safety profiles by taking into account environmental, behavioral, and performance factors along with opportunities for improvement in each.

In large companies, there can be as much as a 14X variance in safety performance between top and bottom locations-Tweet this! By modeling out the “secret recipe” of those top performers, organizations can close the gap between top and bottom. The prospect of having this safety performance equalized can lead to a significant overall impact on an organization’s bottom line. Once again, all of this is enabled by the pool of data. Data-driven models can show us in a quantitative way the impact that factors such as employee tenure and location size have on not just safety metrics, but safety factors themselves such as recordables tracking, and leading indicators such as pain and ergonomics.

Data has put the world of safety on the path towards a predictive future. As the automobile industry has foreshadowed, we may be on the cusp of a golden age in workplace safety. We’ll soon be able to look back and see this evolution from protection, to prevention, to prediction and be amazed at how quickly we’ve gotten here (and also wonder how we ever managed to do things before).

That’s what I think. What do you think? I encourage you to like, comment, and subscribe to this blog to share your thoughts on the topic as well.

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