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  • Joe Hitt, Ph.D.

How to Use Predictive Safety Analytics to Build a Culture of Workplace Safety


A warehouse safety manager holds a clipboard to review workplace data.

Thanks to various regulatory and legal requirements, as well as ethical concerns, workplace safety is top of mind for every labor organization. Without skilled labor, industries like farming, manufacturing, and shipping are brought to a screeching halt. Protecting workers is part of protecting the business and ensuring that operations continue to run smoothly.


Making your workplace as safe as possible unlocks numerous benefits, including increased productivity, better working conditions, and reduced risk. By prioritizing worker safety, businesses simultaneously improve efficiency, mitigate the risk of legal or reputational repercussions, and, most importantly, take care of their employees and their health. Healthy workers don’t need workers’ compensation, and they can continue to perform at their best if they aren’t suffering from back injuries or strained muscles, for instance.


Traditionally, making labor-intensive jobs like construction and manufacturing safer has been addressed with the implementation of safety gear and features like railings, helmets, and other preventative measures. In the modern workforce, technology has begun to play an increasingly important role in the progression and execution of workplace safety initiatives. By gathering data on individual worker health, environmental factors, and more, we can apply predictive safety analytics to reduce risks in the workplace. These advances have the potential to prevent accidents from happening, which calls for changes in the way we approach safety and safety culture in the workplace.


However, it’s not that easy, and these solutions require more than just implementing new technology. To reap the full benefits of predictive safety analytics, you need a culture of workplace safety built around the data that your tech captures. In this blog, we’ll cover both the data you need and the best structure for putting it to use.


What Is Predictive Safety Analytics and How Can it Help?

Broadly, predictive safety analytics is using an algorithm to ingest individual, environmental, and other sources of data to predict the likelihood of an accident taking place. It allows businesses to understand what risks their workers are facing and how to reduce the likelihood of those risks becoming realities. Since it’s based on hard data, it gives businesses a quantitative way to measure, identify, and address risks effectively.


The primary advantage of using predictive analytics for workplace safety is that it can gauge risk before an accident happens. Otherwise, businesses can only react by adding safety measures after an accident occurs, in which case, someone has already been injured, and financial, legal, and reputational consequences are likely soon to follow.


Collecting the Right Data

When we talk about data and risk, the first thing that comes to mind is injury reports. However, this information is considered a lagging indicator of workplace safety, which is to say that it only lets you know that there is a risk after the damage has been done. It’s better than not doing anything, but by only utilizing injury reports, you’ll be stuck trying to prevent problems from happening again when you could be focused on making sure they never happen in the first place.


Wearables, like smartwatches, allow us to collect worker-specific data that can be leveraged to gauge and address risk as soon as it enters the equation. Using wearables, you can monitor factors like worker form, fitness, fatigue, dehydration, and more. These are leading indicators or actionable data that can be used before anything goes wrong and injuries or illnesses occur. By delivering regular reminders to workers who are at risk to take an action, such as drinking water or resting for a moment, wearables can address risks before they become a problem without the need for additional supervision. This solution empowers workers by detecting and triaging leading indicators while passively collecting data that can be used to determine the need for specific safety measures in the workplace.


If you get caught up on lagging indicators, you’ll find yourself scrambling to keep your workers safe. If you use leading indicators, on the other hand, you can gauge risk quantitatively in real time to empower better decision-making.


Fatigue is one of the key predictors of possible injury. Fatigued workers represent a massive risk; whether the job is heavy lifting or operating machinery, fatigued workers present the largest risk to themselves and the business.


What Is Fatigue?

When we use the word “fatigue,” we’re not referring to a qualitative feeling. Measuring fatigue in a qualitative way doesn’t give us any data to fuel our predictive safety analytics models. For our purposes, fatigue is a quantitative measure that we get by dividing an individual’s current VO2 by their VO2 max. If VO2/ VO2 max is higher given the same task, the worker is less efficient, and this is a typical outcome of fatigue.


According to WebMD, VO2 max is “the amount (volume) of oxygen your body uses while exercising as hard as you can.” Everyone’s VO2 max is different, and it’s not a static number, so there’s no cause to screen your employees by their numbers. What’s more important is detecting which employees are operating at a higher portion of their maximum capacity (VO2 max), because those are the ones that are in danger of getting injured.


4 Foundational Elements of Safety Culture

In our experience, setting up the framework for an effective safety culture takes approximately six weeks. When GoX Labs is contracted to help improve workplace safety, this is how we go about building a culture of safety:


1. Understand the Baseline

The first four weeks are all about taking in data.


At GoX Labs, that means four weeks of gathering data from every employee on the floor using wearables. The measurement we focus on the most is fatigue. Our algorithms with many metrics measured have been validated by the US Army as capable of deriving fatigue from VO2 measurements with an accuracy above 95%.


2. Strategize

Once we have the data, we spend the next two weeks strategizing.


Not every employee needs to be monitored, in our experience. Only the ones that are experiencing significant levels of risk need to be supported. The goal is to look at risk as a whole, ascertaining who and where resources need to be diverted to create the most benefit.


By reviewing the data you collect and running it through a predictive safety analytics model, you can create an approach based on objective statistics.


3. Implement

The next step is implementing the strategy.


You support the workers that need it by monitoring their performance and helping them adapt and giving them the support they need to work at their best. This is easiest to achieve with wearables. If, for example, your strongest worker is moving lots of equipment but using the wrong form to do it, the right wearables can remind them to correct it. Their strength is an asset, and not protecting it is tantamount to endangering your business.


4. Rinse and Repeat

The final step is starting all over again from the top.


The workplace is not static. New hires, retirement, lifestyle changes, and even ambient temperature changes at the work site are all factors that come into play when you’re talking about the risk of injury. By regularly retesting your employees, you can make sure that you stay on top of who’s at risk and that your predictive analytics are up to date.


Data is only applicable if it’s recent, especially when it comes to safety. If your initiative succeeds, the employees who you focused on will adapt and/or fix the problems that put them at risk. In the meantime, other employees might have grown bad habits, or new hires may have entered the workplace, which means that you need to measure your team again to stay on top of shifting conditions; who needs support, and why will change over time.


Cultivating Safety Culture

With the process out of the way, here are a few tips for making sure your move toward a culture of workplace safety is successful. Your goal is not to penalize your employees who are at risk; it’s to support them and help them remain healthy and efficient.


To this end, our wearables only notify workers at the end of every hour if they cross a certain risk threshold. Frequent notifications tend to damage morale because they get annoyed, and the fact of the matter is that not every lift and action is going to be perfect; your goal is to reduce the risk of danger through a culture of safety. You can check if your program is working by seeing if the number of notifications is trending downwards. If it’s not, you haven’t established a good culture of safety yet.


The wearables can also send notifications to safety and health personnel, work colleagues, or supervisors who can participate in the improvement. This ensures that it’s not only up to the worker but the whole community to help the worker stay safe and healthy.


Establishing an award system is a good way to establish a positive and reinforcing culture. When a worker or a group improves their safety numbers, provide monetary awards and recognize them for their achievements.


Safety culture is not something you can add to your company overnight. It’s a process, and you can strengthen that process by leveraging predictive safety analytics.


Data Makes a Difference. Let Us Help.

Wearable data and predictive safety analytics based on that data represent powerful tools for improving workplace safety. If you establish a proper culture of safety, you will achieve greater efficiency and increased morale; there are no downsides.


If you’d like to create a culture of safety in your workplace, but need assistance, schedule a demo with us so that we can help you get there.


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