Optimizing Safety Decisions Through Wearables and Other Data
The US Bureau of Labor Statistics recently reported that 5,190 fatal work injuries were reported in 2016, a 7% increase from 2015. As we begin the new year, many companies are taking stock of this sobering statistic to determine how to improve employee safety and mitigate losses. One way your organization can make smarter decisions about job design, equipment, and other factors in order to better protect your people is by collecting information and data through wearable devices and other means.
The Power of Wearables Data
The wearable device market is still developing, but can offer organizations a rich array of data and risk mitigation opportunities. Until now, most workplace loss control efforts have focused on analyzing losses and observing work activities. However, wearables collect data about events that lead to losses, which allows for earlier intervention and injury prevention. Additionally, when a loss event occurs, wearables data can help explain the loss and, if necessary, defend it.
Other Data Sets
Beyond wearables data, three other data sets about your workforce can help you, your ergonomists, and your safety personnel improve working conditions:
- Injury and illness data. This includes the frequency and severity of workplace injuries, lost work days, and workers’ compensation claims expenses.
- Employee discomfort survey data. Surveys could ask simple yes or no questions or could be expanded to Borg scales, which record pain and discomfort frequency and severity for individual body parts on a 10-point scale.
- Predictive risk assessment data. This provides a forward-looking view of potential injuries based on known risks, looking at the presence of workplace risk factors to deliver a score or rating (low, moderate, or high risk) of potential injury to your employees.
Injury and illness data and employee discomfort data have their drawbacks. Injury and illness data can tell you what has happened, but cannot necessarily predict future performance because jobs and work environments change over time. Employee discomfort data can be flawed because it looks at specific points in time — an employee may report significant pain today but none tomorrow — and could be unreliable if employees feel pressure from peers or supervisors to downplay their discomfort. Because they are forward-looking and based on a broader data set, predictive risk assessments tend to be stronger and more reliable, enabling you to take corrective action swiftly before an injury occurs.
Leveraging Data From and Across the Organization
Once you have gathered the various data sets, it is important to share your analysis throughout your organization, including administration, research, manufacturing, distribution, and product design. Your organization should proactively use data to drive daily, continuous improvements, enhance vendor requirements (including improved equipment design), and optimize training. Data can also help you demonstrate to underwriters that your safety programs are effective, potentially leading to more favorable pricing of your insurance coverage. All of this is good news for risk and safety professionals, employees, and bottom lines.