The use of predictive analytics in workers' compensation has emerged as a viable way to better manage claims, as well as to find and fix problem areas that may be systematically driving costs up.

The 2016 RIMS session, "The Proof Is in the Data: Applying Predictive Analytics to Reduce Workers' Compensation Risk," which was presented jointly with JJ Schmidt, senior vice president, York, shared the following insights, examples and advice about using predictive analytics in your company.

Predictive analytics and injured workers

Today's injured employee is likely to be older, in poorer health, and at higher risk for a complex injury or claim than in previous decades.

• By 2020 — just a few years away — an estimated 25 percent of the U.S. workforce will be over the age of fifty-five, according to the U.S. Bureau of Labor Statistics. Unfortunately, higher costs and slower return-to-work are hallmarks of injuries involving America's aging workforce.
• Many patients have more than one chronic medical condition (comorbidity) — for example, diabetes and obesity, often referred to as "diabesity." According to the National Bureau of Economic Research, in 2016, obesity alone costs the U.S. $200 billion dollars a year, more than 10 percent of healthcare costs.

The use of predictive analytics allows claims managers to identify potential complicating factors about an individual's condition up-front, enabling them to take these risks into account and create a more proactive approach to their ongoing treatment plans. The same data and insights can be applied to a return-to-work plan to reduce the risk of re-injury. Insights from predictive analytics can identify "routine" claims that have the potential to become complex, as well as patterns in types of injuries that can be corrected to reduce cost and improve outcomes.

Related: Colorado health care amendment's true cost to workers' comp

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