Why Injury Analytics Aren't just for Soft-tissue Claims

Rising injury claim severity is presenting insurers with significant challenges. Not only are these claims more costly, but they’re often more complex. And the severity trend isn’t slowing down.

Since 2016, bodily injury frequency has decreased by 30.4 percent while severity increased by 37.8 percent. Meanwhile, medical costs have climbed at nearly double the rate of inflation the last ten years, with prices for hospital services increasing even faster.

Insurers need to get a handle on these costly claims, but many of them aren’t leveraging available tools that help them better manage complex claims and contain costs.

Are injury analytics only for soft-tissue claims?

Claims solutions, such as Liability Navigator, help insurers manage injury claims more effectively by using predictive models to compare incoming claims to similar historical claims. That way, insurers can better understand cost drivers, improve consistency in general damages assessments, and enhance negotiations.

Yet, some companies primarily use such tools for soft-tissue injury claims and don’t leverage them for other injuries such as rotator cuffs, knees, and traumatic brain injuries. Let’s examine why applying advanced analytics to all injury claims is critical.

Insights for lower volume claims

Injury claims outside common soft-tissue claims often are more complex, time-consuming, and costly. Having predictive medical and settlement models to assist claim handlers helps expedite the process and contain costs.

While non-soft tissue injuries are less common, there is still plenty of claims data available, and advanced analysis can uncover common trends in medical treatment, recovery plans, and medications. Proper analysis helps guide settlement decisions and predict severity early to lower loss costs and expenses.

For example, if you’re managing a claim of a 62-year-old man who tore his ACL in an accident, a predictive model could quickly analyze multiple data points and compare the claim to others involving claimants with similar age, medical history, pre-existing conditions, as well as in the same jurisdiction. There is enough data available to analyze those types of claims and derive practical insights to guide decisions.

Assisting inexperienced claim handlers

The talent crisis is another reason predictive analytic solutions that compare like claims is critical. Inexperienced adjusters don’t have the expertise to quickly and effectively manage complicated claims. It takes years—even decades—of experience to figure out settlement offers. And the talent issue in insurance isn’t going away. Consider this:

• Roughly 25,000 job openings are expected in claims organizations in the next decade due to retirements
• The median age for claims professionals is 44.9, higher than the overall U.S. average
• Claims is one of the most in-demand functions in property and casualty insurance, second only to tech jobs
• One-third of insurers are looking to fill claims openings with entry-level staff

Less experienced staff need the automated tools to help them make better-informed claims decisions faster, especially for complex claims. They can’t rely on years of experience. They need tools that provide insights for consistent settlements.

Consistency across the claims spectrum

If you think medical and settlement models only work for soft-tissue claims, consider the benefits the technology can have for your entire book of business. Claim severity is rising, and it’s impacting insurers’ bottom line.

By Mike Rivers

Courtesy of Verisk

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