Predictive models that can make cost-saving recommendations are future goal
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Predictive models used by third-party administrators typically capture a variety of data from workers comp claims, ranging from questionnaires injured workers fill out at the start of their claims to systems that comb through a case file for keywords indicating a claimant may have trouble getting back to work.
While data gathered during such processes are useful, TPAs still are developing technology that takes claim information and makes cost-saving recommendations, said Tim DeSett, executive vice president of property/ casualty practices at Lockton Cos. L.L.C. in Kansas City, Mo.
“A lot of organizations are at the initial stages of predictive analytics,” Mr. DeSett said. “I think they're starting to develop how (to) take this big data and actually use it to transform workers compensation claim outcomes.”
Earlier predictive models were good at pinpointing factors that add costs to workers comp claims, such as the claimant's age or if someone has diabetes, said Paul Braun, managing director of casualty claims for Aon Global Risk Consulting in Los Angeles.
However, he said, ad-justers often can intuit such information without the aid of models.
“If somebody says your workforce is 65 and older, it doesn't take predictive modeling to know that it's going to take longer for them to heal,” Mr. Braun said. TPAs now are focused on identifying factors that can be harder for adjusters to spot, he said.
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