The use of predictive analytics in workers' compensationhas emerged as a viable way to better manage claims, as well as tofind and fix problem areas that may be systematically driving costsup.

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

Predictive analytics and injuredworkers

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

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

The use of predictive analytics allows claims managers toidentify potential complicating factors about an individual'scondition up-front, enabling them to take these risks into accountand create a more proactive approach to their ongoing treatmentplans. The same data and insights can be applied to areturn-to-work plan to reduce the risk of re-injury. Insights frompredictive analytics can identify "routine" claims that have thepotential to become complex, as well as patterns in types ofinjuries that can be corrected to reduce cost and improveoutcomes.

Related: Colorado health care amendment's true cost toworkers' comp

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Workers' comp costs are often hidden beneath the surface,like an iceberg. (Photo: iStock)

Defusing the hidden risks in claims

The science of predictive analytics involves overlayingalgorithms onto claim and managed care data to:

• Identify patterns and relationships,
• Gain insight into individual claims and patientpopulations,
• Identify ways to take specific actions for improvement,and
• Potentially alter the clinical pathways for injured workers topromote a faster, safer recovery.

By using scientifically proven methods and combining thesemethods with comorbidity data, geographic factors, patient history,access to best-in-class physicians, and other elements of data,risk managers can have a positive impact on the clinicallifecycle.

Predictive analytics is like the periscope that can see the 90percent of the iceberg that is hidden beneath the surface, beforeit gouges a hole in the ocean liner of claims management, and sinksit.

Related: The aging workforce: Solutions for today's greatestchallenge in workers' comp

The danger of a potentially high-cost claim is that that it —like an iceberg — is hidden from view, disguised as a perfectlynormal case until suddenly it explodes into a complicated, costlyquagmire of missed opportunities for intervention and unnecessarymedical complications.

Predictive analytic programs can identify these high-risk claimsby analyzing all the claim's pieces and how they interact, based onthe data that's in the system but that's difficult for the humanbrain to locate and synthesize. It's the impact of the pieces ofthe claim, usually not any one factor, that affects the durationand cost of the claim.

By creating this holistic view and an early warning system,predictive analytics allows workers' compensation professionals toanticipate potential problems from the outset of a claim, longbefore it escalates out of control. Predictive analytics also canshow the patterns of change that indicate some type of action isneeded, and then provide insight into what that course of actionshould be.

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Many companies are seeing decreases of 5 to 33 percent inworkers' comp claims with predictive analytics. (Photo:iStock)

Does it work?

Using predictive analytics, payers have reported seeingdecreases in average costs between 5 to 33 percent for specifictypes of claims.

The approach followed by York's TeamComp, for example, was amulti-pronged intervention program that included a combinationof:

• alerts generated through predictive analytics,
• rapid responses that included adjuster reviews, claims strategydevelopment and clinical assignments, and
• clear accountability for action through claim manager and vicepresident review, senior leadership tracking, and quality assurancereview.

This program is credited with achieving a 32 percent decrease inaverage payments per claim, a 34 percent decrease in average losttime days, and a 29 percent decrease in average total medicalcosts. For medical only claims, the data shows a 71 percentdecrease in the average total paid per claim, a 59 percent decreasein average medical costs, and a 73 percent decrease in average losttime days.

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Compiling the data can seem like putting a puzzle together.(Photo: iStock)

Putting together the program

Certain pieces of data, when looked at holistically, can predictand direct a claim, including the first report of injury or firstnotice of loss; the claim system; managed care data, including billreview, utilization review, and pharmacy data; and settlement data.In addition, informative data can come from the following:

• therapy benchmarks
• the length of time between medical treatments
• types of medication
• the duration of treatment or disability
• equipment or supply cost thresholds
• psychosocial factors

By finding the patterns, workers' compensation professionals canensure early intervention and deploy the right treatmentsthroughout the life of a claim. With data that can indicate ahigh-risk or a potential situation needing attention, betterquality and more accurate decisions can be made. As a result, theinjured worker receives more appropriate care, and problems can beprevented or controlled before they impact claims, outcomes andcosts.

Key measures of success in predictive analytics programsinclude:

• average claim costs,
• average medical costs,
• average number of disability/lost time days,
• average number of days for claim, open to close, and
• lower costs for surgeries, medical equipment and othertreatments.

As a long term benefit, using predictive analytics can helporganizations develop more effective programs, processes andimprove claims management while lowering costs.

 Related: The aging workforce: Solutions for today's greatestchallenge in workers' comp

Michelle Despres, PT, CEAS II, is the vicepresident and national product leader at Align Networks(Jacksonville, Fla.), a division of One CallCare Management, a provider of specialized cost containmentservices to the Workers' Compensation industry. She can be reachedat [email protected].