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Industry experts agree that medical management helps control the cost of workers compensation claims. Today, the vast majority of employersboth self insured and fully fundedoffer managed care workers compensation programs, and most are well versed on the benefits of case management to reduce both injury costs and duration.

But, employers and payors arent necessarily satisfied with generalities about claims savings estimations and/or results. More and more they are looking for specifics about how much their managed care programs are saving them and ways to increase these savings even more.

Providing these estimates has been difficult in the past, because cases that benefit most from claims management are complexand usually the injury is more severe than in their unmanaged counterparts. Naturally, the costs associated with these complex claims are higher and consequently, employers and payors are likely to question their true savings.

A new approach to claims analysis and savings estimations that incorporates sophisticated data-mining tools provides a more accurate answer to this problem. And it gives risk managers a leg up on developing programs to drive down medical costs, reduce employee absences and improve return-to-work outcomes.

There are many attributes or variables that impact how a company measures the financial impact of workers compensation managed care. These range from the most common, such as disability duration, diagnosis nature and injury severity, to the less frequent, such as geography or job. Often, these unique factors may not be captured by the claims system.

In addition, there are factors such as infrequent but severe injuries or rare catastrophic claims that can account for a significant portion of the total cost of managed claim. If not filtered out, these factors can dramatically skew savings estimations.

Some organizations are turning to methodologies that incorporate data mining to provide more accurate and realistic savings estimations. In general terms, data mining involves transforming raw data into useful business intelligence to reveal trends and predict and explain outcomes.

Data mining software allows analysts to quickly sort through all claims data and spot a wide variety of claim trends and patterns not easily identified by traditional methods, and that provide a more realistic prediction of total claim costs and/or savings.

Additionally, when data mining incorporates predictive modeling to calculate future opportunities for savings, this yields an even more reliable picture of future claim costs.

Data mining tools have evolved considerably over the past two years and their level of sophistication and complexity has increased greatly in the most recent past. Older tools often evaluate fewer data points and are biased by the variables that the analyst chooses to model. Often these are not inclusive of all the data that is available and do not allow for adjustments, making them less reliable sources of information about claim trends and durations.

In many instances, older tools provide information that can lead to overly optimistic savings evaluations. The tools limitations, in turn, restrict what risk managers could do to actively plan for, manage and perhaps mitigate losses. By eliminating these barriers, newer data mining tools provide much more meaningful and reliable results for risk managers.

Data mining tools can flag potential future claim issues as well as analyze historical results, such as injury trends or lengthy claim durations in certain geographic areasfactors risk managers need to evaluate. It is up to the managers to create the solutions and execute strategies to mitigate or prevent additional losses.

A hypothetical example demonstrates the power and promise, of more effective data mining.

A data mining tool used at a large hat factory in Texas identifies a rising trend in the number of carpal tunnel cases among factory workers. The data uncovers a disproportionate number of male Spanish-speaking employeeswhose task is to sew the brims on the hatswith claim durations that exceed industry benchmarks for carpal tunnel in both wrists.

In addition, treatment and network utilization data reveals that the majority of these Spanish-speaking employees are not being treated by Spanish-speaking doctors, making communications about treatment plans and medications particularly challenging.

There are a number of things a risk manager can do with this information to improve results, including:

Address the issue of patient understanding and adherence to treatment plans by making sure that there are sufficient Spanish-speaking providers in the network. In addition, the risk manager would ensure that panel cards in the factory that direct employees to network providers are current and written in Spanish.

Examine bill review results to make sure that network provider discounts are appropriately applied and that the employer isnt being double billed or over-billed for services, and that negotiated treatments and costs are accurately reflected.

Initiate a carpal tunnel prevention program. Focused prevention programs and medical management strategies that address specific conditions can significantly impact lost time costs.

Use an ergonomics consultant to evaluate high-risk situations and modify workstations and/or worksites to prevent future problems.

Establish specific triggers for case management to quickly apply the intervention that will yield the best possible outcome.

Examine network providers/provider practices to check for overutilization of treatment and rehabilitation and unnecessary surgery.

Re-examine the return-to-work case management program to see if there are opportunities for transitional work assignments while employees are recovering. Case managers can also evaluate surgery options where wrists are operated on at different times, thus greatly increasing employees chances for accommodated or transitional work.

This is a simple example of how a data-mining tool can impact disability claims costs. The complexity and breadth of cost-saving initiatives that risk managers can undertake with the right tools will greatly assist with planning, reserve setting and evaluating possible solutions.

Using data-mining-based methodology, employers and payors can identify and solve their organizations unique problems, uncovering opportunities for improvement and savings. This focused, methodical analysis also paves the way for a better and more realistic demonstration of managed care outcomes.

Although we are just beginning to understand the impact that it can have on our industry, data mining already is providing a more accurate representation of cost drivers, a clear depiction of true impact, and direct insight into how to structure a disability management program that has maximum impact on medical and financial outcomes.

is vice president, disability management product development and marketing, for Intracorp, based in Philadelphia.


Reproduced from National Underwriter Life & Health/Financial Services Edition, November 4, 2002. Copyright 2002 by The National Underwriter Company in the serial publication. All rights reserved.Copyright in this article as an independent work may be held by the author.