Nationwide, the average cost of workers' compensation insurance has risen 50% in the last three years. This dramatic surge is due to several complex factors.In the last five years, medical costs in some states have increased 125% per claim, while indemnity benefits rose 32%. Furthermore, inefficient, manual processing continues to add millions of dollars to administrative expenses.

Equally troubling for insurers is the fact that fraud and abuse are on the rise.

Finally, reserves have been inadequate to cover claims costs, pushing carriers into insolvency.

State legislatures and regulators are convening to find solutions, but it's difficult to mandate laws that solve all these issues. Instead, many insurers, managed care companies, third-party administrations, and employers are now turning to advanced analytics and decision-management tools to alleviate many of these problems.

Rules and decision engines

The medical bill review process offers significant potential for healthcare cost-savings, but it has typically been a manual and labor-intensive process.

Today, sophisticated medical bill review and repricing technology integrates an extensive library of fee schedules and a robust database of national and regional preferred provider organizations (PPOs). The technology automatically applies appropriate fees and PPO discounts to every bill, ensuring a consistent first layer of savings.

By using decision-management technology with custom business rules, workers' comp programs further automate the bill review process for a second layer of savings. Bills for simple procedures are automatically posted. Bills that are red-flagged as complex and require a higher level of investigation are automatically routed to the right resources. These red flags include bills reflecting potentially excessive charges, over-utilization, unnecessary services and inappropriate care.

As a result, the bill review expertise that once existed only in the minds of staff members can be incorporated into automated rules and edits. The decision-management engine can be updated as the environment changes. Rules are programmed by the bill review department, instead of by IT staff.

Combined together, rules and decision management enable organizations to incorporate all guidelines and business practices consistently across every bill and every staff member. With this type of decision-management support, organizations are able to automate processing and payment for 70% to 90% of their medical bills.

By leveraging this type of automation, many organizations have been able to double the productivity of their bill review staff, alleviating heavy case loads and overtime.

Predictive neural networks

The size and complexity of workers' comp makes it an attractive target for fraud. The National Insurance Crime Bureau (NICB) estimates that workers' comp fraud costs the insurance industry $5 billion each year.

Currently, it is incumbent upon claims departments to be the first line of defense in detecting fraud.

However, with caseloads that average more than 150 claims at any given point in time, adjusters are too busy to evaluate every claim in a meaningful and effective way.

As a result, carriers and claims professionals are now turning to predictive neural network technology to assist them in accurate detection.

This technology is highly effective because it is capable of analyzing thousands of data elements simultaneously to find subtle, complex and hidden patterns of suspicious behavior. Its analytical and processing strength enables high-volume claims departments to perform a rigorous, objective review of every claim.

Adequate reserves

Establishing adequate loss reserves is yet another area in which predictive neural network technology can be applied.

More than 50% of the insolvencies during the past 10 years have been attributed to inadequate reserving.

In workers' compensation, studies indicate that carriers are currently under-reserved by an estimated $20 billion.

Because of adjuster turnover and varying experience levels, adjust-ers can calculate different reserves for the same claim. Predictive technology levels the playing field, providing information on what the organization has historically paid for similar claims.

The depth and breadth of workers' comp woes-including burgeoning loss costs, reserving deficiency, poor investment returns, increased fraud losses, and rising medical inflation-make legislative solutions difficult to devise.

However, organizations that move quickly to adopt advanced analytics will have a competitive advantage.

Kelly Stephen is vice president of product development, and Kevin Lisle is product manager of property/casualty analytics at Fair Isaac Corp., San Rafael, Calif.

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