How to Measure what Might Have Been

Thursday, October 25, 2012 | 0

The most difficult cost savings measurement is calculating the savings gained from what did not happen. Call it cost-avoidance measurement. Some say it cannot be done because if it has not happened, it certainly cannot be measured.

However, another point of view says that if placed in the context of assumptions based on analysis of the data, assessing savings for avoidance is doable.

Did the risk ever exist?

The first challenge in this tricky process is proving that a costly situation would have actually occurred given the conditions surrounding it. When it did occur, did the interventions mobilized reduce potential costs?

Do-it-yourself

A sample scenario is examining the data and finding that when a certain combination of data elements occurs, the result is consistently a 20% increase in indemnity costs. One can make the reasonable assumption that whenever that data combination appears in the claim, indemnity payments will increase unless intervening action is taken. That is predictive modeling 101.

Hours of IT time can be spent analyzing the data to tease out conditions that consistently result in cost increases. This do-it-yourself approach is not for everyone, and maybe not for anyone.

Rocket science

An alternative is the “rocket science” (mathematically sophisticated) approach of predictive modeling. Formal predictive modeling procedures can be applied to the data to discover costly situations that should be avoided. This is a good approach, but it requires another step: concurrently monitoring the data to identify the risky conditions and taking appropriate action to avoid or minimize them. Nevertheless, the cost savings of avoidance can be claimed using this method.

Short cut

While either of these approaches has the potential to result in appropriately measuring and claiming cost savings, another method abbreviates the process. The shortcut is leveraging industry research and it is easier, less costly and more practical for most organizations.

Workers’ Comp industry research studies offer a format for measuring cost avoidance. During the course of research, large data bases are analyzed, usually applying sophisticated mathematical methodologies that identify risks and costs given certain conditions.

Leveraging research

An example of applicable research is the study conducted by Dr. Ed Bernacki and his team at Johns Hopkins University published in 2010.[1] Using five years of data containing closed claims supplied by Louisiana Workers’ Compensation Corporation, the research team first carved out claims where the reserves had increased from $15,000 to $50,000. The idea was to find the claims that had migrated in a negative way and then look for consistent conditions among them. In this case, the research team was seeking characteristics of poorly performing doctors.

Cost-intensive physicians

Amazingly, it was found that in the migratory claims, 72% of the costs could be attributed to 3.8% of the physicians. The research team named physicians in this group and identified consistent traits and behaviors associated with them. For instance, the physicians in this group were consistently associated with higher medical costs, longer treatment duration, longer claim duration and higher indemnity costs.

Moreover, the cost-intensive physicians tended to treat disorders that have variability of treatment options, that is, no clearly defined treatment pathway. The disorders in that group included carpal tunnel, joint pain, intervertebral disk disorders and psychological disorders. Additionally, certain physician specialties were associated with the characteristics of this group.

The study is rich in detail that can be translated to identify cost-intensive physicians in other databases who are the risky condition in claims. Using the research as a guide, you can isolate data elements that epitomize the characteristics of cost-intensive physicians.

Once identified in the data, cost-intensive physicians can be avoided. Find the cost-intensive physicians using the criteria demonstrated in the study, then avoid them. Determining the amount of savings is a question of establishing organizational policy based on the study.

Measuring savings of avoidance

Each time a cost-intensive physician is avoided, the savings can be calculated. Referring back to the study, preventing reserve migration is the framework of savings. If in the study the reserves migrated from $15,000 to $50,000, the claim savings assumed when a cost-intensive physician is avoided can be set conservatively or aggressively within that range.

Savings policy

The amount of savings declared is a question of determining the organization’s savings policy statement. An organization should establish standards for how it proclaims savings in claims. The savings policy statement, based on the research might read, “Based on the indicators found in industry research, avoiding a cost-intensive physician saves approximately $15,000.”

Monitoring and documenting

Obviously, none of this is useful information unless it is embedded in an operational process of analyzing the data, identifying cost-intensive physicians and documenting avoidance. Traditionally, organizations have relied on their medical provider networks, but it turns out most network administrators do not evaluate provider performance on any basis. So it’s back to do-it-yourself or get help.

Nevertheless, it is possible to measure cost savings of what might have been. When placed in the context of assumptions based on research, assessing savings for avoidance is valid.

Karen Wolfe is president and chief executive officer of MedMetrics, an Internet-based workers' compensation analytics company based in Bend, Ore. This column was reprinted with her permission from the company's Workers' Comp Analytics blog.