One of a risk manager's primary goals is to control and, if possible, reduce the costs associated with various potential and realized liabilities for his employer. In addressing this challenge, a number of questions arise: Is my program performing well already, or is it doing poorly compared with similar programs? How should I spend limited resources for the greatest return? How do I publish the health of my program to my superiors? How do I persuade business partners within my organization to see the value of data and then pursue risk management initiatives? When used effectively, data analytics can help in answering these often complicated questions.
Working with Data Analytics
Understanding the appropriate use, limitations, and potential skewing effects of metrics in an analysis is critical. For example, when trying to answer the question of whether a given company's program is performing well compared to its peers, it makes sense to attempt to have the peer companies match the industry of the company being measured. Even so, there may be other drivers impacting costs, such as geography, staff morale, and the presence of labor unions. These challenges sometimes lead risk managers to give up on finding answers regarding the overall health of their programs based on a peer comparison.
A more effective approach is to focus on action. Instead of asking whether a company's program is good or bad, ask if that company is better or worse, using the peer merely to reflect the combined impact of various inflationary forces in conjunction with a changing legislative environment in various jurisdictions. This approach maintains focus on larger trends in the population that present a clear picture of relative cost drivers.
For instance, in California if a given company's average costs has come down by 10 percent in the past four years while the average change for 20 companies within a given industry shows a 25 percent reduction, it doesn't matter so much if the company being assessed has higher or lower average costs than the peer. There is some indication that there may have been and may still be some opportunity to save additional money in California.
This method can be applied to changes not only over time, but also across categories such as age groups, again, with the goal of identifying opportunities to effectuate changes in cost. If the average cost per claim for your company increases by 50 percent when comparing claimants younger than 35 to claimants older than 55, but the peer group sees an increase of 20 percent, this might indicate that some change of strategy will yield lower costs. In this case, age may indicate that a health-and-wellness program is a viable initiative to promote. The direct workers' compensation cost savings alone may justify the specific initiative and have broader benefits — and not just for the older workers.
Most metrics have an appropriate use, but it is important to understand what they are not telling you in order to make sense of what they are telling you. For example, the average incurred for indemnity claims is often cited as an indicator of the health of a return-to-work program. If, however, there is a change in settlement philosophy, a change in the ability of the TPA to prevent claim conversions (from MO to LT), or a change in prevention that impacts one severity type more than another, then the average incurred for indemnity claims can become skewed and may indicate the opposite of what is really happening. Fewer converted claims often pull what would have been a low-cost indemnity claim out of the indemnity bucket, thus increasing the average cost of indemnity claims, although decreasing the number.
Therefore, it is useful to have balancing metrics for every metric to account for skewing of claim data. Generally, a more precise metric such as the average cost of indemnity claims is balanced by a broader metric such as the average incurred. Now, the average incurred would effectively balance the skewing effect of the TPA doing a better job at claim conversions and, if closure rate is used as a balancing metric, it might help to account for the change in settlement philosophy. The shift in safety might be balanced by looking at the number of indemnity claims and claims in general per cause category divided by some exposure base — for example, the number of full-time equivalencies.
The Importance of Reporting Platforms
In addition to understanding how to use analytics in program assessment, it is critical to have the right reporting platform to facilitate the process of the analytic investigation. The reporting platform must be simultaneously voluminous, diversified, consistent, capable of point-in-time reporting at every level, and highly flexible. A large peer volume allows the analyst to maintain stable results even as more granular categories are investigated. Being capable of point-in-time reporting is critical to maintain like maturity for old years and to compare cost development. It should have a "pivot-style" flexibility so that any grouping or set of groupings can instantly be inserted, removed, or partially filtered. This platform flexibility is critical because a thorough investigation often involves dozens of different views of the data that cannot be anticipated in advance.
The following provides an example of an analyst trying to understand what is driving higher average claim costs in recent years.
The analyst first looks at average costs over time, broken out by state and compared with a peer. This may isolate a state that appears to have a cost increase exceeding what is typical. Within that state, the analyst may then want to see the differences by cause, nature of injury, ICD9 code, body part, tenure, gender, or any other grouping so as to uncover clues that point to the root cost driver. He notes the average cost associated with female claimants has risen consistently and dramatically over the past five years, while that of male claimants has been stable.
Thus far, this investigation has minimally involved a successive break by seven different categories. A further step would necessitate breaking out the cost trends by gender and by age category to see if the cost increase for female claimants is consistent regardless of age. Finally, the analyst will identify whether these trends are isolated to a given facility or set of facilities. Knowing the scope of these breakouts in advance of the findings would not be possible. If the reporting platform is highly flexible, then this investigation would take only minutes. Many traditional platforms may require a separate report for each breakout.
A Research Partner
Most risk managers and their staffs do not have direct access to a platform of this sort and often do not have analysts experienced with the methods needed for sophisticated analytic investigations. Some research institutions provide limited studies or even comparative data sets for a risk manager to carry out investigations. These data sets are often limited in scope and can be very expensive to use. Large, national TPAs are uniquely positioned to fill a niche for their clients in that they have robust databases of claims, managed care, and disability information critical for carrying out these investigations. However, for the most part, this niche has not been further developed. Often, at best, TPAs provide data without thorough investigation, leaving it up to the risk manager and their broker partners to identify cost drivers that can be impacted often without the benefit of data analytics. As evident in the example discussed, direct access to a flexible reporting platform by an informed (and creative) analyst is critical to the investigation. If this investigation was carried out by a committee ordering various cuts of the data by a data provider organization, then the investigation may take months or even longer. To be a true partner, a TPA must be a source of data analytics that demonstrate compelling interpretations focused on developing strategies to spur improvement.
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