Workers’ compensation claims and medical managers are continually challenged by upper management to analyze their drivers of workers’ comp costs. Moreover, upper management wants comparisons of the organization’s results to that of peers.
The request is appropriate. Costs of doing business directly affect the competitive performance of the organization. Understanding drivers of workers’ comp costs is key to making adjustments to improve performance. Still, it’s not that simple.
Executing the analysis is the lesser of the two demands. More challenging is finding industry or peer data that is similar enough to create an apples-to-apples study. In a recent article, Nick Parillo states, “Regardless of the data source, whether it be peer-related or insurance industry-related, risk managers must be focused on aligning the data to their respective company and its operations.” Parillo emphasizes that the data should be meaningful and relevant to the organization.
Aligning the data to the situation can be challenging. Industry or peer data may not be situation-specific enough or granular enough to elicit accurate and illuminating information. State regulations vary, as do business products and practices, along with a multitude of other conditions that make truly accurate comparisons difficult.
Variability in the data available for benchmarking can be especially disconcerting when considering medical cost drivers, which now account for the majority of claim costs. Differences in state fee schedules and legislation such as required utilization review (UR) and the use of evidence-based guidelines can produce questionable comparative results. Additionally, whether the contributed data is from self-insured or self-administrated entities can skew the results.
Other variables that make comparing industry or peer data less valid are unionization, physical distribution of employees, employee age and gender, as well as industry type and local resources available. Potential differences are unlimited.
External sources such as local cultural and professional mores, particularly among treating medical providers, can play a significant role in disqualifying data for comparison. For instance, my company’s analysis of client data has uncovered consistent differences in medical practice patterns in one large state. In one geographic sector, referrals to orthopedists with subsequent surgery and higher costs are far more frequent than in another sector of the state for the same type of injury.
Parillo continues, “Given the uncertainty and limitations on the kinds of peer group data a risk manager would need to perform a truly “apples to apples” comparison, the most “relevant and meaningful” data may be that which a risk manager already possesses: His own.”
Analyzing internal data can be highly productive. First, the conditions of meaningful and relevant are guaranteed, for obvious reasons. The geographical differential across one state was found in one organization’s internal data, which ensures that data variability is not a factor.
Analyses can be designed that dissect the data at hand. Follow up to the above example might include looking for other geographic variables in costs, in injury types and in medical practice patterns. Compare physician performance for specific injury types in the same jurisdiction and then look for differences within. To gain this kind of specificity and relevance, drill down for other indicators.
Evaluate how costs move. Look at costs at intervals along the course of claims for specific injury types. In this case, utilizing ICD-9s is more informative than the National Council on Compensation Insurance (NCCI) injury descriptors. One client found that injury claims that contained a mental health ICD-9 showed a surge in costs beginning the second year. Now, further analysis can begin to discern earlier indicators of this outcome. In other words, dive further into the data to find leading indicators.
Industry data is not likely to contain the detail necessary to evoke subtle mental health information during the course of the claim. Most analysis ignores the subtlety and sequence of diagnoses assigned. Few would uncover the mental health ICD-9 because few bother with ICD-9s at all.
Drilling down, analyze claims that fall into this category for prescriptions, legal involvement and other factors that might divulge prophetic signs. It is an investigative trail that relies on finite internal data analysis.
Too often people disrespect their own data, thinking it is too poor in quality, therefore of little value. It’s true, much of the data collected over the years is of poorer quality, but it still has value. Begin by cleaning or enhancing the data and removing duplicates. Going forward, management emphasis should be on collecting accurate data.
Benchmarking data sourced from the industry may be useful but should not necessarily be considered the most accurate or productive approach. Internal data analysis may be the best opportunity for discovering cost drivers.