Workers’ Compensation claims and medical managers are continually challenged by upper management to analyze their cost drivers. Moreover, upper management wants comparisons of the organization’s results to that of their peers.
The request
is appropriate. Costs of doing business directly impact competitive performance
of the organization. Understanding cost drivers is key to making adjustments to
improve performance. Still, it’s not that simple.
Meaningful and relevant dataExecuting 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.”[1] Parillo emphasizes 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.
Data variability
Variability
in the data available for benchmarking can be especially disconcerting when
considering medical cost drivers that now account for the majority of claim
costs. Differences in state fee schedules and legislation such as required UR
and the use of evidence-based guidelines can produce questionable comparative results. Additionally,
whether the contributed data is from self-insured and/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.
Cultural variables
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 defined
by geography. 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.”[2]
Internal data
Analyzing
internal data can be highly productive. First, the conditions of meaningful and
relevant are guaranteed, for obvious reasons. The differential across one state
was found in one organization’s internal data which insures 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.
Moving costs
Evaluate how costs move. Look at costs at intervals along the course of claims for specific injury types. In this case, utilizing ICD-9’s is more informative than the NCCI injury descriptors. One client found that injury claims which contained a mental health ICD-9 imbedded during the course of the claim, showed an upsurge 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.
Imbedded 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-9’s 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.
Undervalued data
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 cost driver discovery.
Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org
