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, maybe not for any one.
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 short-cut is leveraging industry research and it is an 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 cost-intensive physicians 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, 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 in 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 saving of what might have been. When placed in the context of assumptions based on research, assessing savings for avoidance is valid. To learn more, you are invited to visit MedMetrics or contact karenwolfe@medmetrics.org.
[1] Bernacki, et.al. “Impact of Cost Intensive Physicians
on Workers’ Compensation” JOEM. Vol. 52. No. 1. January, 2010.
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