1. M
uch has been written about the subject of integrating predictive modeling into the
workers’ compensation claims-handling process. Industry news is replete with
examples of carriers and vendors leveraging workers’ compensation data to predict
claim outcomes. Certainly, early identification of high-risk claims represents a huge
opportunity for the industry. However, high-risk claim identification is only the first step in delivering
value. Once identified, successful intervention strategies are required to ultimately impact a claim’s
outcome. It is the combination of claim prediction and intervention that leads to the realization of
value—a better claim outcome. In this article, we discuss one example of how predictive analytics has
been integrated into the claims-handling process in the effort to achieve better outcomes through early
identification and successful intervention strategies.
PREDICTIVE ANALYTICS
A Workers’ Compensation Game Changer
By Brian Billings
Published by the Public Risk Management Association www.primacentral.org MAY/JUNE 2015

2. EARLY IDENTIFICATION,
INTERVENTION AND OUTCOMES
While this article covers predictive models, we will not
explore how they are created other than to mention a
key component—the data. Here, we focus on the path
from prediction to desired business outcome. To start, we
examine how early identification and successful intervention
work together to achieve positive claim outcomes.
If you believe that early intervention can lead to a positive
claim outcome, then a predictive model should address
early identification of those high-risk claims. Arguably,
early identification permits the claim-handler to implement
the appropriate intervention before a claim spirals out of
control. Early identification of high-risk claims is certainly
possible (see Figure 1). However, what about a viable
intervention strategy for those identified high-risk claims?
Is this not equally important? For without an intervention,
you have identified a claim that cannot be acted upon.
Thus, for the claim-handler to “get ahead” of a potential
high-risk claim both early identification by the predictive
model and a viable intervention strategy are necessary.
Understanding how the predictive model fits into the
business process is a critical part of the implementation and
should not be an afterthought.
A claim that is predicted to exceed the current total
incurred is an obvious target for early identification and
intervention. The earlier a claim can be identified, the
earlier that the proper experts can formulate and execute
intervention strategies to mitigate claim costs. In many
cases, early identification can occur at points well below the
ultimate exposure. Such a situation is a perfect opportunity
for the claim- handler to execute possible interventions on
these complex claims. If caught early and the appropriate
inventions implemented, a claim’s potential exposure can be
dramatically reduced.
INTERVENTION STRATEGIES
Combining high-risk claim predictions with appropriate
intervention strategies demands careful consideration.
Risk mitigation requires understanding claim dynamics in
great detail. A claim-handler must unpack the complexity
of a claim, into its component parts, to understand what
is driving a claim’s costs. In addition to a risk score, a
predictive model should give an indication of risk factors
driving that score so that the claim-handler can quickly
identify and research potential claim issues. Once the issues
are identified, the appropriate intervention strategies can be
formulated and deployed.
Two recurring themes, seen in large loss claims, are
chronic pain cases (including Complex Regional Pain
Syndrome and Reflex Sympathetic Dystrophy) and
Failed Back Syndrome. These claims most often include a
regime of opioids. In addition, the claimant is frequently
prescribed medication to counteract the side effects of long
term opioid use—constipation and drowsiness. Other
problematic medical treatments, on these claims, include
injections and spinal cord stimulators. A predictive model
should alert the claim-handler to the presence or potential
of these and other medical related issues.
So what are the steps in formulating an intervention
strategy? In the case of chronic pain and Failed Back
Syndrome claims, the first step is typically a Drug
Utilization Review to understand the efficacy of the
current drug regime and to identify specific cost drivers
and opportunities for mitigation. An independent
medical examination or peer review may also be
necessary to determine if the best course of treatment is
currently being employed. A pharmacy benefit manager
or another pharmacy intervention vendor can contact
the physician to try to change the pain management
regimen. Finally, Cognitive Behavioral Therapy or
Functional Restoration may be considered to help the
claimant better cope with the pain and return to a more
productive lifestyle.
$90,000 90
$70,000 70
$50,000 50
AMOUNT
RISKSCORE
MONTHS FROM ACCIDENT
Prediction Lead Time
FIGURE 1: EARLY IDENTIFICATION OF HIGH RISK CLAIMS
$30,000 30
$10,000 10
$80,000 80
$60,000 60
$40,000 40
$20,000 20
$0 0
6 7 8 9 10 11 12 13
Claim Total Incurred Claim Total Paid Risk Score
14 15
Above is a real example of where a predictive model identified a claim as High Risk at 6 months of age.
The Total Paid and Total Incurred at the time of identification is less than $10k. This particular model is
used to identify claims likely to exceed $50k in Total Paid. The Total Paid hits $50k at about the 15 month
mark. (Note: a Risk Score greater than 50 is considered High Risk)
2 PUBLIC RISK | MAY/JUNE 2015 WWW.PRIMACENTRAL.ORG

3. Predictive Analytics
INTEGRATING INTO THE CLAIMS
MANAGEMENT PROCESS
A critical component of any predictive model project is
integration with the business process. Not only is claims
expertise a critical part of the predictive model development,
but it is also critical to establish a feedback loop with the
claim experts post implementation. For example, the
claim’s department should have online access to the most
current predictions for all open claims. This permits a
claim-handler to review individual predictions to reevaluate
potential exposure. For those claims that warrant additional
attention, a medical professional can be utilized to prepare
a plan of corrective action. The claim-handler should
implement the plan to mitigate exposure.
Every month, the predictive analytics team should meet
with the claim’s department to discuss claims that were
reviewed based on the predictions. This feedback loop
serves as an invaluable tool to help optimize the process of
high-risk claim identification. For example, there may be
claims where the model technically “gets it wrong” with
respect to the prediction. However, the claims department
deems the claim worthy of review for potential exposure.
An example of this situation would be a claim that involves
subrogation. Ultimately, predictive models should be used
in a way that serves the needs of the business. The feedback
loop enables evolution of the process to achieve the desired
business outcomes.
IMPROVING CLAIM PREDICTION
AND ANALYTICS
It is imperative to discuss one major obstacle in the path of
success—the data. Solving data collection and aggregation
issues in the workers’ compensation system is arguably the
single greatest opportunity for improvement in modeling
claim outcomes and analyzing claim risk factors. Data is
the fuel necessary to operate a predictive analytics engine.
In workers’ compensation, this obviously includes the data
captured in a claims management system. However, other
sources of data are equally important. When considering
what data is relevant to a predictive modeling effort, it
helps to explore the various parties that interact with the
claimant during the life of the claim. Hospitals, doctors,
and pharmacies immediately come to mind.
One challenge with utilizing third-party data comes in
linking the data to the appropriate claim. Given varied
computer systems, linking the data can prove difficult.
If done incorrectly, you end up with erroneous data
regarding the claim. This can be disastrous for trying to
accurately predict claim outcomes and can erode trust in
the analytics.
The workers’ compensation system needs to dramatically
improve its data collection, data quality, and data
reporting processes. Using the example above, standard
data exchange protocols should define exactly how data
matching is accomplished. Thus, workers’ compensation
desperately needs a national standard for Electronic Data
Interchange similar to what exists today in group health
claims adjudication.
Early adopters are already seeing the positive impacts
of utilizing predictive models in the claims-handling
process. For starters, predictive models enable
identification of problematic claims much earlier in their
lifecycle. To be successful, processes must be established
to review the high-risk claim predictions and to formulate
appropriate intervention strategies. To achieve the
desired business outcomes, a feedback loop must be
established involving both claim experts and the predictive
modeling team. Lastly and most importantly, claim-
handlers must explore innovative ways of successfully
resolving complex workers’ compensation cases identified
by the predictive models.
Brian Billings is the director of business analytics for Midwest
Employers Casualty Company.
Early adopters are already seeing the positive impacts of utilizing
predictive models in the claims-handling process… To achieve the
desired business outcomes, a feedback loop must be established
involving both claim experts and the predictive modeling team.
Lastly and most importantly, claim-handlers must explore
innovative ways of successfully resolving complex workers’
compensation cases identified by the predictive models.
“
“
Midwest Employers Casualty Company
14755 North Outer 40 Drive, Suite 300
Chesterfield, MO 63017
Phone: 636.449.7000
Website: www.mwecc.com
Contact Brian Billings, Director of Business Analytics
Email: bbillings@mwecc.com
Published in Public Risk, May/June 2015. Copyright 2015. All rights reserved. This file is for web posting and email distribution only; may not be used for commercial reprints.
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