Heard
recently, “Our organization has lots of analytics, but we really don’t know
what to do with them.” This is a common dilemma. Analytics (data analysis) are
abundant, they are presented in annual reports, and published in colorful graphics.
But too often the effort ends there. Nice information, but what can be done
with it? How does it change operations and outcomes?
Obviously,
the basic ingredient for analytics is data. After that comes skill, ingenuity,
and creativity. Nevertheless, without data, there is no business knowledge,
business intelligence, or much in the way of analytics. Fortunately, the last thirty years have been
primarily devoted to data gathering.
Data evolution
Over that
time, all industries have evolved through several phases in data collection and
management. Main frame and mini-computers produced data and with the inception
of the PC in the 80’s, data gathering was the business of everyone. DOS systems were clumsy
in the early PC years and there were significant restrictions to screen real estate
and data volume. Recall the Y2K debacle caused by limiting year data to two
characters.
Happily
for the data gathering effort, progress in technology has been rapid. Advancement
was enhanced first by local and wide area networks, then by the Internet along
with ever more powerful hardware. Amazingly, wireless smart phones today are more
powerful computers than were the PC’s of the 80’s and 90’s. Data gathering has
been successful.
Big data
Now we
have truckloads of data, often referred to as Big Data. People are trying to
figure out how to handle it. In fact, a whole new industry is developing around
managing the huge volumes of data. Once Big Data is corralled, analytic
possibilities are endless.
The
Workers’ Compensation industry has also collected enormous volumes of data. Yet,
little has been done in the industry to actualize the analytics to reduce costs
and improve outcomes.
Imbed analytic intelligence
The
best way to apply analytics in Workers’ Compensation is to create ways to translate
and deliver the intelligence to the operational front lines, to those who make critical
decisions daily. Knowledge derived from analytics cannot change processes or
outcomes unless it is imbedded into the work of adjusters, medical case
managers, and others who make claims decisions.
Consulting
graphics for guidance is cumbersome, interpretation is uneven or unreliable,
and the effects cannot be verified. Therefore, the intelligence must be made easily
accessible and specific to individual workers.
Front
line decision-makers need online tools designed to easily access interpreted
analytics that can direct decisions and actions. Such tools must be designed to
target only the issues pertinent to individuals. Information should be
specific.
Electronic monitoring
To effectively
imbed analytic intelligence into operations, all claims data must be continuously
electronically monitored. To link
analytic findings to claims, all data must be monitored so the system can identify
claims that contain conditions warned by analytics. Then that interpreted
information is linked to operations.
Reverse strategy
When
predictive modeling is employed as the analytic methodology, certain claims are identified as risky. Instead, all claims should be monitored continuously. By monitoring all claims
for events and conditions pre-determined by analytics, no high risk claims can slip
through the cracks.
Personnel
can be alerted of all claims with risky conditions identified through
analytics. At the same time, the analytic delivery system should automatically
document itself.
Self-documenting
The
system that is developed to deliver analytics to operations should automatically
self-document, that is, keep its own audit trail to continually document to
whom the intelligence was sent, when, and why. Furthermore, the system can then
be expanded to document what action is taken based on the information delivered.
Without
self-documentation, the analytic delivery system has no authenticity. Moreover,
those who receive the information cannot be held accountable for whether or how
they acted on it. When the system automatically self-documents, those who have
received the information can be held accountable or commended for their part. Self-documenting
systems also create Additionality.
Additionality
Additionality
is the extent to which a new input adds to the existing inputs without
replacing them and results in something greater. When the analytic delivery
system automatically self-documents guidance and actions, a new layer of information
is created. Analytic intelligence is linked to claims data and layered with
directed action documentation.
Self-verifying
A
system that is self-documenting can also self-verify, meaning results of
delivering analytics to operations can be measured. Claim conditions and costs
can be measured with and without the impact of the analytic delivery system.
Further analyses can be executed to measure what analytic intelligence is most
effective, in what form, and importantly, what action responses generate best
results.
The
analytic delivery system monitors all claims data, identifies claims that match
analytic intelligence, and imbeds the interpreted information in operations.
The data has become a work-in-process knowledge tool while analytics are linked
directly to outcomes.
Karen Wolfe is the founder and President of MedMetrics, LLC, a Workers’ Compensation analytics company.
MedMetrics offers online apps that link analytics to operations, thereby making
them self-documenting, verifiable, and actionable. karenwolfe@medmetrics.org
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