Organizations
are anxious to execute analytics, but their leaders are baffled about how to apply
the knowledge gained. Portraying colorful graphics depicting the results of
analytics in executive reports has zero effect on costs or outcomes. In order
for analytics to have an impact, they must be fused into the operational
process.
Terminology
Simply
stated, analytics is the term used to describe data analysis of any kind.
Analytics should not be confused with predictive modeling which is also analyzing
data, but the goal of predictive modeling is to predict what is likely to
happen when a specific set of circumstances occurs. Stated differently,
predictive modeling predicts what claims are at risk for specific costs and
conditions. In predictive modeling, highly
advanced statistical tools are used to identify the set of conditions that are then
considered predictive. Predictive modeling is one form of analytics.
Analytics
is the broader term applied to data analysis. Aside from predictive modeling, it is designed
to provide the organization with knowledge and insight into their business
processes. Garden variety analytics are used to identify trends and cost
drivers. However, neither predictive modeling nor any other analytics can change
organizational behavior or outcomes.
Operationalize analytics
Analytics
(data analysis) can be powerful as a means of understanding business processes,
organizational strengths, and especially cost drivers, but that is not enough. Analytics
offers understanding, but that is only the first step. To impact the
organization, its workers, and its clients, the insights gained from analytics
must be transformed into timely operational initiatives and enforced through work-in-process
electronic tools.
According
to Rachel Alt-Simmons, SAS, “As competitive pressures increase the need for
organizations to master analytics, internal analytic teams have increased their
statistical sophistication, but are struggling to operationalize their insight.”[1] The problem is they are
missing the very significant step of translating the findings to the
operational process.
Actionable analytics
Analytics,
regardless of the variety, must be linked to operations to make them actionable.
The dots must be connected between analysis, decision, and action. The way to
do that is to translate knowledge to action using designed technology.
Technology-powered
Workers
should not be expected to interpret sophisticated mathematical analyses, but
they can act on the derived re-portrayed information. An example is comprehensive
data analysis of medical provider performance re-presented as a score or rank
compared to their peers. Rather than struggling with multiple analytic indicators
of performance, workers should make informed decisions based on interpreted,
understandable information found immediately at hand.
Analytic delivery framework
Rules-based
technology combined with continuously monitored historic and current data, can
send workers early notification of adverse conditions in a claim. Workers can
be alerted of poorly performing providers, questionable prescriptions, severe
diagnoses, comorbidities, cost benchmarks, and a myriad of other conditions of
known risk as they occur in claims. Moreover, the technology can enforce organizational
standards by including action steps (procedures) with the alerts.
Organizations
using sophisticated predictive modeling initiatives should also take the next
step by applying their results to the analytic delivery framework. Regardless of
the level of statistical sophistication, the information derived must be
delivered in a practical way to claims adjusters, nurse case managers, and
others who make decisions and take action regarding claims. Only then will
analytics empower workers, impact costs, and improve outcomes.
Analytics inspired—technology powered
Results
of analytics must be implemented consistently and structured so that the
intended cost control initiatives are achieved. Analyzing the data and
delivering the results of analysis to workers will inform decisions and actions,
thereby creating maximum value for customers, constituencies, and the
organization itself.
Learn
about MedMetrics analytic delivery
framework or contact karenwolfe@medmetrics.org
[1] Alt-Simmons,
R. Balancing Creativity and Control: Bringing Process Discipline to Predictive
Analytics. SAS. January 21, 2013.
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