Policy & Practice October 2016

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Policy&Practice October 2016 6
from the field
Information systems are more effec-
tive than ever in collecting mass
aggregates of data from all realms of
life—financial, medical, criminal, even
social. In recent years, this capability
has created a buildup of extremely
large and complex data sets called “big
data.” Big data cannot be analyzed by
basic statistical software alone1 but
recent efforts to decipher its large-scale
patterns have led to the development of
“predictive analytics.” Predictive ana-
lytics is the use of electronic algorithms
that learn from big data to predict
future outcomes in a population.2
The health and human service field
is in a highly advantageous position
to benefit from the use of advanced
analytics. Advanced analytics is an over-
arching pattern of statistical analysis
that learns from data to determine
the source of an outcome (statistical
analysis), create hypothetical trends
(forecasting scenarios), predict future
outcomes (predictive analytics), and
recommend optimal solutions for future
scenarios (optimization).3 This often
untapped resource could be used to
further analyze individual- and popula-
tion-level trends to improve outcomes,
impact performance and decision-
making, inform resource allocation, and
customize services to mitigate social,
economic, and health risks across the
broader care delivery system.
Real benefits that can be generated
for health and human services through
predictive analytics include predicting
risk, cultivating diversity, expanding
financial opportunities, and serving
areas of greatest need. For example, in
Dallas, Texas, the Children’s Medical
Center partnered with the Parkland
Predictive Analytics and the Future
of Health and Human Services
By Barbara Tsao
Photo illustration by Chris Campbell
Center for Clinical Innovation research
center to implement a predictive model
that assesses a child asthma patient’s
chance of hospital readmission. In
determining which patients are at most
risk, this model enables doctors to
better establish a plan of care.4
Diversity is another benefit of
advanced analytics. One example is
Google, which has used predictive ana-
lytics to identify the cause of homogeny
within its workforce. When evaluating
its hiring practices, Google found that
brainteaser questions inhibited recruit-
ment from minorities. The company
subsequently adjusted its interview
See Analytics on page 43
NewYork City, ACS, and KPMG pulled
together a team, including an executive
sponsor, subject matter experts, and data
modelers and designers. Upon assessing
their resources, ACS decided to utilize
their existing Data Governance workgroup
and staff skilled in Software as a Service
(SaaS). The model was funded by KPMG
and support resources were provided by
ACS to finance the work. The agency also
realized they did not have the technological
infrastructure (e.g., desktop server)
available to run the new analytic model
so they used KPMG’s data center facility
for the initial data storage and processing
infrastructure. SaaS was the primary tool
used for data transformation and modeling.
Forming an AnalyticsTeam