|[January 10, 2013]
Big Data and Predictive Analytics Can Transform US Healthcare System, According to NYU Stern Study Published in Health Systems
NEW YORK --(Business Wire)--
For more than a decade, banks and retailers have been using "big data"
to draw actionable conclusions from data they collect through commerce.
Now, two NYU Stern researchers say big data can help solve major
societal problems, like reducing the skyrocketing cost of healthcare in
the US while improving quality.
In a study published in Health
Systems, Professor Vasant Dhar, co-director of Stern's
Center for Business Analytics, and his colleague Jon Maguire, use
predictive analytics on a large dataset from the healthcare system to
identify groups most at risk for diabetic complications and predict
treatment costs associated with different treatment patterns. According
to the Congressional Budget Office, healthcare spending tripled as a
percentage of GDP from 1960-2005 and could more than triple again by
2082 to consume nearly half of GDP unless costs are contained. The
researchers argue that big data can show us how to reduce costs and
"Healthcare providers are understandably focused on indviduals and not
fully informed by large-scale data-driven patterns of treatments and
outcomes," said Dhar. "The good news is that we now have troves of
available data of healthcare system use. What we need is a healthcare
system that is willing to let the data speak and show us previously
unknown patterns that emerge even though the reasons for such patterns
may not always be immediately apparent."
In their study, they mined pharmacy and medical claims of more than
65,000 newly diagnosed type-2 diabetes patients, age 18 and older, over
six years. The NYU Stern study revealed the following actionable
patterns that are relevant to patients, healthcare providers and
A large portion of costs arise from very few cases: 68% of utilization
among those newly diagnosed with type-2 diabetes are incurred by the
"sickest" 10% of the population. Predicting the "soon to be sickest
10%" could significantly cut costs and improve outcomes.
People who start with a "lifestyle only" solution after diagnosis (no
medicine for up to six months) tend to have higher complication rates
than those who go on a simple medication such as Metformin, suggesting
that delay may be costly. While some controlled studies suggest that
lifestyle factors such as regular vigorous exercise may prevent the
onset of diabetes, timely medication may be more effective after its
onset than relying on lifestyle alone.
Patients with poorer 'health status' have higher costs and
complication rates. In the study, one proxy for health status was the
number of different therapeutic class prescriptions or pre-existing
diagnoses before a diabetes diagnosis. This surrogate measure and
other related ones should be considered during treatment since they
can impact outcomes.
Prof. Dhar is an expert in the study of predictive analytics, data
mining, big data and digital marketing.
To arrange an interview with Prof. Dhar, contact Jessica Neville,
in Stern's Office of Public Affairs, or Prof. Dhar at email@example.com.
For access to his Health Systems paper, visit their website.
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