STRAIGHT TALK
Weighing in on Big Data’s
Role in Health Care
B
ig data analytics has emerged as a trend
across nearly every industry – from transportation to education and health care –
and experts around the country and across the
world are seeking ways to leverage insights gleaned
from these large data sets to advance their fields.
While there are unlimited opportunities for big data
analytics to transform health care, there are also
many implications to consider and serious complexities to navigate.
A recent Special Issue of Circulation: Cardiovascular Quality and Outcomes focuses on big data
research methods and their impact on cardiovascular care. The issue includes several studies that
used big data analysis and features perspective
pieces from several cardiologists on this hot topic.
“Be assured, research and clinical care are
about to join the digital, mobile, mathematical,
personalized revolution. We all need to ensure
that the changes produce progress for people and
society,” said Harlan M. Krumholz, MD, SM,
in the Editor’s Perspective. Krumholz offered insights and recommendations on training, application, replication, dissemination, interoperability,
funding and collaboration, all of which must be
addressed in order to successfully transition to a
big data analytics strategy.
This era of digital health care opens doors for
applying big data analytics to the emerging area
of precision medicine; however, health care lags
behind other industries in this space for a variety
of reasons. There was consensus among the authors of the perspectives that numerous and significant changes must be made in order to evolve
the research paradigm that has driven medicine
for decades. Several authors stressed that big data
analysis must be held to the same standards as
traditional research methods and ensuring reproducibility was a common concern.
“The rise of big data analytics in health care
settings presents an exciting opportunity to leverage the power of increasingly voluminous health
care data in ways that were simply impossible as
recently as 10 years ago,” said John S. Rumsfeld,
MD, PhD, ACC’s Chief Innovation Officer, and
36 CardioSource WorldNews
Peter W. Groeneveld, MD, MS, in a Cardiovascular Perspective. “However, it is critical to
recognize that the fundamental pitfalls of observational data analysis cannot be ignored, and in
fact, the risks of such pitfalls demand rigorous
scientific testing and novel methods for peer
review of big data analytic models.”
In another Cardiovascular Perspective, Sanjeev P. Bhavnani, MD, and coauthors provide
insight into implications of big data science
“One of the issues with big data is that if
it comes from clinical databases, the accuracy
and completeness of coding can be an issue,”
commented Kim A. Eagle, MD, editor-in-chief
of ACC.org. “Some processes for educating
providers about coding have focused on maximizing revenue, not necessarily on creating a
proper list of diagnoses according to severity and
timeframe. Furthermore, the process of caring
for patients in the EMR is further complicated by
“Be assured, research and clinical care
are about to join the digital, mobile,
mathematical, personalized revolution.”
—Harlan Krumholz, MD
for early career investigators. They stress that
retraining and collaboration are key, and clinical and research teams must be restructured.
The authors point to several examples of new
research models that signal the future, such as
PatientsLikeMe, the United Kingdom’s BioBank
and Apple’s Research Kit. The ACC has partnered
with Patients Like Me in 2015 announced a
partnership to explore innovative ways to make
real-world patient feedback and experience more
central to diabetes research and care.
“Similar to the objectives of established data
sources such as census and public health data
sets, or standardized patient registries such as
(those in the NCDR) where data are structured
and aggregated to monitor po