Big data is the next evolutionary
step for statistical analysis and predictive models. It is made possible by the
large amount of data made available by the internet of things. This data can be
used to “answer what, not why, and often that’s good enough” (The Rise of Big
Data, 2). This is the future of business in healthcare, humanitarian efforts,
civic planning, and all industries. There is so much data now that there are
new job roles being created to manage all this data - the advent of the data
scientist is bringing together diverse skills like programming, statistics and
specific business understanding, among others, to be able to use the copious
piles of data – much of it not in a usable state yet. Big data is all about
handling its sheer volume, velocity of creation and variety of formats that are
stored.
After completing the class
reading and googling for “healthcare and big data” where I found titles like “NHS
director argues big data in healthcare is a ‘moral obligation’”, “Why Health
Care May Finally be Ready for Big Data” and “How Big Data Will Help Save
Healthcare” I was left with the sense that big data is seen as being able to do
no wrong – it will save us all! Most of the articles in in “Business Report on
Big Data Gets Personal” focuses on the advantages of sharing personal data, how
said data is bought and sold today, and how, well, you could identify people
from the data from multiple data points but no one ever would. Those
assumptions feel a little hollow after a year of many big companies – Home Depot,
Chase, JP Morgan, and Sony, just to name a few, have suffered security
breaches. Healthcare data is extremely popular on the black market (Monegain)
and to effectively utilize big data there must be more thorough sharing of data
between healthcare organizations. Security breaches are nearly inevitable and
privacy concerns must be addressed.
Taking it in another direction,
let’s assume there are no nefarious agendas, the use of big data could still
lead to negative unintended consequences. For example, there is concern in the
healthcare industry that predictive data could lead to health insurance
agencies denying coverage for conditions that do not exist, may never exist,
but a predictive model suggests could exist. Predictive models are just that –
predictive and not infallible. We that in 2009, when Google used big data to
find a correlation between internet searches between 2003 and 2008 and flu
outbreaks. The idea being that if they could find a predictive model flu
outbreaks could be analyzed in real-time. This mathematical analysis did the
trick in that they found 45 search terms which often coincided with an
outbreak. With a billion searches a day they wouldn’t have been able to predict
these terms but by using such a large sample they felt confident in the
results. The study concluded, though, with “[…] it seems that Google’s system may
have overestimated the number of flu cases in the United States. This serves as
reminder that predictions are only probabilities and are not always correct […]”
(Cuckler, 3-4).
There are other limitations that
must be conquered. Some of the industries that could benefit the most from
utilizing big data are ill equipped to use the existing data. For example, in
healthcare, from the paper patient records, electronic health records, lab
results and all of the other data there is endless data that could be used to
help drive diagnostic decisions. In an NPR interview with Amy Standen and Jenny
Frankovich, they relate a story about a girl with lupus who might have been at
risk for a blood clot. They faced the dilemma of proactively administering an
anti-coagulant which might cause more problems or waiting for a potential blood
clot to form before administering the anti-coagulant. One of the physicians
used their electronic database with patient records to pull up the number of
lupus patients and the number that had a clot. The course of action was made
based on those results and was considered successful. So is this method in use
by the hospital today? No. Why? Standen says "The system just isn't ready,
the hospital decided. What if Frankovich had used the wrong search terms or the
engine itself had bugs? What if the records has been mis-transcribed? Even
Frankovich agrees that it's just too risky." The potential is there but
this is an industry where the uncertainty, and messy data is a problem. Even if
we assume the records are correct there is little to no standardization across
organizations which means the results could be skewed if the data samples in
each organization were insufficient. For this kind of clinical decision support
healthcare really need the advantages of “N=All” sample sizes.
I do not want to come off as
being anti-big data. In fact, I think the potential for good is limitless and
has the potential to transform and how we interact with each other and other
nations. In healthcare we already see Accountable Care Organizations (ACOs) are
using big data to find trends to reduce the need for patient readmits and
inefficient practices which will reduce healthcare costs (Burg). Another great
example is from Canadian researchers who are using big-data to identify
possible problems with premature babies long before the problem is evident. “By
converting 16 vital signs, including heartbeat, blood pressure, respiration,
and blood-oxygen levels, into an information flow of more than 1,000 data
points per second, they have been able to find correlations between very minor
changes and more serious problems” (The Rise of Big Data, 3). Even if they are
not right 100% of the time those premature babies will get extra care and
attention so the ones who are at risk can be saved at higher rates.
One of my favorite articles from
this week was “Big Data from Cheap Phones” (Business
Report - Big Data Gets Personal,
17-21). In developing countries where there is not existing government
infrastructure, like a census, they are starting to use simple cell phone data
points to under more about the nation and deal with problems. That article
focuses on malaria outbreaks and how with just the data points of cell phones
moving between cell towers they are able to track the outbreak and its flow.
These seemingly simple data points will give them the information to focus
their limited resources on the areas that will have the most impost. This is
similar to the story in the “Rise of Big Data” about how Manhattan is using
data analysis to deal fire-prevention in overcrowded buildings. By studying
previous cases they found data points, which weren’t immediately obvious,
suggested which buildings were more at risk. Their inspectors could focus their
time on those buildings and have more impact (Cuckler, 4-5).
I’ll admit, these examples are
much more motivating to me than how businesses can increase the impact of their
marketing. Big data is here to stay – how can it not with the massive amount of
data generated daily – and while the use and sharing needs to be controlled I
look forward to seeing the improvement it will bring to lives across the globe.
Citations
Burg, Natalie. 'How Big Data Will
Help Save Healthcare'. Forbes.com. 10 Nov. 2014: Web. 23 Jan. 2015.
Business Report - Big Data Gets
Personal. MIT Technology Review. PDF. 20 Jan. 2015.
Cuckler, Kenneth Neil, and Viktor
Mayer-Schoenberger. The Rise Of Big Data. ForeignAffairs.com. May/June
2013. PDF. 20 Jan. 2015.
Monegain, Bernie. "6 steps to keep security issues at bay."
Healthcare IT News. 25 Apr. 2013. Web. 23
Jan. 2015.
Standen, Amy, and Jenny
Frankovich. Big Data Not A Cure-All In Medicine. 5 Jan. 2015. Radio. 24 Jan.
2015.
