Authors: Aggelos Liapis Presented at: BYTE Big Data Community Workshop

1. Big Data in Healthcare
by
Dr. Aggelos Liapis
Research & Innovation Coordinator
Athens University of Economics and Business
E-Business Research Center - [ELTRUN]

2. The Big Data Questions
Big data is generating a lot of hype in
every industry including healthcare.
Leaders in the industry all want to
know about the importance of Big
Data.
They ask questions such as:
• When will I need big data?
• What should I do to prepare for big
data?
• What’s the best way to use big data?

3. When is Data ‘Big’?
Volume Velocity VeracityVariety Value
Data at Rest
Terabytes to
exabytes of
existing
data to process
Data in
Motion
Streaming data,
requiring
mseconds to
respond
Data in
Many Forms
Structured,
unstructured, text,
multimedia,…
Data in Doubt
Uncertainty due to
data inconsistency
& incompleteness,
ambiguities,
latency, deception
€
€
€
€
€
€ €
€
Data into
Money
Business models
can be associated
to the data
Adapted by a post of Michael Walker on 28 November 2012

4. Big Data in Healthcare Today – [Data]
• EMRs alone collect huge amounts of
data however research has shown that
most of the data is for recreational
purposes.
• Only a small fraction of the tables in an
EMR database (perhaps 400 to 600
tables out of 1000s) are relevant to the
current practice of medicine.

5. Big Data in Healthcare Today – [Systems]
• Most health systems can do plenty today
without big data, including meeting most
of their analytics and reporting needs.
• Most healthcare institutions are swamped
with some very pedestrian problems such
as regulatory reporting and operational
dashboards.
• As basic needs are met and some of the
initial advanced applications are in place,
new use cases will arrive (e.g. wearable
medical devices and sensors) driving the
need for big-data-style solutions.

6. Existing Barriers for Using Big Data (1)
Two roadblocks to the general use of big data in
healthcare:
• Lack of technical expertise required to use it
• Lack of robust, integrated security surrounding it.
The value for big data in healthcare today is
largely limited to research because using big
data requires a very specialized skill set.
Hospital IT experts familiar with SQL
programming languages and traditional relational
databases aren’t prepared for the learning curve
and other complexities surrounding big data.

7. Existing Barriers for Using Big Data (2)
In healthcare, HIPAA compliance is non-
negotiable. Nothing is more important than the
privacy and security of patient data.
Unfortunately, security hasn’t been a priority up to
this point and there aren’t many good, integrated
ways to manage security in big data.
When opening up access to a large, diverse
group of users, security cannot be an
afterthought.

8. It’s Coming: Big Data in Healthcare
• When healthcare organizations envision
the future of big data, they often think of
using it for analyzing text-based notes.
• Big data indexing techniques, could
indeed add real value to healthcare
analytics in the future.
• Big data will become valuable to
healthcare in what’s known as the
internet of things (IoT).
• For healthcare, any device that
generates data about a person’s health
and sends that data into the cloud will be
part of this IoT. (e.g. Wearables, mirrors,
smart homes etc.)

9. The Fun Stuff:
• Predictive Analytics
• Prescriptive Analytics
• Drug Discovery

10. The Fun Stuff: Predictive Analytics (1)
• Real-time alerting is just one important
future use of big data. Another is predictive
analytics.
• The use cases for predictive analytics in
healthcare have been limited up to the
present because health organisations
simply haven’t had enough data to work
with.
Big data can help fill that gap!!!

11. The Fun Stuff: Predictive Analytics (1)
• One example of data that can play a role in
predictive analytics is socioeconomic data.
• Socioeconomic data might show that people
in a certain zip code are unlikely to have a
car.
• There is a good chance, therefore, that a
patient in that zip code who has just been
discharged from the hospital will have
difficulty making it to a follow-up appointment
at a distant physician’s office.
• This and similar data can help organizations
predict missed appointments, noncompliance
with medications, and more.

12. The Fun Stuff: Prescriptive Analytics
• Another use for predictive analytics is
predicting the “flight path” of a patient.
• Leveraging historical data from other patients
with similar conditions, predictive algorithms
can be created using programming languages
such as R and big data machine learning
libraries to faithfully predict the trajectory of a
patient over time.
• Once we can accurately predict patient
trajectories, we can shift to the Holy Grail–
Prescriptive Analytics.
• Intervening to interrupt the patient’s trajectory
and set him on the proper course will become
reality.

13. The Fun Stuff: Drug Discovery

15. Useful Resources
Hadoop in Healthcare: A No-nonsense Q and A
Jared Crapo, Vice President
Big Data in Healthcare: Separating the Hype from the Reality
Jared Crapo, Vice President
In Healthcare Predictive Analytics, Big Data Is Sometimes a Big Mess
David Crockett, Ph.D., Senior Director of Research and Predictive Analytics
Using Predictive Analytics in Healthcare: Technology Hype vs. Reality
David Crockett, Ph.D., Senior Director of Research and Predictive Analytics
3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve
Healthcare’s Problems Dale Sanders, Senior Vice President of Strategy

16. Thank You!
Dr. Aggelos Liapis
Research & Innovation Coordinator
Athens University of Economics and Business
E-Business Research Center - [ELTRUN]