Industry Interview Series- Big Data in Healthcare

1. Big Data in Healthcare

3. • Healthcare service model is
transitioning into Patient Centered
care model driven by the
healthcare reforms and the need
to cut costs while improve
outcomes.
• Payment methods based on “Pay
for performance” are driving
collaborative care models like ACO
(Accountable Care Organizations)
and PCMH (Patient Centered
Medical Homes)
Big Trends in Healthcare

4. Big Data in Healthcare Today
• A number of use cases in
healthcare are well suited for a
big data solution.
• Some academic- or research-
focused healthcare institutions are
either experimenting with big data
or using it in advanced research
projects.
• This presentation will examine
what are some of the big trends
in healthcare industry and how
Big Data solutions can enable the
transformations.

5. • In 2001, Doug Laney, now at
Gartner, coined the term “the 3
V’s” to define big data:
• Volume
• Velocity
• Variety
• Other analysts argued that this is
too simplistic but for this
purpose let’s start here.
A Brief History of Big Data in Healthcare

6. • EMRs alone collect huge
amounts of data, but not all of
them are relevant to the current
practice of medicine and its
corresponding analytics use
cases.
• Lots of very useful data sets
relevant for analytics use cases
may come from outside the
organizations, like socio-
economic data, behavioral data,
environmental data etc.
A Brief History of Big Data in Healthcare

8. • 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.
Health Systems Without Big Data

10. • ACOs focus on managed care
and want to keep people at
home and out of the hospital.
• Sensors and wearables will
collect health data on patients in
their homes and push all of that
data into the cloud.
• Healthcare institutions and care
managers, using sophisticated
tools, will monitor this massive
data stream and the IoT to keep
their patients healthy.
Big Data and Care Management

11. • 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.
• Wearables are perhaps the
most familiar example of such
a device.
• Many people now can wear a
fitness device that tracks their
heartrate, their weight, how it’s
all trending, and then their
smartphone sends that data to a
cloud service.
Big Data and the Internet of Things

12. • 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 we simply haven’t had
enough data to work with.
• Big data can help fill that gap.
Predictive and Prescriptive Analytics

13. • 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.
Predictive and Prescriptive Analytics

14. • This and similar data can help
organizations predict missed
appointments, noncompliance
with medications, and more.
• That is just a small example of
how big data can fuel predictive
analytics.
• The possibilities are endless.
Predictive and Prescriptive Analytics

15. • 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.
Predictive and Prescriptive Analytics

16. • 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.
• Real life use-cases
– Major Payor uses member segmentation
analytics to drive Clinical programs that
focus on prevention and proactive
management of chronic diseases among
its members
• Big data is well suited for these
futuristic use cases.
Predictive and Prescriptive Analytics

17. • In conclusion, Big Data solutions are increasing enabling
traditional healthcare service providers transforming into
patient centric, collaborative care providers using analytics to
drive decision making at the point of care
Big Data in Healthcare

18. • Hospital IT experts familiar with
SQL programming languages and
traditional relational databases
aren’t prepared for the steep
learning curve and other
complexities surrounding big
data.
• These experts are hard to come
by and expensive, and only
research institutions usually have
access to them.
Barriers Exist for using Big Data - Expertise

19. • Big data differs from a typical
relational database.
• The biggest difference between big
data and relational databases is that
big data doesn’t have the traditional
table-and-column structure found in
relational databases.
• In contrast, big data has hardly any
structure at all. Data is extracted
from source systems in its raw form
stored in a massive, distributed file
system.
Big Data Differs from Current Systems –
Big Data has Minimal Structure

20. • Due to its unstructured nature
and open source roots, big data is
much less expensive to own and
operate than a traditional
relational database.
• A Hadoop cluster is built from
inexpensive, commodity
hardware, and it typically runs on
traditional disk drives in a direct-
attached (DAS) configuration
rather than an expensive storage
area network (SAN).
Big Data Differs from Current Systems – Big
Data is Less Expensive

21. • References
– www.healthcatalyst.com
– LifeMasters
– sanders d protii, D, Electronic Healthcare 11(2) 2012: e5-e6
Q&A
Thank You