Healthcare Big Data Guide: Today's Uses & Future Potential

Big Data in Healthcare Made Simple:
Where It Stands Today and Where It’s Going

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
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?
• What is Health Catalyst doing with big data?

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’s being done to simplify
big data and make it more
accessible.

A Brief History of Big Data in Healthcare
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.

EMRs alone collect huge
amounts of data, but according
to Brent James of Intermountain
Healthcare most of the data is
for recreational purposes.
Our work with health systems
shows that 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 and its corresponding
analytics use cases.

There is certainly variety in the
data, but most systems collect
very similar data objects with an
occasional tweak to the model.
That said, new use cases that
support genomics will certainly
require a big data approach.

Health Systems Without Big Data
Most health systems can do
plenty today without big data,
including meeting most of their
analytics and reporting needs.
We haven’t come close to
stretching the limits of what
healthcare analytics can
accomplish with traditional
relational databases—and using
these databases effectively is a
more valuable focus than
worrying about big data.

Health Systems Without Big Data
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.

Barriers Exist for Using Big Data
Expertise and Security
Several challenges with big
data have yet to be addressed
in the current big data
distributions.
Two roadblocks to the general
use of big data in healthcare
are the technical expertise
required to use it and a lack of
robust, integrated security
surrounding it.

Expertise
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 steep learning curve and
other complexities surrounding
big data.

Expertise
Data scientists are usually
Ph.D.-level thinkers with
significant expertise.
These experts are hard to
come by and expensive, and
only research institutions
usually have access to them.
Data scientists are in huge
demand across industries like
banking and internet powers
with deep pockets.

Expertise
The good news is, thanks to
changes with the tooling, people
with less-specialized skillsets will
be able to easily work with big
data in the future.
Big data is coming to embrace
SQL as the lingua franca for
querying. And when this
happens, it will become useful in
a health system setting.

Security
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.

Security
The best option for healthcare
organizations looking to implement
big data is to purchase a well-
supported, commercial distribution
rather than starting with a raw
Apache distribution.
Another option is to select a cloud-
based solution like Azure
HDInsight to get started quickly.

Big Data Differs from Current Systems
It’s Unlike Typical Relational Databases
Big data differs from a typical
relational database.
This is obvious to a CIO or an IT
director, but a brief explanation of
how the two systems differ will
show why big data is currently a
work in progress—yet still holds
so much potential.

Big Data Has Minimal Structure
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, somewhat
chaotic distributed file system.

Big Data Is Raw Data
By convention, big data is typically
not transformed in any way.
Little or no “cleansing” is done and
generally, no business rules are
applied. Some people refer to this
raw data in terms of the “Sushi
Principle” (i.e. data is best when it’s
raw, fresh, and ready to consume).
Interestingly, the Health Catalyst
Late-Binding™ Data Warehouse
follows the same principles.

Big Data Is Less Expensive
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 Has No Roadmap
The lack of pre-defined structure
means a big data environment is
cheaper and simpler to create.
So what’s the catch?
The difficulty with big data is that
it’s not trivial to find needed data
within that massive, unstructured
data store.
A structured relational database
essentially comes with a
roadmap—an outline of where
each piece of data exists.

With a relational database, a
simple, structured query language
(i.e. SQL) pulls the needed data
using a sophisticated query engine
optimized for finding data.
With big data, the query languages
are much more complicated.
A data scientist is needed to find
the subset of data required for
applications.

Creating the required MapReduce
algorithms for querying big data
instances isn’t for the faint of heart.
Fortunately, that’s changing at a
fairly rapid pace with tools like
SparkSQL and other query tools
that leverage conventional SQL for
querying.
In short, big data is cheap but more
difficult to use. Relational databases
are expensive but very usable.

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,
and some of the new work
finding information in textual
fields, could indeed add real
value to healthcare analytics in
the future.

Big Data and the Internet of Things
Big data will become valuable
to healthcare in what’s known
as the internet of things (IoT).
SAS describes the IoT as:
a growing network of everyday
objects from industrial machines
to consumer goods that can
share information and complete
tasks while you are busy with
other activities, like work, sleep,
or exercise.

Big Data and the Internet of Things
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 Care Management
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.

The Fun Stuff:
Predictive Analytics, Prescriptive Analytics, and Genomics
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.

The Fun Stuff:
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.

The Fun Stuff:
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.

The Fun Stuff:
Patient Flight Paths and 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.

The Fun Stuff:
Patient Flight Paths and Prescriptive Analytics
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.
Big data is well suited for these
futuristic use cases.

The Fun Stuff:
Genomic Sequencing and Big Data
The use of genomic data is on
the rise in patient treatment. The
cost of sequencing an individual’s
full genome has plunged in
recent years.
Sequencing will become
commonplace and eventually
become a commodity lab test.
Genomic sequences are huge
files and the analysis of genomes
generates even more data.

The Future of Healthcare Data Warehousing
And the Transition to Big Data
With the present limitations for big
data in healthcare and the truly
fascinating future possibilities that
big data enables.
An important question to address at
this point is:
What should a health system do in
the meantime?

Today, health systems’ need for data-
driven quality and cost improvement is
urgent.
Healthcare organizations cannot afford
to wait for big data technology to
mature before diving into analytics.
The important factor will be choosing a
data warehousing solution that can
easily adapt to the future of big data.

A Late-Binding™ enterprise data
warehouse (EDW) architecture is ideal
for making the transition from relational
databases to unstructured big data.
The late-binding approach is very
similar to the big data approach.
In a Late-Binding EDW like Health
Catalyst’s, data from source systems
are placed into source marts.
The data remains in its raw state until
someone needs it.

Real World Example Healthcare’s Transition to Big Data
In conclusion, here is a brief
example of how the transition
from relational databases to big
data is happening in the real
world.
We are working with one of our
large health system clients and
Microsoft to create a massively
parallel data warehouse in a
Microsoft APS Appliance that
also includes a Hortonworks
Hadoop Cluster.

This means we can run a
traditional relational database
and a big data cluster in parallel.
We can query both data stores
simultaneously, which improves
data processing power.
Together, we are beginning to
experiment with big data in
important ways, such as
performing natural language
processing (NLP) with physician
notes, predictive analytics, and
other use cases.

The progression from today’s symmetric multiprocessing
(SMP) relational databases to massively parallel processing
(MPP) databases to big data in healthcare is underway

More about this topic
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
Link to original article for a more in-depth discussion.
Big Data in Healthcare Made Simple:
Where It Stands Today and Where It’s Going

For more information:

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Douglas Adamson joined Health Catalyst in June 2012 as Vice President of
Architecture. Prior to joining Catalyst, Doug worked for GE Healthcare in a number of
roles including Chief Technologist, Chief Architect and General Manager of
Engineering. Doug also spent 14 years working as a software engineer on the Human
Genome Project. He holds a Bachelor of Science degree in Computer Science from
Purdue University in West Lafayette, Indiana with additional graduate work in
Computer Science and Math.

Healthcare Big Data Guide: Today's Uses & Future Potential

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