The Case for Healthcare Data Literacy: It's Not About Big Data

The Case for Healthcare Data
Literacy: It’s Not About Big Data
– Dale Sanders

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
It’s Not About Big Data
At a recent conference having a
small-talk discussion with a
client, he finally asked:
When are you going to talk
about Big Data?”
His disappointment was
immediately apparent when I
responded – “I’m not.”
For the most part, nobody at Health Catalyst talks about
Big Data. As a company in the data and analytics
business, that may seem a little strange.

At Intermountain Healthcare
I led a team that pioneered the
use of analytics and process
improvement to dramatically
improve outcomes.
Many of the concepts that are
key to value-based care and
the accountable care model originated, at least in part, in
the work we did at Intermountain.
So you’d think I’d be the first to be talking about Big Data.
But I’m not.

The concept of Big Data is not
terribly helpful to the work we do.
The data sets we normally deal
with don’t meet the accepted
definition of Big Data.
In many cases the healthcare
data warehouses we analyze
include multi-millions of records.
The Silicon Valley type of big
data sets include huge volumes,
velocities, and varieties of data.

Strictly speaking, healthcare is
not really dealing with Big Data.
We’re just using average data.
I’m convinced that vendors who
hype big data the most are
suffering from some sort of
Freudian insufficiency complex.
The problem is fundamental:
Everybody is discussing Big Data.

Name any problem and we’re
throwing Big Data at it.
• Cancer
• Alzheimer’s disease
• New drug discovery
• ER wait times
Google recently launched an initiative
to use Big Data to cheat death.

You may have noticed that people
in our industry have a habit of
picking up on the next new thing.
Two years ago it was social
networking.
Last year it was the cloud.
This year it’s Big Data.
Next year, it’s going to be…
we’ll just have to see what that is.

For a company in the analytics
business, this period presents a
strategic challenge.
How do you talk about yourself when
everybody else is talking about the
same thing and your customers’
heads are starting to spin?
Luckily, the answer is quite
straightforward:
Talk about reality.

That’s generally how to combat hype.
You talk about solid facts.
You talk about pragmatism.
You give structure to chaos.
Every organization is at a different
point in the progression from
Healthcare 1.0 (the old way) to
Healthcare 2.0, beginning to
scientifically leverage technology
to improve outcomes and cut costs.

We’ve developed a
comprehensive model for
organizations to help with the
transition.
We call it the Healthcare
Analytics Adoption Model.
It’s a core set of foundational
principles that acknowledges
the core challenges facing
most organizations.

Healthcare Data Literacy in Action
It drives me crazy when I hear
people discussing things they’re
doing with data that has little to
no grounding in reality.
A recent news story made me
stop and think that we are
reaching a teachable moment.
A team of reporters took CMS
readmissions data and used it to
develop an innovative sounding
scorecard for surgeons.
My reaction was “Huh. They can tell all this from CMS re-
admission data? That’s extremely impressive.”

The researchers made it look
simple. Surgeons had been
scored using a simple system.
If a patient was readmitted to
the hospital within 30 days, that
counted as a ding.
If a patient died, that counted
as a ding.
The number had been risk-
adjusted, and just to be sure,
they had checked with “experts.”

This score was presented to
consumers as a reliable indicator
of surgeon quality and safety.
Potential limitations not mentioned
included impacts such as:
• Patient population
• Care team role
• Patient behavior
• Problems involved when data used
to bill the government for patient
services is used as a proxy for
patient safety.

In short, there was no context.
An ordinary consumer looking at
the numbers wouldn’t know what
to think and would almost certainly
conclude the wrong thing.
The truth is, it’s complicated.
It’s always more complicated.

A word about my background:
Before moving to lead the
informatics team at Intermountain
Healthcare I spent ten years
working in the Air Force as a
“command, control, communica-
tions, and intelligence officer” and
with the National Security Agency.
That experience shaped the way I
look at information management.
My job involved the collection of massive amounts of data
from every source of data that might contribute to better
military and national security decision making.

Today, when I look at a data set
like the ProPublica report my
first question is:
What’s the source of this data,
and is it a credible source of
data for the decisions that it’s
supposed to improve?”
Experience and education
taught me to pause and
challenge the data until it
either crumbles or prevails.

I learned to look for credible numbers
– to look for red flags indicating the
data might mean something other than
what I thought it did.
And one of the most important things
learned when it came to data analysis
and situational assessment:
When in doubt, underreact!

The ProPublica incident suggests
that those of us who produce and
analyze healthcare data have a
moral obligation to describe the
context and degree of certainty
that exists in the data we produce.
We are obligated to ensure data
consumers understand the
decisions and conclusions they
should and shouldn’t make, based
upon the data consumed.

The average American will not
understand data-driven analysis.
We’re going to have to make data
literacy a goal for ordinary
Americans, politicians and
managers using data as a policy
tool and as a business driver.
We’re going to need to give folks
the tools to evaluate all data
credibility and train them to ask
tough questions about that data.

Thirty years after entering this
business I am more convinced
than ever that technology and
data combined can help drive the
transformation of healthcare and
American business.
We are entering the Human Era of
Data that will dramatically impact
our evolution as a species.
So no, you won’t hear me talking
much about Big Data. Dale Sanders
Executive Vice President – Software

More about this topic
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going
Doug Adamson – Vice President of Architecture
Hadoop in Healthcare: A No-nonsense Q and A
Jared Crapo – Vice President, Health Catalyst
3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve
Healthcare’s Problems Dale Sanders – Vice President, Software
Predictive Analytics: It’s the Intervention That Matters
Health Catalyst – Webinar
Quality Data is Essential for Doctors Concerned with Patient Engagement
Dr. Ed Corbett – Health Catalyst Medical Officer
Link to original article for a more in-depth discussion.
The Case for Healthcare Data Literacy: It’s Not About Big Data

For more information:

Dale Sanders has been one of the most influential leaders in healthcare analytics
and data warehousing since his earliest days in the industry, starting at
Intermountain Healthcare from 1997-2005, where he was the chief architect for the
enterprise data warehouse (EDW) and regional director of medical informatics at
LDS Hospital. In 2001, he founded the Healthcare Data Warehousing Association.
From 2005-2009, he was the CIO for Northwestern University’s physicians’ group
and the chief architect of the Northwestern Medical EDW. From 2009-2012, he
served as the CIO for the national health system of the Cayman Islands where he helped lead the
implementation of new care delivery processes now associated with accountable care in the US.
Prior to his healthcare experience, Dale had a diverse 14-year career that included duties as a CIO
on Looking Glass airborne command posts in the US Air Force; IT support for the
Reagan/Gorbachev summits; nuclear threat assessment for the National Security Agency and
START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse; and co-
founder of Information Technology International. As a systems engineer at TRW, Dale and his team
developed the largest Oracle data warehouse in the world at that time (1995), using an innovative
design principle now known as a late binding architecture. He holds a BS degree in chemistry and
minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the US Air Force
Information Systems Engineering program.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com

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