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Data Science for Healthcare:
What Today's Leaders Must Know

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Data Science for Healthcare
Success in today’s data-driven healthcare
industry will be increasingly defined by
leaders who understand data science.
This knowledge will be critical as
executives build and guide teams toward
a harmonious, well-planned vision for
healthcare improvement that fully
harnesses data’s capabilities.
Up to 30 percent of the world’s warehoused data comes from the healthcare
industry. There’s significant opportunity for healthcare improvement in this
information cache, including an estimated $300 billion in annual cost savings.

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Data Science for Healthcare
But the industry can only welcome these
prospects if health systems fully leverage data
to identify areas for improvement and promote
evidence-based care. Even with this massive
data potential, healthcare too often relies on
outdated technology.
For example, up to 75 percent of medical
communication still occurs via fax machine (in
an era where automotive companies use data
science to add navigation capabilities to cars).
Technology has laid out the opportunities, but,
to realize gains in the digital era, healthcare
leaders must understand data science and the
urgency of investing in data science resources.

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The Bird’s Eye View of What Data Scientists Do
As a broad term, data science means
pulling information out of data, or
converting raw data into actionable
insights.
Data scientists are knowledgeable in their
subject matter (e.g., healthcare clinical
data) and statistics, and use computer
programming skills to tell the computer
how to leverage data to derive insights.

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The Bird’s Eye View of What Data Scientists Do
Data scientists augment traditional data
analysis by automating the process of
insight delivery through code.
This automation can bring efficiency gains
and new depths of insight to analytics,
and enables real-time predictive analytics
by reducing the time it takes to go from
data to prediction.

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The Heart of Healthcare Data Science:
Machine Learning Models that Yield Deeper Insights
The heart of data science is machine
learning models, which are basically
statistical models that can be used to
extract patterns from data.
Data science and machine learning can
also be thought of as using the power of
modern computing to leverage statistics.

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The Heart of Healthcare Data Science:
Machine Learning Models that Yield Deeper Insights
Some machine learning models, such as
regularized regression and decision trees,
lend themselves well to deriving insights
and explaining patterns in data.
Other machine learning models, such
random forests and neural networks (deep
learning), are primarily used for prediction
(e.g., each patient in a population’s
likelihood of readmission after discharge).

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Healthcare Data Science Is the Key to Faster
Diagnosis, Better Treatment
Healthcare has long relied on data and data
analysis to understand health-related issues and
find effective treatments.
For example, researchers have used double blind
placebo-controlled studies as the foundation of
evidence-based medicine.
Such studies generate data about the treatment
under evaluation and analyze that data to
determine whether the treatment is effective, as
well as understand its side effects.
As a method of generating data and insight, this
study process works in a spirit similar to data
science, but is costlier and more time consuming.

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Healthcare Data Science Is the Key to Faster
Diagnosis, Better Treatment
Today, healthcare needs data to optimize patient
outcomes with evidence-based practices more
than ever; those insights are waiting to be
discovered in data that has already been collected.
With data science, the industry can find efficient,
cost-effective ways to harness vast amounts of
existing healthcare data—to maximize its potential
to transform healthcare with faster, more accurate
diagnosis and more effective, lower-risk treatment.

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Data Science for Healthcare in Action
Researchers from Stanford University have
developed a model that can diagnose irregular
heart rhythms (arrhythmias) from single-lead ECG
signals better than a cardiologist.
Clinicians record more than 300 million ECGs
annually, so the data needed for improved
arrhythmia diagnosis already exists.
With data science, health systems can leverage
this information to make more accurate and more
efficient diagnoses.

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Data Science for Healthcare in Action
Another group of Stanford researchers has
developed a diagnostic model for skin cancers that
uses AI to classify images of skin lesions as benign
marks or malignant skin cancers.
This model, which can classify lesions as
accurately as board-certified dermatologists, can
potentially save health systems and patients time
and cost by transforming the multistep process of
diagnosing skin cancer into a single-step data
analysis.
While models are not designed to replace
clinicians, they can provide valuable diagnostic
guidance, making the care process both more
efficient and more effective.

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Data Science for Healthcare in Action
Mission Health wanted to improve the accuracy of
its readmission risk assessment, so it leveraged
machine learning to develop a predictive model
based on its own patient population.
Mission was using the LACE index to predict risk
for readmission, which, while somewhat helpful,
was developed using a patient population from
Canada that was notably different from Mission’s
demographic.
With a machine learning model that used its own
population, Mission improved its readmission risk
prediction to outperform LACE and achieve a
readmission rate 1.2 percentage points lower
than its top hospital peers.

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The Time to Understand and Leverage Data
Science Is Now
Data will continue to be a dominant factor in
healthcare delivery and outcomes improvement.
For organizations to successfully navigate the
complexity of a data-driven world and embrace
improvement opportunities, healthcare leaders
must understand data science; they must become
students of data science, understanding how it’s
working in other companies and its implications
for their health systems.
And, if they haven’t already, leaders must start
developing data scientist skills on their teams.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Data Science for Healthcare: What Today's Leaders Must Know
The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation
John Wadsworth, Technical Operations, VP
Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper)
Dale Sanders, President of Technology
In Healthcare Predictive Analytics, Big Data Is Sometimes a Big Mess Kenneth Kleinberg,
David Crockett, PhD., Research & Predictive Analytics, Sr. Director
Patient Flight Path Analytics: From Airline Operations to Healthcare Outcomes
Dale Sanders, David Crockett, Justin Gressel
Machine Learning, Predictive Analytics, and Process Redesign Reduces Readmission
Rates by 50 Percent Health Catalyst - EDW/Machine Learning Solution

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Michael joined Health Catalyst in June 2017 as a data scientist. Prior to coming to Health
Catalyst, he received his PhD from the University of California, Davis in Ecology using network
analysis to study how people learn and think about sustainable agriculture. Michael also has an
MS in Biology from West Virginia University and a BS in Chemistry from Fort Lewis College.
Michael Levy