When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices. To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.

1. Interoperability in
Healthcare Data:
A Life-Saving Advantage

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Health system-sponsored, regional and
statewide health information exchanges
(HIEs) have come a long way in giving
healthcare providers vital patient information
needed to achieve population health
management (PHM) goals.
Interoperability in Healthcare Data

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Robust healthcare analytics that combine data
from these HIEs with clinical, financial, and
operational data from within the “four walls” of a
health system with other forms of data, such as
patient-generated health data (PGHD) and
patient-reported outcomes (PROs), provide a
more complete picture of a person’s state of
health and healthcare needs.
Interoperability in Healthcare Data

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This article explains how an interoperable
platform approach to population health (e.g.,
the Health Catalyst® Data Operating System
[DOS™]) combines, manages, and leverages
HIE data, healthcare encounter data, and
many other forms of data for a comprehensive
view of patient and population health.
Interoperability in Healthcare Data

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Back in 1998, my mother was diagnosed with
ovarian cancer. Her symptoms were originally
misdiagnosed as a gastrointestinal issue, but within
a short period of time, she was (unfortunately)
correctly diagnosed with stage 4 ovarian cancer.
For the next seven years, my mom visited many
healthcare providers of all types, within the acute
and ambulatory care settings and spanning
primary and specialty care providers.
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle

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During each visit and hospital stay, my mom had
numerous procedures, lab tests, MRIs, and other
scans, a multitude of medications, chemo-cocktails,
radiation therapy, and a lot of love from a caring
clinical community.
Each of these encounters generated significant
amounts of data. Due to the siloed nature of
healthcare in the U.S., however, my mom’s
caregivers had some data about her, but no one
provider had all the data about her.
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle

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Data Is Everywhere, But Nowhere
As a healthcare IT professional, I remember
being extremely frustrated because we had
to explain, and reexplain, my mom’s medical
history to each new provider.
No one clinician could provide optimal care
because they didn’t have a complete picture
of my mom. It was like trying to put together a
jigsaw puzzle with a bunch of pieces missing.
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle

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Data Is Everywhere, But Nowhere
Each provider knew what they did for her
but rarely knew what other providers were
doing and had done previously.
The result was increased costs, duplication
of tests and procedures, unwarranted
variation in what/how care was delivered,
and an overly complicated and disjointed
patient experience.
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle

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Interoperability in Healthcare Data Might Have Changed My Mom’s Experience
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle
My mom passed away in 2005, which was
before the advent of HIEs born just a few years
later as a byproduct of the 2009 HITECH Act.
While each provider had their own data about
my mom, without HIE data and robust analytics,
each provider across the various care settings
acted and made decisions about her treatment
based on incomplete information.
If every caregiver had access to all of her
encounter data, the opportunity to deliver
higher-quality, safer, and lower-cost care
would have dramatically improved.

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Interoperability in Healthcare Data Might Have Changed My Mom’s Experience
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle
If we apply my mom’s patient experience across
thousands, or millions, of people in an ACO or
value-based care arrangement; in a geography,
state, or across the U.S., the opportunity for
adverse quality, safety, and cost outcomes
multiplies almost exponentially.
In fact, trying to manage the health and
healthcare of individuals and populations without
complete information is like Russian roulette—
threatening, dangerous, and unpredictable.

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Interoperability in Healthcare Data Might Have Changed My Mom’s Experience
Population Health Management Can Be
Like Assembling a Jigsaw Puzzle
Adding to the complexity, 80 percent of the
information that describes a person’s health
status (Figure 1), and is essential for managing
population health, is determined by factors
outside a traditional care setting.
This information comes in a variety of data types
and resides in anything from enterprise systems
to consumer devices.
EHRs will always be incomplete data sources,
and while HIEs are closer, a DOS-like platform
approach offers the most comprehensive
picture of patient health.

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Population Health Management Can Be
Like Assembling a Jigsaw Puzzle
Tobacco Use
Diet & Exercise
Alcohol & Drug Abuse
Sexual Activity
Health Outcomes
Health Factors
Policies & Programs
County Health Rankings model @ 2014 UWPHI
Length of Life 50%
Quality of Life 50%
ExpandedSkills&Data
Up&Down
Access to Care
Quality of Care
Education
Employment
Income
Family & Social Support
Community Safety
Air & Water Quality
Housing & Transit
Health Behaviors
(30%)
Clinical Care
(20%)
Social and
Economic Factors
(40%)
Physical
Environment
(10%)
Figure 1: Only 20 percent of factors affecting patient health occur in the clinical setting
Robert Wood Johnson &
University of Wisconsin
Public Health Institute

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Maximizing Data with an
Interoperable Analytics Platform
To maximize the value of comprehensive patient
data, health systems and HIEs need a platform
approach to managing data.
It must be open, interoperable, scalable, and
can apply intelligence to the data at whatever
velocity it comes in, whether the data is
structured or unstructured.

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Maximizing Data with an
Interoperable Analytics Platform
Consider an everyday example: an iPhone comes
with the iOS mobile operating system, which has
numerous native capabilities.
iOS is interoperable with other platforms, such as
Android, and is an open application development
platform.
I have more than 200 apps on my phone from
different software companies, all of which
leverage the native iOS capabilities.
For example, when I travel, I use Apple
maps for navigation, Waze for monitoring
traffic, and Citymapper for getting around
unfamiliar cities in Europe.

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Maximizing Data with an
Interoperable Analytics Platform
All three of these apps leverage the native
GPS that comes with iOS via open APIs, so
each app developer doesn’t need to reinvent
the GPS wheel.
My iPhone also integrates data from my Apple
Watch, recording figures such as heart rate,
sleep patterns, and physical activity; these all
interoperate with my other exercise apps
(e.g., Strava).
These apps provide all sorts of analytics,
suggestions, and predictions, which help me
stay healthy and fit.

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Maximizing Data with an
Interoperable Analytics Platform
Following the iOS interoperability model,
as an interoperable analytics platform,
Health Catalyst’s DOS delivers the
comprehensive health system and HIE
data that care teams need to fully
understand a patient’s situation and make
the most informed treatment decisions.
HIE data feeds into the DOS platform,
capturing patient information along the
continuum of care, from primary care
physicians and specialists to labs, skilled
nursing facilities, visiting nurses, and
emergency departments.

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Maximizing Data with an
Interoperable Analytics Platform
Now, imagine if hospitals and health
systems took a platform approach to
managing data and analytics at an
enterprise scale and were able to
integrate and interoperate with data
from across the healthcare continuum.
Additionally, they could integrate and
interoperate with the 80 percent of
health determinants not found in
a clinical care setting.

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Maximizing Data with an
Interoperable Analytics Platform
Like the iOS or Android operating systems, a healthcare data and analytics
platform includes critical capabilities:
Reusable clinical and business logic
Streaming data
Integrates structured and unstructured data
Workflow integration
Microservices architecture
Machine Learning and artificial intelligence
Agnostic data lake
>
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An Analytics Platform Leverages Complete
Health Information for Better Care
If a healthcare analytics platform like
DOS had been in place when my
mom was in treatment, her healthcare
providers and I would have had a
comprehensive view of her health.
This view would include not only the
myriad data from her hospital and
clinic visits, but other forms of data
from beyond the clinical care setting
(e.g., diet, exercise, and lifestyle).

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An Analytics Platform Leverages Complete
Health Information for Better Care
This data would be “liquid” and easily
exchanged with everyone on my mom’s
care team—and with the rest of my family,
allowing us to be proactive (versus
reactive) in managing her care.
She could have avoided the numerous
repeated and duplicative tests and
procedures, and her care would be better
coordinated.
I wouldn’t have had to be a “human DOS,”
lugging boxes of her medical records to
each doctor visit.

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An Analytics Platform Leverages Complete
Health Information for Better Care
All members of her care team could
communicate openly and securely, putting
my mom at the center of the focus, rather
than each individual encounter.
My mom and I would be able to interact
with the care team in real time, via voice,
text, and video, reducing the number of
onsite visits to hospitals and clinics.
These visits were exhausting, costly, and
time consuming for all of us, but
especially my mom.

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An Analytics Platform Leverages Complete
Health Information for Better Care
She would have been able to use a multitude of
apps to more effectively manage her care and
treatments.
Her data, combined with research and
genomics data, could have helped to
align her with a clinical trial quickly,
bringing the best of modern medicine
to the forefront of her care.

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The Life-Saving Potential of
Interoperability in Healthcare Data
If we think about a broader population than just
my mom, and if we had more data than that
found just within the four walls of the hospital or
health system, we could proactively identify
people at risk for contracting illness well in
advance of the person becoming sick.
We could move from today’s world of managing
illness to managing health and wellness and
keeping people out of the hospital.
Quite literally, an intelligent and interoperable
healthcare data operating system could
possibly have saved my mom’s life.

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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.
Interoperability in Healthcare Data: A Life-Saving Advantage
Finding and Compiling Salient Population Health Data
Dan Soule, VP Product Management
Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability
Dale Sanders, President of Technology
Seven Ways DOS™ Simplifies the Complexities of Healthcare IT
Dale Sanders, President of Technology
Healthcare Analytics Platform: DOS Delivers the 7 Essential Components
Imran Qureshi, Chief Software Development Officer
5 Reasons Healthcare Data Is Unique and Difficult to Measure
Dan LeSueur , SVP of Professional Services

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Joined Health Catalyst in February 2014. Greg has been in the Healthcare IT industry since
1985, starting his career at New England Medical Center in Boston. Over the years, he
worked with innovative healthcare IT vendors and consulting firms such as TSI, Healthlink,
IBM, Picis and most recently, Medicity. Before coming to Health Catalyst, he led a consulting
practice called Storyleaders.
Greg Miller

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company
that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes
needed to improve population health and accountable care. Our proven enterprise data warehouse
(EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more
than 65 million patients for organizations ranging from the largest US health system to forward-thinking
physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”