Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique: 1. Much of the data is in multiple places. 2. The data is structured and unstructured. 3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex. 5. Changing regulatory requirements. The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.

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© 2014 Health Catalyst
www.healthcatalyst.comProprietary and Confidential
5 Reasons Healthcare Data is
Difficult to Measure
By Dan LeSueur, Vice President Technology

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Characteristics of Healthcare Data
Those of us who work with
data tend to think in very
structured, linear terms. We
like B to follow A and C to
follow B, not just some of the
time, but all the time.
Healthcare data isn’t that
way. It’s both diverse and
complex making linear
analysis useless.
What follows are five
characteristics that makes
healthcare data unique

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Multiple Data Locations
Healthcare data tends to reside
in multiple places. From different
source systems, like EMRs or
HR software, to different
departments, like radiology or
pharmacy.
TOP FIVE
PROPERTIES OF
HEALTHCARE DATA
1
Healthcare data also
occurs in different formats
(e.g., text, numeric, paper,
digital, pictures, videos,
multimedia, etc.

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Data Structured/Unstructured
Electronic medical record
software has provided a
platform for consistent data
capture, but the reality is data
capture is anything but
consistent.
TOP FIVE
PROPERTIES OF
HEALTHCARE DATA
2
As EMR products improve and
healthcare providers become
more accustomed to entering
data in structured fields, the
result will be better data for
analytics.

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Inconsistent/variable
Definitions
Multiple clinicians from different groups may
have inconsistent views of treatment for the
same condition.
Additionally, as we learn more about how the
body works, our understanding continues to
change of what is important, what to measure,
how and when to measure it, and the goals to
target.
We’re trying to create order out of chaos.
TOP FIVE
PROPERTIES OF
HEALTHCARE DATA
3

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The Data is Complex
Clinical data from sources like EMRs
give a more complete picture of the
patient’s story.
Healthcare data is an amalgam of
complex individual systems. Managing
the data related to each of those
systems (which is often being captured
in disparate applications), and turning it
into something usable across a
population, requires a far more
sophisticated set of tools than is needed
for other industries.
TOP FIVE
PROPERTIES OF
HEALTHCARE DATA
4

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Changing Regulatory
Requirements
Regulatory and reporting
requirements also continue to
increase and evolve. CMS
needs quality reports around
measures like readmissions,
and healthcare reform means
more transparent quality and
public pricing information.
TOP FIVE
PROPERTIES OF
HEALTHCARE DATA
5

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Healthcare Data Will Get More
Complex
Healthcare data will not get
simpler in the future. Healthcare
faces unique challenges and with
that comes unique data
challenges.
The solution for this challenge is
an agile approach, tuned for
healthcare.

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Agility Compensates for Complexity
and Uncertainty
The generally accepted method of
aggregating data from disparate
source systems so it can be analyzed
is to create an enterprise data
warehouse (EDW).
Health Catalyst uses a Late-
Binding™ Data Warehouse
approach. With this schema, data is
brought into the EDW from the
source applications as-is, and when
needed, transformed into exactly
what the analysis requires.

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Summary
The Health Catalyst Late-Binding™
Data Warehouse system allows you
to aggregate complex data quickly
and develop business rules on the
fly so users can develop
hypotheses, use the data to prove
them right or wrong, and continue
the discovery process until they are
able to make scientific, evidence-
based decisions.

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Other Clinical Quality Improvement Resources
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Dan LeSueur , Vice President Technology has been developing and
implementing the core products and services of Health Catalyst since
February of 2011. He started as a data architect, moved into a technical
director role and is now a Vice President of Client and Technical
Operations. Prior to joining Health Catalyst, Dan owned and operated a
management consultancy for five years that assisted ambulatory practices
in the implementation of
electronic health records and data-driven management methodologies. In this venture
he served as data architect, business-intelligence developer, and strategic advisor to
physicians and practice owners in the strategic management and growth of their
practices. Dan holds Master’s degrees in Business Administration and Health-Sector
Management from Arizona State University and a Bachelor of Arts degree in
Economics from Brigham Young University.