The document discusses three common pitfalls in healthcare analytics: 1) using point solutions that focus on single goals and data slices, 2) relying solely on electronic health record systems, and 3) having independent data marts in different databases. It recommends using an enterprise data warehouse to aggregate data from multiple sources into a single system of truth. The document also describes two common sources of inefficiency: the report factory approach and flavor-of-the-month approach, and recommends a robust deployment system to address these.

1. How to Avoid the
Three Most Common
Healthcare Analytics
Pitfalls and Related
Inefficiencies
Russ Staheli
Technical Director

2. By investing in analytics
you are signing up for
data-driven solutions.
You want to identify and
eliminate those problems
and inefficiencies that
keep you from improving
quality and lowering cost.
Finding a sustainable approach to using analytics
without problems and further waste can be a challenge,
it is possible to avoid these concerns if you better
understand how to choose a good analytics solution.
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Data-driven Solutions
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3. © 2014 Health Catalyst
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Three Common Pitfalls in Analytics
1 Point Solutions
When developing an
analytics platform, some
health systems deploy one
or more best-of-breed or
point solutions.
These applications focus on
a single goal and a single
slicing of the data. They
offer little insight outside the
specific area of focus.
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4. © 2014 Health Catalyst
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Three Common Pitfalls in Analytics
2 Electronic Health Record System
While implementing an EHR is
a crucial step towards data-driven
care, an EHR system
alone is insufficient to enable an
enterprise-wide, consistent data
view from multiple sources.
Clinical, financial, patient
satisfaction, and administrative
data must become a single
source of truth to truly harness
the analytic power of the data.
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Three Common Pitfalls in Analytics
3 Independent Data Marts in Different Databases
Independent data marts from
different databases provide
limited analytics capabilities
because they can only deliver
little sources of truth from the
different system siloes.
For example, when the ADT
(admission, discharge, transfer)
data lives in the EMR, analyzing
associated costs and quality
impacts are extremely inefficient.
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6. For actionable clinical,
financial, and operational
insights that meet your needs
across the enterprise, an
enterprise data warehouse
(EDW) is the best solution
we’ve seen to date.
An EDW captures, aggregates,
and analyzes data in near real-time
from the EHR and other
internal and external systems
that reside in silos.
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Three Common Pitfalls in Analytics
Avoid These Analytics Pitfalls with an EDW
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7. Two Common Sources of Inefficiency
The report factory approach
uses an analytics platform
alone and assumes that if you
build it, people will come.
Reports can begin to back up
in the request queue. If IT
can’t keep up or report
delivery is too slow, the
situation can render your
chosen solution redundant.
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1 Report Factory Inefficiency
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8. Two Common Sources of Inefficiency
2 Flavor of the Month Inefficiency
To avoid becoming a report
factory, sometimes a different,
more measured approach is
used to develop an analytics
platform. This, however, can
result in a project-by-project
or flavor-of-the-month
approach to analytics.
It’s difficult to keep up with
multiple projects and quality
and cost gains made in the
initial projects are quickly lost.
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9. Two Common Sources of Inefficiency
Avoid Inefficiencies with a Robust Deployment System
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A good deployment system
addresses both the flavor of
the month and report factory
inefficiencies.
A good analytics deployment system provides a methodology for
effectively getting clinicians and other stakeholders throughout
the organization to embrace your analytics solution and to use
data themselves to drive decisions.
A robust deployment system leverages existing personnel to
form permanent, cross-functional teams within each area where
the healthcare data analytics will be used. Each team takes
ownership for their own projects in their own space.
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10. © 2014 Health Catalyst
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Link to original article for a more in-depth discussion.
How to Avoid the 3 Most Common Healthcare Analytics Pitfalls and
Related Inefficiencies
More about this topic
4 Ways to Enable Healthcare Data Analysts to Provide Their Full Value
Russ Staheli, Technical Director
Why Point Solutions Strike Out
Ken Trowbridge, Vice President
Problems with EHRs and Built-In Analytics
David Burton, MD, Senior VP and Former Executive Chairman
Why a Partial Data Warehouse Can’t Solve Your Transformation Challenges
Dan Burton, CEO
3 Essential Systems to Overcome Population Health Analytics Challenges
Jared Crapo, Vice President
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For more information:
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12. Other Clinical Quality Improvement Resources
Russell Staheli joined Catalyst as a data architect in October 2011. He
started his career as an Intern and later Outcomes Analyst at Intermountain
Healthcare in the Institute for Health Care Delivery Research supporting
the Advanced Training Program for Executives & QI Leaders (ATP) and the
Primary Care Clinical Program. Before coming to Catalyst he worked as a
Management Engineer Programmer Analyst for the Duke University Health System in
their Performance Services department supporting their Infection Control and
Epidemiology efforts. While there, he also worked as an external consultant to advance
the analytical work of the Duke Infection Control Outreach Network (DICON), a
collaborative of over 30 community hospitals. Russ holds an Master of Public Health in
Health Policy and Administration from University of North Carolina Chapel Hill and a
Bachelor’s degree in Health Services Research from the University of Utah.
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