The document outlines four critical steps for healthcare organizations to improve their analytics effectiveness: tightening insights at the top, loosening data governance at the bottom, building transparency into data processes, and enhancing data visualization. It emphasizes the necessity for actionable insights to drive better outcomes in healthcare, especially as organizations face rising pressures to utilize their data effectively. Addressing these steps can lead to smarter decisions and stronger patient outcomes by utilizing existing data more efficiently.

1. Action Figures
Ready for less talking and more
doing when it comes to using
analytics insights in healthcare?
Start Here.
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2. Introduction
Step 1: Tighten at the Top
Step 2: Loosen at the Bottom
Step 3: Build in Windows, Not Walls
Step 4: Paint a Picture
Think, and Do
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3. YOU’VE TALKED
about the huge potential
impact that analytics
capabilities can make
in your health care
organization.
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So Where are the Results?
YOU’VE HEARD
your peers talk about all
that potential, too –
for years now.
YOU’VE READ
all about it from reporters,
big consulting firms and
anyone else with access
to a keyboard and an
Internet connection.

4. After years of making big investments in analytics, many leaders in healthcare are still
wondering when they’ll see the results their organization has been expecting. In fact,
many of them would be happy to simply see some action – any evidence that new,
analytics-driven insights are at least setting a chain of events in motion that will lead
to improved outcomes.
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According to a global study by Mercer/HCI*, a hefty 69% of organizations report less
than moderate success with analytics initiatives. But in healthcare, it’s a particularly urgent
challenge. Healthcare organizations are awash in data. They finally have analytics
capabilities in place to start making sense of it all, along with the potential to not only
change patient outcomes, but to run more effectively – at a time when the entire industry
is facing unprecedented pressure to deliver on both fronts. They don’t just need to
generate new insights. They need to show results.
* https://www.mercer.com/our-thinking/why-analytics-projects-often-disappoint.html
THIS ISN’T UNIQUE TO THE HEALTHCARE INDUSTRY.
Business and IT Leaders Across Industries Are All
Struggling to Act on the Insights Being Generated
By Their Analytics Tools.

5. The good news is that healthcare organizations already have the
fundamental building blocks in place to act on analytics insights.
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MANY JUST NEED
A STRATEGY FOR
PUTTING IT ALL
TOGETHER.
You’ve got all the pieces to
achieve a high ROI from analytics.
Now, you can create the strategy
for putting it all together.
“
”
That’s exactly why we created this practical guide, highlighting four
critical steps for getting from here to stronger outcomes as quickly
and effectively as possible.

6. Step 1:
Tighten
at the Top
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Even When They’re Delivering a Ton of
Insight, They’re Offering Zero Guidance to
Decision Makers on What Actions to Take.
Regardless of the industry, analytics initiatives tend to share the
same problem. When it comes to prescribing a course of action,
they’re simply too open-ended:
That’s a big deal, especially when you consider that most
organizations still have a long way to go in terms of creating an
“analytics culture,” in which decision makers across the
organization are accustomed to receiving and acting on a steady
stream of analytics-driven insights. “Here’s the information – do
whatever you think is best” isn’t a useful approach in this context.
Imagine a fire hydrant just spraying water indiscriminately, and
you’ll get the idea – some passers-by may fill their buckets with
water, some may just get soaked, and in the end, the vast
majority of the water goes right down the drain.

8. One of the Most Important
Next Steps for Any Analytics
Efforts in Healthcare Is to
Point the Way Toward
Specific Actions.
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That requires knowing exactly who’s
receiving analytics insights, what types of
choices they’re making day in and day
out, how and when they prefer to receive
insights, and which actions they might
take upon receiving them. There’s an
impressive infrastructure at work below
the surface of these analytics initiatives –
those who are able to tighten up the
insights and directions emerging from
the top of this infrastructure are the ones
who will most likely see smarter decisions
and stronger outcomes, faster.
GET SPECIFIC
When receiving analytics-driven
insights, incentives may be indirectly
visible through the shame of “red”
numbers – but the information being
delivered doesn’t explicitly tell the
decision maker “if these numbers
continue on trend for another month,
you’ll lose $1000 of your year-end
bonus.” The more specific analytics
rules can make the context in which
insights are delivered, the more
likely they’ll be acted on.

9. Step 2:
Loosen at
the Bottom
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10. While healthcare organizations need to tighten
up the insights emerging at the top of their
analytics infrastructure, they need to do the
opposite at the data-gathering bottom. At its
core, this is a data governance issue. Consider
that the data governance function is historically
paternalistic and rules-oriented – with good reason!
Garbage In,
Garbage Out
“
”
has been the guiding mantra for data governance for years, because healthcare technology
leaders have always understood that their data systems have to be fed the right type of
data in the right formats in order to make sense of it. That’s why they insisted that every
step forward was tightly controlled and reviewed with multiple layers of oversight.
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11. BUT THIS DYNAMIC IS CHANGING QUICKLY,
BASED ON MAJOR ADVANCES IN ANALYTICS CAPABILITIES.
Data systems are able to accommodate and interpret a wider range of data than ever
before. As a testament to the old adage “one person’s trash is another person’s treasure,”
what may have been considered “garbage” only a few years ago may actually be useful
today. And that means health care organizations need to rethink the rules regarding what
type of data enters their systems, and how it does so. Ultimately, they need to find new
ways to loosen up some of the current constraints of their data governance strategies.
ODDLY ENOUGH, THE KEY TO ALL THIS MIGHT BE FOUND
IN THE HUMBLE DOMAIN OF DATA GOVERNANCE.
In fact, your approach to data governance may be able to transform your entire analytics
capacity. It stands to reason that if you are able to take in more of a wider range of data
types and actually put it to use, your healthcare organization could be generating a whole
new class of unexpected insights, helping identify exactly why patients aren’t complying
with prescribed courses of care, how to improve clinical operations in the ER, you name it.
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12. Step 3:
Build in Windows,
Not Walls
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13. Building Transparency
Into Your Data.
Regardless of the industry, analytics initiatives tend to share
the same problem. When it comes to prescribing a course
of action, they’re simply too open-ended:
WHY IS THIS SO IMPORTANT?
Because with new sources and types of information will come
more questions – the people who are using that data to inform
their decisions want to be sure they can trust it. Without the
assurance that they can defend their decisions, executives,
managers and others are more likely to revert to old modes
of decision making rather than adopting new, advanced
approaches relying more heavily on analytics – such a risk-
averse approach is just considered to be safer, even if less
powerful. This unexpected hurdle can derail even the most
buttoned-up analytics initiatives in healthcare.
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14. ForThis to Work,Your People
Have to Be Good Stewards
of Their Own Knowledge –
for Their Own Sake, and for
the Sake of the Organization.
Otherwise, it’s anyone’s guess whether the
underlying data can be trusted. This will only
happen if your people are provided with
intuitive tools for doing so, in an organized,
integrated fashion, across the organization.
Wikipedia offers a good model here. For any
given Wikipedia entry, a number of people
may have contributed to the knowledge base
– the underlying data governance rules are
quite open. And in every case, it’s easy for
any user to examine the source of the data
and make their own decisions about its
veracity.
ENTICE THEM INSTEAD
When thinking about how to build transparency
into data sources, consider the Wikipedia model.
Wikipedia contributors are volunteers – they
develop, edit and polish content because they
want to, not because of any mandate, and
certainly not because they’re being paid.
(They’re not.) They do it because they care.
This is a fundamental human trait – one shared
by health care workers, who seek autonomy,
want the value of their insights to be recognized,
and above all want to improve the lives of
patients. Give them a clear path to updating,
commenting on and improving critical care-
related data, and they will. Don’t mandate it.
Entice them by simply making it easy.
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15. Step 4:
Paint A Picture
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That’s Not News.
THE POWER OF GOOD DATA VISUALIZATION IS A
WELL-DOCUMENTED ASPECT OF ANY SUCCESSFUL
ANALYTICS STRATEGY.
But in healthcare, what is news is how visualizations are already
being put to work among leaders in the field. Part of their power lies
in their ability to spark action. It’s one thing to squint to find a useful
data point buried in a report alongside dozens or hundreds of other
data points, but when you see a scatter chart showing an alarming
outlier in your department, for example, you’re more likely to act on
the data.
How many patients with a diagnosis don’t have their next
appointment scheduled? Which individual supplies or parts are
driving the most significant cost increases for patients?
Questions like these are more easily discussed, analyzed and shared
with others when they’re visualized – not just numbers on a report.

17. Today There are A Host of Commercially Available
Advanced Visualization Tools That Can be Plugged
into Existing Analytics Solutions, and More Entering
the Marketplace Every Day.
PLUS, THEY CAN BE EVEN MORE POWERFUL WHEN COMBINED
WITH SOME OF THE OTHER STEPS MENTIONED HERE.
For example, in the context of increased transparency around data governance,
imagine a scenario in which a decision maker notices a declining trend on a
visual dashboard, then simply clicks it to learn more about the underlying data.
There are even a growing number of visualization tools already outfitted for the
specific challenges of the healthcare industry.
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Not Using Them?
You Should Be.

18. Think –
and Do
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These Insights Should Be the Air They
Breathe, Providing a Steady Call to Action
Just as Quickly as Data Becomes Available.
IN HEALTHCARE, ANALYTICS INSIGHTS SHOULDN’T BE A
REPORT YOUR DECISION MAKERS RECEIVE ONCE A DAY
OR EVEN ONCE AN HOUR.
IN THIS ENVIRONMENT, DATA CONTEXT IS
NEARLY AS IMPORTANT AS THE DATA ITSELF.
Which report or other data source did these numbers come from?
Who uses those reports, who enters the data into them, and how and
when did they do it? Smart decision makers in health care never take
such information at face value, because they know the consequences
of making the wrong decision based on faulty data. The faster they
can understand its provenance and full context, the better prepared
they’ll be to make timely decisions on it. Seen in this light,
Data Context is the Foundation of Action.

20. THE ORIGEN MODELDELIVERSAN OPEN,COLLABORATIVE,WIKIPEDIA-LIKE EXPERIENCE
from the analyst managing the rules used to compute top-level business metrics, to the
developer who wrote the code, to the administrator entering data, all the way to the six-
sigma blackbelt running a critical organizational initiative. As health care organizations
open the data collection aperture to accept more types of information from more sources,
Origen gives users an intuitive, easy-to-use tool for understanding its full context.
THE LEVEL OF TRANSPARENCY AND OPENNESS INTRODUCED BY THE ORIGEN
PLATFORM IS TRANSFORMING THE WORLD OF HEALTHCARE ANALYTICS –
and it could transform your organization as well, without disrupting all the progress
you’ve made so far.
If You’re Ready to Turn That Mountain of Facts
and Figures into Action, We Should Talk.
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That's Where Amitech Solutions' Origen Platform
Can Make All the Difference.

21. About the Author
PAUL BOAL
Paul serves as Amitech’s Vice President of Delivery,
leading management of the delivery program,
practice growth and maturity and business
development. Paul’s 15+ years of experience with
healthcare information management and analytics
solutions includes the development, promotion
and implementation of enterprise strategies
across a range of information management
disciplines including enterprise data management,
master data management, data warehousing and
business intelligence, and custom analytics
applications. In addition to his position as an
adjunct professor at both Washington University
and St. Louis University, Mr. Boal is currently
focused on helping healthcare companies adapt
to an evolving industry via integrated big data and
advanced analytics strategies and solutions.

22. Amitech is a leading healthcare data analytics
and information management consulting firm.
We strive to make healthcare more proactive,
higher quality, and less expensive for everyone
by helping our clients get the absolute most
from their data.
Amitech Solutions
One CityPlace Drive, Suite 282
St. Louis, Missouri 63141
1.866.870.8920
amitechsolutions.com
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