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© Health Catalyst. Confidential and Proprietary.
Empowering Leaders with
Self-Service Analytics
Jason Jones, PhD
Chief Analytics & Data Science Officer
Health Catalyst
© Health Catalyst. Confidential and Proprietary.
Key Takeaways
• Analytic competitiveness can be defined and broken down into
achievable parts​
• Errors of interpretation are predictable and can be avoided​
• Standard tools exist to drive correct, consistent, and
transparent insight​
• Using these tools takes us from “flying blind” to winning with
superior visibility and maneuverability
© Health Catalyst. Confidential and Proprietary.
Skills
Aim
Harris, J., & Davenport, T. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Review.
Experimental
Can apply principles of experimentation to their
business
Numerate
Don’t need to be statisticians, but must understand
proper use and interpretation of approaches
Should be adept at understanding what data are good
enough, sources, and data-generating flow
Data Literate
Analytic
Competitor
Drive strategic advantage from applying analytics to
solve problems
Self-Service Analytics
Analytics is the process of discovery, interpretation, and communication of meaningful
patterns in data to drive effective decision-making
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Poll Question
When was the last time you suspect your organization made an avoidable, wrong decision?
A. Last week
B. Last month
C. Last quarter
D. Last year
E. Maybe never
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Poll Question
How are you preventing avoidable, wrong decisions?
A. Have or are hiring specialized analysts
B. Have or are training specialized analysts
C. Have or are hiring for or training decision makers
D. Have or are investing in analytic tools
E. This is a gap I worry about
F. Other
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3 Types of Analytic Tools
Bifocals
Read history and
see the horizon
X-ray
See what is there
but missed easily
or uncovered
painfully
fMRI
Visualize what
cannot be seen
(Self-service)
Data
Correct,
Consistent, &
Transparent
Insight
We continue to see repeated failures in driving correct insights from available data
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What are we seeing?
Do we have a problem?
(Bifocals) Benjamin Franklin as a Man of Letters
https://archive.org/details/benjaminfranklin00mcma/
page/n9/mode/2up
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Demo Time: Intro Screen 1
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Demo Time: Intro Screen 1
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Interactive
Use link via Zoom
Feel free to use a fake email address
(e.g., tweedle@mail.com)
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96% of people have not been able to answer correctly…
• What is our baseline performance?
• (Where) have there been any persistent shifts in perfo
• Which points are extreme outliers?
• Where do we expect to be in 6 months?
Time Series Data—Stroke Mortality…are we okay?
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Time Series Data—Stroke Mortality…not okay!
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What could we not see?
How might we improve?
(X-Ray) Marie Curie in one of her mobile X-ray units in October 1917
https://www.smithsonianmag.com/history/how-marie-curie-
brought-x-ray-machines-to-battlefield-180965240/
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With end-user expertise, we can shift understanding and improveme
• Which units (providers) should we focus on?
• How is performance clustering?
• What type of improvement strategy do we need?
• Where is performance going?
Grouped Data
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Grouped Data, Enhance (including time element)
With end-user expertise, we can shift
understanding and improvement
approach…
• There are 5 distinct performance
groups
• Almost no one is performing at
the “system average”
• Top performers continue to
improve
• Bottom/mid performers continue
to get worse
…because we haven’t trained them
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What do we wonder?
Is this drug/device/program
worth it?
(fMRI)
By National Institute of Mental Health - US Department of
Health and Human Services: National Institute of Mental
Health, Public Domain,
https://commons.wikimedia.org/w/index.php?
curid=17982524
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Left plot shows important imbalances
(potential confounding):
 Important differences in race, marital
status, prior income, age, no degree
 Small differences in education level
 Random number has no difference, as
expected
Confounding:
 Want to draw conclusion about how
A (e.g., training) impacts B (e.g.,
income)
 But a confounder C (e.g., prior
income) is related to both A and B.
A
C
B
Observational Data and Confounding
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Observational Data and Confounding…Addressed
Randomized trial results
$8,037 increase in income
Unadjusted observational data
 $2,845 decrease in income
 50% decrease in exiting poverty
 10% increase in having any income
Matched/weighted results
 $7,908/$6,446 increase in income
 No difference in exiting poverty
 No difference in having any income
 Multiple outcome measures provide
more context and confidence
 Matched/weighted results agree with
experimental ones
 Randomized designs are optimal
 Without randomization, methods
should be applied to test and adjust
for group differences
Adjustment approach 1
Adjustment approach 2
Adjustment approach 1
Adjustment approach 2
Adjustment approach 1
Adjustment approach 2
“My patients
are sicker
”
Approach 1
Approach 2
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We should provide great data
and great insight
Self-service is a critical path
because we often don’t have
analysts in the decision loops
© Health Catalyst. Confidential and Proprietary.
Key Takeaways
• Analytic competitiveness can be defined and broken down into
achievable parts​
• Errors of interpretation are predictable and can be avoided​
• Standard tools exist to drive correct, consistent, and
transparent insight​
• Using these tools takes us from “flying blind” to winning with
superior visibility and maneuverability
Questions?
© Health Catalyst. Confidential and Proprietary.
jason.jones@healthcatalyst.com