U.S. healthcare is badly missing the soft, human side of healthcare analytics, especially as it impacts clinicians. How do we fix that? This webinar explores those ideas. You won’t hear Dale talk about SQL, inner joins, outer joins, R, Python, logistic regression, random forest, or convolutional neural networks but instead, in this webinar he talks about the principles and philosophy of analytics. For the most part, we’ve figured out the technology of analytics. That is all left-brain thinking—analytical, logical and methodical in nature—and it is literally getting easier every day with new data technology. But, in healthcare, we’re missing the right-brain thinking—creative and artistic in nature—that has almost nothing to do with technology but has everything to do with the human side of pursuing “data driven healthcare.” Right-brain thinking is required for the oddities and shortcomings of healthcare data, and how to manage those shortcomings in the context of delivering data to the humans who we hope will consume it. The right-brain relates to the personality characteristics of the people who are leading your analytics strategy. It relates to the leadership culture of the organization and where that culture resides on a scale of transparency, internally and externally. The right-brain relates to behavioral economics, evolutionary psychology, human decision making theories, and the fundamental factors that motivate or demotivate human behavior. The right-brain relates to concepts like experimental design and PICO—patients, interventions, comparisons, and outcomes—that, if followed, can make your analytics more truthful and believable. It has to do with the way we negotiate and structure performance-based contracts that are loaded with quality metrics that either measure things that can’t be measured accurately or may measure the wrong thing, altogether. You see, right-brained thinking in this left-brain world of analytics relates to a bunch of things, but mostly it relates to the Golden Rule of Data. Do unto others with data as you would have them do unto you.

1. Healthcare Analytics:
Right-Brain Advice in a Left-Brain World
Dale Sanders
Chief Technology Officer
Health Catalyst
July 2019
Creative Commons Copyright

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• If healthcare analytics is fundamentally about decision support
and changing or maintaining human behavior based on those
decisions
• And healthcare data quality, other than imaging, is overall very
poor, given the criticality of the mission and complexity of
decision making
• There’s a big gap between the data we do have vs. data we
should have to support the criticality of the mission
• And changing human behavior with poor data is incredibly
difficult, if not worse … poor data, if presented as good,
entrenches the wrong behavior
• Then, healthcare analytics success must be accompanied by
an inordinately heavy emphasis on human engagement
Today’s Assertion
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3. Agenda
• The State of Healthcare Data and
Analytics
• Reading List for Human-Side of
Analytics
• Lessons Learned from My Career
• Air Force, Space & Defense
• Intermountain Healthcare
• Northwestern Medicine
• Cayman Islands
• Health Catalyst

4. Healthcare Data &
Analytics:
Brief State of Affairs

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Why have we not seen the analytics
revolution that I predicted in 2002?

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The Human Health Data Ecosystem
• The digital world of health is
largely stuck in the lower left
two clouds.
• Our digital understanding of
the patient requires the entire
data ecosystem.
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Quality of
Healthcare
Data
Relative Ease of Human Behavioral Change
This is where we
should be, given
the criticality of
the mission.
This is where
we are.

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Let’s kick-off a $175B, 10-year
mission to digitize the patient …
not the billing process.
That feels like our generation’s
moonshot.
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IEEE Future Directions; “Can We Have a Digital Twin?”

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Acknowledge Our Poor Data Quality to Physicians
“Here’s the truth: We know that all of these quality measures are a giant
pain in the neck. We know they are burning you out and stripping you of
your sense of mission, autonomy and purpose. We also know that most
of these measures are clinically irrelevant. It’s a spiral of frustration. You
are forced to collect irrelevant data, then we measure your performance
according to that irrelevant data. Going forward, we will minimize all of
that burden, including the pressure we put on you, AND we commit to
giving you the data you want, when you want it, and how you want it so
that you can practice to the top of your profession.”
If I were still a C-level in the trenches of healthcare analytics
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10. © 2018
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The State of Healthcare Data
Quantitative Data Examples
• Lab results
• Some measurements in
diagnostic imaging
• Vital signs
• Dosing
• Genomics
Qualitative Data Examples
• Diagnosis codes
• Clinical notes
Significant amounts of qualitative data
And we only sample data about 3x per
year, and on patients seeking
treatment in a traditional clinical
encounter, none from those.
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• The maturity of any body of science and engineering is directly
proportional to the mathematic models which describe that body’s
predictability and reproducibility.
• Observation, measurement, and data collection precede the
development of mathematical models.
• We must acknowledge the overall lack of data and maturity of
mathematical models in healthcare and adjust our approach to
decision making according to the reality of our current state.
Acknowledge Our Lack of Mathematical Maturity
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Maturity of Mathematical Models
Economics
Psychology
Biology
Quantum
Physics
Electrical, Computer,
Aeronautical, and
Aerospace Engineering
Newtonian
Physics
Healthcare
If not for randomized clinical trials,
which have problems of their own
with predictability and reproducibility,
our “Mathematical Maturity” would
score even lower.
Meteorology
Facebook
Ads Feed J

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• Mouse models != human models
• Cell mis-identity and contamination
• p-values of 0.05 vs. 0.005
• Lack of open data sharing to allow
scrutiny and reproducibility
A Sobering Assessment of Published Research
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This our CTO’s analog life. This is healthcare’s digital
view of our CTO’s life.
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The Patient View: An Unclear Picture

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On Top of Poor Data
We have the usual complexities of human behavior
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Psychology
of Change
Tribalism
Social Norms
of the Tribe
Homo Economicus
& Evolutionary
Biology
Behavioral
Economics
“Truth” According
to Data
“Truth” According
to Perception
This tends to be the
most dominant
influence on
behavior.

16. A Reading List:
Decision Making,
Human Behavior,
and More
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Undergrad student: “What’s the most
valuable class you’ve ever taken?”
Me: “Without a doubt, an epistemology
philosophy class, “What is Truth?”
taught by Dr. Paul Pixler, because it
taught me to think, and peel away the
false layers of belief until reaching
fundamental truths. But it also confused
the hell out of me. I’ve never been too
sure of anything since then.”

18. • What are we doing with analytics and
decision support to satisfy these basic
human needs in clinicians, patients, and
administrators?
• In the US, our national analytics strategy
is detracting from these basic human
needs.
Mastery, Autonomy, Purpose
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History doesn’t
repeat itself.
Human nature
does, which leads
to repeated history.
“Therefore the laws
of biology are the
fundamental
lessons of history.
We are subject to
the processes and
trials of evolution,
to the struggle for
existence and the
survival of the
fittest to survive.”
1963-1969
True “Moon Shot” =
Incredibly Complex
Organizational Challenges x
Unprecedented Engineering
Challenges x Political
Challenges x Human Lives
at Stake x Hopes of the
World
• 410,000 employees and
contractors
• 20,000 companies and
universities
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One of the Best Single References…
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Give people freedom of
choice, but nudge, don’t
shove, with data towards
the preferred behavior.
This book is mostly about
personal self-awareness,
but can readily apply to
empathy and awareness in
others.
Data is persuasive when
it fits your world-view. If
presented insensitively,
data entrenches the
opinion that’s targeted for
change.
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Attributes of a Change Negotiator
Believability: Is this person honest, sincere, trustworthy, and
transparent? If I don’t know them well enough, is there evidence
of that in their background?
Relatability: Is this person similar to me? Can this person
empathize directly with my situation and/or role? Can I relate to
this person along other dimensions of empathy, e.g., upbringing,
religion, age, gender, race, ethnicity, experiences, hobbies,
education, etc.?
Credibility: Even though we’re similar, does this person bring a
diverse view, expertise or knowledge that I don’t have; and that I
value, respect, and need?
Inspired by a RAND Corp study, ~1985…?
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23. Lessons Learned
From the
Field
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Air Force, Space, Defense
Like Healthcare
• Strong personalities
• Life and time critical
• Subjective and objective information
Not like healthcare
• Leadership insistence on more data, more intel,
before making high-risk decisions
• Digitizing aircraft, satellites, and ICBMs, is easier
than digitizing humans
Military Decision Making
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Boeing 777 Data Ecosystem
• 11,600 reportable faults
• 270 prognostic alerts
• >/=1Tbyte per flight
• Aircraft Health Maintenance
System (AHMS)
• Prognostics and Health
Maintenance (PHM)
The aircraft industry started formally planning their strategic data acquisition strategy for
aircraft health maintenance in the early 1990s, which drove the development of sensors.

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Vested Contracting
• Funded by US Air Force
• Traditional, legalese-style of
contracting and treaty
negotiations was not working
• Leaner, faster agreements based
on shared principles,
collaborative economic interests,
and conflict governance and
resolution
• Open books financials
Contracting and negotiating like humans, not robots
1. Focus on outcomes, not transactions.
2. Focus on the WHAT, not the HOW.
3. Agree on clearly defined and measurable outcomes.
4. Pricing model with incentives that optimize both businesses.
5. Insight and resolution versus oversight governance structure.
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Lessons Learned: Intermountain Healthcare
• The economics of an IDN create a
healthy balance and shared incentives
between hospitals, clinics, and
insurance.
• That economic balance makes
behavioral change easier.
More importantly:
• Utah’s state symbol is the beehive, and
that’s not about honey. It’s about
working hard, together, selflessly for the
common good.
• Intermountain’s culture is communal.
• Reducing clinical variation fit the culture.
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Lessons Learned: Northwestern Medicine
• My first executive level meeting was a
disaster.
• The Intermountain-centric message for
analytics didn’t fly.
• Dean Landsberg: “Dale, you just told all
those physician researchers that they
should reduce their variation in care.
They came here to experiment with
controlled variation. Come back with a
different message.”
• Pivoted the analytics strategy to
research, and we became Data Heroes.
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Make the Best Out of the Data with Good Process
PATIENT OR PROBLEM
How would you describe a similar group of patients?
What are the most important characteristics of the
patient?
INTERVENTION
What main intervention are you considering? What do
you want to do with this patient?
COMPARISON
What is the main alternative being considered, if any?
OUTCOME
What are you trying to accomplish, measure, improve
or affect?
PICO: Ask the questions
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In 2007, this was our approach to
“Meaningful Use” at Northwestern
Medicine.
Purposely constrained to one page, based
on principles that could be measured, but we
didn’t make measurement the core
message.
We made common sense the core message.
This was the seed that turned into the weeds
of Meaningful Use.
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Drained the analytics energy out of the industry.
We are obligated to unwind the legacy of MU as quickly as we created it.
Start over.
Starting in 2009,
Meaningful Use Became Meaningless Use
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• It was a nudge, not a push, to embrace
analytics.
• The autonomy of government-employed
physicians in a small labor pool, made
behavioral change difficult.
• The culture was caught between the US and
UK influence.
• Culturally, they treated me very well, and I
loved them, but there was always a sense that
the American Guy had his own agenda, and
would only be around for a short time.
Lessons Learned: Cayman Islands
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Lessons Learned: Health Catalyst
• Our Intermountain skills play well
in some places, but not in others.
• Most clients are not in an IDN
economic model.
• Many cultures are not as
”beehive” as Intermountain.
• Clinical variation reduction has
taken a back seat to quality of
care analytics tied to
reimbursements, and preventive
care.
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34. Market Segment Core Behavioral and Cultural Economic Drivers
Integrated Delivery
Network
Higher quality, lower cost for their covered lives; but they can’t distinguish between covered and uncovered lives, so they
operate most closely to our value proposition. Good combination of care optimization plus care prevention.
Academic Medical
Center
Heads in beds for high acuity, complex and rare patients; grant funding and research; clinical trials enrollment;
publications. They typically have reimbursement levels from payers that are 300-400% higher than CMS, but there is a
shift to reference pricing that they are starting to feel. Quality measures as it relates to brand and status.
Community Hospital Payer mix frequently dominated by Medicare/Medicaid (60-70%) and commercial payers with reference-based pricing;
higher bad debt ratios. Typically an important employer in the community so reducing staff through efficiencies is not
naturally appealing. Cutting costs through more efficient operations is a high priority.
Direct to Employer
Healthcare
This is the segment that is naturally aligned with maximizing health for the lowest cost possible.
Risk Management
Entities, including
PHMOs
This segment is naturally aligned with our mission, but in the big scheme of the economic layers, it’s yet another layer that
someone is paying for. Benefits accrue to payers and physicians/hospitals through split margins on MLR savings with the
Risk Management entity.
ACOs/CINs Performance on Quality Measures and upside risk contracts; downside risk is almost non-existent right now in today’s
market.
Large Physician
Practices
Physician productivity and per physician revenue.
Life Sciences Pharma, rare disease biotechs and digital therapeutics: risk-based contracts, adoption of guidelines in areas where they
are market leaders, pragmatic trials, filling more prescriptions through medication adherence; pharmacovigilance:
distinguishing via root cause analysis true adverse events; pragmatic trials; patient selection algorithms; clinically
identifying and justifying off-brand use; clinical trials acceleration/adoption; finding rare disease patients to treat.
Adjust Your Analytics Strategy to the Cultural Economic Drivers

35. © 2019
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Catalyst
Wrapping Up
• Find your Data Empathy, especially
for physicians.
• Acknowledge the limitations of
existing data and analytics, then
adjust your decisions accordingly.
• Give physicians some form of
analytic hope… give them some
data that they want and need.
• Look for and adapt to the social
norms of the tribe… avoid head-on
collisions.
• Nudge, don’t push.
• Campaign for digitizing the patient.
https://kellyepperson.com/brain/
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36. © 2019
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Catalyst
Sept. 10-12, Salt Lake City, Grand America Hotel
• National Keynotes
• Digital Innovation Showcase
• 28 Educational, Case Study, and Technical Breakouts
• 24 Analytics Walkabout Projects
• Machine Learning Marketplace
• Networking (bringing back ”Braindate”)
• CME Accreditation for Clinicians
• 5-Star Grand America Hotel Experience
• 96 Total Presentations
Healthcare Analytics Summit 19
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