Advancing Health Equity:
A Data-Driven Approach Closes the
Gap Between Intent and Action
Jason Jones, PhD
Chief Analytics and
Data Science Officer
Trudy Sullivan, MBA
Chief Communications and Diversity,
Equity, & Inclusion Officer

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Advancing Health Equity
Beginning in 2020, the dramatically higher
COVID-19 infection and morbidity rates
for communities of color compared to
white communities heightened awareness
about the human and financial costs of
inequitable healthcare.

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Advancing Health Equity
In response, more healthcare organizations
are prioritizing advancing health equity and
investing in diversity, equity, and inclusion
programs and leadership—all of which
require data to showcase the most
significant gaps and healthcare disparities.

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Advancing Health Equity
Equity is one of six quality dimensions in
healthcare and has been since the National
Academy of Medicine (NAM) published
“Crossing the Quality Chasm” in 2001.
Since 2018, the Center for Medicare and
Medicaid Services (CMS) has presented
Health Equity Awards to recognize
organizations committed to overcoming
disparities through a strategic approach to
identifying, prioritizing, and addressing
improvement areas.
Effective quality assessments include
equity as a requisite.

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Advancing Health Equity:
Quality Care Must Be Equitable
According to NAM, quality healthcare must be
safe, effective, patient centered, timely, efficient,
and equitable.
NAM defines equitable as:
Providing care that does not vary in quality
because of personal characteristics including
gender, ethnicity, geographic location, and
socioeconomic status.”

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Advancing Health Equity:
Quality Care Must Be Equitable
Yet, healthcare organizations often
rely solely on passion and
perseverance to address equity.
There is a gap in reliance on analytics
and augmented intelligence (AI) to
identify and address inequitable care.
As an industry, would not pursue
improvement in the other dimensions,
such as safety and effectiveness,
without relying on data, and we
must close the equity gaps by
leveraging data.

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Advancing Health Equity:
Quality Care Must Be Equitable
NAM’s statement on quality care
provides a hint as to how to close the
gap. If the industry changes “care that
does not vary…” to “care that cannot
be predicted by…,” we open the
possibility of using an AI toolset..

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Advancing Health Equity:
Quality Care Must Be Equitable
Health equity becomes the predictive model
we hope we would not need to build.
We feel “good” about being able to predict
a patient’s readmission based on their
medication complexity (we might intervene
with medication simplification, support, or
education).
However, we do not feel good about higher
readmission risk due to personal character-
istics, to include primary spoken language
or zip code.

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The Universal Benefits of
Advancing Health Equity
All healthcare organizations benefit from
data to improve health equity and
formalize and operationalize equal
access to and delivery of healthcare
for all patients.
Along with improving clinical outcomes,
bolstering health equity improves health
systems’ operational and financial
performance.
A 2016 IHI white paper projects that, left
unchecked, health disparities could
reach an economic burden in the United
States of $353 billion by 2050 (Figure 1).

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The Universal Benefits of
Advancing Health Equity
Figure 1: Economic burdens of health disparities if unchanged in the U.S.

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COVID-19: A Stark Real-World
Example of Health Inequity
COVID-19 demonstrated that U.S. health
systems are falling short in health equity.
Vulnerable communities have borne the
pandemic’s brunt from infection rates and
risk of death to access to treatment,
testing, and vaccination.
For example, based on early pandemic
data reported in JAMA, residents in
predominantly Black counties in the United
States experienced three times the COVID-
19 infection rate and six times the death
rate of chiefly white counties (Figure 2).

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COVID-19: A Stark Real-World
Example of Health Inequity
Figure 2: COVID-19 impact predominantly Black versus white counties.

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COVID-19: A Stark Real-World
Example of Health Inequity
Not surprisingly, the disparity in COVID-19
impact among Black populations reflects
decades of documented disparities in
healthcare.
A report from the Kaiser Family Foundation
states that the Black and American Indian
or Alaska Native (AIAN) individuals
continue to fare worse across most
examined health status measures,
including physical and mental health
status (Figure 3).

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COVID-19: A Stark Real-World
Example of Health Inequity
Note: Measures are for 2018 or the most recent year for which data is available. "Better" or "Worse" indicates a significant difference
from Whites at the p<0.05 level. No difference indicates no statistically significant difference. "Data Limitation" indicates data are no
separate data for racial/ethnic group, insufficient data for a reliable estimate, or comparisons not possible due to overlapping samples.
AIAN refers to American Indians and Alaska Natives. NHOPI refers to Native Hawaiians and Other Pacific Islanders. Persons of
Hispanic origin may be any race but are categorized as Hispanic for the analysis; other groups are non-Hispanic.
Figure 3: Number of measures for which diverse racial and ethnic groups fared better, the same, or worse than whites.

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COVID-19: A Stark Real-World
Example of Health Inequity
Meanwhile, Black and AIAN individuals’
infant mortality rate is roughly two times
higher than for white individuals.
Black teens and adults have a more
than eight times higher HIV diagnosis
rate, and Hispanic teens and adults
have a more than three times higher
HIV and AIDS diagnosis rate.

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COVID-19: A Stark Real-World
Example of Health Inequity
Healthcare disparities, including those
related to COVID-19, are part of broader
underlying social and economic inequities,
making it more critical than ever for
organizations to have data and analytics to
understand the personal characteristics,
goals, preferences, and circumstances of
those they serve.

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COVID-19: A Stark Real-World
Example of Health Inequity
Primary drivers for increased insights
include the social determinants of
health (SDOH), the conditions in which
people are born, grow, live, work, and
age—all areas healthcare data hasn’t
traditionally captured.
As COVID-19 further underscores
health inequities between U.S. racial
and ethnic groups, the industry can’t
ignore the demand for comprehensive
patient data and open analytics.

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Health Equity Best Practices:
How a Data-Informed Approach Can Deliver Quality for All and
Support Short- and Long-Term Economic Success
Making better data-informed decisions is one
of the first steps toward achieving greater
equity—not just during a crisis like a
pandemic but in everyday healthcare.
A data-informed approach to health equity
directs focus and rational accountability
targets to secure resources and provides
tools to optimize impact.

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Health Equity Best Practices:
How a Data-Informed Approach Can Deliver Quality for All and
Support Short- and Long-Term Economic Success
For example, Health Catalyst’s data-driven health equity
solution, currently being piloted, combines an analytics
framework with the services expertise to accomplish
the following:
 Using data to understand which measures demonstrate
the most significant disparities and which personal
characteristics drive these disparities.
 Setting measurable goals for improvement.
 Partnering to make meaningful, measurable progress
in driving clinical, operational, and financial improvements.

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Health Equity via AI
There is justified concern about AI
exacerbating disparities if algorithms rely on
biased assumptions or data that can
reinforce inequities. At Health Catalyst, we
share this concern.
We also have found ways of using AI to
reduce disparities. We do this by ensuring
AI empowers organizations to identify
where to focus their attention and resources.

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Health Equity via AI
Healthcare is accustomed to using predictive
models, for example, to anticipate readmission
or cardiovascular disease risk, based on
clinical factors, such as comorbid burden,
medication complexity, or genetics.
Someone will likely heal more quickly from
a splinter than a car accident.

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Health Equity via AI
We have identified a disparity if we can
predict care or outcomes based on
personal characteristics.
When we fail to predict care or outcomes
based on personal characteristics,
including race, ethnicity, age, gender,
sexual orientation, zip code and more,
we have demonstrated health equity.

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Closing the Gap Between Health Equity
Intention and Action
Approaching health equity as a
prediction problem opens the entire
predictive modeling toolbox to
organizations wishing to reduce
disparities but lacking quantitative
support to pinpoint areas of
improvement.
This is an unexpected example of
how high-quality data analytics
replaces anecdotes and assumptions
to drive massive, measurable, data-
informed improvement.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent and Action
Health Equity: Why it Matters and How to Achieve it
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Health Catalyst Editors
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Advancing Health Equity – Data Driven Strategies Reduce Health Inequities
Health Catalyst Editors
2021 Healthcare Trends: What Leaders Need to Know from COVID-19 to New Administration Policies
Daniel Orenstein, JD, Senior VP, General Counsel, and Secretary
Stephen Grossbart, PhD, Senior VP and Chief Quality Officer

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Jason Jones is passionate about achieving the Quadruple Aim through better and
easier use of data in healthcare, including helping organizations to find analytic focus;
helping providers feel that the systems they work for have their backs; and helping
people to understand and have their goals and preferences respected for the hopefully
brief periods during which they are “patients.” Previously, Jones served as Vice
President, Information Support for Care Transformation, at Kaiser Permanente (KP). In
that capacity, he brought together and co-led the national Hospital and Healthplan Quality and Finance
analytic functions and led development of national quality strategy and care delivery IT investments.
Prior to that, he was KP’s Executive Director of Clinical Intelligence and Decision Support and a
Research Scientist in KP’s Southern California region. Before joining KP, Jones was a Senior Medical
Informaticist for Intermountain Healthcare. He also held analytic and marketing positions at Bayer
Healthcare in Wayne, N.J., and Ingenix (now Optum) Pharmaceutical Information Products in Salt Lake
City, where he developed a model for converting United Healthcare data into a saleable asset for
external customers conducting outcomes research. Throughout his career, Jones has taught graduate
courses in statistics to medical informaticists at the University of Southern California and at the
University of Utah. He has published dozens of peer-reviewed papers in medicine, predictive modeling,
and outcomes improvement.
Other Clinical Quality Improvement Resources
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Jason Jones, PhD

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Sullivan leads Communications and Diversity & Inclusion efforts as a Health Catalyst
leadership team member. She holds a Masters in Business Administration from Kellogg
School of Management at Northwestern University, and a BA in History from the
University of Portland, Oregon. Sullivan brings experience in B2B, B2C + B2G
environments, from innovative high-tech companies, including biomedical,
semiconductor and aerospace & defense. She spent more than a decade in healthcare,
including serving more than one hundred Pacific Northwest hospital customers, as CEO of an American
Red Cross blood services region. Sullivan’s functional experience includes Operations Management;
Human Resources; Marketing; Business Development; Strategy; M & A; Investor, Government +
Community Relations; and Brand Development.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Trudy Sullivan, MBA

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Other Clinical Quality Improvement Resources
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Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement
company that helps healthcare organizations of all sizes improve clinical, financial, and operational
outcomes needed to improve population health and accountable care. Our proven enterprise data
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