Effective Patient Stratification: Four Solutions to Common Hurdles

Effective Patient Stratification:
Four Solutions to Common Hurdles

© 2020 Health Catalyst
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Solutions to Patient Stratification
Effective and nuanced patient stratification is
the key to successful population health efforts.
Whether a provider system, payer, employer,
or another risk-bearing entity is managing
a given population, leveraging data to
effectively identify patients to target for an
intervention is crucial.

Solutions to Patient Stratification
This involves leveraging as many data
sources as one has accessible, aggregating
the data, and then applying logic, groupers,
algorithms, and machine learning to group
patients into cohorts that will benefit most
from an intervention.
Stratifying patients in this way—by their
impactibility—is a prerequisite to a
population health infrastructure yielding
financial and clinical returns.

Four Solutions to Common Patient
Stratification Population Health Challenges
Listed below are four common population health
challenges and solutions to support them.
These approaches help support accurate and
effective patient stratification to enable systems
to overcome roadblocks and effectively carry out
population health initiatives.
1. Consider Both the Physical and the Mental
2. Prove and Measure Return on Investment
3. Complete Data Sets
4. Transparent, Customizable Technology

Understanding the chronic disease state
of a patient population is critical to any
population health strategy.
Too often, population health teams only
focus on the most common physical
chronic conditions, such as hypertension,
heart failure, and diabetes.

While these conditions are key drivers
of healthcare utilization, there are often
comorbid mental health conditions that
significantly contribute to a patient’s
burden of disease.
In fact, high-cost patients with mental
health conditions tend to incur
significantly more expenses than high-
cost patients without mental health
conditions.

Understanding how behavioral health
conditions affect a patient’s overall health
can help care teams manage current
symptoms and prevent worsening of the
condition, as well as manage the impacts
on other health concerns.

Proving a return on investment (ROI)
is a critical element of any population
health program.
ROI is necessary to make a business
case for population health, to achieve
clinician buy-in, and to scale adoption.
Demonstrating the ROI of population
health initiatives can be challenging,
but several approaches will help.

From the get-go, a health system may choose
to stratify patients based on evidence-based
clinical and utilization variables.
By leveraging the body of knowledge in the
scientific literature and lay press, a system
may cohort patients based on known
impactibility that has been proven elsewhere;
the wheel need not be re-invented.

By simultaneously measuring outcomes
for an intervention cohort and a control
cohort, PHM teams can calculate ROI
in real time.
The control cohort could include
patients who are in a queue for a
PHM intervention, who decline the
intervention, or even the entire risk
population (this is not a perfect
comparison or control group but will
provide directional ROI information).

Finally, in the long-term, a more
controlled and adjusted analysis
conducted retrospectively can
evaluate the precise clinical impact
and financial savings for a given
patient population, accounting for
regression to the mean, and with
statistical significance.

As healthcare continues to shift from
volume to value and health systems work
to overcome the COVID-19 pandemic,
care delivery and access are also
evolving.
It is commonplace for patients to receive
care from multiple providers inside and
outside of the hospital, making it difficult
for health systems to aggregate all
patient data from numerous sources.

Incomplete, fragmented data leads to care
decisions that don’t consider the complete
picture of a person’s health.
To make the most informed decision, care
teams need access to comprehensive
data sets.
With a reliable enterprise data warehouse
that aggregates and organizes data from
multiple care sources, patient cohorting
can achieve a higher level of accuracy.

Population health strategies are heavily
reliant on accurate algorithms to stratify
patient groups.
Providers and population health managers
alike seek to understand the inputs and
weighting underlying a given algorithm in
order to buy in to the cohorts that it creates.

A lack of transparency can result in a lack of
faith in the output of algorithms and can also
result in a complex, cumbersome process
when an algorithm needs to be updated.
As such, an algorithm that is both validated,
flexible, and transparent is key.

This can provide members of
the care team with complete
transparency into the population
definitions as well as the ability
to easily adjust those definitions
as needed.
The Population Builder™:
Stratification Module allows for
this agility and transparency.

Patient Stratification Lays the Foundation for
Population Health Success
Identifying the right patients is critical to any
population health initiative.
By leveraging a data-driven population health
tool like the Population Builder: Stratification
Module, care teams can be confident that
they are identifying impactable patients in a
comprehensive and transparent way and can
feel empowered to invest in and measure
interventions to target the identified cohorts.

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

More about this topic
Link to original article for a more in-depth discussion.
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Dr. Amy Flaster joined Health Catalyst in August 2016 as the Vice President of Care
Management Services. In this role, she is concurrently employed by Partners
Healthcare as an Assistant Medical Director of Population Health Management. She
continues to see patients as an internist at the Brigham and Women’s Hospital in
Boston and is an Instructor of Medicine at Harvard Medical School. Prior to joining
Health Catalyst, Amy completed her residency in the Division of General Medicine and
Primary Care program at the Brigham and Women’s Hospital. Amy has previously
co-founded a healthcare IT startup (TrueNorth Healthcare) which operates in the end-of-life space,
and has worked as an advisor to other startups through her work with the BWH iHub incubator. She
has worked extensively on provider innovation and transformation through her work with the Brigham
and Women’s Physicians Organization. Amy has earned a BA from Dartmouth College, an MD from
Harvard Medical School and an MBA from Harvard Business School.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Amy Flaster, MD, MBA

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
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
warehouse (EDW) and analytics platform helps improve quality, add efficiency and lower costs in
support of more than 65 million patients for organizations ranging from the largest US health system
to forward-thinking physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”

Effective Patient Stratification: Four Solutions to Common Hurdles

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Effective Patient Stratification: Four Solutions to Common Hurdles