Even the healthiest among us would benefit from some level of care management, but resources are limited and patients must be stratified to facilitate prioritized enrollment into care management programs. Therefore, health systems need to identify not only high-cost, high-risk, and rising-risk patients, but also patients who are truly impactable. This article explains how systems can use healthcare analytics, at varying levels of maturity, to improve patient stratification and, ultimately, achieve the gold standard: Level 1 (where to start): use healthcare analytics to identify high-cost, high-risk patients in a population. Level 2: use healthcare analytics to identify patients with rising-risk profiles. Level 3 (highest level of maturity): use healthcare analytics to identify patients who are truly impactable (the patient stratification gold standard). Analytics is key to achieving the patient stratification gold standard, but should enhance (not replace) clinical judgement. Stratification lists need to go through workflows in which clinicians remove patients that aren’t appropriate for enrollment.

1. In Pursuit of the Patient Stratification Gold Standard:
Getting There with Healthcare Analytics ̶ Maggie O'Keefe

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Care Management for Patient Populations
As health systems take on risk and shift to
value-based payment models, providing
effective care for a population of patients is
imperative.
Care Management is a pillar of population
health management strategies—succeeding
in this arena is increasingly important to
thriving in this risk-based industry.
Care management is costly and requires
significant investments in infrastructure and
expenditure of resources to achieve
targeted clinical and financial outcomes.

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Care Management for Patient Populations
Even the healthiest among us benefits
from some degree of care management,
but limited resources lead to an
unfortunate reality.
Patients must be stratified in ways that
facilitate prioritized enrollment into care
management programs.

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Care Management for Patient Populations
This presentation explains why identifying
patients who are truly impactable is key to
maximizing cost-effectiveness and sustainability
of care management programs.
And why leveraging analytics and clinical
judgement will help systems achieve the
patient stratification gold standard.

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Care Management for Patient Populations
This presentation explains why identifying
patients who are truly impactable is key to
maximizing cost-effectiveness and sustainability
of care management programs.
And why leveraging analytics and clinical
judgement will help systems achieve the
patient stratification gold standard.

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Problems with the Common Care Management Approach
Today, many health systems rely on
qualitative assessments to identify
and enroll patients in care
management programs.
Some systems identify patients by
calling every individual within two
days of discharge.
At other systems, primary care
providers are responsible for refer-
ring patients to care management.

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Problems with the Common Care Management Approach
Although there is value in using qualitative means
(clinical judgement) to determine which patients
need care management, relying solely on such
mechanisms isn’t likely to create the optimal
patient panels for two key reasons:
1: Patients who are part of an at-risk
population, but aren’t receiving all
necessary care within the health system,
are likely to be missed. This may be due
to not qualifying for any single provider’s
patient panel, or because their frag-
mented records make it difficult for a
provider to appropriately ascertain the full
continuum and complexity of their needs.
2: A myriad of biases and heuristics are
at play when systems rely entirely on
qualitative inputs.
For example, patients asking for more
attention may get enrolled instead of
those who are reticent to engage
independently, but who would benefit
more from care management.

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How to Improve Patient Stratification with Healthcare
Analytics: Three Levels of Maturity
Health systems can use healthcare analytics for
incorporating quantitative information to more
effectively stratify patients into the appropriate
care management program:
Level 1: Identify high-cost, high-risk patients
Level 2: Identify patients with rising-risk profiles
Level 3: Identify patients who are truly impactable

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How to Improve Patient Stratification with Healthcare
Analytics: Three Levels of Maturity
LEVEL 1
The first level of maturity in using healthcare
analytics to improve patient stratification is
identifying high-cost, high-risk patients in
a population.
With the top five percent of patients responsible
for nearly half of healthcare dollars spent, and
patients with multiple chronic conditions costing
up to seven times those patients with only
one, focusing care management here is a
logical way to leverage an analytics system.

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How to Improve Patient Stratification with Healthcare
Analytics: Three Levels of Maturity
LEVEL 2
The next level is identifying patients with
rising-risk profiles.
If systems can recognize these individuals
earlier, then it may be possible to intervene
before their health status worsens and
they become part of the high-cost, high-
risk group.

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How to Improve Patient Stratification with Healthcare
Analytics: Three Levels of Maturity
LEVEL 3
The highest level of maturity in using
analytics to improve patient stratification,
is identifying those patients who are
truly impactable.
This is the primary goal of patient
stratification—the gold standard.
Achieving this gold standard requires
more than simply identifying patients
with a high-cost or rising-risk status.

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How to Improve Patient Stratification with Healthcare
Analytics: Three Levels of Maturity
LEVEL 3
Many of the highest cost patients will remain
extraordinarily expensive regardless of
intervention.
Others with high costs in a given time frame
will regress to the mean without any
additional care management.
The gold-standard patient stratification
process identifies patients for whom care
management support results in improved
clinical and financial outcomes that
would have otherwise not occurred.

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Why Healthcare Analytics Is Key to Achieving the Patient
Stratification Gold Standard
Achieving the gold standard of patient
stratification capabilities, in which impactable
patients are effectively identified, relies on a
hybrid quantitative/qualitative approach.
Analytics can provide visibility into the full
continuum of care by drawing from disparate
healthcare data sources.
Combining claims data with clinical records
greatly enhances the precision with which
patient profiles can be created.
Stratification algorithms can bring in other data
such as patient socioeconomic, if available.

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Why Healthcare Analytics Is Key to Achieving the Patient
Stratification Gold Standard
While data is imperative, a key method to quantitatively derive
impactability is employing analytics to illuminate trends of patients
care management programs have successfully impacted in the past.
By creating this learning system, common traits among patients who
respond to intervention begin to emerge:
Clinical similarities
(e.g., conditions or treatments).
Programmatic strengths
(e.g., specific care plans or care managers with community ties who connect with patients)
Social factors
(e.g., IMPACT score or demographic characteristics)
>
>
>

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Healthcare Analytics Should Enhance (not Replace)
Clinician Judgement
There is no question that clinician
knowledge is a critical component of any
successful patient stratification program.
Many health systems are not going after
the entire high-cost or rising-risk
population—instead, they’re using their
own prioritization based on experience
and understanding of their market to
further segment that population.

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Healthcare Analytics Should Enhance (not Replace)
Clinician Judgement
For example, a care management
program may be targeted at the
highest cost diabetics with several
co-morbid conditions, excluding
cancer or end-stage renal disease.
Stratification algorithms may soon
do more to drive these targeted
decisions, but in the short-term,
qualitative determination of where
to begin remains important.

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Healthcare Analytics Should Enhance (not Replace)
Clinician Judgement
Qualitative determination (clinical
judgement) is key because any stratific-
ation algorithm is ultimately no more than
a highly refined assumption about who
the most impactable patients are.
There are many nonclinical, nuanced
factors that influence clinician-led
enrollment today that are unlikely to
become standard data elements, either
because of privacy concerns, limited
relevance to standard clinical care, or
items not easily quantifiable.

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Healthcare Analytics Should Enhance (not Replace)
Clinician Judgement
While some cutting-edge health systems are
using creative proxies for these factors—
identifying patients with addresses that
change often, for example—many of these
determinations will happen on a case-by-
case basis.
Any stratification list needs to go through a
workflow in which individuals, such as care
managers, can remove patients they know
are not appropriate for enrollment, and add
patients the algorithm has missed.

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The Analytics-Driven Pursuit of Patient Stratification Will
Improve Outcomes
Identifying patients who will benefit most
from care management—achieving the
patient stratification gold standard—is an
iterative process.
Although there is incredible value in health
systems’ current strategies, creating the
most effective patient stratification process
requires employing healthcare analytics in
an increasingly sophisticated fashion.

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The Analytics-Driven Pursuit of Patient Stratification Will
Improve Outcomes
Health systems can start by using analytics
to identify high-cost, high-risk, and rising-risk
patients fairly quickly.
Ultimately, however, health systems must
work toward employing a learning system—
in tandem with qualitative information—to
best deduce where the opportunities lie as
they pursue patient stratification that truly
improves health outcomes

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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.
In Pursuit of the Patient Stratification Gold Standard: Getting There with Healthcare Analytics
Understanding Risk Stratification, Comorbidities, and the Future of Healthcare
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Introducing the Breakthrough Health Catalyst Care Management Product Suite
Paul Horstmeier, Senior VP
The Six Care Management Challenges Healthcare Must Overcome
Bobbi Weber, Product Manager Care Management and Patient Relationship
How Care Management Will Save Richard’s Life—and Billions in Healthcare Costs
Dr. John Haughom, Senior Advisor

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Maggie O'Keefe joined Health Catalyst in July 2015 as Associate Director, Accountable Care.
Prior to coming to Health Catalyst, she worked for CCNC Services as a Clinical Data Analyst.
Maggie has a Master’s degree in Social and Behavioral Sciences from the Harvard School of
Public Health and a Bachelor’s degree in Religious Studies from Yale University.
Other Clinical Quality Improvement Resources
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