Care management is a tool for population health that focuses scarce healthcare resources on the sickest patients. Care management leaders need to know who those sickest patients are (or may be). The static risk models typically used for stratifying patients into risk categories only paint a partial picture of health and are ineffective for modern care management programs. Custom algorithms are now capable of predicting risk based on multiple risk models and multiple data sources. They help care management teams confidently stratify patient populations to paint a complete picture of care needs and efficiently deliver care to those who need it most. This article explains how custom algorithms work on static risk models to normalize risk scores and improve patient stratification, care management, and, ultimately, population health management.

1. Custom Care
Management
Algorithms that
Actually Reveal Risk

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Care Management Strategies
Care management is a population health
strategy tool that helps patients achieve
their healthcare goals and overcome
socioeconomic barriers to care.
These achievements are guiding
principles of value-based care.
Though organizations deploy care
management programs differently, it is
the bedrock of every successful
population health approach.

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Care Management Strategies
A primary care management component
is patient stratification: the process of
separating patient populations into high-
risk, low-risk, and rising-risk groups,
which is how health systems identify
patients most likely to benefit from a care
management program.
But patient stratification, and the entire
population health strategy, is only as
effective as the data and risk models it
employs.

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Care Management Strategies
Accurate and effective risk scoring
depends on multiple models, multiple
data sources, and custom care
management algorithms capable of
blending them all into a comprehensive
patient stratification visualization.

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Care Management Risk Modeling Under
Value-Based Care
Care management practitioners and
goals have changed as healthcare
has moved from fee-for-service
(FFS) to value-based care.
Under FFS, a payer uses care
management techniques to identify
members of the population, target
them for enrollment, and plan
appropriate care services.
The payer uses claims and clinical
data, which are run through a
black box risk model, to assign
static risk scores.

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Care Management Risk Modeling Under
Value-Based Care
Under value-based care, healthcare
organizations (HCOs) have more
complex risk modeling needs because
their goals center on the health of
multiple patient populations.
These organizations’ goals involve
multiple payers and multiple risk
models, which tend to complicate
care management.

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Care Management Risk Modeling Under
Value-Based Care
Because most organizations have
limited care management resources,
identifying those patients who will
respond best to care management
tactics is important.
HCOs need to incorporate a dynamic
risk scoring methodology over the top
of these static risk models to meet the
patient stratification demands of
value-based care.

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The Problem with Static Risk Model Scores
Different risk models produce unique risk scores.
For example, Conifer and Milliman Advanced Risk
Adjuster (MARA) are two popular models used by
payers to produce static scores.
A patient with a Conifer risk score greater than 30
is considered high-risk, as is a patient with a
MARA risk score greater than 3.5.
But comparing these scores is like comparing
apples to oranges. Though they have similar
meaning, they are on completely different scales.
Also, these models were originally developed
without access to EMR data.

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The Problem with Static Risk Model Scores
Typical clinical risk models also produce unique
scores, such as predictive risk, admissions risk,
Charlson/Deyo, and HHS-HCC. These models
were built with limited access to clinical datasets.
In managing population health, care managers
need to effectively compare patient risk levels,
then target and coordinate care appropriately.
But static, incongruent scores make it nearly
impossible to compare risk levels to stratify
patients and create precise lists of patients for
assigning to care management programs.
Normalizing risk scores is necessary for
precise stratification.

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How to Normalize Static Risk Model Scores
with Custom Care Management Algorithms
HCOs need an analytics solution that
can ingest the vast amounts of data that
come from their patient populations.
The solution must also work with
disparate data sources, including
claims, EMRs, and clinical applications.
A solution that can nimbly handle large
datasets from a broad representation of
data sources establishes the process
for normalizing risk scores.

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How to Normalize Static Risk Model Scores
with Custom Care Management Algorithms
To normalize scores (i.e., standardize them
for comparison in a risk stratification process),
care management teams must be able to
build custom care management algorithms
from all datasets, so they can accurately
stratify patients into risk categories.
Comprehensive stratification algorithms
consider chronic conditions, risk, utilization,
medication, and social determinant variables,
each of which must be weight-adjustable.

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How to Normalize Static Risk Model Scores
with Custom Care Management Algorithms
For example, the utilization variable could be
weighted by any combination of ED visits,
hospital admissions, skilled nursing facility
stays, specialist visits, or ICU stays.
The risk variable could be weighted by
predicted risk, rising risk, readmission risk,
HHS-HCC risk, and Charlson-Deyo risk.
A custom algorithm must play on the
strengths of multiple risk models and data
sources, while removing the fragmentation
imposed by individual, mismatched scores
and disparate data sources.

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Effective Patient Stratification Accounts
for Patient Complexity
Typical risk models don’t take into account the
complexity of patients. Instead, they rely on
static datasets.
A clinical or claims-based patient stratification
algorithm only looks at a portion of the patient
profile, which limits the care management
team to only a partial view of the risk
variables impacting each patient.
Custom care management algorithms
reveal patient populations with complex
care needs that otherwise would be missed
using traditional risk scoring models.

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Effective Patient Stratification Accounts
for Patient Complexity
Though robust geographic-based socioeconomic
datasets still need to be collected, the value of
being able to ingest this data cannot be overstated.
Clinical and claims data alone cannot tell the care
management team if a patient can afford to pay for
medication or understand discharge instructions.
Patient stratification care management software
should include modules for consuming all these
data sources, especially socioeconomic data, to
generate a list of patients that can then be reviewed
and assigned to a care management program.

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Effective Patient Stratification Accounts
for Patient Complexity
In a properly designed patient stratification tool,
custom algorithms should also use machine
learning to predict which patients will respond to
care management tactics; e.g., who will be
readmitted and who will be most impacted by
a care program.
Care management teams should be able to save
algorithms and use them for generating updated
lists of care management patient candidates daily.
Algorithms should be developed in an open-source
environment and shared between HCOs to support
diverse, widespread population health initiatives.

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Population Health Demands Next-Level
Patient Stratification
Population health initiatives require knowing as
much about patient populations as possible.
More robust data enables this understanding,
shows the care gaps, stratifies the sickest patients,
and reveals how they are utilizing resources.
Using multiple risk models, multiple data sources,
and dynamic patient stratification algorithms, care
management teams can confidently target
populations for care management resources:
a critical process for meeting the financial and
clinical challenges of value-based care.

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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.
Custom Care Management Algorithms that Actually Reveal Risk
Patient Stratification
Health Catalyst Product – Care Management Suite
In Pursuit of the Patient Stratification Gold Standard: Getting There with Healthcare Analytics
Maggie O'Keefe, Product Line Director, Operations and Performance Management
Defining Patient Populations Using Analytical Tools
Kathleen Merkley, Senior VP of Professional Services
A Guide to Care Management: Five Competencies Every Health System Must Have
Russ Staheli, Analytics, VP
How to Define Population Health Management
Health Catalyst Population Health Solutions

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Kathleen Clary joined Health Catalyst in May 2017 as VP of Care Management & Patient
Engagement. Prior to coming to Catalyst, Kathleen worked for MultiCare Health System as
the Administrator of Patient Navigation & Care Coordination. Kathleen has a degree in
Bachelors of Nursing from Mount Marty College, a Masters of Nursing from University of
Washington, Tacoma, a Doctorate of Nursing Practice in Systems Leadership from Rush University,
Chicago and currently enrolled (graduate 2018) at the Army War College to earn a Master’s in
Strategic Studies.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Kathleen Clary, BSN, MSN, DNP