1.
© 2014 Health Catalyst
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© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Three Approaches to Predictive
Analytics in Healthcare
By David Crockett

2.
© 2014 Health Catalyst
www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Three Approaches to Predictive
Analytics in Healthcare
For predictive analytics to
be successful in healthcare,
it must have three simple
characteristics:
• It must be timely
• It must be role-specific
• It must actionable
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At Health Catalyst, we are using three types of predictive analytics that
directly support clinical decision-making and inform administrative
priorities and action.
Risk scores
(stratification)
What-if scenarios
(simulation)
Geo-spatial analysis
(mapping)

3.
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Risk stratification scoring can
assist in prioritizing clinical
workflow, reducing system waste,
and creating financially efficient
population management. Well-
established risk stratification
scores of low-risk, high-risk, and
rising-risk can play a key role in
several healthcare scenarios.
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Risk Stratification
A good vendor will work with your server
administration team to understand the size
and footprint of your environment.

4.
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Simulation
Another type of predictive approach we use
with our client partners is simulation/what-if
scenarios. These tools can be invaluable
when decision-makers want to ask simple
“what if” questions about a given clinical area
or administrative function.
For example, our Key Process Analysis (KPA) application calculates
the amount of opportunity dollars available to capture as variation is
reduced in a specific clinical care process.
Predictive analytics used in a simulation environment allows
clinicians and administrators a safe glimpse into “what if” and the
likely outcomes of a given combination of events.

5.
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Mapping
Geographic information
systems (GIS) and geo-spatial
analysis recently passed its
50th anniversary mark, yet
healthcare and geomedicine is
just now beginning to embrace
these data analysis tools.
This is a very visual and
effective approach to analytics
and decision-making.
Mapping layers and predictive analytics are
routinely used to forecast weather, optimize
supply chains, and support military deployment.

6.
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What Really Matters for Healthcare
Predictive Analytics
Our message is simple: prediction is the
easy part, it’s the intervention that matters.
Return on investment does not reside in
data itself, but in timely interpretation of
that data followed by appropriate
intervention.
Learn how data mining is used in healthcare predictive analytics or see
why sometimes big data is a big mess in healthcare analytics.

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© 2013 Health Catalyst
www.healthcatalyst.com
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Why Predictive Modeling in Healthcare Requires a Data Warehouse
Also by David K Crockett
David K. Crockett, Ph.D. is the Senior Director of Research and
Predictive Analytics. He brings nearly 20 years of translational
research experience in pathology, laboratory and clinical
diagnostics. His recent work includes patents in computer prediction
models for phenotype effect of uncertain gene variants. Dr. Crockett
has published more than 50 peer-reviewed journal articles in areas
such as bioinformatics, biomarker discovery, immunology, molecular oncology,
genomics and proteomics. He holds a BA in molecular biology from Brigham
Young University, and a Ph.D. in biomedical informatics from the University of
Utah, recognized as one of the top training programs for informatics in the
world. Dr. Crockett builds on Health Catalyst’s ability to predict patient health
outcomes and enable the next level of prescriptive analytics – the science of
determining the most effective interventions to maintain health.