While many organizations use patient registries from EMRs to determine their patient population, there is a better way. Using GIS location technology, a health system can identify its care population based on geography and drive times. Health Catalyst uses Dartmouth Atlas hospital referral regions, a hierarchy of facility levels with appropriate drive time isochrones, and medical specialties-based central place theory to develop a more comprehensive view of a health system’s minimum bounding geometry. Using this method, ACOs derive a better understanding of their enrolled patients and eligible payer groups resulting a better basis for strategy and decision making.

2. GIS Location Technology for Healthcare
A patient population is
usually defined by who has
already been seen for care.
Care providers will sometime
use the patient registries in
their EMRs to define patient
populations.
There is a better way using GIS location technology and
supporting data sources that may be especially useful for
ACOs and other organizations concerned with managing
population health—optimized service area boundaries.
© 2014 Health Catalyst
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3. GIS Location Technology for Healthcare
A more precise approach
would be to first objectively
define the geography within
which the health system
operates, and second to
define the subsequent
population belonging to that
overall service area.
This is known as network
coverage optimization, and it
provides a more robust way to
define boundaries and identify
populations.
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www.healthcatalyst.com
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4. GIS Location Technology for Healthcare
Once the geography-based
care population is well defined,
value-based strategies become
more feasible.
Within a rapidly changing
healthcare market, it makes
sense to leverage location
analytics for more robust
strategic assessments for
healthcare systems, physician
groups, health exchanges, and
other payer groups.
© 2014 Health Catalyst
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5. GIS Location Technology for Healthcare
Determining Adequate Service Coverage
One simple approach
to gauge adequate
service coverage
across population
density is to visualize
access times for
various healthcare
facilities as shown in
Figure 1.
© 2014 Health Catalyst
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Figure 1: An example showing drive time access to healthcare facilities within a given service area.
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6. GIS Location Technology for Healthcare
Determining Adequate Service Coverage
Health Catalyst developed a
more comprehensive
approach using objective
and independent methods
for defining geography.
This system was used to
generate the minimum
bounding geometry of the
overall reach of a given
healthcare system as
shown in Figure 2.
Figure 2: An example of using minimum bounding geometry to define the reach of a health system.
© 2014 Health Catalyst
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7. GIS Location Technology for Healthcare
Determining Adequate Service Coverage
To better measure the
overall viability of this
health system, a network
coverage score was
calculated using zip code
and population-based
statistics.
This score represents a
system’s enrolled patients
as compared to the total
population.
© 2014 Health Catalyst
www.healthcatalyst.com
Figure 1: An example showing drive time access to healthcare facilities within a given service area.
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8. GIS Location Technology for Healthcare
Determining Adequate Service Coverage
Lastly, consumer data
from sources such as
Census.gov, Esri Tapestry
market segmentation, and
Medicare.gov were used
to characterize the
population defined by the
final resulting boundary
as shown in Figure 3.
Figure 3: Leveraging socioeconomic and demographic data will enhance the understanding
of health system boundaries and covered population.
© 2014 Health Catalyst
www.healthcatalyst.com
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9. Optimized Service Area Boundaries Result in a
Better Understanding for Population Health
Management Strategy
© 2014 Health Catalyst
www.healthcatalyst.com
So, how does this really work?
A healthcare system can
define its service area, by zip
code, to determine the total
population of that geography.
Then it’s easy math to
determine the percentage of
the population already under
care and the available new
patient opportunity.
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10. ACOs Benefit from GIS Technology
© 2014 Health Catalyst
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Using GIS-powered analytics
to define patient populations,
ACOs derive a better under-standing
of their enrolled
patients and payer groups.
This process can yield better
decisions in population
health management, leading
to improving quality and
lower costs.
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11. © 2014 Health Catalyst
www.healthcatalyst.com
Conclusion
GIS location technology can be
used to readily identify objective
service area boundaries relevant
to a specific healthcare system.
This automated method of
defining the service area also
identified the exact population for
which health coverage is
currently provided.
This health network optimization tool was built in collaboration with GISi (a platinum
Esri partner) and is available by invitation at http://cloud.gisinc.com/hc/.
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11

12. Link to original article for a more in-depth discussion.
A New GIS-driven Approach to Optimize Service Area
Boundaries for ACOs
A Definitive Guide to Accountable Care Organizations
Dr. David A. Burton, Senior Vice President and Dr. John Haughom, Senior Advisor
Predictive Analytics: It’s the Intervention That Matters (Webinar, Slides, or Transcript)
Dale Sanders, Senior Vice President and
David K. Crockett, Ph.D., Senior Director of Research and Predictive Analytics
© 2014 Health Catalyst
www.healthcatalyst.com
More about this topic
Accountable Care Transformation Framework (an executive report)
Dr. David A. Burton, Senior Vice President
ACO Success Requires Precise Patient Population Definitions
Luke Skelley, MSN, Vice President
The Power of Geo-Analytics (and Maps) to Improve Predictive Analytics
David Crockett, Senior Director of Research and Predictive Analytics
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13. – John Haughom, MD, Senior Advisor, Health Catalyst
© 2014 Health Catalyst
www.healthcatalyst.com
For more information:
Download Healthcare: A Better Way.
The New Era of Opportunity
“This is a knowledge source for clinical and
operational leaders, as well as front-line
caregivers, who are involved in improving
processes, reducing harm, designing and
implementing new care delivery models, and
undertaking the difficult task of leading
meaningful change on behalf of the patients
they serve.”
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14. Other Clinical Quality Improvement Resources
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.
© 2014 Health Catalyst
www.healthcatalyst.com
Click to read additional information at www.healthcatalyst.com
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