Why Adding External Data to Your EDW is Critical to Driving Outcomes Improvement

Why Adding External
Data to Your EDW is
Critical to Driving
Outcomes Improvement
– Jared Crapo

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Data Integration is Crucial to Outcome
Across the healthcare industry,
organizations are seeking ways to
improve the quality and lower the
cost of care.
To assist in this endeavor, many
of these organizations are using
myriad of data and analytics and
investing in enterprise data warehouses (EDWs).
Aggregating internal data into the EDW from disparate clinical,
administrative, and financial systems is the first critical step to
identifying quality improvement and cost savings opportunities.

At-Risk Contract Performance Required
CMS and Claims Data
Many healthcare organizations are
entering, or are planning to enter, into
some type of at-risk contract such
as—a bundled payment program, a
Medicare Advantage plan, or an ACO.
Successfully integrating external and
internal claims data enables leaders to
effectively oversee these contracts.
Data from CMS and commercial
payers represent the most common
kind of external data.

CMS and Claims Data
CMS provides a robust data set for a
range of claims types. Integrating this
data into the EDW gives leaders a
complete view of the organization.
Comparative Medicare claims and
organizational performance analysis
for the organization’s patients versus
those patients under the care of
another healthcare system are crucial.
The information allows decision
makers to enter into future at-risk
contracts with a clear understanding of
the improvements required.

CMS and Claims Data
Incorporating claims data from commercial
payers for specific populations covered by
at-risk contracts can answer some
important questions.
• Is the organization paying for services
provided out of network?
• What other choices does the patient have
when seeking treatment?
• Is there a significant difference in spend for
in-network and out-of-network care?
• How can the organization minimize leakage
in order to achieve cost savings?

CMS and Claims Data
Integrating claims data is not without its
challenges. Claim data sets typically lag
the delivery of care by at least 60 days.
Some claim data sets are de-identified,
limiting the insights gleaned from them.
The incremental investment required to
integrate claims data into the EDW is
necessary, even vital, to attain success
in risk-based contracts.

Performance Benchmarking Versus
Other Health Systems
sets of reports to subscribers which, when incorporated in
the EDW, can allow organizations to conduct their own
performance benchmarking.
Healthcare organizations are
very keen on benchmarking.
The majority of hospitals and
health systems subscribe to
some type of benchmarking
service.
These services deliver fixed

The main challenges with loading
benchmarking data are anonymization
and the aggregate nature of
benchmarking data.
Anonymization means benchmarking
services often do not send data for a
particular, named hospital. The data
provided is for “a community hospital
with between 200-300 beds.”

Benchmarking data is usually not as
fine-tuned as clinical or claims data.
For example, receiving information
containing the individual pneumonia
readmissions rates for 17 specific
hospitals, the reports provide the
average pneumonia readmissions rate
for a group of 17 anonymous hospitals.
Given these challenges, along with its
limited utility, integrating benchmarking
data should be a lower priority for most
organizations.

Demographic Data Addresses the
Impact of Changing Patient Populations
Analyzing demographic data has
always been a part of healthcare as
hospitals strive to meet the needs of
patients in the communities they
serve.
The emergence of shared
accountability agreements
established financial incentives for
institutions to deliver high-quality,
patient-centered care at lower costs

EDW and analytics technologies
allow healthcare organizations to
request more-specific data sets,
creating the opportunity for a more
thorough analysis of their local
patient population.
Applying the strategies of Population
Health Management, institutions can
proactively identify and assist high-
risk patients; improving the health of
not only these particular patients, but
of the patient population as a whole.

The U.S. Census is an excellent,
and freely available, source for this
type of information.
It is essentially accurate, updated
frequently, and the data correlates
well with health information.
Companies like Experian and
TransUnion offer a much richer—
and much more expensive—data
set. Leaders should keep in mind,
however, that the value of this type
of data may not be as high as the
cost to acquire and analyze it.

Another valuable, and inexpensive,
alternative is Area Health Resource
Files published by CMS.
This collated data set brings together
a wide range of health-related
information by county.
An organization needs a high level
of analytical expertise in consumer
and household data to understand,
communicate, and take action on the
information provided by these data
sources.

The EDW: As-Is Versus What-If
New models of care delivery are
driving hospitals and medical practices
to build EDW solutions to answer
complex, mission-critical questions.
Armed with data from a trusted, single
source of truth, leaders, physicians,
nurses, and frontline employees are
empowered.
Together, they can critically evaluate
and aggressively pursue the best
opportunities for improving outcomes.

A Big Future for External Data
Healthcare systems continue to
develop competencies for capturing,
disseminating, and taking action on
data from external sources.
The process to determine how to
make such data meaningful is in its
infancy, but its potential to tangibly
improve the quality of care, increase
patient satisfaction, while lowering
costs is great.

A Big Future for External Data
Aggregating data in to an EDW
from internal, disparate, clinical,
financial, and administrative
systems is the first critical step to
identify opportunities for quality
improvement and cost savings.
As the transition to value-based
care continues to gain momentum,
success will be determined by how
effectively external data is
integrated in to the EDW.

More about this topic
How Partners HealthCare is Leveraging Episode-Based Data to Improve Care Delivery
Health Catalyst
Effective Healthcare Data Governance: How One Hospital System is Managing its Data
Assets to Improve Outcomes ― Health Catalyst
How to Integrate Patient Satisfaction Data to Deliver Quality Healthcare and Improve
Operational Efficiency ― Health Catalyst
Surviving Value-Based Purchasing in Healthcare: Connecting Your Clinical and
Financial Data for the Best ROI
Bobbi Brown – Vice President of Financial Engagement
Patient Identification and Matching—An Essential Element of Using an Enterprise Data
Warehouse to Manage Population Health ― Health Catalyst
Link to original article for a more in-depth discussion.
Why Adding External Data to Your EDW Is Critical to Driving Outcomes
Improvement

For more information:

Jared Crapo joined Health Catalyst in February 2013 as a Vice President. Prior to
coming to Catalyst, he worked for Medicity as the Chief of Staff to the CEO. During
his tenure at Medicity, he was also the Director of Product Management and the
Director of Product Strategy. Jared co-founded Allviant, a spin-out of Medicity, that
created consumer health management tools. In his early career, he developed
physician accounting systems and health claims payment systems.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com

Why Adding External Data to Your EDW is Critical to Driving Outcomes Improvement

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Why Adding External Data to Your EDW is Critical to Driving Outcomes Improvement