1.
3 Approaches to Master Data Management in
Healthcare: What’s Best for You?
By Brian Eliason and Jason Burke
© 2014 Health Catalyst
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© 2014 Health Catalyst
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2.
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Master Data Management
IT leaders considering Master Data
Management (MDM) for their
healthcare organization already
know the importance of having a
solid MDM approach.
Their questions for us include:
“What is the right MDM strategy for my organization?”
“Will an enterprise data warehouse (EDW) solve my
MDM problems?”
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3.
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What is Master Data Management
Master data is critical business
data shared among multiple
systems.
In healthcare, we divide master
data into two types:
Identity data—such as patient, provider,
and location identifiers
Reference data—which includes common
linkable vocabulary like ICD, DRG,
SNOMED, LOINC, RxNorm, and order sets
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4.
MDM comprises the processes,
governance, policies, standards,
and tools that consistently define
and manage the critical data of
an organization to provide a
single point of reference.
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What is Master Data Management
Master data management is,
at its most basic, the process
of linking identity data and
reference data across multiple
IT systems into a single,
consistent point of reference.
A more formal definition:
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5.
Three main drivers are making
MDM more important than ever in
the healthcare industry:
Mergers and Acquisitions (M&A):
Because data configuration of multiple
providers are usually so different, MDM is
needed to merge the data.
Health information exchanges (HIEs):
To successfully exchange information
across locations and organizations, HIEs
have to be able to reconcile master data.
ACOs: To understand and manage their
patient populations, ACOs bring together
health system data and payer data. This
process demands a solid MDM.
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Why is MDM So Important?
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6.
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Three Approaches to MDM
The next question that any
healthcare organization (ACO
or not) must address is how to
tackle MDM.
Currently, three main
approaches are available:
IT system consolidation
Upstream MDM implementation
Downstream master data reconciliation in
an enterprise data warehouse (EDW)
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7.
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Three Approaches to MDM
IT System Configuration
What it is:
A popular way to address many of
the MDM challenges within an
organization is to abandon best-of-
breed solutions in favor of
monolithic EMR and ERP
solutions.
These solutions are the Epics and
Cerners (of the clinical realm) and
the Lawsons and Peoplesofts
(of the business processes realm).
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8.
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Three Approaches to MDM
IT System Configuration
Pros:
In addition to its relative comprehensiveness,
you get another benefit of this approach:
when MDM is handled at the level of these
transactional systems, master data is
reconciled at the time of the transaction.
Cons:
A drawback of IT consolidation is its
complexity and expense. These systems are
not cheap, and the changeover consumes
significant resources. Also, there may be a
need for more MDM between data sources.
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9.
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Three Approaches to MDM
Upstream MDM Implementation
What it is:
In an upstream MDM implement-ation,
organizations keep their
disparate IT systems but map
their master data through a third-party
tool such as an enterprise
master patient index (EMPI).
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10.
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Three Approaches to MDM
Upstream MDM Implementation
Pros:
Although master data problems aren’t
reconciled in the source (as is possible with IT
consolidation), they are reconciled very near
the source. In addition, these systems allow
for extensive manual adjudication.
Cons:
Upstream implementations tend to be
complicated, large, expensive, and slow-moving
IT projects. This approach tends to
have a high failure rate. In most cases
providers favor IT consolidation.
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11.
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Three Approaches to MDM
When an EDW Is the Right Solution for MDM
What if the two approaches we
just described don’t appeal to an
organization? Is an EDW the
right alternative for an MDM
strategy?
We would say, “maybe.”
At Health Catalyst, we implement
an EDW platform, analytics
applications, and processes that
enable healthcare organizations
to use their data to drive higher-quality,
lower-cost care.
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12.
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Three Approaches to MDM
When an EDW Is the Right Solution for MDM
Pros:
The main benefit of using an EDW to master
data is that it is a very achievable solution to
the problem.
Cons:
The main drawback of this approach,
however, is that the mastered data is only
available for analytics. An EDW will not solve
master data challenges at the level of
transactional systems.
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13.
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Three Approaches to MDM
When an EDW Is the Right Solution for MDM
Here are the main instances
when it is best to use an EDW
for master data management:
When an organization needs to do
analytics, but doesn’t have another
MDM solution in place.
When an organization inevitably starts
integrating data sources from outside its
consolidated infrastructure or EMPI.
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14.
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MDM In Action: Claims Data
The most common example we’ve
encountered involves claims data.
More and more of our health
systems are participating in ACOs.
Today’s solutions for managing
master data may not be enough to
encompass the healthcare data
providers need to leverage.
An EDW can step in and bridge
any gaps in an organization’s
MDM strategy.
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15.
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More about this topic
5 Reasons Healthcare Data Is Unique and Difficult to Measure
Dan LeSueur, Vice President Technical Operations
The Best Data Architecture: Know When to Bind Your Healthcare Data
Steve Barlow, Co-founder and Senior Vice President Client Operations
A New Way to Look at Healthcare Data Models
Cherbon VanEtten, Director of Education
The Best Way to Make Payer and Provider Healthcare Data Accessible
Bobbi Brown, Vice President Financial Engagement and Luke Skelley, Vice President
3 Best Practices for Payer-Provider Collaboration to Improve Patient Care
Bobbi Brown, Vice President Financial Engagement
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16.
Other Clinical Quality Improvement Resources
© 2013 Health Catalyst
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Brian Eliason brings more than 10 years of Healthcare IT experience to Health
Catalyst, specializing in data warehousing and data architecture. His work has been
presented at HDWA and AMIA. Prior to coming to Catalyst, Mr. Eliason was the
technical lead at The Children's Hospital at Denver with experience using I2B2.
Previously, he was a senior data architect for Intermountain Healthcare, working closely with the
disease management and care management groups. Additionally, he helped Intermountain
bridge clinical programs with the payer-arm, Select Health. Mr. Eliason holds an MS in business
information systems from Utah State University and a BS from Utah Valley University.
Jason Burke brings over eight years of health care experience at University of Utah
Healthcare to Health Catalyst. At the University of Utah, he spent over four years as a
data architect on the Enterprise Data Warehouse and over four years as the business
system administrator of the Enterprise Performance Management tool used to annually
budget over $1.2 billion. He has also worked with a multitude of business intelligence tools and
was responsible for executive level reports and dashboards. Mr. Burke holds a MBA with a
Management of Technology certificate and a BS from the University of Utah.