3 approaches to master data management in healthcare: what's best for you

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3 Approaches to Master Data Management in Healthcare: What’s Best for You?By Brian Eliason and Jason Burke


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?”


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


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:

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.


Why is MDM So Important?

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.


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)


Three Approaches to MDMIT System ConfigurationWhat 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).


Three Approaches to MDMIT 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.


Three Approaches to MDMUpstream MDM ImplementationWhat 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).


Three Approaches to MDMUpstream 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.


Three Approaches to MDMWhen an EDW Is the Right Solution for MDMWhat 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.


Three Approaches to MDMWhen 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.


Three Approaches to MDMWhen an EDW Is the Right Solution for MDMHere 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.


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.


More about this topic

5 Reasons Healthcare Data Is Unique and Difficult to MeasureDan LeSueur, Vice President Technical Operations

The Best Data Architecture: Know When to Bind Your Healthcare DataSteve Barlow, Co-founder and Senior Vice President Client Operations

A New Way to Look at Healthcare Data ModelsCherbon VanEtten, Director of Education

The Best Way to Make Payer and Provider Healthcare Data AccessibleBobbi Brown, Vice President Financial Engagement and Luke Skelley, Vice President

3 Best Practices for Payer-Provider Collaboration to Improve Patient CareBobbi Brown, Vice President Financial Engagement

http://www.healthcatalyst.com/5-reasons-healthcare-data-is-difficult-to-measure

http://www.healthcatalyst.com/5-reasons-healthcare-data-is-difficult-to-measure

http://www.healthcatalyst.com/best-data-architecture-know-when-to-bind-healthcare-data

http://www.healthcatalyst.com/a-new-way-to-look-at-healthcare-data-models

http://www.healthcatalyst.com/a-new-way-to-look-at-healthcare-data-models

http://www.healthcatalyst.com/payer-provider-healthcare-data-accessible-quality-measures

http://www.healthcatalyst.com/challenge-payer-provider-collaboration

© 2013 Health Catalystwww.healthcatalyst.com

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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.

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