Map Manager
HealthMay 09, 2018

Breaking down data silos: How reference data is the cornerstone of a data management strategy

Providers and payers face growing pressure to control costs, increase profitability, and succeed with value-based care. Healthcare executives must turn to data as their strongest ally to inform decision-making and drive performance improvement.

Yet many organizations fail to extract the full value of their data assets due to fragmented operations, ineffective data governance, and IT structural limitations that create data silos across an enterprise. The importance of overcoming this challenge was recently underscored in a webinar hosted by Health Data Management. My colleague Brian Diaz, Senior Director of Strategy, and I took an in-depth look at how healthcare organizations can bring together strategy, systems, and infrastructure to support management of a crucial enterprise asset: reference data.

Demonstrating reference data is both current and accurate

Reference data—represented by standard terminologies, proprietary codes, and custom content—form the foundation of reliable analytics, helping healthcare organizations break down data silos and organize information coming from disparate sources. Using diabetes as an example, we explained that a variety of identifiers can represent the condition across enterprise systems—diagnosis codes, medications, or lab results, for instance. Reference data in the form of industry standards such as LOINC, RxNorm, and SNOMED CT enable healthcare organizations to accurately and completely aggregate all this data.

The key is having the confidence that reference data is current and accurate, and is used consistently across front-office and back-office systems so all functions are working with a single source of truth—a significant challenge for many healthcare organizations. When reference data is not properly managed, healthcare organizations can end up with inconsistent versions and use of different standards across an enterprise.

How to get started with an RDM initiative

To improve the outlook, we shared a five-point approach designed by Health Language that establishes a framework when approaching a Reference Data Management (RDM) initiative by answering key questions:

  • Data governance: How do I align my enterprise around a single source of truth?
  • Acquisition and promotion: Am I up to date with the latest standards?
  • Content authoring: How do I author local data in a consistent manner?
  • List/value set management: How do I align business rules when standards are updated?
  • Integration and distribution: How do I ensure systems are receiving the updates they need?

This framework was then applied to both the provider and payer markets covering key use cases for RDM. For payers, this included enterprise code set management, medical policy management, claims processing and patient inquiries to call centers. For providers, the presentation highlighted terminology management, modeling proprietary content to industry standards and managing cohorts.

We also discussed key steps for getting started with an RDM initiative and how establishing a single source of truth can help organizations drive operational efficiencies, reduce costs, and optimize analytics. Finally, we stressed the importance of having strong data governance process in place to make the RDM project successful.

Watch the webinar Breaking Down the Data Silos to learn more about how our Health Language solutions can improve the quality of your data and help you maximize reimbursement, meet regulatory compliance, and enhance quality initiatives.

SNOMED CT® is a registered trademark of the International Health Terminology Standards Development Organisation (IHTSDO).
LOINC® is a registered trademark of Regenstrief Institute, Inc.

Speak To An Expert


Brian Laberge
Solution Engineer, Health Language
Brian supports the company’s Health Language solutions by ensuring that solutions help customers with their challenges, as well as works with the Sales Team and clients to understand their needs. 
Health Language Data Interoperability
Manage and maintain your enterprise healthcare data in a single platform for authoring, modeling, and mapping to industry standards to enable semantic interoperability.
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