Health Language Blog

How Terminology Mapping Drives Semantic Interoperability

Posted on 08/21/14



The increasing use of electronic health records (EHRs) opens opportunities for data sharing and collaborative care that simply didn’t exist when patient information was confined to paper charts.

Emerging healthcare business models such as accountable care organizations (ACOs) and patient-centered medical homes (PCMHs) rely on the exchange of data among primary care physicians, specialists, hospitals and other providers. Health information exchanges (HIEs), whether localized or regional in scope, naturally depend on the ability to move clinical data among the participating parties. Population health management, a component of the nation’s healthcare reform initiative, calls for a robust data analysis infrastructure that can pull in anonymized patient data from myriad healthcare systems.

Ironically, the electronic systems that liberate data from paper records often end up restricting communications. Different healthcare providers tend to use different EHR systems, selecting from the dozens of products available in the market. Systems tend to represent data differently, which leads to interoperability issues. A recent report published in the Journal of the American Medical Informatics Association noted “615 observations of errors and data expression various” across the 21 EHR technologies examined.

Indeed, the ability to share and effectively use data across clinical care processes has become a significant challenge.

Aiming For Semantic Interoperability

Semantic interoperability is the key to getting disparate systems to share data in a useful way. Technical interoperability already exists to some degree in healthcare. The Health Level 7 (HL-7) series of standards, for example, provides guidance on how messages should be structured. But the content of those messages is a different story. While a doctor knows that dropsy describes the same illness as  congestive heart failure, a computer typically can’t make that distinction. Semantic interoperability, however, aims to create a common vocabulary that will pave the way for accurate and reliable communication among computers.

What’s needed is a semantically normalized information model. Such a model is  critical for the success of a number of forward-thinking healthcare initiatives.

Those include:

  • Evidence-based care protocols
  • Quality improvement programs
  • Population health management
  • HIE, ACO and PCMH organizations and models.
  • Pay-for-performance programs
  • Data warehousing and analysis

Making It Happen: Terminology Mapping

Terminology mapping enables semantic interoperability, helping the healthcare sector reach the objective of fluent machine-to-machine communication. This function lets IT systems, such as EHRs, map different terms to a shared semantics, or meaning.

Here’s how terminology mapping accomplishes interoperability at the semantic level:

Provides a trusted source

Healthcare organizations that adopt a unified approach for managing terminology can leverage a single source of terminology truth across an enterprise. The trusted source helps providers achieve a normalized data set that makes sure departmental systems are using the latest terminologies.

Incorporates terminology into applications

Terminology mapping also provides a method for incorporating standard clinical terminology within healthcare apps. The ability to integrate terminology into a healthcare provider’s core systems promotes semantic interoperability among organizations.

Manage a range of terminology content sets

Healthcare doesn’t speak one language. Terminology content sets in play today include SNOMED CT (R), ICD-9, ICD-10, LOINC, CPT, RxNorm, HCPCS and DSM-IV-TR to name a few. A structured approach to mapping terminology can help an organization keep tabs on the multitude of content sets.

Communication Challenges

The healthcare domain presents one of the most complicated IT environments for achieving smooth computer-to-computer communications. And there’s a lot at stake with several high-profile initiatives closely linked to the ability to share data.

Is the lack of semantic interoperability limiting your ability to exchange information and pursue collaborative care? How do you deal with communications challenges? Leave your comments below.

data normalization

Topics: semantic interoperability

About the Author

Brian Diaz is the Senior Director of Strategy, Health Language, part of Wolters Kluwer, Health. Brian has over 17+ years of leading product and marketing teams for SaaS-based healthcare companies focused on interoperability, data quality, and diagnostic imaging. Brian has a computer engineering degree with the University of Minnesota.