Health Language Blog

How to Overcome Semantic Interoperability Hurdles

Posted on 12/17/14 | Comments

Semantic interoperability spans the communications gap among divergent health IT (HIT) systems and data sources.

Electronic data and HIT systems have proliferated across the healthcare sector in recent years. Unfortunately, HIT innovation has developed in isolated pockets, with initial development taking place in billing and claims, followed by localized development of ancillary clinical systems such as laboratory, radiology, and pharmacy, and, more recently, electronic health record (EHR) systems.

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Topics: semantic interoperability

6 Problems of Departmental Approaches to Terminology Management

Posted on 12/12/14 | Comments

Executives leading individual departments within a health system may hit upon the need for terminology management.

A hospital pharmacy, for example, may decide to move away from local drug terminologies toward the RxNorm standard to support an HIE initiative. A laboratory may abandon localized labs nomenclature and adopt LOINC to support an interoperability project with their EHR. Clinicians may drop hand-written patient documentation in favor of SNOMED-CT for problem lists in order to attest for Meaningful Use. Or perhaps two departments will agree upon the use of particular terminology standards to accurately represent their patients within their clinical quality measures.

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Topics: clinical terminology management

Why It's Important to Automate the Terminology Mapping Process

Posted on 12/03/14 | Comments

Communication among healthcare IT systems has reached a critical stage as providers and payers rollout more applications to improve patient outcomes and boost operational efficiencies.

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Topics: data normalization

What Are Code Groups and How Do They Relate to CQMs?

Posted on 11/19/14 | Comments

The Building Blocks of Clinical Rules

Code groups (also referenced as value sets) are just that: groups of codes that come from one or more standard vocabularies. According to the National Institutes of Health (NIH), value sets are used to define clinical concepts, such as “clinical visit” or a “reportable disease.”  The sets consist of codes expressed as numerical values and terms are taken from standard terminologies such as SNOMED CT, RxNorm, LOINC and ICD-10-CM. 

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Topics: code groups, CQMs

Understand the Three Levels of Interoperability

Posted on 11/12/14 | Comments

Healthcare may have been among the last industries to automate, but the sector has made up for lost time.

Indeed, electronic data and healthcare IT (HIT) systems have expanded rapidly in recent years. But technical innovation has developed in isolated pockets. The first wave of HIT development took place in the billing and claims departments. Localized development of ancillary clinical systems such as laboratory, radiology, and pharmacy followed the rollout of administrative systems. More recently, the healthcare industry’s focus has shifted to electronic health record (EHR) systems.

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Topics: semantic interoperability, interoperability

Why You Should Care About Semantic Interoperability in 2015 and Beyond

Posted on 10/24/14 | Comments

Semantic interoperability represents the pinnacle of machine-to-machine communication, enabling disparate IT systems to share data in a useful way.

In the healthcare sector, semantic interoperability is critical for bridging the terminology gap among divergent health IT (HIT) systems and data sources. This capability aims to create a common vocabulary that will provide accurate and reliable communication among computers.

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Topics: semantic interoperability

Challenging Terminology Domains for Data Normalization

Posted on 10/22/14 | Comments

The task of achieving semantic interoperability ranks among the toughest problems in health IT.

The rapid adoption of IT systems has digitized masses of clinical data, but at a cost. Different health IT (HIT) systems tend to represent data in different ways, which creates numerous interoperability issues. Accordingly, the healthcare community faces significant hurdles when it comes to efficiently sharing and using data across clinical care processes, business functions and systems.

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Topics: data normalization

What to Look for in a Data Normalization Platform

Posted on 10/15/14 | Comments

Data normalization solutions aim to overcome a difficult problem: the explosive growth of disparate healthcare IT systems and the resulting fragmentation of data.

The systems rapidly proliferating across the healthcare ecosystem each have their own ways of representing clinical terms. The range and variety of terminologies -- from highly localized codes to international standards -- complicate attempts to share and aggregate data. Healthcare organizations must overcome this terminology obstacle if they are to realize the national vision of increased interoperability, transparency and collaboration within the healthcare field.

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Topics: data normalization

5 Trends Driving Data Normalization

Posted on 10/13/14 | Comments

Data normalization addresses a key problem in healthcare informatics: the lack of a single, universally accepted standard terminology that defines the meaning of every type of healthcare data. Solutions supporting data normalization use automated mapping to quickly match incompatible terminologies to a shared vocabulary. This technology helps healthcare organizations overcome semantic uncertainty and the ambiguity that arises when multiple terms describe the same concept.

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Topics: data normalization

4 Medication Use Cases that Require a Data Normalization Solution

Posted on 10/09/14 | Comments

Now that you are collecting all this data, what do you do with it?  Well, one of the first challenges involves normalizing the data collected from disparate sources.  A data normalization solution offers healthcare organizations the ability to semantically map between disparate reference terminologies, classification systems, local proprietary coding systems, and unstructured text. A semantic map allows both you and the computer systems to understand what the codes and words from your data actually mean.  

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Topics: data normalization