Health Language Blog

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.

Read more...

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.

Read more...

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.

Read more...

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.

Read more...

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.  

Read more...

Topics: data normalization

3 Ways Semantic Interoperability Improves Your View of Patient Data

Posted on 10/03/14 | Comments

Semantic interoperability has long been a key missing piece of the healthcare industry’s data-sharing puzzle. Semantic Interoperability is the ideal state where the meaning of data can be effectively shared between systems despite the numerous ways the data may be represented in any individual system [1, 2].

Read more...

Topics: semantic interoperability

3 Reasons Data Normalization is Critical for Data Warehousing

Posted on 09/26/14 | Comments

The “big data” technology trend that has swept across various industries is also impacting the healthcare sector. 

With big data, organizations grapple with the task of analyzing extremely large data sets and identifying important patterns. The healthcare industry’s big data focal point is the clinical data repository, or CDR. A CDR functions as a data warehouse, pulling together patient-oriented health data from a variety of IT systems. Use cases include monitoring the caregiving process, collecting data for quality programs, creating predictive models, determining the cost of care and population health management.

Read more...

Topics: data normalization

3 Uses of Data Normalization for Real-Time Care Alerts

Posted on 09/22/14 | Comments

Data normalization seeks to harmonize data from different sources so that data can be made available to, and consumable by, a provider’s IT system.  Alerts are based on aggregating data from across disparate systems and applying rules to that data - if the interrogated data meets certain conditions, the alert is fired.

Read more...

Topics: data normalization

Data Normalization Use Case: Mapping Disparate Labs to LOINC

Posted on 09/16/14 | Comments

Patient data, scattered across the healthcare community, is notoriously difficult to pull together.

Disparate systems obstruct the ability to aggregate data from multiple sources or to share data among healthcare organizations. Data normalization, however, seeks to harmonize data from different sources into standard terminologies. A data normalization solution can provide a shared vocabulary that can ease data exchange and help improve data analytics-driven initiatives such as population health management.

Read more...

Topics: data normalization

5 Benefits of Data Normalization with MediSpan, RXNorm and NDC

Posted on 09/12/14 | Comments

Prescription drug nomenclature over the years has been embedded in myriad terminologies and coding systems.

The lack of standards has made it difficult to share and aggregate drug-related data. But that situation is changing. Terminology standards such as RxNorm and NDF-RT are being adopted for the use in HIT applications dealing with drug information. Many existing systems already use NDCs as well as proprietary drug terminologies such as Medispan.  

Read more...

Topics: data normalization