Health Language Blog

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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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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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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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].

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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.

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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.

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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.

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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.  

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3 Ways Data Normalization Helps Improve Quality Measures and Reporting

Posted on 09/09/14 | Comments

Healthcare providers and payers are increasingly on the hook for monitoring the effectiveness and safety of care and generating reports for internal use and external regulators.

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3 Ways Data Normalization is Key to Link Claims and Clinically-Sourced Data

Posted on 09/05/14 | Comments

Working with electronic health data is no walk in the park.

Just ask the numerous healthcare organizations that want to share data among disparate IT systems or pull data from multiple data sources for analytical or reporting purposes.  A significant challenge is being able to leverage both claims and clinically-sourced data.

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