Health Language Blog

3 Benefits of an Enterprise Terminology Management Platform

Posted on 03/03/15 | Comments

Fragmentation is a defining characteristic of today’s healthcare ecosystem.

A health delivery organization may manage 40 or more separate IT systems, each of which has its own clinical terminology content and infrastructure. Those terminology silos make it difficult for organizations to leverage isolated clinical data, which impacts downstream activities such as data analytics. The problem intensifies when a health system seeks to share data with other healthcare partners. In that scenario, the relevant data resides in numerous isolated systems scattered across multiple healthcare organizations.

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Topics: enterprise terminology management

Can Value Sets be Used Beyond CQMs?

Posted on 02/27/15 | Comments

Value sets, also called code groups, are essentially bags of codes that represent clinical concepts.

Those sets consist of terms and their associated numerical codes, which are derived from various terminologies such as ICD-10, SNOMED CT®, RxNorm and LOINC®. Each bag, or grouping, of codes could represent a population of patients with a particular disease (myocardial infarction) or a particular class of drug (aspirin).

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Topics: CQM

How Enterprise Terminology Management Can Be Used for Clinical Alignment

Posted on 02/12/15 | Comments

Healthcare organizations have deployed myriad healthcare IT systems in recent years, hoping to improve patient outcomes and improve operational efficiencies.

Unfortunately, most of the systems scattered across the healthcare community today are not semantically interoperable. That is, even with EHR certification under “meaningful use”, health IT systems still often use different terminologies to say the same things. The resulting fragmentation hinders communication among systems and makes it difficult for healthcare organizations to create a comprehensive view of clinical data.

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Topics: enterprise terminology management

How Caradigm is Leveraging Data Normalization to Enable Population Health

Posted on 02/04/15 | Comments

Population health management seeks to identify at-risk populations, tailor interventions for individuals and measure the clinical impact.

It’s a complex task that relies on the ability to collect and aggregate patient data and use that data to measure the quality of care. The job also calls for health systems to generate reports on those quality measures for internal use and external regulators.

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Topics: data normalization

How SNOMED CT Compliance Will Benefit Your Patients

Posted on 01/28/15 | Comments

Historically, a doctor recorded a patient’s medical issues on a problem list included as part of the patient’s paper chart.

Chronic illnesses and major medical issues were included on the list. Such paper records faced severe limitations, however. The chart was housed in one physical location, restricting accessibility. Each healthcare provider organization working with the patient would maintain its own records -- including problem lists -- leading to a highly fragmented view of the patient. Caregivers moving towards a more collaborative care delivery model would soon find paper records an undesirable and insecure way to share patient data. In addition, these paper-based problem lists were not always consistently maintained.  

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Topics: SNOMED CT

How to Use Code Groups for Cohort Rules Management

Posted on 01/22/15 | Comments

Code groups have a number of uses and one of the more prevalent examples is the creation of cohort identification rules.

Those code groups, bags of codes that represent clinical concepts, are often associated with clinical quality measures (CQMs). But cohort identification ranks among the top uses outside of CQMs.  Healthcare delivery systems must create cohort identification rules within IT systems so that their care management programs can properly identify at-risk patients. This is important for both big data analytics and population health management.

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Topics: cohort rules management

A Banking Analogy that Explains Semantic Interoperability

Posted on 01/16/15 | Comments

Without semantic interoperability among disparate healthcare IT systems, sharing data in a useful way is impossible.

While a doctor knows that dropsy describes the same illness as congestive heart failure, a computer typically can’t make that type of distinction. Semantic interoperability, however, creates a common vocabulary that paves the way for accurate and reliable communication among computers.

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Topics: semantic interoperability

Pillars of an Enterprise Terminology Management Platform

Posted on 01/09/15 | Comments

Enterprise Terminology Management is an umbrella term that covers a range of technologies and services that aim to achieve a common goal: help healthcare organizations overcome the interoperability issues associated with multiple, incompatible medical terminologies.

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Topics: enterprise terminology management

How Code Groups are the Building Blocks of Important Health IT Initiatives

Posted on 12/29/14 | Comments

Code groups (also known as value sets ) are codes and terms taken from standard terminologies which are used to define clinical concepts.

In essence, code groups are collections of codes that represent such ideas as a category of drugs or a reportable disease. For example, a health system could group together all of the ICD-9, ICD-10, SNOMED-CT and RxNorm codes that could indicate patients with diabetes. In this case, “patients with diabetes” is the clinical concept defined by the code group.

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Topics: Meaningful use requirements, code groups, value sets

The Need for Enterprise Terminology Management in 2015

Posted on 12/23/14 | Comments

The healthcare sector lacks a common clinical vocabulary that spans the rapidly growing population of IT systems.

The problem has both local and cross-industry dimensions. At the local level, a given health delivery organization may manage 40 or more separate systems, each of which has its own clinical terminology infrastructure. The disparate terminologies make it difficult for such an organization to leverage the data in a meaningful and consistent way. The problem becomes magnified at the inter-organizational level when two or more health systems attempt to share and analyze data.

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Topics: enterprise terminology management