HealthNovember 19, 2014

What are code groups and how do they relate to CQMs?

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. One can think about value sets and code groups as clinical building blocks that healthcare organizations can assemble to take on the difficult challenges of healthcare IT. Each code group building block contains codes that represent different clinical concepts. A code group, for example, might encompass a number of codes from various terminologies that represent a clinical concept such as a patient with myocardial infarction. The clinical concepts, in turn, can be used to construct quality measures, population health identification rules and other business rules.

Clinical quality measures

The Centers for Medicare and Medicaid Services (CMS) defines CQMs as “tools that help measure and track the quality of health care services provided by eligible professionals, eligible hospitals and critical access hospitals ... within our healthcare system.”

A CQM may be employed to measure health outcomes, clinical processes, patient safety, care coordination and population health, among other elements of patient care. The point of CQMs is to provide a mechanism to keep tabs on whether the healthcare system is able to deliver safe and effective services. In order to qualify for incentives under the federal government's Meaningful Use program, providers must submit CQM data via a certified EHR system.

NIH states that value sets have many uses, but notes that “a primary purpose” of the value sets represented in the VSAC is to support the 2014 CQMs designated for Meaningful Use.

“Most of the value sets are therefore used to define the patient populations that should be included in the denominators and in the numerators when computing a clinical quality measure,” according to NIH.

Over time, VSAC’s value sets will expand beyond Meaningful Use. NIH said that VSAC content will gradually grow to include value sets for other use cases and new measures. To access VSAC, a user must first acquire a free Unified Medical Language System Metathesaurus License.

Quality demands

Healthcare providers and payers increasingly find themselves needing to monitor the effectiveness and safety of care and generating reports for internal use and external regulators. Meaningful Use, population health management, and emerging care delivery models such as accountable care organizations all call for quality monitoring and reporting. In this environment, CQM data has become an important tool for healthcare organizations. Code groups can help those organizations with their CQM efforts.

Health Language offers a content database that houses more than 790 code groups that participate in CQMs defined by the National Quality Forum. CMS, as well as other entities, use NQF’s CQMs for value-based reimbursement and Meaningful Use compliance. Healthcare organizations can load and manage the code groups within Health Language’s Language Engine. Once loaded, the code groups can support run-time access, local terminology modeling, and the creation of custom business rules based on any supported terminology or classification system. Speak to an expert to learn how Health Language solutions can help your organization improve the quality of your data. 

CPT® is a registered trademark of the American Medical Association (AMA).
SNOMED CT® is a registered trademark of the International Health Terminology Standards Development Organisation (IHTSDO).
LOINC® is a registered trademark of Regenstrief Institute, Inc.

Sarah Bryan
Director of Product Management at Health Language, Wolters Kluwer, Health
Sarah supports the company’s Health Language Health Language solutions by understanding challenges managing healthcare terminologies to enable the semantic interoperability necessary for data accuracy
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