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Measure Specification

Develop Specifications

The measure developer begins construction of measure specifications by outlining the target/initial population, numerator, denominator, denominator exclusions, numerator exclusions, denominator exceptions, and measure logic. Then, the measure developer gives the measure concept increasing amounts of detail, including precisely defined data elements and the appropriate values or value sets. Every part of the measure specification requires explicitly defined elements with accompanying analysis to identify constraints and criteria of the specification. Additional considerations for both numerator and denominator include alignment with other measures conceptually and technically. 

Measure Specification Process

Developing specifications is an iterative process. When specifying a measure, the measure developer

  • Considers the data elements necessary for the proposed measure and conducts preliminary feasibility assessments.
  • May request preliminary input from standards subject matter experts (SMEs) regarding data model, terminology, data elements and content, Clinical Quality Language expression, and impact on clinician workflow.
  • Drafts initial specifications, which the technical expert panel (TEP) and possibly other interested parties, such as work groups, SMEs, and other measure developers, will review and may suggest changes. Specifications at this stage likely include high-level numerator and denominator statements and initial information on potential denominator and numerator exclusions, if applicable.
  • Continues to detail specifications and refines them throughout the development process.

Specifications of special measure may differ slightly in their execution. For more information about special measures, see the Resources.

Specification Details

Measure developers should specify measures with sufficient details to be distinguishable from other measures and to support consistent implementation. 

Examples of Building Blocks for Specifications

The building blocks of a measure in the specifications may include, but not limited to 

  • Measure name/title
  • Measure description
  • Target/initial population 
  • Denominator statement and definitions
  • Denominator exclusions
  • Denominator exceptions
  • Numerator statement and definitions
  • Numerator exclusions
  • Time interval 
  • Stratification scheme, or how to split results to show differences across groups
  • Risk adjustment methodology
  • Calculation algorithm, or how to calculate results 
  • Sampling methodology
  • Data source(s) 
  • Key terms, data elements, codes, and code systems
  • Level of analysis
  • Attribution model, or how to attribute data to measured entities
  • Care setting

Information Sources for Specifications 

Different information sources influence development of specifications for a measure. These inputs improve the precision of specifications and increase validity and reliability of the measure.

Examples of Information Sources
  • Literature review
  • Clinical practice guidelines
  • Clinical decision support artifacts
  • Existing measures 
  • Interested party input, e.g., TEP, SME
  • Public comment
  • Alpha testing
  • Beta testing

Use Positive Evidence

Inquiries for measure specifications should be based on the principle of positive evidence, defined as data used to confirm a given criterion was met. This principle is particularly relevant when there are no data or there are conflicting data. Where, for instance, a numerator criterion is “low density lipoprotein (LDL) cholesterol is less than 100” and there is no LDL cholesterol result in the patient record, then there is no positive evidence, and the criterion is not met. When, for instance, a denominator criterion is “ejection fraction is less than 40%” and there is both an ejection fraction of less than 40% and an ejection fraction of greater than 40% in the same patient’s record, then because there is positive evidence of an ejection fraction less than 40%, the criterion is met.

Attribution Model 

The measure developer should consider the attribution model early in development. The attribution model can affect which patients to include in the population addressed by a value-based purchasing program or included in the denominator of a quality measure. The CMS consensus-based entity offers these considerations for attribution approaches:

  • Is the attribution model for the new measure evidence-based? 
  • To what degree can the new accountable unit influence the outcomes? 
  • Are there multiple units to which the attribution model will be applied? 
  • What are the potential consequences? 
  • What are the qualifying events for attribution, and do those qualifying events accurately assign care to the right accountable unit? 
  • What are the details of the algorithm used to assign responsibility? 
  • Did the measure developer consider multiple methodologies for reliability? 

Composite Measures for Accountability Programs
Cost and Resource Use Measures
Electronic Clinical Quality Measures (eCQM) Specification, Testing, Standards, Tools, and Community
Multiple Chronic Conditions Measures
Patient-Reported Outcome Measures
Population Health Measures

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