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A (7) | B (2) | C (29) | D (13) | E (8) | F (5) | G (3) | H (10) | I (6) | J (1) | K (4) | L (3) | M (18) | N (4) | O (3) | P (15) | Q (5) | R (9) | S (14) | T (6) | U (3) | V (5) | W (1)

Data Aggregation

Data aggregation is the combining data from multiple sources to generate performance information.

Data Criteria

Data criteria are the data elements from the data model.

Data Element

A data element is a basic unit of information with a unique meaning and subcategories (data items) of distinct value. National Institute of Standards and Technology. (n.d.). Data element. Computer Security Resource Center. Retrieved November 1, 2023, from

Data Element Validity (part of Scientific Acceptability)

Data element validity is the extent to which the information represented by the data element or code used in the measure reflects the actual concept or event intended. For example

  • The measure developer uses a medication code as a proxy for a diagnosis code.
  • Data element response categories include all values necessary to provide an accurate response.

Data Fidelity

Data fidelity describes the accuracy, completeness, consistency, and timeliness of data, e.g., high-fidelity, low-fidelity. Gulen, K. (2023, April 21). The power of accurate data: How fidelity shapes the business landscape? Data Science. Retrieved November 1, 2023, from

Data Sources

Data sources are the primary source document(s) used for data collection (e.g., billing or administrative data, encounter form, enrollment form, patient medical record).

De novo Measure

A de novo measure is a new measure that is not based on an existing measure.


The denominator is a statement describing the population evaluated by the performance measure and is the lower part of a fraction used to calculate a rate, proportion, or ratio. It can be the same as the target/initial population or a subset of the target/initial population to further constrain the population for the purpose of the measure. CV measures may refer to this as measure population.

Denominator Exception

A denominator exception is any condition that should remove a patient, procedure, or unit of measurement from the denominator of the performance rate only if the numerator criteria are not met. A denominator exception allows for adjustment of the calculated score for those measured entities with higher risk populations. A denominator exception also provides for the exercise of clinical judgment and the measure developer should specifically define where to capture the information in a structured manner that fits the clinical workflow. The measured entity removes denominator exception cases from the denominator. However, the measured entity may still report the number of patients with valid exceptions. Allowable reasons fall into three general categories: medical reasons, patient reasons, or system reasons. Only proportion measures may use denominator exceptions.

Denominator Exclusion

Denominator exclusions are cases the measured entity should remove from the measure population and denominator before determining whether numerator criteria are met. Proportion and ratio measures use denominator exclusions to help narrow the denominator. For example, the measured entity would list patients with bilateral lower extremity amputations as a denominator exclusion for a measure requiring foot exams. Continuous variable measures may use denominator exclusions but may use the term measure population exclusion instead of denominator exclusion.

Direct Reference Code (DRC)

A direct reference code is a specific code referenced directly in the eCQM logic to describe a data element or one of its attributes. DRC metadata include the description of the code, the code system from which the code is derived, and the version of that code system.

Discriminant Validity

Discriminant validity is the degree to which a test of a concept (a quality measure) is not highly correlated with other tests designed to measure theoretically different concepts. Demonstrate discriminant validity by assessing variation across multiple comparison groups (such as health care providers) to show that a performance measure can differentiate between disparate groups it should theoretically be able to distinguish.

Dry Run

A dry run is full-scale measure testing involving all measured entities representing the full spectrum of the measured population. The purpose is to finalize all methodologies related to case identification/selection, data collection, and measurement calculation, and to quantify unintended consequences.