Glossary

Data capture, or electronic data capture, is the process of extracting information from a paper or electronic document and converting it into data readable by a computer. Hyland. (n.d.). What is data capture? Retrieved March 20, 2024, from https://www.hyland.com/en/resources/terminology/data-capture/what-is-data-capture

A data element in quality measurement or improvement refers to a specific piece of information that is collected and stored in a healthcare system. Each data element represents a single unit of data and can include various types of data such as patient demographics, clinical measurements, lab results, and treatment details.

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, use of a medication code as a proxy for a diagnosis code and data element response categories that include all values necessary to provide an accurate response.

See also measure validity.

A data model is an abstract model organizing elements of data and standardizing how they relate to one another. For instance, a data model linking guideline information with clinical data for the patient. Taylor, D. (2023). Data modelling: Conceptual, logical, physical model types. Retrieved March 20, 2024, from https://www.guru99.com/data-modelling-conceptual-logical.html

The De Novo Measure Scan (DNMS) is an advanced feature within the Environmental Scan Support Tool (ESST), available through the controlled-access Centers for Medicare and Medicaid Services (CMS) Measures Inventory Tool (CMIT) website. A CMIT login is required to use this functionality.

DNMS is an on-demand tool to support measure developers in conducting early and ongoing environmental scans during the development of new quality measures. It leverages a Clinical Quality Measure (CQM) ontology to define and structure measure concepts. Key ontology components include the target population, health status or utilization focus, change concept, expected outcome of the change concept, and care setting.

Using structured search terminology derived from these concepts, DNMS helps users build and refine new measures. It also applies artificial intelligence to identify, prioritize, and retrieve the most relevant literature from PubMed, PubMed Central, and CINAHL, streamlining the evidence review process.

Decision Model and Notation (DNM) is a standard published by the Object Management Group. It is a standard approach for describing and modeling repeatable decisions within organizations to ensure decision models are interchangeable across organizations. Oliveira, W. (2018, August 21) What is decision model and notation (DMN)? Retrieved March 20, 2024, from https://www.heflo.com/blog/process-modeling/decision-model-and-notation-dmn/

The denominator is the lower part of a fraction used to calculate a rate, proportion, or ratio. It can be the same as the initial population or a subset of the initial population to further constrain the population for the purpose of the measure. Continuous variable measures do not have a denominator, but instead define a measure population.

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 use a denominator exception.

A denominator exclusion is a case the measured entity should remove from the measure population and denominator before determining if numerator criteria are met. Proportion and ratio measures use denominator exclusions to help narrow the denominator. For example, a measure developer 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.

The Health Level Seven International® (HL7®) Data Exchange for Quality Measures (DEQM) Implementation Guide (IG) provides standards for sharing healthcare quality information using Fast Healthcare Interoperability Resources® (FHIR®). It defines how systems can exchange electronic clinical quality measure (eCQM) data in a consistent and interoperable way. For example, it supports the electronic transfer of quality reporting data from healthcare providers to payers and other organizations.

The DEQM IG supports multiple reporting scenarios, including:

• Individual reporting (data for a single patient)
• Subject list reporting (lists of patients meeting certain criteria)
• Summary reporting (aggregate results for a provider or organization)
• Gaps in care reporting (identifying patients who may need recommended services)

The DEQM Individual MeasureReport profile is designed as a FHIR-based alternative to the Quality Reporting Document Architecture (QRDA) Category I format for patient-level reporting. The DEQM Summary MeasureReport profile serves as a FHIR-based alternative to QRDA Category III for aggregate reporting.

The guide is maintained by HL7’s Clinical Quality Information (CQI) Work Group through an established standards development and balloting process. Updates are coordinated with related IGs, including the Quality Measure IG and the Quality Improvement Core (QI-Core) IG, to ensure alignment across quality measurement standards.

Derivative products, with respect to clinical practice guidelines, are products with content derived from the content of the practice guideline, e.g., clinical decision support, patient/family guides, pocket cards, phone apps for clinicians, continuing education programs.

CMS defines digital quality measures (dQMs) as quality measures that use standardized, digital data from one or more sources of health information that are captured and exchanged via interoperable systems; apply quality measure specifications that are standards-based and use code packages; and are computable in an integrated environment.

A direct reference code (DRC) is a specific code referenced directly in the electronic clinical quality measure logic to describe a data element or one of its attributes. DRC metadata include the description of the code, the code system including the code, and the version of that code system.