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Machine learning

Machine learning is a branch of artificial intelligence and computer science focusing on the use of data and algorithms to imitate human learning, gradually improving in accuracy. IBM. (n.d.). What is machine learning? Retrieved March 20, 2024, from

Meaningful Measures Initiative

CMS’s Meaningful Measures Initiative identifies high priority areas for quality measurement and improvement, with the goal of improving health outcomes for patients, their families, and measured entities (e.g., clinicians, hospitals). Its purpose is to deliver value by empowering patients to make informed care decisions while also reducing burden on measured entities.

Measure developer

A measure developer is an individual or organization responsible for the development, implementation, and maintenance of a measure. Measure developers may create, edit, and submit measures for consideration by CMS to include in programs. CMS encourages measure developers to use the Blueprint content on the Measures Management System Hub as a guide in creating their measures and to collaborate with other measure developers to share best practices/new learnings freely, e.g., CMS measure development contractors, hospital systems, medical associations, or federal health agencies.

Measure observation

The measure observation is the computation reporting entities should perform on the members of the measure population after removing the measure population exclusions. Only continuous variable measures use measure observation.

Measure score

The measure score is the numeric result computed by applying the measure specifications and scoring algorithm. The computed measure score represents an aggregation of all appropriate patient-level (for example, proportion of patients who died, average lab value attained) or episode-level data (for example readmission measures) for the measured entity (hospital, health plan, home health agency, clinician, etc.). The measure specifications designate the measured entity and to whom the measure score applies.

Measure steward

A measure steward is an individual or organization that owns a measure and is responsible for maintaining the measure. Measure stewards may also be measure developers. Measure stewards are also the ongoing point of contact for people interested in a given measure e.g., medical specialty society or federal health agency.

Measure testing

Measure testing is empirical analysis to assess the evaluation criteria (e.g., importance, feasibility, scientific acceptability - reliability and validity, usability and use) of a measure as specified. It includes analysis of issues posing threats to the validity of conclusions about quality of care such as exclusions, risk adjustment/stratification for outcome and resource use measures, methods to identify differences in performance, and comparability of data sources/methods.

Measure Under Consideration (MUC) List

The Measures Under Consideration (MUC) List is a list of quality and efficiency measures the Department of Health & Human Services is considering adopting, through the federal rulemaking process, for use in the Medicare program. The MUC list is made publicly available by December first each year for categories of measures described in section 1890(b) (7) (B) (i) (I) of the Social Security Act (SSA) as amended by Section 3014 of the Patient Protection and Affordable Care Act.

Measure validity

Measure validity is when the measure accurately represents the evaluated concept and achieves the intended purpose (to measure quality). For example, the measure

  • Clearly identifies the evaluated concept (face validity)
  • Includes all necessary data elements, codes, and tables to detect a positive occurrence when one exists (construct validity)
  • Includes all necessary data sources to detect a positive occurrence when one exists (construct validity)

Measured entities

Measured entities are the front-line clinicians, including health information technology professionals, and their organizations, who collect quality measurement data. Measured entities are the implementers of quality measures. The effect of quality measure data collection on clinician workflow can be negative. There may be effects on their payments, positive and negative, with respect to reporting and actual performance on quality measures. Because of these potential effects, measured entities should be involved in all aspects of the Measure Lifecycle.

MIPS Quality ID

The Merit-based Incentive Payment System (MIPS) assigns the MIPS quality identification to a quality measure in use in MIPS. CMS uses the MIPS Quality ID in MIPS documentation including Physician Payment System proposed and final rules.