Glossary

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 https://www.ibm.com/topics/machine-learning

The Measure Authoring Development Integrated Environment (MADiE) is a software tool redefining the electronic clinical quality measure (eCQM) development and testing process by making it a self-contained process including dynamic authoring and testing within a single application. MADiE allows eCQM developers to author eCQMs using Quality Improvement (QI)-Core, the Quality Data Model (QDM), Fast Healthcare Interoperability Resources® and to test and verify eCQM behavior. MADiE helps eCQM developers execute eCQM logic against the constructed test cases and evaluates if the eCQM logic aligns with the intent of the eCQM.

The Measure Authoring Development Integrated Environment (MADiE) User Group is an open forum to discuss the status of the tool and gather community and user feedback on planned enhancements and releases. The User Group is scheduled to meet on the third Thursday of every month. Register for the MADiE User Group via the meeting appointment.

The Measure Collaboration (MC) Workspace is a web-based tool bringing together a set of interconnected resources, tools, and processes to promote transparency and better interaction across interested parties that develop, implement, and report electronic clinical quality measures.

The Measure Collaboration (MC) Workspace User Guide provides instructions for the use of each module in the MC Workspace.

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.

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.

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.

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.

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 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.

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.    https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/overview

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 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.

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 identification (ID) in MIPS documentation including Physician Payment System proposed and final rules.

The Measures Management System (MMS) is a standardized system for developing and maintaining the quality measures used in CMS’s various quality initiatives and programs. The primary goal of the MMS is to provide guidance to measure developers to help them produce high-caliber health care quality measures.