Quality Measures

Last updated: 07-11-2025

What are Quality Measures?

Quality measures help us measure or quantify healthcare processes, outcomes, patient perceptions, organizational structure, and systems. These measures are related to one or more quality goals for health care (e.g., effective, safe, efficient, patient-centered, equitable, and timely care). CMS uses quality measures in its quality improvement, public reporting, and pay-for-reporting programs for specific healthcare providers.

However, value-based care has become synonymous with long lists of quality measures that healthcare providers must track and many overlapping programs and measure definitions. CMS has launched new “simplified” reporting programs to help address these administrative burdens, such as the Alternative Payment Model Performance Pathway (APP) and ACO REACH.

Quality measures are typically developed based on research and clinical practice evidence. Measures are developed by:

• Public agencies (e.g., CMS and the Agency for Healthcare Research and Quality)
• Private nonprofits (e.g., the National Committee for Quality Assurance)
• Professional medical associations
• Private groups

Components of a Quality Measure

Quality measures have many standard sections:

• Measure ID: Measures can have several different identifiers. These are created by the measure steward (i.e., the organization that authored and maintains the measure). For example, the identifiers for Breast Cancer Screening are NQF 2372, MIPS CQM Quality ID #112, and eCQM CMS125.
• Measure Description: A brief description of the purpose of the measure.
• Denominator: The population to which the measure applies (i.e., the number of people who should have received a service or action such as a screening). The denominator is the lower part of a fraction used to calculate a rate.
• Numerator: The portion of the denominator population that received the service or action for which the measure is quantifying. The numerator is the upper part of a fraction used to calculate a rate.
• Exclusions/Exceptions: An exclusion is a reason that removes a patient from both the numerator and denominator because the measure would not appropriately apply to them. Exceptions are due to medical reasons (e.g., patient is comatose), patient reasons (e.g., patient refuses), and system reasons (e.g., shortage of a vaccine).
• Measure Period: The timeframe in which the service or action should have occurred.
• Value Sets: The healthcare codes used to define the clinical concepts used in the measure. These codes are from standard systems such as ICD-10, CPT, LOINC, RxNorm, SNOMED, etc.

Methodology

Below is a simplified flow chart for calculating a quality measure.

Below is an example of the concepts and logic in the Breast Cancer Screening quality measure using the 2020 Medicare LDS 5% data set.

Data Quality Issues

Many organizations that have the technical staff needed to build the logic for a quality measure may still run into data quality issues. Some common data quality issues are listed below.

• Aggregating and deduplicating data sources.
• Matching patients across these various data sources.
• Missing data or not having enough data:
  • Missing key data points required for the measure, such as date or birth or gender.
  • Missing types of data, such as labs or medications.
  • Some measures may go back several years for screening data, such as Colorectal Cancer Screening).
  • Some measures may require other data sources, such as labs or survey data, that are not easy to obtain or work with.
  • Merging claims into encounters to accurately look for institutional stays or residing in a long-term care facility for more than 90 days during the performance period.
• Normalizing healthcare codes to the value set required by the measure.
• Mapping custom data from an EMR, such as observations and report or document tags, to a proper code from the measure value set.

Introduction Video

In this video, we walk through the high-level concepts of the Breast Cancer Screening quality measure and common data quality issues that may come up when calculating a measure.

Tuva Quality Measures Data Mart

Code on Github

The Quality Measures data mart is where we are building publicly available quality measures. If there is a publicly available measure you would like to see added you can submit an issue on GitHub.

Measure Name	Measure ID	Specification	Status
Documentation of Current Medications in the Medical Record	CMS Star C06, MIPS CQM 130	Link	Released
Hospital-Wide All-Cause Readmission (HWR)	CMS Star C15, MIPS CQM 479	Link	Released (Readmissions mart)
Medication Adherence for Cholesterol (Statins)	CMS Star D10, NQF 0541	Link	Released
Medication Adherence for Diabetes Medications	CMS Star D08, NQF 0541	Link	Released
Medication Adherence for Hypertension (RAS antagonists)	CMS Star D09, NQF 0541	Link	Released
Pain Assessment and Follow-Up	CMS Star C07, MIPS CQM 131	Link	Released
Statin Therapy for the Prevention and Treatment of Cardiovascular Disease	CMS Star C16, MIPS CQM 438	Link	Released
Statin Use in Persons with Diabetes (SUPD)	CMS Star D12	Link	Released

The data mart includes logic that allows you to choose a measurement period end date.

• quality_measures_period_end defaults to the current year-end
• snapshots_enabled is an optional variable that can be enabled to allow running the mart for multiple years

To run the data mart without the default, simply add the quality_measures_period_end variable to your dbt_project.yml file or use the --vars dbt command. See examples below.

dbt_project.yml:

vars:
    quality_measures_period_end: "2020-12-31"
    snapshots_enabled: true

Data Dictionary

summary_counts

Reporting measure counts with performance rates.

Column	Data Type	Description	Terminology

summary_long

Long view of the results for the reporting version of all measures. Each row represents the results a measure per patient. A null for the denominator indicates that the patient was not eligible for that measure.

Column	Data Type	Description	Terminology

summary_wide

Wide view of the results for the reporting version of all measures. This model pivots measures on the patient level (i.e. one row per patient with flags for each measure. The false flags can be treated as care gaps as exclusions have been included in the pivot logic.

Column	Data Type	Description	Terminology

Intermediate Tables

The intermediate tables contain the logic for calculating each quality measure. The subfolder for each quality measure contains that measure's specific logic for calculating the denominator, numerator, and exclusions. Many measures use the same logic for calculating exclusions, such as dementia or hospice. This shared logic can be found in the shared exclusions subfolder.

Example SQL

Quality Measure Performance

select
      measure_id
    , measure_name
    , performance_period_end
    , performance_rate
from quality_measures.summary_counts
order by performance_rate desc

Exclusion Reason Breakdown

select
      measure_id
    , exclusion_reason
    , count(person_id) as patient_count
from quality_measures.summary_long
where exclusion_flag = 1
group by
      measure_id
    , exclusion_reason
order by
      measure_id
    , exclusion_reason

Patient Pivot

select * from quality_measures.summary_wide