Glossary

A sample is a subset of a population. The subset should be chosen in such a way that it accurately represents the whole population with respect to some characteristic of interest. A sampling frame lists all eligible cases in the population of interest (i.e., denominator) and how they are selected.

Scientific acceptability is the extent to which the measure, as specified, produces consistent (i.e., reliable) and credible (i.e., valid) results about the quality of care when implemented.

Scoring is the method(s) applied to data to generate results/score. Most quality measures produce rates; however, other scoring methods include categorical value, CV, count, frequency distribution, non-weighted score/composite/scale, ratio, and weighted score/composite/scales.

Semantic validation is the method of testing the validity of an eCQM whereby the measure developer compares the formal criteria in an eCQM to a manual computation of the measure from the same test database.

Sensitivity, as a statistical term, refers to the proportion of correctly identified actual positives (e.g., percentage of people with diabetes correctly identified as having diabetes). See also Specificity.

With respect to quality measurement, the signal is the information of interest and noise is the random, unwanted variation. The signal-to-noise ratio measures the strength of a desired signal relative to the background noise.

Spearman's ρ is a nonparametric measure of rank correlation. It assesses how well the relationship between two variables can be described using a monotonic function. Spearman's coefficient is appropriate for both continuous and discrete ordinal variables.

Specifications are measure instructions addressing data elements, data sources, point of data collection, timing and frequency of data collection and reporting, specific instruments used (if appropriate), and implementation strategies.

Specificity, as a statistical term, refers to the proportion of correctly identified negatives (e.g., percentage of healthy people correctly identified as not having the condition). Perfect specificity would mean the measure recognizes all actual negatives (e.g., all healthy people recognized as healthy). See also Sensitivity.

Stratification divides a population or resource services into distinct, independent groups of similar data, enabling analysis of the specific subgroups. This type of adjustment can show where disparities exist or where there is a need to expose differences in results.

A structure measure, also known as a structural measure, is a measure assessing features of a health care organization or clinician relevant to its capacity to provide health care.

Supplemental data elements are those items not captured in other eCQM fields. CMS has four required data elements - payor type, ethnicity, race, and ONC Administrative Sex.

Synthetic data are artificially generated data used to replicate the statistical components of real-world data but do not contain any identifiable information. Macaulay, T. (2019). What is synthetic data and how can it help protect privacy? Retrieved November 1, 2023, from https://www.techadvisor.com/article/738852/what-is-synthetic-data-and-how-can-it-help-protect-privacy.html 

A systematic literature review is a review of a clearly formulated question using systematic and explicit methods to identify, select, and critically appraise relevant research. A systematic literature review also collects and analyzes data from studies included in the review. Two sources of systematic literature reviews are the AHRQ Evidence-Based Clinical Information Reports and The Cochrane Library.