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A (6) | B (2) | C (20) | D (11) | E (8) | F (3) | G (2) | H (8) | I (7) | J (1) | K (1) | L (3) | M (16) | N (4) | O (3) | P (12) | Q (5) | R (9) | S (12) | T (4) | U (2) | V (5)

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 of the Measure Properties
Scientific acceptability of the measure properties 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.

Score (Measure Score)
The score, as defined in the CMS CBE’s Glossary of Terms , is “the numeric result that is computed by applying the measure specifications and scoring algorithm. The computed measure score represents an aggregation of all the appropriate patient-level data (e.g., proportion of patients who died, average lab value attained) for the entity being measured (e.g., hospital, health plan, home health agency, clinician). The measure specifications designate the entity that is being measured and to whom the measure score applies.” (p. 12)

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
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). Refer to Specificity.

Specifications are measure instructions that address 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 that the measure recognizes all actual negatives (e.g., all healthy people recognized as healthy). Refer to 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.

Structure Measure
A structure measure, also known as a structural measure, is a measure that assesses features of a healthcare organization or clinician relevant to its capacity to provide healthcare.

Synthetic Data
Synthetic data are artificially generated data used to replicate the statistical components of real-world data but do not contain any identifiable information (Macaulay, 2019).

Systematic Literature Review
A systematic literature review is a review of a clearly formulated question that uses 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.