Validation is testing to determine whether the measure accurately represents the evaluated concept and achieves the purpose for which the measure developer intended (i.e., to measure quality). Measure developers use validation in reference to statistical risk models where they compare model performance metrics between two different samples of data called the development and validation samples.
Validity (Scientific Acceptability of measure properties subcriterion)
Validity includes measure validity (when the measure accurately represents the evaluated concept and achieves the intended purpose, meaning to measure quality) and data element validity, which is the extent to which the information represented by the data element or code used in the measure reflects the actual concept or event intended.
Validity testing is empirical analysis of the measure as specified demonstrating data are correct and/or conclusions about quality of care based on the computed measure score are correct. Validity testing focuses on systematic errors and bias.
Validity threats are measure specifications or data that can affect the validity of conclusions about quality. Potential threats include patients excluded from measurement, differences in patient mix for outcome and resource use measures, measure scores generated with multiple data sources/methods, and systematic missing or “incorrect” data (unintentional or intentional).
A value set is a subset of concepts drawn from one or more code systems, where the concepts included in the subset share a common scope of use (e.g., Anticoagulant Therapy).