TABLE 1.

Central elements of semantic DQA conceptual model

Phase	Construct	Definition	Examples
Semantic DQ Design Principles	Clinical Data Factors	Expresses clinical concept for which data quality (DQ) must be measured Considers the ways in which underlying workflow affects potential variables Connects clinical concepts and data provenance	Hypertension can be measured through diagnoses, medications (prescriptions or administration of antihypertensives), or blood pressure measurements in EHR data
	Analytic Uses	Weighs the impact of the clinical concept undergoing DQ assessment Considers the scope: how widely the DQ check will be implemented	Main exposure variables or outcomes may be more important than minor covariates
	DQ Principles	Addresses the combination of established DQ theory with current needs Develops roadmap to determine appropriate DQ method Focuses the results of variable testing	Benchmarking hypertension metrics across institutions for face validity requires a different set of tools than attempting to use external sources to test the plausibility of blood pressure values Common DQ principles include outlier detection, completeness of records, variable concordance, and plausible distribution of facts
Semantic DQ Practice	Representation	Translates clinical concepts to data‐adapted variable definitions	More precise clinical definitions should be considered—eg, hypertension defined as use of antihypertensives may be important to measure specificity and hypertension defined as a series of blood pressure measurements allows more flexibility in analytic modeling
	Assessment Lenses	Supplies specific assessments to evaluate the validity of variables	Common lenses to consider in clinical research are epidemiology, diagnoses, clinical care, and health care utilization.
	DQ Methods	Applies statistical or descriptive methods to evaluate DQ principles	Methods can range from simple (eg, proportions or frequency distributions) to complex (eg, PCA, clustering, or other machine learning) Results can be categorical or can rely on visualization. Thresholds for acceptable DQ can be pre‐determined or part of the applied methodology.

Note: Green elements address development of clinical content for testing, while blue rows address application of DQA testing methods.