Table 1.

Description of the data quality (DQ) dimensions.

Dimension	Description	Subthemes
Accuracy	“The degree to which data reveal the truth about the event being described” [31]	Validity, correctness, integrity, conformance, plausibility, veracity, and accurate diagnostic data
Consistency	“Absence of differences between data items representing the same objects based on specific information requirements. Consistent data contain the same data values when compared between different databases” [31]	Inconsistent data capturing, standardization, concordance, uniqueness, data variability, temporal variability, system differences, semantic consistency, structuredness, and representational consistency
Completeness	“The absence of data at a single moment over time or when measured at multiple moments over time” [79]	Missing data, level of completeness, representativeness, fragmentation, and breadth of documentation
Contextual validity	Assessment of DQ is “dependent on the task at hand” [18]	Contextual DQ, fitness for use, granularity, and relevancy
Accessibility	How “feasible it is for users to extract the data of interest” [18]	Accessible DQ and availability
Currency	“The degree to which data represent reality from the required point in time” [32]	Timeliness