Table 4 Notable limitations of data quality frameworks

Lack of accessible and agreed standards: No agreed standard data quality framework (that is straightforward to apply) and no defined data coding/mapping standard

Shortfalls of “SNOMED Clinical Terms” in practical applications

Lack of resourcing and activities to support primary care providers to implement data quality improvements at point of data capture

The ‘resource drain’ for researchers or data custodians to implement a comprehensive data quality framework

Inconsistencies or lack of transparency around data transformation related to data extraction tools, leading to data quality issues including inconsistent or inaccurate results

Uncertainty among data custodians on types and definitions of data ‘de-identification’, leading to the possibility of secondary users re-identifying individuals in datasets

Technical limitations of received data structures and data tools limiting data recipients’ ability to analyse and report received data