Data quality audit of a clinical quality registry: A generic framework and case study of the Australian and New Zealand Hip Fracture Registry

The value of a clinical quality registry is contingent on the quality of its data. This study aims to pilot methodology for data quality audits of the Australian and New Zealand Hip Fracture Registry, a clinical quality registry of hip fracture clinical care and secondary fracture prevention. Methods A data quality audit was performed by independently replicating the data collection and entry process for 163 randomly selected patient records from three contributing hospitals, and then comparing the replicated data set to the registry data set. Data agreement, as a proxy indicator of data accuracy, and data completeness were assessed. Results An overall data agreement of 82.3% and overall data completeness of 95.6% were found, reflecting a moderate level of data accuracy and a very high level of data completeness. Half of all data disagreements were caused by information discrepancies, a quarter by missing discrepancies and a quarter by time, date and number discrepancies. Transcription discrepancies only accounted for 1 in every 50 data disagreements. The sources of inaccurate and incomplete data have been identified with the intention of implementing data quality improvement. Conclusions Regular audits of data abstraction are necessary to improve data quality, assure data validity and reliability and guarantee the integrity and credibility of registry outputs. A generic framework and model for data quality audits of clinical quality registries is proposed, consisting of a three-step data abstraction audit, registry coverage audit and four-step data quality improvement process. Factors to consider for data abstraction audits include: central, remote or local implementation; single-stage or multistage random sampling; absolute, proportional, combination or alternative sample size calculation; data quality indicators; regular or ad hoc frequency; and qualitative assessment.