Login / Signup

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

Aidan Christopher TanElizabeth ArmstrongJacqueline CloseIan Andrew Harris
Published in: BMJ open quality (2019)
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.
Keyphrases
  • quality improvement
  • electronic health record
  • big data
  • healthcare
  • patient safety
  • deep learning
  • hip fracture
  • clinical evaluation