Which of the following best describes methods used by cancer registries to ensure data quality and completeness?

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Ensuring data quality and completeness in cancer registries relies on a combination of automated checks and human review. Automated edit checks/validation run during data collection to catch syntax errors, missing fields, out-of-range values, and logical inconsistencies, helping to standardize coding and flag records for review. But automated systems can’t judge whether the source documentation truly supports every data point or resolve ambiguous cases. That’s where manual re-abstraction and chart reviews come in: trained staff verify data against the original records, fill in missing information, resolve discrepancies, and confirm case inclusion and accuracy. Relying on data entry by interns alone would not provide sufficient quality control, and manual re-abstraction alone, while important, is less scalable and may miss systematic issues that automated checks can reveal. The strongest approach combines both automated checks and manual re-abstraction and chart reviews.