Which of the following is an example of quality control of data accuracy?

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Quality control of data accuracy focuses on ensuring that the values recorded in the dataset truly reflect what is in the source documents and are coded correctly. A recoding audit fits this focus precisely because it directly reviews how data have been coded after initial entry, checking that the coded values align with the original records and with established coding rules. By re-checking a sample of records, cross-referencing with pathology reports or medical records, and catching miscodes or inconsistent coding, a recoding audit improves the trustworthiness of the data values themselves.

Historical data review looks for patterns, trends, and potential drift over time, which is useful for identifying broader data quality issues, but it isn’t a focused check of the accuracy of individual data points. Benchmarking compares your performance or data quality metrics to external standards, which is about relative performance rather than validating the truth of recorded data. Monitoring the DCO rate tracks how many cases are identified only through death certificates, which relates to data completeness and case capture, not the precise correctness of coded data values.