Which procedure is primarily used to ensure data completeness?

The correct procedure used to ensure data completeness is reabstracting studies. This method involves taking previously abstracted data and having another trained abstractor independently perform the abstraction process again using the original source documents. The primary goal is to compare the two sets of data for discrepancies and determine the completeness of the data collected.

Reabstracting studies specifically help identify areas where data may have been omitted or inaccurately recorded, thereby providing insights into the thoroughness and robustness of the data collection process. By identifying and rectifying these discrepancies, organizations can enhance the quality and reliability of their cancer registry data, ensuring that all pertinent information is captured effectively.

While reliability studies focus on the consistency and accuracy of data across different abstractors, and data input audits examine the accuracy of data entry processes, these methods do not specifically target the completeness of the data itself. Source document verification involves checking the information captured against the primary source documents, which is important, but it does not measure completeness in the same targeted manner as reabstracting studies do. Thus, reabstracting studies stand out as the key procedure to ensure data completeness in this context.