A healthcare quality professional is asked to evaluate the accuracy of a publicly reported data...

The significant variation in accuracy percentages among different reviewers (72% to 95%) strongly suggests a problem with interrater reliability. Interrater reliability refers to the degree of agreement or consistency between different reviewers or data abstractors assessing the same data set. Large discrepancies imply that reviewers are interpreting or applying the measure differently, leading to inconsistent results (The Joint Commission, 2024; NAHQ CPHQ Study Guide).

Measure definition (A) issues would typically cause systematic errors affecting all reviewers similarly, not wide discrepancies.

Construct validity (C) relates to whether the measure assesses what it intends to, which is different from reviewer agreement.

Random selection (D) concerns the method of choosing data samples and does not explain reviewer discrepancies.

Improving interrater reliability usually involves clarifying data definitions, enhanced training, and consistent abstraction protocols.