Which practice supports ongoing data quality after surveillance data collection?

• A. Increase data collection time
• B. Feedback and corrective actions
• C. Delete outliers without review
• D. Publish results without review

Answer: (B)

Keeping surveillance data reliable after collection hinges on a feedback-driven quality improvement loop. By continually reviewing the data for gaps, inconsistencies, and potential errors and then sharing findings with those who collect and enter the data, you create a mechanism for timely corrections. These corrective actions—refining data collection forms and definitions, providing targeted training, implementing validation rules, and adjusting procedures—keep data quality improving over time, leading to more accurate, complete, and timely information for monitoring and decision-making.

Increasing data collection time may raise volume but doesn’t directly fix quality issues. Deleting outliers without review risks discarding valid variation or masking errors. Publishing results without review bypasses essential quality checks and can spread flawed data.