Data ManagementGlossary Term
ICH E6(R3) Section 5.5
The systematic process of identifying, tracking, and resolving data discrepancies, inconsistencies, or missing information in clinical trial databases to ensure data quality and completeness.
Query management represents a critical component of clinical data management, ensuring that data quality issues identified during review are systematically communicated to sites, appropriately resolved, and documented in the trial database. Data queries are generated when review of entered data reveals potential errors, inconsistencies with other data points, missing information, or values that fall outside expected ranges. The query management process must balance thoroughness in pursuing data quality with efficiency in minimizing burden on site personnel.
Queries may be generated automatically by programmed edit checks within the EDC system or manually by data management personnel during systematic data review. Automatic queries typically identify obvious issues such as out-of-range values, logically impossible dates, or required fields left blank. Manual queries address more nuanced issues requiring human judgment, such as clinical inconsistencies, unclear or ambiguous responses, or discrepancies with information from other sources. Well-designed edit checks can prevent many errors at the point of entry, reducing the burden of subsequent query management.
The query management workflow typically involves query generation with a clear description of the issue and requested action, routing to appropriate site personnel, site response with clarification or correction, review of the response for adequacy, and query closure when satisfactorily resolved. All steps must be documented with audit trails capturing who performed each action and when. Query metrics, including the number of queries generated, time to resolution, and recurring issues, provide valuable indicators of data quality and site performance, informing risk-based monitoring decisions and identifying areas requiring additional training or process improvement.
Automatic query
"When the coordinator entered a blood pressure of 180/40 mmHg, the EDC system automatically generated a query requesting confirmation of the diastolic value, as it fell outside the programmed acceptable range and differed substantially from prior measurements."
Manual query
"During data review, the data manager identified that the participant's reported symptom onset date preceded the enrollment date by two weeks and issued a manual query requesting clarification of whether the dates were correct or required correction."
A CDISC standard that defines the structure and content of analysis-ready datasets derived from SDTM data, supporting efficient generation of statistical analyses and displays for regulatory submissions.
A secure, computer-generated, time-stamped electronic record that automatically captures the creation, modification, or deletion of data, including the identity of the operator and the date and time of the action.
An international nonprofit organization that develops and supports global data standards for clinical research, enabling consistent and efficient exchange of clinical trial information.
The process of detecting, correcting, and resolving inaccurate, incomplete, or inconsistent data in the clinical trial database to ensure data quality and reliability for analysis.
The degree to which data are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle.
ICH E6(R3) Section 5.5
The systematic process of identifying, tracking, and resolving data discrepancies, inconsistencies, or missing information in clinical trial databases to ensure data quality and completeness.
Query management represents a critical component of clinical data management, ensuring that data quality issues identified during review are systematically communicated to sites, appropriately resolved, and documented in the trial database. Data queries are generated when review of entered data reveals potential errors, inconsistencies with other data points, missing information, or values that fall outside expected ranges. The query management process must balance thoroughness in pursuing data quality with efficiency in minimizing burden on site personnel.
Queries may be generated automatically by programmed edit checks within the EDC system or manually by data management personnel during systematic data review. Automatic queries typically identify obvious issues such as out-of-range values, logically impossible dates, or required fields left blank. Manual queries address more nuanced issues requiring human judgment, such as clinical inconsistencies, unclear or ambiguous responses, or discrepancies with information from other sources. Well-designed edit checks can prevent many errors at the point of entry, reducing the burden of subsequent query management.
The query management workflow typically involves query generation with a clear description of the issue and requested action, routing to appropriate site personnel, site response with clarification or correction, review of the response for adequacy, and query closure when satisfactorily resolved. All steps must be documented with audit trails capturing who performed each action and when. Query metrics, including the number of queries generated, time to resolution, and recurring issues, provide valuable indicators of data quality and site performance, informing risk-based monitoring decisions and identifying areas requiring additional training or process improvement.
Automatic query
"When the coordinator entered a blood pressure of 180/40 mmHg, the EDC system automatically generated a query requesting confirmation of the diastolic value, as it fell outside the programmed acceptable range and differed substantially from prior measurements."
Manual query
"During data review, the data manager identified that the participant's reported symptom onset date preceded the enrollment date by two weeks and issued a manual query requesting clarification of whether the dates were correct or required correction."
A CDISC standard that defines the structure and content of analysis-ready datasets derived from SDTM data, supporting efficient generation of statistical analyses and displays for regulatory submissions.
A secure, computer-generated, time-stamped electronic record that automatically captures the creation, modification, or deletion of data, including the identity of the operator and the date and time of the action.
An international nonprofit organization that develops and supports global data standards for clinical research, enabling consistent and efficient exchange of clinical trial information.
The process of detecting, correcting, and resolving inaccurate, incomplete, or inconsistent data in the clinical trial database to ensure data quality and reliability for analysis.
The degree to which data are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle.