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What is Query Management and how does it ensure Data Accuracy
Is data being collected according to the protocol design? Are there any data errors or fields that are being left empty? Which of my sites or team members are generating the most invalid data? These are the usual worries that every researcher has when conducting a clinical study yet most are only capable of answering them once data collection has ended, and they are performing data analysis. Correcting invalid data months after it's collected can be a nightmare and extremely consuming. Dirty data leads to weaker conclusions and sometimes the need to redo part, or all, of the data collection. Is there any way that a researcher can identify and resolve problems in their data while it's gathered?
The answer to this is a query management system (QMS). After reading this blog post, you should have a clear understanding of what queries are in clinical research and why automatic query management is a researcher's best friend.
What are Data Queries?
A data query is an error or discrepancy generated when a validation check, either done manually or by a computer program, detects a problem with the data. A query management system is a tool that tracks data queries so they can be adequately individualized and resolved. QMS substantially minimizes and even eliminates the risk of invalid data being unnoticed. When a data query (e.g., data issue) is created it should be persistent, which allows it to be tracked over time, and only be resolved in the following ways:
- by correcting the error, in other words entering a new value that is valid
- by marking the data in conflict as correct
Implementing a query management workflow
Clinical data collection may be done in different ways, some researchers use paper forms, excel while more experiences one's Electronic Data Capture, here we expose how query management can be set up with different data collection methods:
Paper Case Report Forms (CRFs)
When collecting data with paper Case Report Forms (CRFs) the task of transcribing the data from paper to a database software may be carried out by a separate team. This team is responsible for confirming that data has been correctly collected and will often do this twice in a process called double data entry, to minimize the possibility of mistakes being unnoticed and new errors being of created during data transcription. Any discrepancies that may be found during double data entry may be kept on a separate Excel file, so they are tracked and resolved by contacting the person that originated that issue. Collecting research data with paper forms is not only prone to mistakes with handwriting interpretation but also makes the task of validating data extremely time-consuming. We have published an article specifically on why researchers should not use paper forms.
Online survey tools.
Creating data validation rules with online questionnaires like Qualtrics and Survey Monkey is possible. Validation rules may be established to force or remind a user that a field is required or that a value is out of range. Data validation minimizes the possibility of errors and required fields being left empty. Although data validating can be automatic, isolating data issues with an online survey tool needs manual filtering and a high level of attention to assure no data problems are unnoticed.
Excel is often used for research data collection. Researchers value the fact that Excel is almost as ubiquitous as a computer and that it works without the need for internet. Excel’s data validation is very powerful; you can use formulas that nest arithmetic calculations and combine multiple fields or variables.
As mentioned above, data validation is only the first step in a query management workflow, and since Excel does not generate reports of data issues, to raise and resolve queries one has to rely on manual and time-consuming work.
Electronic Data Capture
Electronic Data Capture (EDC) is software specially designed for the collection of clinical data in electronic format, often for use in human clinical trials. EDCs, like Teamscope, have built-in query management and comply with Good Clinical Practice (GCP). We recommend to always use a validated EDC for collecting sensitive and research data. EDC eliminates the need for paper forms and drastically simplifies data monitoring.
How queries work on Teamscope
Teamscope’s query management is built on top of our data validation system.
Your first step is to add a Condition to any data field:
Data queries are automatically generated when a value is invalid, matches warning criteria or is required and has been left empty:
From Teamscope Web you can see:
- Who in your team created this data query
- When it was originated
- The data that caused
- The condition that triggered it: Invalid, Warn or Required
- The status: Open or Resolved
By clicking on the status of a query you may change from Open to Resolved, and vice versa.
Alternatively, whenever the data issue is fixed, the platform automatically ✨changes the status of the query from Open to Resolved:
When a researcher finds out months or weeks after data collection that there are issues with his/her data, this not only means the studies’ outcome will be weaker but potentially data collection will have to be redone. The best way to mitigate this risk is to use an Electronic Data Capture system that has the capability of not only validating data as its inputted but most importantly keeping track of the data entries that have issues. A query management system (QMS) automatically isolates data issues and allows researchers to react to them immediately, giving them full control of data quality and accuracy.
Teamscope’s Query Management System is fully automated and easy to use. Want to have a custom demo with one of our specialists? Sign up here.
- Wikipedia contributors. (2018, October 3). Clinical data management. In Wikipedia, The Free Encyclopedia. Retrieved 20:36, January 2, 2019, from https://en.wikipedia.org/w/index.php?title=Clinical_data_management&oldid=862284831
- Bellary S, Krishnankutty B, Latha M S. Basics of case report form designing in clinical research. Perspect Clin Res 2014;5:159-66