How to Optimize Query Management in Clinical Trials

Queries are an essential part of clinical data management in clinical trials. Robust query management ensures that queries improve data quality without driving up costs or slowing down processes.

Clinical data queries identify possible issues as data is captured into an electronic data capture (EDC) system. This query process can include primary, secondary, and exploratory endpoints; safety data; and study-specific validation reviews. 

In this article, find out:

• What issues queries can catch during clinical data collection
• What are examples of queries in clinical research
• How expensive and resource-heavy ineffective queries can be
• How to build your queries for maximum efficiency

What are queries in clinical trials?

Queries are a common tool sponsors and CROs use to clarify or validate uncertain data. 

Queries can catch: 

• Wrong dates
• Wrong units of measurement
• Missing or unreadable data 
• Data outside the acceptable range
• Inclusion/exclusion criteria 

Queries make data fit for purpose, with 51% of queries linked to anomalies in dates, signatures, identifiers, or data legibility. 

Meanwhile, 43% of queries are linked to absent source data, specified in the protocol and Clinical Endpoint Reporting Guidelines as necessary for adjudication. Inclusion and exclusion checks identify if data exists that should have excluded the subject from the study. Additionally, queries can check for protocol deviations.

Medrio has built-in query support, learn more about our solutions here.

Clinical Research Query Examples

Queries can be considered a notification tool for providing alerts, identifying problems, and recognizing data discrepancies in your database.

There are two types of queries: manual and system-generated. Manual queries are reactive. System-generated queries are proactive.

Examples of queries include: 

• Manual queries
• System-generated queries
• Custom queries

What is a manual query?

Manual queries are reactive. They are initiated manually by specific users to review data entry and confirm or revise that data as required. 

For example, Data Managers may review listings created as reports from the entered data and compare these to protocol expectations.  If something falls out of the expected value set, the Data Manager will raise a manual query.

Another example is a coding query. If a Medical Coder finds an issue that prevents them from coding, they can raise a coding query manually.

What is a system-generated query?

System-generated queries are proactive. They are built-in checks that prevent the input of incorrect or incomplete data. 

For example, system queries may ensure that the entered visit date is not in the future. If the system flags a future date, it will ask the user to review and confirm. 

System-generated queries include:

• Univariate Checks: These are blank, missing data, or range checks. They identify if data, such as lab values or vital signs, are blank, missing, or out of range. 
• Multivariate Checks: These checks review multiple variables on the same form, on different forms, from visit to visit, or within a log row(s) or other repeating structure.

What is a custom query?

Custom queries are data checks outlined within the protocol or other data validation requirements. They check specific data, such as primary endpoints or cross-page checks. 

For example, a custom query may flag that a lab sample cannot be taken before the study drug administration. If data is entered, it may flag the entry for review and confirmation.

Explore how Swing Therapeutics uses queries to improve data capture.

Inefficient Query Management

Despite their value, queries can be overused. Inefficient query configurations that disrupt workflows, compete with one another, or create unnecessary data reviews can drive up costs and increase timelines.

It’s important to find a balance when creating queries. Too many or too few queries can negatively impact data entry, the data cleaning process, and monitoring visits.  

Too many edit checks can create additional costs and data issues. Edit checks can overlap or be redundant, ultimately leading to frustration. Too few data queries can result in poor data quality.

Competing queries can create:

• Additional study costs
• Unnecessarily high query trends
• Date discrepancies
• Out-of-scope statistics reviews

Query effects on the budget

Depending on how queries are set up, the effort for data cleaning may yield a negative return on investment. 

A study of 200 participants could yield up to 10,000 queries. Since it costs between $28 and $71 to fix just one query, that’s up to $500,000 or more. What’s more shocking? One meta-analysis of Phase III trials found that less than 2% of the data points changed due to the queries. 

Currently, queries are highest for oncology studies, Phase I trials, and lab data.

Query effects on time and resources

Addressing just one query can take up to 23 weeks. Additionally, 1 in 5 queries get resubmitted, creating inefficiencies in clinical trial processes.

Not only can queries be time-consuming, but they may require multiple individuals to review and close out. 

Each query resolution may require multiple people, including:

• Clinical research assistant
• Data manager
• Quality control personnel
• Site personnel

High query trends often indicate that the site may require additional training or that the query itself needs to be more effective.

Effective Query Writing

Effective query writing should create efficiencies while ensuring high-quality data.

Programmed edit checks should not create burden or complexity. System-added checks should be understood and thoroughly tested for effectiveness during user acceptance testing. Always use the site user perspective when testing.

Additional tips for how to build better queries include:

• Partner with vendors to build a database that collects, protects, and preserves your data.
• Configure checks so that they’re simple and don’t disrupt workflows.
• Prioritize critical data and endpoints, not irrelevant data, when building edit checks.
• Rely on skip logic and form rules to reduce or eliminate unnecessary queries and data. 
• Ensure edit checks complement, not compete with each other.
• Use edit checks to clean data when entered.
• Create reports to monitor query trends and effectiveness.
• Use an EDC with built-in monitoring workflows.

Learn how Freenome uses effective query management to support high-quality data for large-scale trials.

Query Reporting

Reporting can help Data Managers and Clinical Research Associates (CRAs) to analyze and identify site trends. This process also allows teams to review and determine the effectiveness of queries. 

Different reporting capabilities will be available depending on the CDMS/EDC system you select. 

Some of the most impactful types of reports include:

• Top queries report
• Query aging report
• Query rate by form or field reports
• Query rate by site

Top Queries Report

This report displays a list of the most common queries in the study. It helps identify the cause of queries and sheds light on needed configuration changes or training opportunities.

A “Top 10 Queries” report allows the team to review and determine the effectiveness of queries.

Query Aging Report

A query aging report is used to assess the query resolution process by site. It shows how well a site responds to queries compared to other sites.

Query Rate by Form or Field Report

These reports will show the number of queries generated by form or field, along with the query rate as a percentage of entered data.

Query Rate by Site

This report will show the number of queries generated by sites with the query rate as a percentage of entered data

Learn how Roche Diagnostics uses reporting and data standardization to improve clinical trial outcomes. 

How Medrio CDMS/EDC Supports Query Management 

Medrio CDMS/EDC is an intuitive, configurable solution that supports effective query management.

With Medrio, you can:

• Ensure clean data: Manual and system queries consistently protect data quality to ensure clean data with less effort.
• Enable efficient processes: Automated notifications and thoughtful workflows streamline query management processes.
• Collaborate with experts: Industry, technology, and subject matter experts help you with the nuances of query building. 

Medrio features that support effective query management include:

• Configurable query designations and assignments
• User acceptance testing for query testing
• Ability to manually query data
• Default query designations for data review
• Real-time query firing (ranges, future dates)
• Reports that include filterable query listings 

Medrio CDMS/EDC integrates directly with Medrio eCOA/ePRO, Medrio eConsent, and Medrio RTSM. 

See Medrio CDMS/EDC in action by requesting a demo. Or learn how your peers have seen query success in this Medrio CDMS/EDC case study.

Further Guidance for Building Effective Queries and Clinical Data Management

The features and functionality of the technology you select can greatly impact the effectiveness of your queries. Make your queries work harder for you with Medrio’s built-in query support.