How to Reduce Lab Queries in Clinical Research: Strategies for Site Success

In clinical trials, lab queries happen when there is a problem with the data related to patient samples. This could be a missing value, an incorrect identifier, or a discrepancy in the visit date. These queries are raised to clarify or validate the information before it can be used in the study.

Although lab queries help protect data, they can create significant issues for research sites. Handling them is often time-consuming, especially when the site staff or coordinator must manually investigate the issue. This can delay lab results, interrupt the workflow, and even affect a patient’s progress in the study.

In this article, we’ll explore how to reduce lab-related queries in clinical research. You’ll learn how to standardize data collection, digitize sample tracking, improve query resolution workflows, and streamline the process through collaboration and regular checks. These practices can help your team optimize performance and reduce the burden of lab queries.

Common Causes of Lab Queries

Manual and Disjointed Processes

Many lab queries begin with the way sample data is collected and handled. At most research sites, different site personnel may take part in the process. One person might collect the sample, while someone else completes the form. This handoff increases the risk of inconsistency and errors.

When data is written by hand, it’s easier to make mistakes or leave out essential data points. Manual queries are often raised because a form is missing, a sample is mislabeled, or the electronic data capture (EDC) entry does not match the original source data.

Sample Labeling and Requisition Errors

Incorrect labeling is another leading cause of queries in clinical trials. Sometimes, the wrong patient ID, study ID, or sample identifier is used. Other times, the requisition form is incomplete or attached to the wrong kit.

In some cases, a kit meant for one patient is used for another. When this happens, labs receive samples that are out of order or linked to the wrong protocol window. These deviations trigger queries and delay processing.

Impact of Lab Queries on Clinical Trial Execution

Lab queries may seem like minor data issues, but they can cause serious setbacks in clinical trial execution. Each unresolved query can lead to delays, additional workload, and risks to data quality—all of which affect how smoothly a study runs.

• Delays in Sample Processing and Reporting: 

When a query is raised due to a missing value, incorrect identifier, or mismatched visit date, labs often pause processing until the issue is resolved. This delay can affect downstream activities like patient treatment decisions, database locks, and interim data reviews.

• Increased Workload Across Teams: 

Lab queries demand attention from multiple parties: sponsors, lab personnel, and site staff, often requiring back-and-forth communication and manual data entry. This extra effort takes valuable time and slows other critical site operations.

• Potential Impact on Patient Progress: 

Lab data is essential for assessing patient safety and eligibility for the next trial phase. A participant’s treatment may be postponed if results are delayed because of unresolved queries. In some cases, this can impact trial timelines, outcomes, and patient retention.

This is especially important considering that 60 to 70% of the objective information on a patient’s chart comes from laboratory data (Shiferaw & Yismaw, 2019)¹. Delays in reporting lab results don’t just stall workflows; they can affect critical treatment decisions and overall study progress.

Query Management: How to Reduce Lab Queries in Clinical Research

Reducing lab queries starts with improving how data is collected, tracked, and reviewed. Strong query management in clinical research means preventing problems before they happen and fixing them quickly when they do. The strategies below focus on helping sites reduce lab-related errors, support compliance, and protect overall data quality.

1. Standardize Lab Data Collection and Entry

Clear and consistent data entry is one of the best ways to avoid lab queries. Sites should follow well-defined protocols that include standard units of measurement, precise definitions, and expected ranges for all data points.

Using validated, pre-filled forms helps reduce errors and prevent missing data. These forms can include built-in checks to flag unusual values before submitting them to the electronic data capture (EDC) system.

Proper training is just as necessary. All site staff should know how to collect and record sample data accurately. They should also understand what to do when values seem unusual or don’t follow the protocol. Training ensures everyone follows the same steps, reducing confusion and discrepancies.

2. Digitize Lab Sample Management

Many EDC queries start when sample data is recorded or tracked using paper forms or spreadsheets. To avoid this, sites should adopt a centralized platform for managing lab kits and sample tracking.

A digital system allows real-time access to each sample’s status, collection time, and location. This helps site personnel and data managers check for problems early and resolve them faster.

Automated tools can also automatically capture key information, such as the identifier, collection date, or who collected the sample. This reduces the chance of manual errors and improves query resolution when issues are found later in the clinical trial.

3. Build Proactive Lab Query Resolution Workflows

A strong query resolution process starts with being proactive. One way to do this is by setting up automated alerts when a discrepancy or deviation is detected in the system. These alerts help site staff act quickly and avoid delays.

Sites should also define clear workflow steps for handling lab queries. This includes setting up escalation paths, assigning roles, and adding deadlines to ensure nothing is overlooked.

Using shared dashboards or tracking tools helps teams manage queries linked to anomalies, especially when many samples are moving through the system. These tools help teams prioritize tasks and keep query management on track throughout the clinical research process.

4. Strengthen Collaboration Between Sites, Labs, and Sponsors

Good communication is key to reducing queries in clinical trials. All parties, including coordinators, labs, CROs, and sponsors, should follow a clear reporting and resolution plan.

Teams can use shared dashboards or platforms to view sample status, system-generated queries, and query resolution progress. This shared visibility makes it easier to respond in real time and keep everyone updated.

5. Perform Ongoing Quality Checks and Reconciliation

Even with strong systems in place, errors can still happen. That’s why performing regular quality checks² on lab results, sample data, and tracking records is important. These checks help maintain high data quality throughout the clinical trial.

Sites should use reconciliation tools to compare electronic data capture (EDC) entries, lab system outputs, and source data like requisition forms. Cross-checking between systems helps spot problems early.

By identifying and fixing issues in real-time, sites can address small mistakes before they become formal queries. This approach supports faster query resolution, helps sites stay aligned with best practices, and ensures data integrity.

Wrapping Up: Turning Lab Challenges Into Site Success

Reducing lab queries is not just about fixing errors. It’s about building more intelligent systems, improving teamwork, and supporting data integrity at every clinical trial stage. When research sites focus on standardizing processes, using digital tools, and staying proactive, they can prevent many problems leading to EDC queries.

By following the strategies in this guide, such as improving data entry, using platforms to digitize tracking, and setting up clear workflows, sites can reduce manual queries, speed up the query resolution process, and protect the quality of their clinical research.

One tool that supports all these strategies is CDConnect™. It’s a powerful software for managing clinical trial data. CDConnect™ helps you track data in real-time, connect with devices, and manage decentralized workflows more easily. 

It brings all your study data together in one place, making it easier to spot issues and streamline the query process. It also helps you stay compliant while keeping participant data safe and secure.

FAQs About Lab-related Queries 

What Is a Clinical Trial Execution Platform and How Does It Help Reduce Lab Queries?

A clinical trial execution platform is a digital system that supports the end-to-end management of clinical trials. It is the backbone of modern management processes, combining data capture, tracking, and analytics into one interface. 

By using a unified platform, research teams can reduce errors, standardize procedures, and minimize lab queries caused by inconsistent or delayed information.

How Do System Queries Differ From Manual or Reactive Queries?

System queries are automatically generated when collected data does not meet set validation rules. In contrast, staff create manual queries after reviewing records. Some reactive queries are raised only after a problem disrupts the site’s operations. 

System-generated queries often allow quicker detection and correction of issues before they impact study timelines.

How Can Sites Reimagine Requisition Workflows to Prevent Incomplete Data?

Many requisition workflows still rely on paper-based processes, which increase the risk of incomplete data. To improve accuracy, sites should shift to digital systems with step-by-step prompts and required fields. This reimagined approach reduces delays and helps ensure that each lab sample is linked to the correct form and participant record.

Who Reviews Data Entry to Ensure It Matches Protocol Expectations?

Designated users, such as coordinators or data managers, are often assigned to review data and confirm that all entries match protocol expectations. These users review data for accuracy, such as proper time stamps and sample identifiers. Verifying each field helps reduce queries related to missing or inconsistent information.

Why Should Data Managers Regularly Review Listings During a Clinical Trial?

Data managers may review listings to identify outliers or unusual trends in the collected data. This step helps them optimize query handling by spotting patterns that could point to errors across multiple records. Reviewing listings regularly ensures better control over management processes and supports clean, reliable results.

Sources:

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC6480504/#:~:text=The%20laboratory%20plays%20a%20crucial,Institute%2C%20Bahir%20Dar%2C%20Ethiopia. 
2. https://pmc.ncbi.nlm.nih.gov/articles/PMC11416741/#:~:text=There%20is%20a%20growing%20emphasis,QA%20in%20clinical%20trial%20success. 

CDConnect Team

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