Query management is an essential part of clinical data management in clinical trials. Queries are questions raised by data managers or stakeholders (e.g. CRA, CTM, Medical Monitor, Coder) when they identify discrepancies or missing information in the collected data. A query is used as a tool for clarifying missing, inconsistent or ambiguous data.
The quality of data produced in a clinical trial plays a crucial role in the success of a study. Queries form part of the data cleaning process, ensuring discrepancies are identified and resolved before analysis. Effective query management is also a regulatory expectation under ICH E6(R3) Good Clinical Practice – Principles and Annex 1. Data discrepancies, if left unchecked, can lead to flawed data, jeopardising the validity of the entire study. Query management is essential for ensuring data completeness and accuracy. It helps to identify and correct errors, inconsistencies and discrepancies, improving the quality of data collected during clinical trials.
Query management involves identification, generation, monitoring, resolution, and documentation. This article aims to provide a clear understanding of query management workflows, its importance, types of queries, best practices, and emerging trends in query technology.
Understanding Query Management and Workflow
Effective query management is critical to ensure that data gathered during clinical trials is accurate and reliable. It involves identifying, resolving, and documenting discrepancies.
Query management serves as the communication bridge between data managers, CRAs or CTMs, and site investigators. Queries raised by stakeholders are forwarded to site staff for their review. The site is responsible for providing clarification on questions asked or correcting the data. Data managers/CRA/CTM review responses and resolve queries when the update or explanation is acceptable.
Query management not only identifies and resolves data discrepancies but also document them for future reference. Documenting queries accurately aids in tracking trial progress and maintaining regulatory compliance.
The process of query management involves the following steps:
- Detection
First step in query management is identifying data discrepancies. These could be wrong dates, unit measurements, missing or unrelated data, data outside of acceptable range, inclusion/exclusion criteria issues, or typographical errors. Detection can occur through automated validation or manual review. - Generation
Once a discrepancy is identified, the next step is to create query text. The text contains a clear question regarding the issue and the required action, whether it is to confirm, correct or update the data. It should be concise. Once finalised, the query is generated. - Assignment
Once generated, a query is assigned to the relevant department. For instance, data managers missing data queries to site staff, while CRA/CTMs forward source-data queries. - Monitoring
Assigned queries are monitored until resolution. For example, data managers track their own queries until the data is corrected or a satisfactory response is received. - Resolution
Query owners resolve their queries once the site has corrected the data or provided a satisfactory response. - Audit trail
The audit trail documents the query lifecycle and helps to spot recurring data issues and trends. This is a regulatory requirement under GCP ICH E6(R3) for maintaining traceability.
To maintain accountability and traceability, every query is tracked by audit trail recording:
- Who raised the query
- When query was raised
- To whom the query was assigned
- What action was taken on the data and by whom
- Who resolved the query
- When the query was resolved
Types of Queries in Clinical Trials
Queries are formal communication tools used to seek clarification or correction of missing, inconsistent, or ambiguous data. Below are the types of queries:
Automatic or System Generated Queries
Nowadays most clinical trials use Electronic Data capture (EDC) systems to capture the data. During set-up, checks are built in to prevent incorrect or incomplete data. Automatic queries proactively flag discrepancies at the point of entry. They are divided into the following types:
- Univariate Queries
These queries review a single variable within the same form. For example, on the Vital signs form a blank heart rate field triggers a query for completion. - Multivariate Queries
These queries review multiple variables on same or multiple forms. For example, if the collection date is entered but several lab results are missing, or a Death form is completed without a corresponding End-of-Study form, a multivariate query will be raised.
Manual Queries
Manual queries are reactive and arise when reviewers find issues that automated checks miss. If inconsistencies are found a manual query is raised. Medical coders, CRAs, and CTMs may also raise manual queries when coding issues or source-data discrepancies are detected.
Custom Queries
These queries are specific to the trial and outlined in the protocol or other data review checks. They are mostly related to primary and secondary endpoints. For example, lab samples need to be collected 24 hours before drug administration. If not, a custom query will flag the issue.
Best Practices & Strategies for Query Management
Efficient and strong query management plays an important role in maintaining high quality data in clinical trials. By resolving discrepancies promptly, it preserves data integrity. Below are a few best practices to maximise the efficiency of query management:
- Standardise Processes
Implement standardised procedures for both data entry and review. Clear guidelines minimise downstream issues that can delay timelines. Well-defined entry instructions and clinically relevant review rules help staff detect discrepancies early. Automated queries during study setup reduce manual queries and enable real-time cleaning. Equally important is the development of standardised training programs for new team members, to ensure all staff are aligned from the outset, enabling a culture of accuracy and quality across the study. These expectations are supported by regulators such as the MHRA and FDA (MHRA, 2023).
- Optimise Query Configuration to Eliminate Redundancies
An overload of irrelevant or redundant queries places unnecessary pressure on site staff, leading to delays. Configure queries with clarity and precision, ensuring they’re concise, jargon-free, and neutral in tone. Check the audit trail to avoid duplicates and raise queries promptly. Clear, targeted queries reduce site burden and speed resolution, improving data quality.
- Prioritising Queries by Impact to Expedite Resolution
Not all queries carry the same weight. Those linked to critical data require immediate attention. To manage this effectively, teams can develop real-time study metrics and dashboards to triage queries by impact, and establish turnaround time targets to track response speed. Provide clear escalation guidance to site staff, medical monitors, or CRAs. Maintaining a proactive stance on data quality through continuous monitoring across the trial lifecycle allows teams to catch issues early, minimising disruptions and driving reliable results.
- Foster Cross-Functional Training and Collaboration
Query management is a team effort and involves multiple teams like data managers, site staff, medical monitors, CRA, CTMs, and medical coders. Joint training, regular cross-functional meetings, and shared tools keep everyone aligned, promoting faster query resolution and better data quality.
Measuring the Impact of Queries
Measuring query impact is essential. Poor management can disrupt workflows, inflate costs, and delay timelines. Both excessive and insufficient queries hinder data entry and cleaning, so balance is critical.
Queries impact various aspects of clinical trial such as:
- Additional Study Budget
Unclear or redundant queries demand extra reviews and rework, extending monitoring, delaying database lock, and triggering budget overruns. Poor query management can jeopardise both timelines and financial performance. - Resource Management
Excessive or avoidable queries absorb valuable staff time, causing fatigue and diverting attention from proactive data review. Near database lock, this can call for overtime or the need for extra staffing, which can raise cost and error risk. - Delay in Timeline
Slow query resolution delays data cleaning, pushing back interim analyses, database lock, and subsequent regulatory submissions, ultimately affecting time to market. - Increased Site Burden
Too many irrelevant or confusing queries can cause frustration, divert attention from patient care, and discourage future participation. - Risk to Data Quality and Regulatory Compliance
Unclear or delayed query resolution can compromise the integrity of trial data, affecting both the reliability and credibility of the dataset and study results. Both the FDA and EMA expect clear documentation of how discrepancies are identified and resolved (FDA, 2021; EMA, 2023). Poor query management could lead to findings during inspections or audits, putting trial compliance at risk.
The below methods can be used to evaluate query impact:
- Query Turnaround Time Metric
Tracking this interval highlights slow-moving queries, supports corrective actions, and shows how resolution speed affects overall timelines.. - Query Volume and Frequency Metric
Analysing these metrics across time and sites uncovers protocol or training issues. High volumes can indicate unclear CRFs or inexperienced staff, while unexpectedly low volumes may signal under-reporting or missed discrepancies. - Query Reporting
Structured reports, such as Query Ageing, Query Rate by Site, and Query Rate by Form, all provide oversight, highlight delays, and pinpoint areas needing intervention. - Occasional Review of Programmed Edit Checks
Regular audits remove outdated or duplicate rules, ensuring automated checks target only clinically relevant errors and reducing site burden. - Monitor Data Quality After Query Resolution
Comparing data before and after resolution confirms issues are fully corrected and processes are effective.
Emerging Trends in Query Technology
Traditionally, query management has been largely manual and time-consuming. However, emerging technologies such as artificial intelligence (AI), natural language processing (NLP), automation, and real-time analytics are reshaping how clinical trials are conducted by reducing manual work, enhancing accuracy, and improving compliance.
Below are some emerging trends in query technology:
- AI and NLP-Powered Query Generation
Integration of artificial intelligence (AI) and natural language processing (NLP) holds significant promise to automate query management. These technologies automatically scan large datasets, detecting anomalies or missing information. AI-powered NLP models can read and understand medical language, then turn it into smart search queries to help find the most relevant clinical trials or research articles. They also generate concise queries in plain language, in turn reducing manual workload. Any AI/NLP tool used in this context must be validated according to GAMP 5 principles (ISPE, 2022).
- Real-Time and Risk-Based Monitoring
Conventional query management involves entering data first, followed by raising queries. In contrast, real-time monitoring reviews data at the moment of capture, while risk-based monitoring targets high-risk areas to maximise resource efficiency without compromising data integrity or patient safety. The result is instant review, faster query generation and resolution, and more targeted oversight.
- Integrated and Remote-Friendly Platforms
With the rise of decentralised and hybrid trials, query technology is evolving to support remote site access, eSource integration, and mobile data entry. These platforms allow CRAs, data managers, and investigators to manage data and queries from any location, improving collaboration and oversight for sponsors and CROs. Mobile apps, integrated with AI, support real-time data entry and query review, accelerating resolution. Patients can ask questions about symptoms, appointments, prescriptions via mobile apps while telemedicine apps integrate query management with video calls and chatbots to provide quick advice or book consultations.
- Use of Predictive Analytics for Query Reduction
Predictive analytics uses machine learning to anticipate discrepancies, pinpointing high-risk sites, forms, or users. By forecasting issues, teams can deliver targeted training, adjust monitoring plans and refine forms before problems emerge, reducing query volume and boosting efficiency.
In risk-based monitoring, predictive models flag sites likely to generate high query volumes, gauge the impact of protocol amendments, and detect patterns such as frequent adverse-event errors or missing lab values. They mine free-text, flag out-of-range data, and via mobile-enabled EDC systems, send real-time alerts that guide staff towards cleaner data entry.
Predictive analytics shifts query management from a reactive process to a proactive strategy, improving both data quality and operational efficiency throughout the trial lifecycle.
Conclusion
In conclusion, effective query management is the foundation of successful clinical trials. It ensures that the data used to evaluate new treatments is accurate, dependable, and meets regulatory requirements. By focusing on strong query management, clinical trials can deliver reliable results that support medical progress and improve patient care. Clinical trials are changing and so should the way we manage data. By upgrading to smarter, AI-powered tools and mobile-enabled platforms, we can streamline query resolution, cut costs, and ensure regulatory compliance. Ultimately, moving from reactive fixes to proactive quality assurance will produce high-quality data. This supports informed decisions and benefits patients in line with GCP principles.
Quanticate's clinical data management team optimise every step of the process, from automated detection and concise query generation to risk-based prioritisation, real-time monitoring, and full GCP-compliant audit trails. Harness automated tools and predictive analytics to resolve discrepancies faster, minimise site burden, and protect data integrity. Ready to transform query management into a proactive quality engine? Submit an RFI today.
