In this QCast episode, co-hosts Jullia and Tom unpack query management in clinical trials. They explain what queries are, how they move from detection to closure, and why a well designed process is essential for reliable data, realistic site workload, and inspection readiness. They walk through the query lifecycle, the main query types, and practical ways to design checks, wording, and metrics so query management supports quality rather than becoming an administrative burden.
What Query Management Is and Why It Matters
Query management is the structured process of finding, clarifying, and resolving issues in trial data. A query is the formal question raised when something looks missing, inconsistent, or implausible. Effective query management does more than correct a field. It captures who raised the query, how the site responded, what changed, and when. This audit trail helps sponsors show that their data are complete, reliable, and suitable for safety review and decision making, in line with current expectations for data integrity and the ability to reconstruct the trial.
Understanding the Query Lifecycle and Audit Trail
A typical lifecycle runs from detection, through generation and assignment, to monitoring, resolution, and closure. Issues are picked up either by programmed edit checks in the electronic data capture system or by reviewers such as data managers, monitors, and medical staff. Clear, neutral wording makes queries easier for sites to understand and act on, while correct routing ensures they reach the right person. Monitoring focuses on open queries and late responses, with particular attention to patterns at specific sites or in certain forms. A robust audit trail then links each query to its originator, assignee, data changes, and final closure, providing inspectors with a transparent record of how discrepancies were handled.
Types of Queries and Designing Checks that Help
In practice, teams work with three main categories. Automatic queries come from predefined edit checks in the electronic data capture system and can range from simple missing values to more complex cross form inconsistencies. Manual queries are raised by people when something looks wrong but is too nuanced for a rule, such as an adverse event that does not fit the narrative. Study specific or custom queries focus on parameters that are critical for that protocol, such as key endpoints or tight time windows. When these elements are well designed, automation and expert judgement complement each other. Poorly tuned checks, by contrast, can flood sites with low value queries and drain time from more important review.
Focusing on Value through Process and Metrics
Clear case report forms and precise data entry guidance reduce avoidable errors. Concise queries that cover one point at a time and state exactly what is needed cut down on back and forth. Prioritisation ensures that issues affecting safety, eligibility, or key endpoints receive faster attention than minor discrepancies. Meaningful metrics, such as query turnaround time, queries per subject or per form, and lists of ageing queries by site, help teams see where processes are working and where redesign or training would help. Periodic reviews of edit checks, informed by these metrics, keep the system lean.
Practical Improvements for Study Teams
Reviewing and tightening standard query wording can improve site responses almost immediately. Cross functional agreement on which fields are truly critical allows teams to focus their strictest checks and fastest turnaround targets on a small, important set of data. Simple, shared dashboards of open queries and turnaround times promote joint ownership between sponsors, vendors, and sites. Where metrics show repeated issues at certain sites or on particular forms, targeted refresher training can reduce query volume and improve data quality. Across all of these activities, treating query management as part of study design, rather than a back office clean up task, turns it into a proactive tool for quality and smoother database locks.
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Today we are focusing on query management in clinical trials. When people hear “queries”, many think of those pop-up messages in an EDC system. Before we get into detail, how do you define query management, and why does it matter for study teams and regulators?
Jullia
So, I see query management as the structured process for finding, clarifying, and resolving issues in trial data. A query is the formal question raised when something looks missing, inconsistent, or implausible. That could be a blank field for a required vital sign, an adverse event with no outcome, or a date that does not fit the visit schedule.
Good query management does more than fix the number on the screen. It records who raised the query, how the site responded, what changed, and when. That audit trail shows that the dataset used for analysis is complete and reliable. Current good clinical practice guidance expects sponsors to protect data integrity and to be able to reconstruct what happened during a trial. Query management is one of the main ways teams show that their data are fit for safety review, decision making, and submission.
Tom
That makes sense. Many listeners will recognise parts of the process from day-to-day work, but maybe not the whole flow. Can you walk us through a typical lifecycle, from spotting a discrepancy to closing the query, and what a solid audit trail needs to show?
Jullia
So, at a high level there are six stages. First is detection. Issues are found automatically through programmed edit checks in the electronic data capture system, or manually during review by data managers, clinical research associates, clinical trial managers, or medical monitors.
Second is generation. The reviewer writes clear query text that describes the issue and asks a specific question. The wording should be neutral, polite, and easy for site staff to understand.
The third stage is assignment. The query is routed to the right person. Often that means the site coordinator or investigator for source issues, and sometimes a central team such as medical coding. Clean routing saves time and avoids confusion.
Fourth is monitoring. Data managers track open queries, follow up when responses are late, and look for patterns, such as one site that struggles with the same field.
Fifth is resolution. The site updates the data or explains the situation. The query owner checks that the answer is complete and consistent with the protocol and source expectations.
Finally, the audit trail brings it all together. It should show who raised the query and when, how it was answered, what was changed in the data, who closed it, and when. Regulators rely on that history to see how discrepancies were handled and to confirm that changes were legitimate.
Tom
Thanks, Jullia. Once teams understand that lifecycle, they often ask about the different types of query. Not every query carries the same weight. Could you talk us through the main types people see in practice, and how they show up in day to day work?
Jullia
Of course. So broadly, we see three groups. Automatic queries, manual queries, and study specific or custom queries.
Automatic queries are set up in the electronic data capture system during build. Simple rules look at a single field. If a required blood pressure value is missing, the system raises a query. More complex rules compare fields on the same form or across forms. For example, a death date that comes before the last recorded visit, or a laboratory date without corresponding results. These checks are powerful, but if they are too strict or poorly designed they can flood sites with low value queries.
Manual queries are raised by people when something looks odd but is too subtle for a programmed rule. That might be an adverse event that does not match the concomitant medication, or a free text comment that suggests a missing event. Data managers, clinical research associates, trial managers, coders, and medical reviewers all play a part here.
Custom queries are built around what matters most for that protocol. They often focus on primary or key secondary endpoints, strict eligibility criteria, or time critical procedures. Together, these three categories create a net that combines automation with expert judgement.
Tom
That combination is where a lot of tension sits. Sites talk about query overload, while sponsors worry about missing issues if they switch checks off. From your perspective, what does good practice look like so teams reduce noise but still protect data integrity and timelines?
Jullia
The real gains come from prevention, clarity, and focus. First, prevent avoidable errors by designing clear case report forms and giving precise data entry guidance. If fields are intuitive and expectations are obvious, you cut down the number of queries that ever need to be raised.
Second, be disciplined about query wording. Each query should cover one issue, be short, and state exactly what is needed. Avoid vague questions and avoid raising duplicates. When queries feel fair and understandable, sites respond more quickly and accurately.
Third, prioritise. Not every discrepancy has the same impact. Issues that affect subject safety, eligibility, or key endpoints should have strict timelines and clear escalation paths. Less critical issues can follow a slower cycle.
Finally, build collaboration and training into the process. When data management, monitoring, coding, clinical, and safety colleagues agree what “good data” looks like, they raise fewer unnecessary queries and focus more on risks that matter. Regulators expect a risk-based approach with documentation that shows how important discrepancies were managed, so this helps both operations and compliance.
Tom
Let’s pick up that point on focus. Teams often pull basic reports, such as total query counts, because they are easy to extract. What do you see as more meaningful measures when you want to know whether query management is actually improving quality rather than just generating admin work?
Jullia
I would start with query turnaround time. Measure how long it takes from raising a query to closing it. Long resolution times often point to specific sites, forms, or query types that need attention.
Next, normalise query rates. Look at queries per subject, per visit, or per form. High rates may mean confusing forms or training gaps. Very low rates on high-risk data can be a warning sign that there is not enough review or that checks are missing.
Then, review ageing. A list of older open queries, grouped by site or query category, helps you target follow up before database lock.
I also encourage teams to look at impact. Large numbers of low value queries increase workload for sites, monitors, and data managers. They can delay database lock and raise costs. Bringing project management and finance into metric reviews helps to make that impact visible.
Lastly, use these insights to refine the system. Retire edit checks that create many trivial queries, adjust rules that miss important issues, and track whether data look cleaner after changes are introduced. That feedback loop keeps query management lean and effective.
Tom
That gives people some clear signals. If we turn those into a short list of practical actions, what would you suggest as quick wins for someone planning a new study or trying to improve a live one?
Jullia
So, I’d suggest three immediate steps. First, review and tighten query wording. Take a sample of frequent queries, rewrite them so they are shorter and clearer, and agree that style within the team. Train reviewers to follow that pattern so sites get consistent, simple messages.
Second, agree on critical data. Bring together data management, statistics, clinical, pharmacovigilance, and medical writing to decide which fields drive safety decisions and primary analyses. Aim your strictest edit checks and fastest turnaround goals at that short list. Keep checks lighter on less critical fields.
Third, set up a simple metric pack. For example, a weekly view of open queries by age and site, median turnaround time for high priority queries, and queries per subject on key forms. Share that view with internal teams and with sites. It encourages joint ownership and makes it easier to see where small changes could help.
If resources allow, add targeted training based on what you see in the metrics. Focus on the sites or topics that generate the most queries and offer a short, practical refresher.
Tom
Before we close, it would be helpful to leave listeners with a couple of key thoughts. If you had to sum up the main messages on query management that people should take back to their next study meeting, what would you choose?
Jullia
I’d highlight two points. First, treat query management as part of study design, not just as a back office task. Good forms, aligned expectations, and thoughtful edit checks mean fewer queries, quicker resolution, and better-quality data. That leads to smoother analyses and more credible results.
Second, pay attention to what your queries are telling you. They are not only tickets to close. They are signals about how well sites understand the protocol, where processes are fragile, and where training or design changes might help. If you use those signals to improve your approach, query management becomes a proactive quality tool rather than a last-minute scramble.
Jullia
With that, we’ve come to the end of today’s episode on query management in clinical trials. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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