Clinical data review is often described as checking trial data for accuracy, completeness and consistency. In practice, it reaches further than that. In this QCast episode, co-hosts Jullia and Tom discuss how clinical data review supports active study oversight, subject safety, medical review and reliable decision-making throughout a trial, especially when data is arriving from multiple systems and sources.
The episode looks at the pressure points that can make review harder: delayed data entry, fragmented systems, manual hand-offs, unclear escalation routes and the misconception that automation can replace clinical and operational judgement. Getting clinical data review right means planning the review model early, focusing effort where risk is highest, and giving reviewers enough context to understand what the data is actually saying.
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Key Takeaways
Clinical Data Review is Part of Study Oversight
Clinical data review is most valuable when it happens close enough to trial activity to support action. Missing fields and inconsistent entries still matter, but reviewers also need to identify patterns, late entries, unexpected relationships and signals that may affect participant safety or trial conduct. A delayed adverse event entry or abnormal lab value can have operational and medical implications if it is not seen in context at the right time.
Connected Data Gives Reviewers Better Context
Modern studies often draw data from EDC, ePRO, eCOA, central labs, imaging, biomarkers, wearables and other sources. Reviewing each domain in isolation can make it harder to understand the relationship between a visit, a lab result, a dosing change and an adverse event. A useful review process allows authorised teams to move between subject-level detail, site behaviour and wider study patterns without relying on disconnected extracts or manual trackers.
Risk-Based Review Depends on Planning
A risk-based approach helps teams decide which data and relationships need the closest attention for safety, protocol conduct and reliable results. Automation, dashboards and alerts can support that work, but only when the review approach is clearly defined. Teams need to agree who reviews what, how often, what triggers escalation, and how findings, queries and audit trail insights move through the workflow.
Full Transcript
Episode 46: Clinical Data Review
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
Now clinical data review sounds straightforward on the surface. You look at trial data, check it, and deal with the issues. But I suspect that definition misses quite a lot. So where would you start?
Jullia
Well I’d start with the purpose, rather than the task. Clinical data review is the ongoing examination of trial data to see whether it’s accurate, complete, consistent, and suitable for the decisions the study team needs to make. It supports data quality, of course, but it also supports subject safety, trial conduct, medical review, and confidence in the final interpretation.
Really, it’s more so part of active study oversight. A reviewer isn’t only looking for missing fields or obvious inconsistencies. They’re looking for patterns, gaps in context, unexpected relationships, and signals that might affect how the study is being run. That could be repeated late adverse event entries at one site, abnormal lab values that need medical context, or a dosing change that doesn’t quite line up with the visit record.
Tom
People also often refer to ALCOA++ in this space. How does it show up in actual review?
Jullia
So ALCOA++ is a way of describing reliable data. It stands for attributable, legible, contemporaneous, original and accurate, with added expectations around completeness, consistency, durability, availability and traceability. In a real study, that might mean checking whether an adverse event was entered close to when it occurred, whether the change history is visible, whether the source and timing are clear, and whether the data can still be accessed and understood later.
Timing changes the value of the review. If a central lab upload shows repeated abnormal values, or a protocol deviation is entered late, the study team needs to see it soon enough to ask questions, protect participants, and correct behaviours. Data review only has real operational value when it happens close enough to the activity it describes.
Tom
So where does timeliness become a safety issue?
Jullia
It becomes a safety issue when delayed data limits the team’s ability to understand what is happening to a participant. If an adverse event is entered late, or a lab result is not reviewed in context with dosing and medical history, the study team may not have the full picture at the right moment. The data may still be captured eventually, but the opportunity to act quickly may already have passed.
Tom
Older review methods used to be much more manual. Why has the model had to change so much?
Jullia
It’s mostly because the volume, speed and variety of trial data have changed. Older models relied heavily on paper records, handwritten case report forms, site visits, and static listings produced after the fact. That could work when studies had fewer sources and less frequent data flow. But modern studies might include EDC, ePRO, eCOA, central labs, imaging, biomarkers, wearable devices, and sometimes electronic medical record inputs.
Tom
Could you give a concrete example of how that changes the review itself?
Jullia
Well think about a dose adjustment visit. The subject attends the visit, labs are drawn, the dose changes, and an adverse event is reported two days later through the EDC. If those data points sit in separate systems, the connection may be missed until the next review cycle. A more current view lets the reviewer ask, “Does the lab result explain the dose change? Was the adverse event expected? Is the timing consistent with the visit schedule?”
That is a different mindset from checking one domain at a time. Labs, adverse events, concomitant medications, protocol deviations, visits, dosing and operational metrics all tell part of the story. A reviewer may need to move from a population-level trend to one subject’s timeline, then across to site behaviour, then back to the wider pattern.
Tom
A common misconception is that modern clinical data review means automating everything and removing human judgement. Is that fair?
Jullia
I don’t think it’s fair, and it’s a risky assumption. Automation helps with consistency, speed and scale, especially for routine checks and alerts. But the reviewer still has to interpret context. A system might flag an outlier lab value, but a person decides whether it fits the medical picture, whether the site needs a query, whether monitoring should follow up, or whether the finding affects risk oversight.
Tom
Now what makes a dashboard genuinely useful?
Jullia
It has to help the reviewer move quickly to the right question. Dashboards, alerts and visualisations are useful when they allow drill-down, comparison and traceability. They’re less useful if they create another layer of noise or hide the underlying data. A good tool should make review more focused, not simply add another place where teams have to look.
Tom
Where does risk-based review fit into this?
Jullia
Treating every data point as equally important can dilute attention, especially in complex studies. A risk-based approach asks which data are critical for safety, protocol conduct and reliable results, then makes sure those data and relationships receive the right level of attention. You may still review widely, but the intensity and frequency are guided by risk.
Tom
Can you make that tangible?
Jullia
Yes, so in an oncology study, dose changes, serious adverse events, abnormal labs and concomitant medications may need close, connected review. In another study, endpoint timing or visit window compliance may be more central. The review plan should reflect the study design, the protocol, the case report forms, the data sources and the decisions the team will need to make while the study is running.
Tom
That suggests clinical data review can’t be designed properly at the last minute. So what needs to be agreed early?
Jullia
Well the review approach belongs in planning. It should be reflected in the data management plan and, where appropriate, supported by the protocol and operational documents. Teams need to decide who reviews what, how often, which tools they’ll use, what triggers escalation, and how queries or findings move through the workflow.
Tom
Who tends to be involved? Is this more so a clinical data management activity?
Jullia
Clinical data management is central, but review connects several functions. Medical monitors may focus on subject safety and clinical interpretation. CRAs may look at site behaviour and follow-up actions. Data managers may focus on completeness, consistency and query management. Biostatistics and programming teams may support listings, visual outputs or data standards.
Tom
Where does that break down most often?
Jullia
Well, it often breaks down when each function is looking at a slightly different picture. One group may look at an EDC export, another may wait for a lab file, another may rely on a dashboard, and someone else may track issues in a spreadsheet. The more manual those hand-offs become, the harder it is to reproduce analyses, compare across domains, and know whether everyone is looking at the same status.
So, the pressure doesn’t just focus on the amount of data. It’s also access, timing and coordination. “Too much data” is the easy complaint, but it’s rarely the whole story. The harder problems are delayed access, fragmented sources, inflexible reporting, and limited workflow support. As more data arrive outside the traditional EDC flow, those coordination problems become harder to ignore.
Tom
Now you touched on standards earlier. So where do the likes of CDISC, CDASH and similar formats come into the conversation?
Jullia
They help with structure and exchange. If data is collected and mapped consistently, it becomes easier to integrate sources, compare domains and prepare for downstream analysis. Standards don’t remove every review challenge, but they make the data environment easier to manage. They also support traceability, which is essential when teams need to understand where a value came from and how it changed.
Tom
What about audit trails? They’re sometimes treated as something to inspect only when there’s a problem.
Jullia
Audit trails can be very informative before a problem becomes serious. They show how data is being entered, changed and managed over time. If a site is repeatedly correcting the same type of field, or if key data is being entered long after visits, that can point to training, process or oversight issues. It’s a window into how the protocol is being operationalised.
Tom
There’s also the practical side of query management. How does query turnaround fit into good review?
Jullia
Query turnaround tells you whether issues are being resolved quickly enough for the study to keep moving with confidence. If queries on critical safety fields remain open for too long, that is not just an administrative delay. It may affect medical review, monitoring follow-up, interim decision-making or database lock readiness later.
Tom
Let me challenge that slightly. Some teams may say, “We’ll clean everything before lock. Why invest so much effort earlier?”
Jullia
Well it’s because late cleaning can confirm what went wrong, but it can’t always change what happened. If a site misunderstood an adverse event form for three months, or visit schedule deviations weren’t spotted until late, the team may have lost the chance to correct behaviour. Earlier review reduces the chance that small issues become repeated study-wide problems.
Tom
Before we get near the end, what would you want a sponsor or study team to take away from this?
Jullia
I’d keep it simple. Clinical data review is part of live study oversight. Reviewers need to see the relationships behind the data, especially when labs, dosing, adverse events and visits are telling one story together. And the review model needs to be planned around the study’s risks, data sources and decision points, rather than assembled once problems have already built up.
Tom
And as tools become more advanced, with automation and machine learning becoming more common, what should teams be careful about?
Jullia
They should avoid treating the tool as the strategy. Intelligent automation can help identify anomalies and prioritise review, especially as trials include more electronic and decentralised inputs. But it still needs a controlled framework around it: clear ownership, access controls, auditability, privacy protections and documented escalation routes.
Tom
What’s the simplest way for teams to keep that grounded?
Jullia
Keep the review model tied to real study risks. Faster systems and larger data flows don’t change the basic responsibility. Teams still need to know whether the data are reliable enough to support safe oversight and credible decisions. When the review approach is planned early, clearly owned and connected to the questions the study team actually needs to answer, the whole trial runs with more control.
Jullia
With that, we’ve come to the end of today’s episode on clinical data review. 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.
About QCast
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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