Clinical data review has always been one of those parts of a trial that takes more time than anyone expects. An industry report by NIH states it can make up nearly 30-40% of the total workload, and if you’ve ever combed through multiple listings or tried to reconcile a stubborn discrepancy, that number probably feels pretty accurate.
And the reality is, trials aren’t getting any simpler. We’re collecting data from more sources than ever, and each new source adds another layer for teams to sort through. It’s no surprise that reviewers are constantly looking for ways to make the process easier and less fragmented.
This is precisely why Interactive Review Listings, or IRLs, have become such a talking point in data management circles. They give study teams one clean space to look at their data, explore what’s happening, and figure out what needs attention. We can all agree that finding a simpler, more intuitive way to review information is not just helpful, it is absolutely essential.
In this blog, let’s explore the genuine value IRLs bring to the review process, how teams across the industry are using them right now, and how platforms like SDQ (Saama’s Smart Data Quality) fit perfectly into this new method of working.
Why IRLs are becoming everyone’s go-to review method
Clinical data today comes from everywhere. EDCs, labs, ePRO tools, imaging systems, wearables, you name it. And trying to make sense of all this using only static listings can make the process feel scattered and slow.
IRLs give teams a single, interactive place where they can:
- Filter and explore data instantly
- Jump between patient-level and variable-level views without exporting anything
- Investigate potential issues right in the system
- Work with teammates in the same space instead of emailing screenshots around
Basically, IRLs make the whole process feel less scattered. And that alone can shave hours off a reviewer’s week.
If you’ve ever wondered what a faster, cleaner review process really looks like in action, we have just the webinar for you. It will help you explore how teams are using AI to cut through the noise and focus on what matters.
How Saama’s SDQ fits into the IRL workflow
So what does a strong IRL actually look like in practice? Here’s how Saama brings all of this together inside SDQ.
- A single workspace for all listings
Subject-level listings, cross-domain listings, and aggregated views all exist within one unified space. Reviewers no longer need to switch between multiple tools to understand their data.
- All critical data review signals in one dashboard
SDQ brings together every major signal reviewers track during a study. These include:- Data changes
- Queries
- Aging
- Overdue actions
- Protocol deviations
- These signals are presented in an interactive dashboard that highlights what requires attention and provides context about the source and impact of each finding. This helps reviewers not only see what is happening but understand why it is happening.
- Flexible listing creation without coding
Teams can use out-of-the-box global listings, inherit listings from previous studies, or create custom listings using generative AI. Reviewers simply describe what they want in natural language, and the listing is generated without needing a programmer.
- Integrated task and discrepancy management
Users can create, assign, track, and resolve tasks directly within the IRL workflow. All queries, regardless of source, appear in one location so teams do not lose context or time.
- Secure vendor collaboration
Vendors only see the tasks assigned to them, not the entire study, which keeps collaboration simple and secure.
- Clear, complete audit history
Every listing action, including creation, edits, and comments, is captured in a downloadable audit log for full traceability.
IRLs are shaping the future of clinical data review
IRLs are no longer optional. They’re becoming the preferred workflow for teams focused on:
- Faster review cycles
- Fewer back-and-forth systems
- Clearer visibility into data quality drivers
- A single source of truth across all review activities
As AI and automation take over routine work, IRLs will remain the place where human judgment drives insight. Together, they push clinical data review toward becoming faster, more connected, and far less stressful.
Ready to replace data chaos with clarity? See the SDQ platform in action. Request your demo today, write to us at [email protected].
