In this QCast episode, co-hosts Jullia and Tom explore automation in clinical trials and why it has become such a practical priority for modern study delivery. They clarify what automation means in day-to-day trial work: the use of connected digital systems to handle repeatable tasks, move data more reliably between platforms, and support faster operational and clinical decisions. The conversation focuses on where teams feel the pressure most clearly, including fragmented system landscapes, manual handoffs between functions, inconsistent query workflows, and the knock-on effect those issues can have on data quality, oversight, and timelines.
They also discuss what automation is really there to do. It helps reduce avoidable friction across recruitment, consent, data cleaning, reconciliation, safety workflows, and reporting, while giving teams better visibility of what is happening across the study. At the same time, automation does not remove the need for human judgement. Along the way, Jullia and Tom highlight common misconceptions and failure modes, such as treating automation as a technology project rather than an operational one, assuming integration will work without clear ownership, or overestimating what AI can do without proper controls, traceability, and review.
What Automation in Clinical Trials Means and Why It Matters
Automation in clinical trials refers to the use of digital systems and workflow tools to support repeatable study tasks with greater consistency and less manual effort. That can include data transfer between systems, query routing, consent tracking, reconciliation steps, safety workflows, and operational alerts when expected activity has not happened. Its value comes from making trial delivery more reliable as studies become more complex and data sources become more fragmented. For many teams, the main benefit is not speed alone, but stronger control over quality, traceability, and oversight.
Where It Helps Most in Day-to-Day Delivery
Automation tends to create the clearest gains in areas with high repetition, high volume, or frequent avoidable delay. Common examples include lab data uploads, discrepancy checks, query turnaround, consent version control, and routing study tasks to the right team with a visible audit trail. These improvements reduce the operational burden created by disconnected systems and manual handoffs, while helping teams focus their time on review and decision-making. They are especially useful in studies with decentralised or hybrid elements, where visibility across multiple activities becomes harder to maintain manually.
Integration, Governance, and Human Oversight
Successful automation depends on more than deploying a tool. Teams need clear ownership of data flows, agreement on which system acts as the source of truth, and well-defined processes for validation, review, exception handling, and change control. AI-enabled automation can support activities such as risk detection, document handling, or surfacing workflow issues earlier, but it still requires explainability, traceability, and appropriate human oversight. Good practice starts with a narrow, measurable operational problem, builds around usable workflows, and keeps compliance, training, and auditability in scope from the start.
Episode 39: Automation in Clinical Trials
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
Automation in clinical trials can mean a lot of different things depending on who’s talking. Some people mean AI, some are referring to workflow tools, while some just mean fewer spreadsheets and fewer manual handoffs. Now what are we actually talking about today?
Jullia
So I’m inclined to keep it broad, but practical. Automation in clinical trials is the use of connected digital systems to handle repeatable tasks, move data through the study more reliably, and support faster decisions. That can sit in recruitment, consent, data capture, query management, safety workflows, lab data transfer, monitoring, or reporting.
Now where this shows up day to day is usually quite ordinary. A lab upload lands in the database without someone manually reformatting files. A discrepancy is flagged as the data comes in, instead of three weeks later. Or a dosing change is routed to the right team with a timestamp and an audit trail, so people aren’t chasing emails to work out what happened.
Tom
That makes sense, because the concept can sound quite big when a lot of the value is really just in very basic friction being removed. So is that why teams are pushing harder on it now?
Jullia
Yes, more so partly because the volume and complexity have changed. Trials are drawing data from more places, and they’re expected to move quickly without losing control of quality. You’ve got site-entered data, central lab feeds, patient-reported outcomes, wearable data in some studies, randomisation systems, document platforms, and safety systems. So if those stay disconnected, the burden lands on people.
Now the other driver is that timelines don’t really get kinder when a trial becomes more complex. Teams still need clean, reviewable data, they still need traceability, and they still need confidence that the process will stand up under inspection. Automation helps by making routine steps consistent. So that doesn’t necessarily remove human judgement, but it gives people a more stable operational base to work from.
Tom
You’ve touched on quality there. Now people often hear efficiency first, but really the stronger argument is usually control. What tends to improve first when automation is done properly?
Jullia
So it's usually consistency, and that honestly matters more than it sounds. If you automate standard checks, route tasks in the same way every time, and make sure systems record who did what and when, you reduce the small avoidable errors that accumulate over a study.
For example, a common scenario is adverse event entry or concomitant medication coding. If those steps depend too heavily on manual follow-up, turnaround becomes uneven and you start seeing preventable backlog, missed context, or repeated queries. Now put sensible rules and workflow around that, and the team can spend more time reviewing what actually needs expertise. And that’s really the bit people underestimate. Good automation often protects expert time rather than replacing it.
Tom
And then the obvious question. Where do you usually see the highest return first, because not every sponsor is going to redesign the whole trial stack in one go?
Jullia
No, and they shouldn’t feel they have to. So the best starting points are usually the areas with high repetition, high volume, or a lot of avoidable delay. Recruitment support can be one. Consent workflows can be another. Data cleaning is also a very common one, especially where edit checks, reconciliation steps, and query routing are still more manual than they need to be.
You also see quick gains in data flow between systems. If a team is repeatedly downloading files, renaming them, checking version control, and uploading them elsewhere, that’s a signal. The moment it matters is when those manual transfers affect review speed or create uncertainty about the current state of the data. Even automating one part of that chain can make oversight easier.
Tom
I’m glad you mentioned consent, because people sometimes treat eConsent as a patient-facing convenience feature and stop there. But it can affect downstream operations as well, can’t it?
Jullia
Very much so. When it’s implemented well, it improves more than the participant experience. It helps with version control, document completeness, confirmation that the right version was used, and visibility of the consent pathway across the study. Now that’s useful operationally and matters for compliance.
And the same goes for remote or hybrid study activity more broadly. If a visit schedule shifts, if diary completion is tracked digitally, or if alerts are triggered when expected data hasn’t arrived, the operational picture becomes clearer. While this doesn’t make a study simple, it does make it easier to see where attention is needed. And for decentralised elements, that visibility is often what keeps things manageable.
Tom
I think that leads nicely into integration. Everyone likes the idea of better systems but fewer people like the reality of getting them and the device data to actually behave together.
Jullia
Yes, that’s where it can get complicated. Automation is only as good as the connections around it. So if systems don’t exchange data cleanly or the data model is inconsistent, you can end up automating confusion. Then you end up with speed without clarity.
So really, what teams need is a sound architecture and clear ownership. Which system is the source of truth for each data type? How are changes propagated? What gets validated, reviewed, and reconciled, and by whom? And a short aside here for importance: some automation projects stall because the technical design gets most of the attention while process design is left vague. Then the tooling is live, but nobody has really agreed how exceptions should be handled. That’s usually where confidence drops.
Tom
Right, so technology and governance have to move together. But once AI enters the conversation, people can get either overexcited or overly cautious. What’s the grounded view?
Jullia
So the grounded view is that AI can be useful, but only in the right place and with proper oversight. It may help with patient matching, pattern detection, document handling, or surfacing risks earlier. Those can all be valuable. But teams still need to understand what the model is doing, where the output came from, and how decisions are being reviewed.
Like one place teams get caught is assuming that a tool is acceptable because it saves time. But time saved is not the standard. The focus should be whether the process remains explainable, traceable, and appropriate for the trial. If an eligibility suggestion is generated by a model, somebody still needs a defensible workflow around review. If automated text or classification is used, the accountability doesn’t disappear. Rather, it sits more heavily on the team to define controls properly.
Tom
Could you give me an example of a trial activity where automation helps a lot, but you still absolutely need a human to stay close to it.
Jullia
I’d say query management is a good one. You can automate a lot of the mechanics around queries. A missing lab date can trigger a check. An out-of-range value can be routed for review. A site can receive a prompt quickly instead of waiting for a manual pass through the data.
But the human part is still essential when the issue is clinical, contextual, or potentially safety-relevant. A raised liver enzyme on its own might look like a straightforward data point. In context, with dosing history, concomitant medication, and visit timing, it may need a very different level of attention. Automation can surface the issue fast and consistently. Experienced review is what determines what it means.
Tom
Now that’s probably the misconception worth flagging. People hear automation and assume fewer people are needed in the process, when often the smarter outcome is better use of the right people.
Jullia
Exactly. The roles evolve more than they disappear. Teams still need data managers, programmers, clinicians, statisticians, safety specialists, and operational leads who understand both the science and the systems around it. What changes is the shape of the work. Less repetitive administration, more review, design, exception handling, and oversight.
And that shift needs support. If an organisation brings in new tools without training, without SOP updates, and without clear validation and change control, people end up working around the system instead of through it. Then you lose both efficiency and trust. Good adoption is usually quiet. People use the workflow because it genuinely fits the job.
Tom
Now before we wrap up, let’s give listeners something practical. Say a team is interested in automation, but doesn’t want a grand transformation programme. What would you tell them to do first?
Jullia
I’d start by mapping where time is going and where avoidable risk sits. Then choose one or two processes that are repetitive, measurable, and painful enough to be worth changing. That might be query turnaround, consent tracking, data transfer from a lab, or reconciliation between study systems.
Then a few quick checks can help streamline things. Make sure process ownership is clear. Decide what success looks like before anything is switched on. Keep validation and auditability in scope from the start. And don’t ignore the user experience for sites and study teams, because friction just moves around if the workflow isn’t usable.
Tom
So, if I had to compress that into one line, pick a narrow problem, design the workflow properly, and make sure the control framework keeps pace with the technology.
Jullia
Yes and the broader point is that automation works best when it’s tied to trial delivery, not treated as a technology exercise on its own. Teams need cleaner flow of information, stronger visibility, and more reliable execution.
So if I were pulling together the main takeaways, I’d leave listeners with three things. First, automation is most useful where manual repetition is slowing down quality-critical work. Second, integration, governance, and training matter just as much as the tool itself. Third, human judgement stays central, especially where participant safety, interpretation, and compliance are concerned. When those pieces line up, automation can make a trial easier to run and easier to trust.
With that, we’ve come to the end of today’s episode on automation 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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