Clinical trial trends are increasingly shaping practical choices around study design, data collection, participant experience, and oversight. In this QCast episode, co-hosts Jullia and Tom look at how decentralised and hybrid models, AI and automation, real-world evidence, adaptive designs, RBQM, advanced therapies, outsourcing, and cybersecurity are affecting the way modern studies are run.
The operational pressure comes from balancing potential gains with the controls each approach requires. A wearable may reduce site visits but create new validation and data-review demands. AI may help surface records for review, but it still depends on data quality and human judgement. New trial designs, outsourcing models, and digital systems can also change where accountability sits, how sites are prepared, and which risks need closer oversight.
The value of a new approach depends on whether it answers the clinical question and suits the patient population, site network, and data requirements. Decentralised models, adaptive designs, and AI-supported workflows can all be useful, but only when their role is clearly defined and their limitations are understood.
Remote participation can make trials easier for patients by reducing travel and site visits, but it can also introduce extra work around device validation, protocol consistency, and data review. The same principle applies to automation, where faster processing still requires controls, exception handling, and human oversight.
RBQM, site readiness, vendor oversight, and cybersecurity all point back to the same operational discipline: sponsors need to understand where risks sit and how they are being controlled. Outsourcing or adding technology may change how work is delivered, but it does not remove the need for clear accountability and proportionate oversight.
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
Clinical trial trends can become a fairly broad topic quite quickly. If you’re running studies now, what’s actually changing in a way that affects day-to-day decisions?
Jullia
A lot of it comes down to execution. Sponsors have more options around how trials are designed, how data is collected and how work is outsourced, but every new option brings decisions around quality, oversight and patient burden.
Technology is part of that, of course, but adopting something because it’s new isn’t particularly useful. The question is whether it fits the trial, the patient population and the evidence you need to generate.
Tom
I feel like decentralised trials are a good example to talk about. There was a period when remote participation was talked about almost as the direction every trial was heading in. Has that settled down a bit?
Jullia
I think it has. Decentralised and hybrid approaches are now just being increasingly used where they make sense, rather than trying to be shoehorned in every single study. Remote visits, home health support, wearable devices and electronic clinical outcome assessments can reduce travel and make participation easier. But it’s also important to consider that you’re also introducing questions around device validation, protocol consistency and the reliability of data collected outside the site.
Tom
So reducing visits doesn’t automatically mean reducing trial complexity?
Jullia
Exactly. See, you might make the experience easier for the participant while creating more complexity elsewhere. Like, say you replace a site assessment with a wearable measurement at home. The patient avoids a journey, which is useful, but the study team now needs confidence that the device is being used correctly, the data is complete and the measurement is comparable across participants. There may also be a much larger volume of data to review.
Tom
And I guess in that same vein, participant experience itself seems to be getting treated as more of an operational issue too as opposed to an engagement exercise?
Jullia
Yes. Travel burden, complicated visit schedules, reimbursement delays and unclear communication can affect whether someone enrols and whether they stay in the trial. That then affects recruitment timelines and data completeness. So patient-centricity has to feed into trial planning right from the start.
Tom
AI is probably the other trend people hear about constantly. What makes a useful clinical trial application?
Jullia
Usually, it’s a clearly defined task. AI and machine learning can support things like protocol review, recruitment feasibility, site selection, data review, medical coding or risk-based monitoring. If you know what problem the tool is solving, you can assess whether the output is useful and how it should be checked. Broad claims that AI will simply make trials faster or cheaper are much harder to evaluate.
Tom
Could you give me a simple example?
Jullia
Well, think about data review. A system might help flag unusual patterns or records that need attention, but somebody still needs to understand why something was flagged and whether action is required. If an adverse event entry looks unusual, for example, an algorithm can help surface it. Basically, it doesn’t remove the clinical judgement needed to understand the record.
Tom
Human review feels especially important because generative AI can also produce answers that sound convincing and are simply wrong, right?
Jullia
Yes, and predictive models can perform poorly if the underlying data is incomplete or not representative of the population where the model is being used. That’s why governance is equally important here. Teams need to know where AI or automation is being used, how outputs are checked and who remains accountable. Automated clinical data workflows also need the usual controls around validation, audit trails and exception handling.
Tom
There’s another data trend here as well, which I assume is the broader use of real-world evidence?
Jullia
Yes, although it’s important to frame real-world evidence, or RWE, as complementary evidence rather than a replacement for controlled trial evidence.
Data from electronic health records, claims, registries, wearables and observational studies can provide context on how treatments are used in routine care. They may also provide information about groups that were less well represented in a traditional trial.
Tom
And where do patient-reported outcomes fit? They can sometimes get grouped into the general patient-centricity discussion.
Jullia
They’re more specific than that. Patient-reported outcomes, or PROs, capture information directly from patients about things such as symptoms, functioning, treatment burden or quality of life.
They can add something important when a traditional clinical endpoint doesn’t fully describe whether a treatment makes a meaningful difference in daily life. The measure still has to be appropriate for the population and suitable for how the study is being run.
Tom
Trial design itself is changing too isn’t it? One misconception I sometimes hear is that adaptive or Bayesian approaches mean conventional randomised trials are becoming outdated. Is that a fair interpretation?
Jullia
Not really, it’s better to think about having a broader set of designs available for different clinical questions. Response adaptive randomisation, for instance, can allow treatment allocation probabilities to change based on accumulating information. BOIN designs can support dose-finding decisions in early-phase studies. Master protocols, including umbrella, basket and platform trials, can allow related questions to sit within a shared protocol structure.
Each approach brings statistical and operational requirements of its own. The design still has to match the question and have a clear scientific and regulatory rationale.
Tom
And in oncology, dose optimisation is part of that shift as well?
Jullia
Yes, Project Optimus has increased the focus on dose optimisation rather than relying primarily on the maximum tolerated dose. That means thinking more carefully about the balance between efficacy, safety and longer-term tolerability during development.
Tom
We’ve talked quite a lot about new ways of collecting data and running studies. Does that make risk-based quality management more important?
Jullia
It does. See, risk-Based Quality Management, or RBQM, extends the risk-based approach beyond monitoring alone. ICH E6(R3) reinforces quality by design and proportionate, risk-based approaches across trial planning and conduct. That means identifying what really matters to participant protection and reliable trial results, then concentrating controls and oversight accordingly.
Tom
Could you give me an operational example of where that wider view changes something?
Jullia
Well, site readiness is a good one. A site can meet the basic feasibility criteria but still struggle because staff capacity is limited, there’s a competing study or the team hasn’t had enough training on a particular assessment.
If that risk is identified early, the sponsor can address it before it starts showing up as slow recruitment, protocol deviations or inconsistent data. The same principle applies to vendors. Outsourcing an activity doesn’t remove sponsor accountability for the quality of that work.
Tom
I suppose that becomes particularly relevant with rare disease and advanced therapy trials, where the operational requirements can be quite specialised?
Jullia
They can. Rare disease studies may have very small populations, heterogeneous data and recruitment challenges, so endpoint selection and long-term follow-up need careful thought.
Cell and gene therapy trials can add requirements around specialist storage, administration, safety monitoring and coordination between manufacturing and clinical teams. Here, site capability becomes part of feasibility because not every site will have the infrastructure or experience needed to deliver those studies.
Tom
It’s true that we’ve also seen diversity and inclusion become much more prominent in trial planning, right?
Jullia
Yes. There’s greater attention on whether trial populations reflect the patients likely to use a treatment, and on barriers that make participation harder for particular groups.
That affects recruitment planning, eligibility criteria and how sponsors think about access to sites. The regulatory position can vary depending on the context, so it’s important to work from current guidance rather than relying on broad statements about diversity requirements.
Tom
Before we finish, outsourcing is another area that’s been changing. FSP, full-service and hybrid models all seem to coexist now rather than one replacing another. Is that correct?
Jullia
That’s right. Functional Service Provision gives sponsors access to dedicated functional teams while retaining more internal control. Hybrid approaches can combine that with elements of full-service outsourcing.
For smaller biotech companies especially, the decision may depend on internal capability, cost pressure and how quickly specialist resources need to scale. Really, there isn’t one outsourcing model that fits every programme.
And the more systems and vendors you bring into a study, the more cybersecurity and data privacy become part of that operational picture. Decentralised trials, wearables, eCOA systems, EDC platforms, laboratories and other vendors all create data flows that need to be understood and controlled. Access controls, audit trails, data transfers and supplier oversight therefore need to sit within the trial’s wider risk-management approach.
Tom
Now if we pull all of this together, what are the main things you’d want listeners to take away?
Jullia
Clinical trial trends are most useful when they solve a defined problem. Whether it’s AI, a decentralised model or an adaptive design, the method needs to fit the study rather than drive it.
Then, reducing burden in one area can create new work somewhere else. Remote data collection is a good example because better patient access can come with additional validation and oversight requirements. That gives us a useful filter for any new trend.
With that, we’ve come to the end of today’s episode on Clinical Trial Trends. 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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