In this post-Clinical Trials Day spotlight, Ken McFarlane, VP of strategic consulting at CluePoints, reflects on how the clinical trials landscape is evolving. From AI-driven monitoring tools to shifting regulatory priorities, the company shares insights on where innovation is taking the industry – and what needs to change to keep progress moving.

What does Clinical Trials Day mean to your organization, and how did you mark it this year?

Clinical Trials Day is about acknowledging the contributions of the amazing professionals, patients and partners powering progress in research. It was also an opportunity to reflect on how far we’ve come over the past 278 years and look ahead to the next phase of clinical research.

Back in 2012, the company was founded on the premise that the clinical trial operating model needed to evolve to better serve those professionals, patients and partners – with a firm belief that a merger between human intelligence and artificial intelligence would transform the research landscape.

Thirteen years later, through a continued commitment to technology and the people who use it, we remain focused on helping customers navigate an ever-evolving environment.

How has your clinical trial strategy evolved over the last five years?

The regulatory environment has changed significantly, with the FDA offering greater support for risk-based quality management approaches and the release of ICH E6 (R3), which re-emphasizes proportionate risk-based approaches and quality by design. The shift in focus from data integrity to data reliability encourages more critical thinking.

We’ve also acknowledged the need for better change management and upskilling talent to adapt to a new operating paradigm.

Over this time, the company has expanded its platform capabilities and services to support customers well beyond traditional risk-based models – ensuring they achieve their desired outcomes.

What development milestone from the past year are you most proud of, and why?

We’re especially proud of launching three new products in 2024 that are transforming clinical trial reviews. The Site Profile & Oversight Tool enables adaptive site monitoring, helping teams quickly identify anomalies and take action. The Intelligent Medical Coding solution uses advanced deep learning to boost accuracy and efficiency. And the Medical Safety Review tool streamlines the medical analysis of study data.

Another major milestone was the development of the Time Similarity Test, which enhances detection of unusual patterns in ePRO data by analyzing audit trail timestamps. It flags sites where data is entered at unusually similar times, which can indicate fabrication or non-compliance. By identifying such issues earlier, sponsors can reduce timelines and costs while improving data quality and readiness for regulatory review.

What role does AI or machine learning currently play in your trial planning or execution?

We’re actively using AI, including large language models, to augment statistical approaches and tackle tasks that have traditionally been time-consuming and manual.

For example, AI-generated suggestions in medical coding eliminate the need for manual dictionary searches and streamline synonym management. This not only saves time but ensures consistency across datasets.

We’re also exploring agentive models. These aren’t replacements for human input but tools that support automation in ways that boost quality and reduce time spent on repetitive tasks.

What’s your biggest prediction for how clinical trials will change by 2030?

If we continue using AI and technology in practical ways, I hope the focus shifts from treatment to something even more important to patients: cures.

With these tools, we’re getting closer to answers and cures for rare and currently incurable diseases — or, at the very least, treatments that can dramatically improve lives. Having seen the toll of conditions like cancer, dementia and lupus on loved ones, I’m personally committed to helping the industry push toward that goal.

If you could change one thing about the current trial ecosystem, what would it be?

I’d give sponsors, CROs and their partners more room to test and refine new models — without the pressure of having to implement them on studies considered too critical for experimentation.

We need space to try, fail, learn and improve. This isn’t about promoting agility for the sake of it, but about recognising that mastery comes with practice. Change isn’t delivered through a single tool or quick fix. It takes time, and teams need the freedom to work through it.