Clins and outs

Why ClinOps has been thrust into the spotlight

Clinical operations (ClinOps) are the lifeblood of any strong clinical trial programme, orchestrating the many moving parts that bring innovative therapies to market.

Yet, as trials become more complex and data-driven, the challenges facing ClinOps teams are evolving rapidly.

From managing diverse data sources to navigating intricate trial designs, the need for operational efficiency and innovation has never been greater.

As we move into 2025, AI continues its emergence as a transformative force in clinical trial operations. While its applications in protocol development and study design are already gaining traction, the potential to embed AI and GenAI deeper into operational workflows is immense.

Without a doubt, real-world evidence (RWE) is reshaping the clinical trial landscape and enabling organisations to enhance trial designs, streamline operations and improve outcomes.

ClinOps teams are now tasked with integrating diverse data sources, from electronic medical records and genomic data sets to data generated by smartwatches and other wearables.

While these new information streams provide richer, more comprehensive insights into patient populations, they also require sophisticated tools and methodologies to manage and analyse effectively as well as solid data management practices to create reliable and useful real-world evidence.

The rise of targeted therapies and precision medicine has also disrupted traditional clinical trial processes.

Adaptive trial designs, biomarker-driven studies and other innovative approaches offer the potential for more precise and effective treatments but also introduce new layers of complexity.

Embedding regulatory guidance into AI models, for instance, can streamline protocol development and ensure compliance, even as trial designs grow more intricate.

AI, including GenAI, is already revolutionising how clinical trials are planned and executed.

From predicting trial feasibility to optimising site selection, or processing increasingly large real-world data sets, AI-driven models are informing ClinOps teams about the likelihood of reaching enrolment targets and strategies to minimise costly delays.

In January, the FDA took its first official action relating to the use of AI in drug development when it issued new draft guidance on the use of AI in supporting regulatory decision-making on a drug or biological product’s safety, efficacy or quality.

This new guidance recognises the swift pace of technological advancements and cements the notion that AI, when deployed with the appropriate safeguards, has significant and broad potential to advance clinical research and meaningfully accelerate product development.

A recent study highlighted how AI-powered tools have already shown significant promise in enhancing trial efficiency, particularly in automating patient eligibility assessments and reducing manual workloads.

As these tools become more refined, their potential to drive operational efficiencies and improve decision-making continues to expand.

As regulators around the world establish new policies encouraging the broader utilisation of AI in randomised control trials and other ‘traditional’ research processes, clinical operations teams must embrace change.

One key change may lie in evolving the skillsets required for ClinOps success – complementing domain expertise in areas like patient recruitment, site start-up and data management with data scientists, generative AI specialists and machine learning engineers.

With the convergence of AI, data integration and patient-centric strategies, 2025 offers an opportunity for organisations to build more efficient and innovative trial programmes – laying the groundwork for the next era of clinical research.

Rachel Hardin is Head of Life Sciences Business & Market Development at SAS.
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