Insights

Artificial intelligence (AI) is proving particularly useful in identifying ​potential candidates for ​appropriate clinical trials.​​ In oncology, for example, overall estimated patient participation rate in clinical trials is only around 7%. AI can help alleviate the burden of prescreening at the site level, freeing up clinical research coordinators’ time to focus on the patients who are most likely to meet inclusion/exclusion criteria. 

As more and more clinical research professionals come to grips with the day-to-day, warts-and-all reality behind the glittering productivity promises offered by artificial intelligence (AI), one thing that has become clear is that AI is not just about reviewing clinical trials data and regulatory paperwork—it’s about reviewing the reviewers themselves. 

While clinical research offers a broad range of career paths, site-based professionals may face challenges transitioning to roles at non-site companies. Developing a transition roadmap—using self-assessment tools, tracking metrics, gaining cross-functional exposure, and reframing outcomes in the company’s language—can help in achieving career goals.

There continues to be a shared responsibility across sites and sponsors to align with the recently updated ICH E6(R3) guideline for Good Clinical Practice, which is designed to foster a culture of quality as clinical research methods evolve. Understanding the guideline's Quality by Design (QbD) and Critical to Quality (CtQ) elements is key to implementing its tenets.

Lynn D. Van Dermark, RN, BSN, MBA, RAC, FACRP, shares her pride in contributing to ACRP’s legacy of building training systems and defining standards that elevate excellence in clinical research.