Saama

This eBook will discuss three key ways that researchers can implement AI to address data challenges, improve patient safety and outcomes, and accelerate processes.

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data. Additionally, this solution would need to aggregate data into one “single source of truth” to inform better decision-making.

This white paper will explore the key challenges impacting clinical trial medical reviews from the perspective of Dr. Gareth Tomlinson, PhD, Director, Clinical Research Scientist at Taiho Oncology, Inc.

AI can be applied to patient data in real time, allowing medical reviewer teams to actively monitor patient safety, with the AI alerting medical review teams to identify potential adverse events (AEs), data anomalies, critical issues for patients on treatment, and more.

Over the last two decades, clinical trials have increased in complexity by approximately 61%. Additionally, data volume has grown to be too complex and resource consuming to be processed manually. As a result, patient data is dispersed over so many various systems, that it’s impossible to get a clear picture of the patient, making it difficult to understand how they’re responding to treatment – thereby impeding a patient’s time to recovery.