EMMANUEL BELABE
Senior Vice-President for Customer Success, Global Customer Support, and Solution Consulting at ArisGlobal, New Jersey, United States

Abstract

Adverse event (AE) case intake, in its current form, is one of the most overlooked and broken workflows in biopharma, so it is ripe for disruption by next-generation AI technologies. After several years of bold promises, Generative AI and Large Language Models are now tangibly delivering human-like decision-making, leading to earlier and more accurate conclusions about Safety events, to enable real-time interventions. ArisGlobal’s Emmanuel Belabe assesses how far the technology has come in delivering intelligent, automated case intake; the tangible difference this is already making; and the wider opportunity for reinventing pharma R&D operations.

In developed economies, it is expected, and assumed, that any authorized drug or medicinal therapy designed for human use is unequivocally safe. Pharmacovigilance processes – the discipline of continuously monitoring the effects of drugs once on the market – are designed to uphold that position over time, once a product has been authorized for patient consumption.

Approaches to post-market Safety monitoring have changed little in decades, however, despite soaring volumes of available information – submitted in an increasing array of formats, via a proliferating range of channels.

Many companies still take the approach of “booking” cases or determining whether the mandatory elements are present, to quickly assign an identifier. This approach doesn’t take into account the actual contents of a case, forcing Pharmacovigilance teams to apply the same treatment to all information. This means all cases are assigned the same priority in the early stages; there is no discretion to allocate teams’ bandwidth according to a potential case’s complexity or risk. Keeping track of all of the potential signals, assessing their validit ...