Automating to innovate
By 2020, automation is likely to reduce case-processing efforts by over 80% and decrease costs by at least 50%. At the same time it will improve the accuracy, quality, and consistency of adverse event data. Fast-forward another five years, and AI technologies will be radically transforming PV science through intelligent signal detection and evaluation.
Automated processes will generate and test hypotheses, predict likely safety issues, and suggest potential interventions to minimize those risks. These advancements will support a much stronger emphasis on the science of preventing ADRs, which is the most important role of PV. In the future, PV professionals will be able to devise more precise definitions for the proper use of a drug—and more sophisticated approaches for controlling that use.
AI solutions designed for PV will make this easier. Through machine learning, the accuracy of these solutions will automatically and continuously improve as PV teams apply them to growing volumes of data. At the same time, pharma companies will actively seek service providers with capabilities to apply these advanced technologies in an end-to-end model, to outsource their PV and regulatory activities related to managing and maintaining drug information.
Harnessing AI to transform PV into a sustainable, scalable operating model will free up resources to focus on innovation, and advance the knowledge base associated with newer products. That way, they can truly influence the healthcare ecosystem to optimize patient care and ensure sustainable value.
This article was co-authored by BK Kalra, senior vice president, business leader for consumer goods, retail, life sciences & healthcare, and Eric Sandor, global leader for Cora PharmacoVigilance at Genpact
A version of this point of view previously appeared in ThePharmaLetter.