Life Sciences

Cora PharmacoVigilance

Digital enhances patient safety

The top priority of the pharmacovigilance (PV) function in life sciences companies is to ensure that the highest quality, compliance, and data privacy standards are met and maintained across global operations at all times.

Many life sciences organizations have not only seen their adverse event (AE) volume double over the last five years, but expect to see a similar increase in the near future due to new approaches such as intelligent and personalized medicines. In this context, the current manual approach to case processing is not sustainable or scalable for ensuring cost effective operations at the highest levels of quality.

Rising costs and complexity, coupled with the manually intensive nature of case processing in PV, makes it a perfect candidate for automation. Technologies such as optical character recognition (OCR), robotic process automation (RPA), artificial intelligence (AI), natural language processing (NLP), and machine learning (ML), have great promise in this space, but need to be augmented with PV domain expertise.


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Designed specifically for the PV domain, Genpact's Cora PharmacoVigilance product applies advanced digital technologies such as RPA, NLP, and ML to reliably and effectively extract and code AE data from unstructured and partially structured source documents.

Cora PharmacoVigilance enables complete end-to-end case processing , and transforms signal detection in the case of AEs, through automated hypothesis generation and testing against a wide range of data sources. For most AE cases, our “touchless" AE processing methodology utilizes a configurable workflow engine to automatically generate notifications and follow-ups without any human intervention.

For cases that need human review, our guided user interface helps the practitioner through the data fields that need verification, enabling more streamlined quality control. As practitioners make corrections to case data, Cora PharmacoVigilance continues to learn and improve in accuracy.

Integrating AI across the information value chain enables a self-learning, virtuous circle of improving data quality and insight. Our team ensures a low-risk approach for introducing automation incrementally, and transitioning clients from manually-intensive processes to a highly simplified and automated PV operating model, all while maintaining strict compliance standards.