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White Paper
The Future of Safety
How automation and machine learning are transforming Pharmacovigilance
Mar 28, 2019
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Introduction

Despite rapid advances in technology, pharmacovigilance (PV) units still handle the majority of adverse event and safety information data collection and processing through manual efforts. Once the data is captured, it is often stored in siloed databases making it difficult if not impossible to aggregate and analyze in meaningful ways.

According to the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA), the amount of adverse event reporting is increasing by more than 15 percent annually. Adding to this complexity, the number and variety of data sources PV units are expected to review is growing exponentially, making it increasingly difficult for PV professionals to keep up.

Many of these data sources are unstructured in nature where information has to be culled from narratives rather than directly lifted from forms and reports. These unstructured sources include emails, social media posts, journal articles, audio files, and handwritten documents, some of which require language translation prior to extracting relevant details.

The deluge of new data, increasing requirements from regulators, and declining PV budgets are forcing the PV community to seek new solutions to improve the access, timeliness and cost-effectiveness of safety data management. 

 

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