Traditional information gathering across the pharmaceutical industry, collected directly from healthcare professionals (HCPs), patient registries and regulatory databases, provides insightful information for drug safety risk analysis.
However, relying solely on a limited source of structured data only tells part of the pharmacovigilance (PV) story. In recent years, as the digital landscape has expanded, patients are increasingly flocking to non-traditional reporting channels to express concern and seek guidance regarding drug safety events.
As such, there exists a wealth of untapped information and direct feedback from patients across patient support programmes (PSPs), social media platforms, online forums, discussion groups and much more. To access and leverage this data, clinical research stakeholders are increasingly turning to automation and artificial intelligence (AI) to extract and interpret information from these consumer channels. To understand how these technologies are revolutionising data collection and analysis, it is critical to recognise the current drug safety landscape and the use of traditional data collection methods.
The traditional landscape of AE detection
Historically, adverse event (AE) reporting has occurred directly through patients and HCPs. Traditional AE reporting has relied on manual and fractured systems and protocols that lead to an incredibly time-consuming process. This reporting has relied on structured data capture networks in a pre-approval landscape or from HCPs in a post-approval landscape, creating a gap in information sharing and potentially hindering early detection of safety concerns.
Adding insult to injury, not only do AE detection and reporting methods create a challenge in structuring accurate results, but data reveals that experts still lack a strong understanding of the current AE landscape.
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