All pharmaceutical products approved to be used in a market setting have proven benefits but are also associated with adverse effects. The detection of such unknown risks is important to ensure the safety of patients. Signal detection and management in pharmacovigilance necessitates the ongoing monitoring of individual case safety reports (ICSR) to identify case reports of adverse events (AE) that are worthy for further exploration. Traditionally, signals are detected either qualitatively or quantitatively. Qualitative detection involves a deep analysis through manual assessment of the ICSR in an individual or cumulative manner, whereas the quantitative method involves statistical techniques such as disproportionality analysis.
In this two-part white paper series, we propose a hybrid approach where the algorithm is fueled to assess a signal and manage it for effective grading based on a qualitative approach.
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