Pharmacovigilance (PV) plays a crucial role in ensuring drug safety by continuously monitoring and evaluating adverse drug reactions (ADRs). Traditionally, PV has relied on manual case assessments, literature reviews, and spontaneous reporting databases. However, as healthcare data expands exponentially, leveraging advanced tools and databases has become essential for improving the efficiency and accuracy of safety

Strengths and Challenges Spontaneous reports are the cornerstone of pharmacovigilance systems due to their ability to: However, they face challenges: Enhancing Reporting Systems To overcome these limitations, strategies include: The Impact of Patient Reporting Patient involvement has transformed pharmacovigilance by providing: Studies show that integrating patient reports with healthcare professional data creates a more comprehensive

Traditional Methods Spontaneous Reporting Systems (SRSs) remain the backbone of pharmacovigilance. These systems rely on healthcare professionals and patients to report adverse events. Traditional methods include: While these methods have been successful, they are limited by issues like underreporting, inconsistent data quality, and a lack of standardized reporting across regions. Emergence of Data Mining In

What is Signal Detection? Signal detection is a cornerstone of pharmacovigilance, the science of monitoring, detecting, assessing, and preventing adverse effects of medicinal products. A “signal” refers to information suggesting a new causal association between a drug and an adverse event, warranting further investigation. Defined by WHO in 2002, signals typically arise from patterns of

The Importance of Signal Detection in Drug Safety In today’s world, ensuring the safety of medicinal products is more critical than ever. Pharmacovigilance—the science of monitoring, detecting, and preventing adverse effects—plays a vital role in safeguarding public health. At the heart of this field lies signal detection, a systematic approach to identifying and addressing potential