Sollers College

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Automation is increasingly being recognized as a powerful tool in pharmacovigilance (PV), particularly in the processing of Individual Case Safety Reports (ICSRs). With the high costs, large workforce demands, and quality risks involved in manual processing, automation—via AI, ML, RPA, and cognitive computing—offers a transformative solution. From case intake and processing to reporting, automation can streamline operations, reduce errors, and enhance compliance. However, regulatory risks and the complexity of medical evaluation still pose implementation challenges.

The strength of AI and Big Data in signal detection lies in their capability to rapidly and accurately analyze large volumes of data. This facilitates the identification of subtle patterns and anomalies that human analysts might overlook, leading to more efficient and effective decision-making across diverse fields. Additionally, automated NLP and AI reduce manual effort, allowing experts to concentrate on critical risk assessment.

How Artificial Intelligence is Transforming Drug Safety & Pharmacovigilance

Technology breakthroughs, greater access to data, and growing patient involvement in health decisions are transforming global pharmacovigilance systems.

In pharmacovigilance, clinical pharmacists can use AI to employ completely new competencies. Moreover, AI may facilitate patient-provider communication by reducing processing times.

Pharmacovigilance, Drug Safety, and Clinical Research Training for Future Professionals

Pharmacovigilance and Social Media: Transforming Drug Safety Monitoring

Pharmacovigilance is evolving with the power of automation and artificial intelligence (AI), enabling pharma companies to streamline operations such as adverse event case intake, benefit-risk analysis, and literature screening. With rising disease complexity and regulatory scrutiny, automation in PV enhances compliance, accelerates reporting, and ensures high-quality data for decision-making. This infographic explores key factors driving PV digitalization and the role of AI, machine learning (ML), robotic process automation (RPA), and big data analytics in revolutionizing drug safety.

Discover how the integration of natural language processing (NLP) is transforming pharmacovigilance by analyzing user-generated content to identify adverse drug reactions (ADRs). This infographic highlights the potential of text mining and AI strategies to enhance drug safety and improve data collection processes. Explore key findings and outcomes that underscore the value of computational linguistics in advancing healthcare monitoring.

Research is essential to innovation in medicine, and clinical trials constitute a significant portion of ongoing medical research.

The use of clinical trial management software by physicians, researchers, and other healthcare professionals helps organize, conduct, and oversee clinical trials.

In the early waves of COVID-19, medications approved for other purposes were repurposed due to a shortage of vaccines. Patients with COVID-19 have been treated with medications not approved for the disease.

This course provides training for students to learn how the Clinical Trial Management process works, developing complete Regulatory activities involved in Creating Documents and in Planning, Organizing, Monitoring, Recording, Analysis and Reporting of Clinical Trials. The program provides additional opportunities to learn about recent advancements as well as specializations in Risk-based Monitoring concepts.

A lot of exciting and crucial changes have taken place in the field of clinical trial management in recent years! Here are some of the key trends shaping the field...For more information: Sollers college

Before biological products are submitted for market clearance, #pharmacovigilance (PV) is one of the most important steps in the medication development cycle. It makes sure that the products fit the intended safety profile.