A recent article projected that almost 78% of large pharmaceutical companies regularly leverage Artificial Intelligence (AI) to scan social media platforms for drug and side‑effect mentions. It clearly states that Pharmacovigilance (PV) is surpassing traditional reporting of adverse events and increasingly banking on a variety of digital sources for data gathering.
The variety of digital sources includes social media channels, health applications, online pharmacy stores, etc. The data collected from real-world and social media reports are helping pharmaceutical companies to:
- improve drug safety monitoring,
- spot safety signals sooner, and
- make better risk management decisions
Sources of Real-World Data
Real-world data in pharmacovigilance (PV) means healthcare information collected outside of clinical trials. This data shows how medicines work in everyday settings. Main sources include:
- electronic health records (EHRs),
- insurance claims,
- patient registries,
- pharmacy databases,
- wearable devices, and
- patient-reported outcomes
Using real-world data in drug safety lets organizations track larger and more diverse groups of patients, find rare or long-term side effects, and understand how medicines are used in real life. As regulators see more value in this evidence, combining these data sources is now a key part of modern pharmacovigilance.
Social Media Adverse Event (AE) Monitoring
Patients often talk about their treatment experiences on online communities and social media platforms. This makes social media a new source of safety information. By regularly keeping an eye on these platforms, companies can spot possible side effects, medication misuse, problems with sticking to therapy, and patient concerns earlier than with traditional reporting.
But social media data can be messy, incomplete, and hard to check. That’s why strong data validation is needed to make sure the information is reliable before using it in pharmacovigilance.
Sophisticated tools like Natural Language Processing (NLP) and Artificial Intelligence (AI) help analyze a wide variety of online content. So, safety teams can sort through cases while still relying on human validation experts for clinical review.
Data Validation and Integration
To combine real-world data plus social media information well, organizations need standard ways to check and manage the data. Before adding it to safety databases, organizations should review the data for its
- completeness,
- consistency,
- duplicacy, and
- medical accuracy
When datasets are combined, pharmaceutical companies can better spot new safety trends, study different patient groups, and track long-term treatment results.
At the same time, automating pharmacovigilance tasks like case intake, literature review, duplicate checking, and workflow management helps professionals focus on clinical review and meeting regulations.
Future Trends in Pharmacovigilance (PV)
The future of PV will be progressively data-driven and technology-enabled. Sustained advancements in AI in pharmacovigilance, real-time analytics, interoperable health systems, and patient-generated data will further enhance adverse event reporting and active drug safety surveillance.
As organizations continue to incorporate real-world data in pharmacovigilance with digital monitoring capabilities, they will be better equipped to strengthen risk management, improve patient safety, and support informed regulatory decision-making throughout the product lifecycle.