10 Key Pharmacovigilance Challenges in the EU and How to Address Them

Pharmacovigilance challenges are becoming more complex as safety teams manage growing data volumes, evolving regulations, and increasingly diverse safety sources.

Intro

Pharmacovigilance (PV) continues throughout the entire lifecycle of a medicinal product. After a medicine reaches the market, its safety profile continues to evolve as new information emerges from spontaneous reports, scientific literature, real-world data, and other safety sources. Monitoring drug safety, therefore, requires continuous evaluation of evidence rather than simply collecting adverse event reports.

Even well-structured pharmacovigilance systems can face methodological and operational challenges. These may include incomplete safety data, difficulties identifying potential safety signals, growing case volumes, complex regulatory frameworks, and limited resources. For pharmaceutical companies operating across the EU, these challenges can become more demanding as teams must maintain regulatory compliance while coordinating activities across different countries and healthcare systems.

Understanding these key and future challenges can help safety teams strengthen existing processes, improve data quality, support effective risk management, and ultimately help ensure patient safety.

What Are the Main Challenges in Pharmacovigilance?

Pharmacovigilance teams face challenges that can affect how effectively they detect, assess, and prevent medication-related risks. Some limitations come from the nature of safety data itself and the spontaneous reporting systems used to collect it. Reports may be incomplete, lack important clinical details, or be influenced by reporting patterns, making it difficult to identify a clear safety signal.

Other challenges in pharmacovigilance are linked to processes, regulatory compliance, workload, technology, and available pharmacovigilance resources. Teams must handle large and growing volumes of safety information while meeting strict timelines and maintaining consistent documentation. At the same time, requirements may differ across markets, making global monitoring more complex.

These challenges can affect regulatory reporting, risk assessment, and the ability to respond consistently to emerging safety issues. Understanding the challenges of pharmacovigilance helps organisations identify where processes may need stronger controls, better technology, or additional expertise.

The main challenges of PV can be summarised as follows:

Challenge	Main impact
Incomplete or poor-quality safety data	Makes case assessment and signal detection more difficult
High data volume	Increases workload and the risk of delays
Complex regulatory requirements	Creates compliance and documentation risks
Manual processes	Consume time and increase the potential for human error
Limited resources	Can affect monitoring quality and response times
Multiple data sources	Makes data integration and consistent assessment harder
Technology limitations	Restrict automation, scalability, and visibility

1. Underreporting of Adverse Drug Reactions

One of the most persistent challenges in pharmacovigilance is the underreporting of adverse drug reactions (ADRs). Spontaneous reporting systems depend largely on healthcare professionals, patients, and other reporters to submit suspected adverse events. However, not every suspected reaction is reported, leaving important gaps in available safety data.

Research consistently demonstrates substantial ADR underreporting, but its extent is not a single fixed percentage. Reporting rates can vary depending on the type and severity of the event, the drug involved, the healthcare setting, awareness of reporting requirements, and other factors. Some reactions may be more likely to attract attention and be reported, while less obvious or expected events may receive fewer reports.

This creates an important limitation for ADR reporting and drug safety surveillance. Low reporting does not necessarily mean that an event is rare or clinically unimportant. Conversely, increased reporting may sometimes reflect greater awareness rather than a genuine increase in risk.

Improving reporting mechanisms and making reporting easier may help address some of these gaps. Mobile health apps may also increase adverse drug reaction reporting rates by making reporting more accessible to patients and healthcare providers.

For spontaneous reporting systems, underreporting and reporting bias therefore need to be considered when interpreting safety data and evaluating potential signals.

2. Incomplete, Duplicate, and Heterogeneous Safety Data

Poor-quality safety data can complicate pharmacovigilance decisions. Adverse event reports may contain missing patient information, an incomplete medical history, limited treatment or exposure details, or no follow-up information. Reports can also arrive in inconsistent formats, with information presented as structured or unstructured data across multiple channels and different countries.

Another issue is the presence of duplicate reports. The same case may be submitted through different sources or processed in different existing systems. Identifying duplicates can be challenging when reports contain different levels of detail or use different terminology. In the EU, duplicate management is specifically addressed in GVP Module VI Addendum I.

These issues directly affect pharmacovigilance data quality. Missing or inconsistent information can complicate case validation and causality assessment, while duplicates may distort signal detection and aggregate safety evaluation. Data integration can become particularly difficult when safety information is collected through multiple reporting mechanisms and stored in different formats.

Data privacy must also be considered when transferring safety information between systems, organisations, and countries. Robust quality checks, follow-up processes, and duplicate-management procedures are therefore essential to ensure that individual case safety reports support reliable safety assessments.

3. Assessing Causality and Quantifying Risk

Determining whether a medicine actually caused an adverse event is one of the key challenges in PV. A temporal association between drug exposure and an event does not automatically establish causality. Patients may be taking concomitant medicines, have underlying diseases, or have other factors that provide alternative explanations. Limited clinical information can make this assessment even more difficult.

These limitations are particularly important when working with spontaneous reports. Such reports generally do not provide a reliable exposure denominator, meaning they cannot, on their own, show how frequently an event occurs among all patients exposed to a medicine. As a result, estimating incidence or comparing absolute risks based solely on spontaneous reporting data can be problematic.

Causality assessment and pharmacovigilance risk assessment, therefore, require careful consideration of the available clinical evidence and relevant alternative explanations. Spontaneous reporting remains essential for identifying potential safety signals and generating hypotheses, but it has methodological limitations for establishing causality or quantifying the true incidence and magnitude of a risk.

A robust safety evaluation may require additional data sources, including clinical research, clinical trials, epidemiological studies, and real-world evidence. These sources can complement spontaneous reports and provide additional context for understanding a medicine’s safety profile in clinical practice and post-marketing surveillance.

4. Detecting and Prioritising Safety Signals

Pharmacovigilance signal detection is essential for identifying previously unknown or changing risks associated with medicines. However, finding meaningful patterns in large volumes of safety information is not straightforward. Safety teams must distinguish potentially important findings from background noise while considering the quality, consistency, and clinical relevance of the available data.

Why Signal Detection Is Difficult

High volumes of safety reports can make it difficult to identify meaningful patterns, particularly when datasets contain duplicate cases, incomplete reports, or heterogeneous sources. Data overload can make prioritisation more difficult, while rare adverse reactions may generate only a small number of reports. Weak signals can also be difficult to distinguish from random variation, and background noise may contribute to false-positive findings.

Improving Signal Detection

Effective safety signal detection combines multiple approaches rather than relying on a single method. Statistical screening can identify unusual reporting patterns, while medical review and case-series analysis help assess their clinical relevance. Scientific literature and real-world evidence can provide additional context, while expert assessment helps determine whether findings represent true safety signals and warrant further investigation.

Continuous monitoring supports the timely identification of emerging safety issues. A structured signal management process helps teams evaluate, prioritise, document, and follow up on potential risks throughout the product lifecycle.

5. Monitoring Global and Local Medical Literature

Medical literature is an important source of safety information, but monitoring it at scale creates significant operational challenges. The volume of scientific publications continues to grow, requiring safety teams to screen global databases as well as local journals. Publications may appear in multiple languages and formats, making it difficult to identify relevant drug–event information consistently.

A further challenge is that potentially reportable ICSRs may be hidden within publications that do not initially appear relevant. Duplicate publications can also complicate screening and case identification. When these activities are performed manually, the workload can become substantial, particularly for companies monitoring multiple products across different markets.

Global vs Local Literature Monitoring

Global literature monitoring typically focuses on major international databases and publications, while local monitoring covers national journals, databases, and other sources required or relevant to specific markets. Both can be necessary because important safety information may not be indexed in major global databases. This is particularly important when monitoring products across different countries, where relevant publications may appear only in local sources. The EMA also monitors specified scientific and medical literature to identify suspected adverse reactions, highlighting the regulatory importance of literature surveillance.

Natural language processing can aid in extracting adverse event data from large volumes of unstructured scientific literature, although human review remains important for confirming relevance and accuracy. Effective pharmacovigilance literature monitoring, therefore, requires more than finding publications. Teams need consistent literature screening, documented decisions, traceability, and an auditable process. A structured local literature monitoring approach can help address these requirements while reducing the burden of manual medical literature monitoring.

6. Keeping Up With EU Pharmacovigilance Requirements

Keeping up with EU pharmacovigilance requirements can be challenging because the regulatory framework is comprehensive and continues to evolve. Good Pharmacovigilance Practices (GVP) provide a structured framework for pharmacovigilance activities, while requirements from the European Medicines Agency (EMA), EudraVigilance, and national regulatory authorities must also be considered.

For a marketing authorisation holder (MAH), compliance involves more than understanding the current rules. Safety teams need to monitor regulatory updates, assess their impact on existing procedures, update SOPs and internal processes, and ensure that employees receive appropriate training. Changes may also affect the Pharmacovigilance System Master File (PSMF), documentation, regulatory reporting, and oversight activities. They may also have implications for a product’s risk management plan and post-marketing surveillance.

For example, GVP Module VI Addendum II on masking personal data in individual case safety reports became applicable on 25 July 2025. Keeping track of such updates is therefore an ongoing responsibility rather than a one-time compliance exercise. Monitoring evolving regulations and national requirements is an important part of maintaining an effective pharmacovigilance system.

Effective regulatory intelligence helps teams identify relevant changes early and determine what actions may be required. Companies may also use pharmacovigilance consultancy to support regulatory interpretation and implementation.

Ultimately, staying compliant requires continuous monitoring, clear ownership, documented processes, and a practical understanding of how GVP, EMA, EudraVigilance, and national requirements interact.

7. ICSR Processing, Follow-Up, and Reporting Timelines

Pharmacovigilance case processing involves a series of steps that must be completed accurately and within applicable reporting timelines. A typical workflow includes case intake → validation → duplicate check → data entry → coding → medical review → follow-up → quality control → submission. Each stage can introduce operational challenges, particularly as case volumes increase.

Initial reports may contain incomplete patient, product, or event information, requiring follow-up attempts to obtain clinically relevant details. At the same time, teams need to identify potential duplicates, prioritise cases appropriately, perform accurate data entry and coding, and maintain effective quality control. Manual activities can increase workload and create additional opportunities for inconsistencies or delays.

ICSR management, therefore, requires a coordinated process that balances speed, data quality, and compliance. GVP Module VI provides guidance on the collection, management, and submission of reports of suspected adverse reactions. For reports submitted through EudraVigilance, the EMA also monitors compliance with legally defined electronic reporting timelines.

Accurate regulatory reporting depends on effective reporting mechanisms, appropriate quality controls, and timely case processing. Strong processes and appropriate technology can help teams manage increasing volumes while maintaining consistent individual case safety report processing. A structured ICSR management approach can support adverse event reporting from initial intake through final submission.

8. Limited Resources, Expertise, and Vendor Oversight

Effective pharmacovigilance depends on sufficient pharmacovigilance resources, specialised expertise, and appropriate systems. For many MAHs, the challenge is not simply the number of employees. Still, managing increasing workloads with limited internal capacity. Case volumes may fluctuate, creating workload peaks that require additional support, while multi-country operations can add further complexity.

Human resources are particularly important when a pharmacovigilance team operates across different countries or needs to manage sudden increases in workload. Teams may also need specialised expertise across case processing, signal management, literature monitoring, regulatory requirements, quality management, and inspection readiness. Continuous pharmacovigilance training is therefore important when processes, regulations, or technologies change.

Pharmacovigilance outsourcing can help MAHs increase scalability and access specialised expertise without having to build every capability internally. However, outsourcing does not remove the MAH’s responsibility for effective oversight of its pharmacovigilance system. Clear responsibilities, quality controls, performance monitoring, documentation, and vendor oversight remain essential.

Depending on the market and operating model, companies may also need dedicated QPPV support. For example, local QPPV services can support local pharmacovigilance activities, while pharmacovigilance training can help maintain team competence and inspection readiness.

9. Integrating Real-World Data Into Safety Evaluation

Modern drug safety monitoring increasingly uses multiple sources of evidence alongside spontaneous reports. These may include healthcare databases, patient registries, observational studies, and other sources of real-world data (RWD). When analysed appropriately, RWD can contribute to real-world evidence (RWE) and provide additional insights into medicine use, patient outcomes, and potential safety risks.

Data integration across these sources can be challenging. Data quality may vary between databases, while differences in data structures can create interoperability issues. The populations represented in a dataset may not fully reflect the wider patient population, making representativeness an important consideration. Observational data can also be affected by confounding and other sources of bias.

Before using RWD for a specific purpose, teams need to assess whether the data are fit for purpose and understand the limitations of the available evidence. Data generated through healthcare systems and routine clinical practice can complement evidence from clinical research, but interpretation may require specialised epidemiological and clinical expertise.

RWD and RWE should not replace spontaneous reporting. Instead, they complement spontaneous reports and other pharmacovigilance data sources, helping safety teams build a broader evidence base for risk assessment and ongoing medication safety monitoring.

10. Using AI and Automation Without Compromising Quality

AI and automation can help pharmacovigilance teams manage growing volumes of safety information. Potential applications include literature screening, case triage, data extraction, prioritisation, and the reduction of repetitive manual work. These advanced technologies can help teams process large datasets more efficiently and focus human expertise on tasks requiring clinical or regulatory judgment.

However, adopting artificial intelligence does not remove the need for quality controls. Validation is essential to demonstrate that an AI-enabled process performs reliably for its intended use. Teams also need sufficient transparency and traceability to understand how outputs are generated and document relevant decisions.

Data quality presents another challenge. Poor input data can affect automated results, while false positives may increase workload, and false negatives could result in potentially relevant safety information being overlooked. Data privacy is also important when AI tools process patient-level or other sensitive safety information. Human oversight, therefore, remains essential for decisions with clinical or regulatory consequences.

Effective AI governance should define responsibilities, monitoring requirements, quality controls, and escalation procedures. Pharmacovigilance teams must also ensure that automated processes remain aligned with applicable regulatory expectations.

How Can Pharmacovigilance Teams Address These Challenges?

Addressing pharmacovigilance challenges requires a practical framework that combines standardised processes, reliable data management, appropriate automation, regulatory intelligence, and continuous quality monitoring.

Standardise Pharmacovigilance Processes

Clear SOPs, standardised workflows, and defined responsibilities help ensure that pharmacovigilance activities are performed consistently. Quality controls should be built into key processes to identify errors early and support PV compliance. Regular reviews can also help teams adapt procedures when regulatory requirements, products, or operational needs change.

Centralise Safety Data

Centralising relevant safety data can reduce fragmentation across systems, teams, and sources. A more connected data environment improves traceability and makes it easier to access information during case assessment, signal review, and aggregate safety evaluation. Effective data integration can also reduce duplicated work and improve consistency across existing PV systems.

Automate Repetitive Workflows

Automation can be applied where it provides clear operational value, such as in literature screening, case triage, data processing, and regulatory monitoring. The aim is to reduce repetitive manual work and allow specialists to focus on higher-value activities. However, automation should operate within a controlled workflow, with appropriate human oversight for quality, clinical judgment, and regulatory decisions.

Maintain Continuous Regulatory Intelligence

Regulatory monitoring should be an ongoing activity rather than a periodic exercise. Teams can track relevant EMA and GVP updates, national requirements, and other regulatory developments. When requirements change, organisations should assess their impact, update SOPs and workflows, and provide appropriate training to affected staff.

Monitor Quality and Performance

Regular performance monitoring helps teams identify weaknesses before they become significant compliance issues. Useful metrics may include case-processing timelines, literature-screening performance, signal-management KPIs, quality findings, and CAPAs. Maintaining clear records and reviewing these indicators also supports audit and inspection readiness.

Conclusion

The challenges in PV are becoming increasingly interconnected as safety teams manage growing data volumes, complex regulatory requirements, global literature, and evolving technologies. Addressing these challenges requires reliable processes, high-quality data, appropriate automation, and continuous human oversight. For many teams, the key is finding practical ways to reduce repetitive work without compromising quality, traceability, or compliance. By combining AI-powered automation with structured workflows and expert review, DrugCard helps pharmacovigilance teams manage safety information more efficiently while maintaining a controlled and auditable process.

FAQ

What are the main challenges in pharmacovigilance?

Key challenges include underreporting, incomplete safety data, signal detection, literature monitoring, regulatory complexity, ICSR processing, limited resources, real-world data integration, and AI implementation.

What are the limitations of spontaneous reporting systems?

Spontaneous reporting systems can be affected by underreporting, reporting bias, incomplete information, duplicate reports, and the lack of reliable exposure denominators.

Why is underreporting a problem in pharmacovigilance?

Underreporting can leave gaps in safety information and make emerging risks harder to detect. Reporting patterns can vary by medicine, event, healthcare infrastructure setting, and other factors.

What are the main pharmacovigilance challenges for MAHs in the EU?

EU MAHs must manage GVP and EMA requirements, EudraVigilance reporting, national obligations, ICSR processing, literature monitoring, vendor oversight, and inspection readiness.

Can AI solve pharmacovigilance challenges?

AI cannot solve every pharmacovigilance challenge, but it can reduce repetitive work and support literature screening and case triage when combined with validation and human oversight.