f automation is a bullet train, culture and processes are the tracks that will keep pharmacovigilance on course.

Pharmacovigilance (PV) has always had a vital role to play in detecting, understanding, and preventing adverse events. However, it is now transforming from a necessary but reactive safety and cost-absorbing function to a proactive value-creation function that saves time, makes better use of resources, and transforms data into actionable insights.

New technologies offer the chance to deal with high data volumes, increasing trial complexity, and demands for faster commercialization. However, seizing these opportunities is not without its challenges. Success depends on cultural changes, strong leadership, and a cyclical approach to improvement, driven by use of adaptive technologies and expertise at every stage.

The Challenges

PV is vital to understand the benefits and risks of treatments and improve patient safety and care. Emerging in the wake of the 1961 thalidomide disaster, it allows the industry to understand whether a drug or treatment works and is safe.

Despite this crucial role, PV has been slow to adapt to a changing clinical environment. A 2024 survey of PV professionals indicated 66% were taking a reactive approach to adverse event (AE) review and 18% were relying on manual or outdated methods.

Other historic challenges are holding back PV. Parallel safety organizations with different functionality and overlapping organizational structures create greater complexity. Inconsistent reporting structures with differences across business units lead to complex resource negotiation and unclear accountabilities. Outdated sourcing results in low-risk activities, executed in low-cost geographies, for products with an established safety protocol.

Deploying the right person, in the right job, with the right level of experience can also be a challenge. Smaller biotech companies report losing PV talent to large pharmaceutical companies, and PV is seen as a high or medium priority for talent attraction and retention across the industry.

Aside from the practical challenges, there are cultural hurdles to overcome. There can be a lack of understanding about the consequences of failing to get things right and the impact on patient safety. There can also be resistance to change as we move away from long-established practices and embrace new innovative solutions.

The Technology Solution

New technologies offer the opportunity for more efficient case intake, improved employee productivity, faster hypothesis-to-testing cycles, and improvements in risk-benefit profiles. We are already seeing some progress. One in four PV professionals report that their organizations are currently operating with 20% automation in case processing. This is expected to rise to 60% and above in the next 12 months.

But if we imagine new technologies as a ladder, basic process automation is just the first rung. The next rung is robotic process automation, which reduces or eliminates manual tasks to increase consistency and quality. Next is cognitive robotic process automation requiring less data structure and increasing scalability.

The final rung is AI, which increases process efficiency and removes human error to optimize cost and productivity. For example, AI can be used to identify articles for Individual Case Safety Reports (ICSRs) and signal detection.

There are other ways technology can transform PV. Platform approaches can help to overcome inconsistent reporting structures, enabling the provision of services in a standard, more manageable, transparent, efficient way. Cloud-based systems can facilitate seamless data exchange. Virtual working can enable geographical footprint consolidation.

The Process and Culture Solution

The transformation of PV is not just about harnessing new technologies. It is also vital to put the right cultures and processes in place. We need to merge two skillsets—technical and technology expertise in the areas mentioned above and PV expertise—within a framework that can facilitate change management.

In terms of processes, instead of reactively outsourcing to manage capacity, we need to develop strategic sourcing processes based on better quality deliverables from low-cost geographies, reducing the need for outsourcing provider oversight. We need to develop offshore and outsourcing partner capabilities with tech-enabled processes to provide strategic input and begin signal detection earlier in the development lifecycle.

In terms of supporting the transformation of PV through culture, we need to use up-to-date, credible messaging which ensures that everyone understands the importance of PV. Change must be driven by strong leadership and based on a clear strategy and data-driven decisions to avoid staff misinterpreting the aims of change. We should consistently measure progress and celebrate success. Peer involvement should be encouraged, including the sharing of expertise and experiences.

Finally, we need to engage employees in the transformation. We need to emphasize that quality is for everyone, keep getting feedback, and be informed by employees about how we can be more proactive. A future-ready PV system will be based on a cyclical approach driven by expertise at every stage.

Case Study: Standardizing and Improving Safety Surveillance

A Top 10 pharmaceutical company faced challenges in standardizing safety surveillance across their pharmaceutical, vaccine, and consumer health units. Their challenges included consolidation of the vaccine and pharmaceutical safety signal teams, facilitating the divestment of the consumer health division, integrating multiple data sources, completing complex data migrations from legacy applications, replacing established methods and in-house data visualizations, and attempting to simplify their IT landscape.

To address these challenges, a signal detection platform with comprehensive integration capabilities was deployed over two phases. This phased approach minimized time-to-benefit and allowed gradual uptake across different units. A comprehensive strategy ensured seamless data migration and integration, and the system was configured to facilitate the separation of the consumer health business.

Platform deployment, which falls under the third rung of cognitive robotic process automation, supported the pharma company’s strategic objectives, allowing it to standardize safety surveillance practices and streamline operations. Reported benefits included:

• Increased operating efficiency through unified signal processes
• Successful combination of the Pharma and Vaccine operating units
• Enhanced process visibility and improved audit readiness
• Significant simplification of the in-house IT landscape.

Conclusion

To prepare for the automation/AI future, the PV community must ensure that the “future state” operating model is flexible and scalable. Systems must be interoperable with external large data sources to undertake enhanced signal detection with larger and more diverse patient data. We need to strengthen interfaces with related partner functions to gain process efficiencies.