Navigating the Future of Pharmacovigilance with Hyperautomation

The pharmaceutical industry is grappling with several trends contributing to the increasing occurrence and complexity of Adverse Events (AE). Factors such as focus on niche therapeutic areas, increasing product complexity, patient-centric approaches, the rise of Real-World Evidence (RWE) from digital channels and evolving regulations have all made managing AEs more challenging. In fact, AE reporting to the US Food and Drug Administration (FDA) has more than doubled between 2013 and 2023.¹

Most organizations’ pharmacovigilance systems are ill-equipped to address these new challenges, leading to significant spending on case processing. Many still rely on legacy and siloed applications that require costly upgrades. However, cognitive technologies – powered by Artificial Intelligence (AI), Generative AI (Gen AI) and intelligent workflows – offer powerful solutions for automating case intake, processing and signal detection.

Hyperautomation, with its ability to seamlessly orchestrate digital transformation across multiple systems, technologies and workflows, is key to building next-generation pharmacovigilance. This approach enables an automated, intelligent and scalable infrastructure that reduces costs, enhances safety and improves the overall patient experience.

The Changing Pharmacovigilance Landscape

A convergence of business imperatives, technological innovations and regulatory requirements is propelling the adoption of digitalization in pharmacovigilance. This includes several inter-connected factors.

The steady growth in AE reports can be linked to increased consumer awareness of drug safety, greater adoption of wearable devices and new reporting channels. These new data sources and channels add complexity but offer an opportunity to access real-time patient data and enhance drug safety.

AI and Gen AI provide advanced capabilities for automated AE identification across (and extraction from) various channels, unstructured formats and sources, including social media, wearables and literature. These technologies facilitate end-to-end case management by automating case creation and processing through intelligent workflows. They enable automated aggregate reporting, coding and support for signal detection and monitoring. Additionally, advanced analytics can be applied to predictive safety management and reporting.

Regulatory changes are driving the demand for enhanced pharmacovigilance capabilities, particularly in countries establishing new pharmacovigilance frameworks. Simultaneously, regulatory bodies are prioritizing standardized frameworks that leverage RWE for critical signal detection and safety insights to enhance drug safety.

Owing to increasing operational expenditure in recent years, pharmacovigilance firms are adopting flexible cost-saving strategies. These involve resource optimization measures and hybrid human-AI solutions to streamline workflows.

Advanced technology-driven collaboration is driving integrated AE data analysis and communication across borders, facilitating accurate monitoring and the proactive management of potential risks across the global pharmaceutical market

The industry’s shift toward narrow therapeutic areas and increasingly complex product portfolios has amplified the challenge of managing AEs. Specialized therapies have unique safety profiles, requiring tailored monitoring and reporting, while a diverse product range adds complexity to tracking, analyzing and responding to AEs across multiple treatments.

As patients take a more active role in managing their health, pharma companies are increasingly focused on patient-centered care and safety. This shift requires the pharmacovigilance ecosystem to capture and process AEs not only from healthcare providers but also across various digital touchpoints, such as apps, wearables and online platforms, ensuring a more comprehensive safety monitoring system.

Hyperautomation: The Path to Effective Digitalization in Pharmacovigilance

This fragmented approach results in sub-optimal outcomes, limiting the ability to handle growing AE caseloads and comply with regulatory demands. Without a unified data source, organizations miss opportunities to leverage analytics for improved safety and product innovation. Budget constraints and limited expertise compound these inefficiencies.

Hyperautomation offers a shift from this traditional, piecemeal technology adoption by enabling an interconnected and cognitive system. Using a modular approach, it integrates AI, Gen AI, ML, Robotic Process Automation (RPA), low-code / no-code tools and other software solutions built on a unified data platform to orchestrate end-to-end digitalization across the pharmacovigilance value chain.

By implementing hyperautomation, pharmacovigilance processes can be seamlessly integrated – from case intake / processing to reporting and signal detection / processing. This enhances efficiency, reduces costs and enables cognitive RWE processing to assess the benefits and risks of signals and prevent safety issues. Ultimately, hyperautomation helps build a future-ready, patience-centric pharmacovigilance function that optimally manages AEs and patient safety through data-driven insights.

Leveraging Hyperautomation across the Pharmacovigilance Lifecycle

A convergence of business imperatives, technological innovations and regulatory requirements is propelling the adoption of digitalization in pharmacovigilance. This includes several inter-connected factors:

1. Case Intake

Real-time, omni-channel case intake solutions powered by AI and RPA technologies enable pharmacovigilance teams to simplify and expedite the process of ingesting data from diverse sources, including e-mails, web forms, contact centers and Electronic Medical / Health Records (EMR / EHR). Gen AI-enabled solutions drive automated case management from social media, literature and other unstructured source. Bots, e-mail automation and intelligent workflows allow automatic data extraction, case creation and appropriate routing

2. Individual Case Safety Reports Processing

Organizations grapple with complex cross-border regulations and issues with quality / variance in output data. Advanced AI and ML techniques can streamline these processes by automating triaging, case prioritization, case processing and clinical validation from various data sources, such as patients and healthcare professionals. Automated workflows ensure efficient review and quality control, while data validation, duplicate checks and quality assurance processes are also seamlessly automated, enhancing overall accuracy and efficiency.

3. Aggregate Reporting

Aggregate reporting in pharmacovigilance is a cumbersome and error-prone task due to the manual consolidation of data and the multitude of unique reporting requirements and formats. The inefficiencies and delays caused by manual processes in identifying signals can negatively impact patient safety. Real-time, self-serve platforms powered by AI / Gen AI can streamline data collection with standardized formats while ensuring compliance adherence. Gen AI also analyzes patterns in the vast volumes of AE reports to detect potential safety signals, inconsistencies and unexpected correlations that help pharmacovigilance teams prioritize timely investigation into potential risks. It also offers easy and conversational information gathering, convenient information summaries and automated capture of Medical Dictionary for Regulated Activities (MedRA) codes.

4. Safety and Risk Management Epidemiology

Traditional pharmacovigilance methods rely on retrospective data analysis, limiting real-time monitoring and early adverse drug reaction detection, impacting:

• Patient safety monitoring
• Data translation into evidence
• Early detection of risks

Integrating IoT devices and sensors enables pharmacovigilance teams to continuously monitor patient safety data, detect real-time safety signals and proactively mitigate risks. Additionally, AI identifies vulnerable patient subgroups and phenotypes, enabling more tailored interventions for improved patient safety outcomes.

Gen AI further enhances real-time signal detection and reporting by analyzing large and diverse datasets in real-time. This capability provides rapid insights from sources such as social media, EMRs and literature. It also automates the generation of Patient Safety Update Reports (PSUR), streamlining the reporting process and improving efficiency.

5. Safety Database Services

Most pharmacovigilance databases are hampered by processing constraints resulting from outdated technology, making it difficult to stay updated with current information. Legacy modernization services and AI-driven contact center solutions ensure compliance and real-time data availability. These platforms streamline the integration and analysis of heterogeneous data sources, such as health records, omics data and patient-reported outcomes, enhancing the accuracy of safety and risk assessments.

Moreover, AI-driven analyses of real-world data aid in regulatory decision-making and post-marketing surveillance, enabling regulators and healthcare stakeholders to make informed decisions about drug safety and effectiveness grounded in clinical practice.

Figure 1: Hyperautomation Components Driving Pharmacovigilance

As seen in Figure 1, hyperautomation offers an opportunity to improve pharmacovigilance processes and workflows and establish a centralized and evidence-based center for patient safety and drug development intelligence.

Consider these real-world examples that demonstrate the potential of hyperautomation in transforming pharmacovigilance practices.

Re-defining Pharmacovigilance: The Way Forward

The fusion of hyperautomation with real-time signal processing and IoT monitoring will herald a new era of proactive risk assessment. Integrated IoT sensors will enable the continuous monitoring of patient safety data, swiftly detecting potential risks and proactively addressing them to prevent escalation. Furthermore, advanced analytics and machine learning algorithms will analyze real-time IoT data, identifying emerging safety concerns and providing actionable insights to stakeholders.

Currently, 47 percent of organizations depend on automation without AI / ML for case processing, aggregate reporting and signal and risk management.² There is significant scope for advanced technology adoption to transform pharmacovigilance processes. Collaborating with a partner equipped with a domain-led platform suite, robust data-to-insights capabilities and tailored industry solutions can accelerate a pharma company’s journey toward hyperautomation.

Explore how your company can confidently navigate the evolving pharmacovigilance landscape by embracing next-gen hyperautomation technologies and paving the way for a safer and more resilient pharmacovigilance function.

References

1. FDA Adverse Events Reporting System (FAERS) Public Dashboard - FDA Adverse Events Reporting System (FAERS) Public Dashboard

2. Pharmacovigilance Industry Survey Report for 2023: Trends on Transformation | Fierce Pharma