echnology significantly enhances the advancement of pharmacovigilance, facilitating the development and adoption of more effective practices, and paves the way for a forward-looking roadmap. The 10th annual DIA India Pharmacovigilance Conference 2023, under the theme Redesigning and Transforming PV for Augmenting Patient Safety, explored the influence of technology on the present and future pharmacovigilance (PV) landscape.

The role of artificial intelligence (AI) stands out as a significant component in this context. A patient “twin” represents a digital or computational counterpart or simulation of an individual patient. In healthcare, a patient “twin” can be generated by analyzing an individual’s medical history, examination data, and population statistics, offering the potential for proactive illness prevention. To tackle the lengthy and expensive process of bringing new drugs to market, a similar digital strategy could scrutinize the entire drug development journey and introduce automation to streamline or otherwise optimize data flow for better efficiency.

Conference discussions highlighted that, by embracing regenerative AI, both healthcare and PV service providers aim toward a quadruple goal:

1. Faster identification of chronic diseases to enhance population health: AI can contribute to early assessment and risk detection in patients, image and signal analysis, continuous monitoring, and remote patient management.
2. Reducing healthcare costs: AI can optimize administrative tasks such as appointment scheduling, billing, and insurance claim processing, and enhance operational efficiency, save time, and lower administrative costs for healthcare providers.
3. Improving the overall patient experience: AI can analyze patient data, including medical records, genetic information, lifestyle factors, and treatment outcomes, to generate personalized treatment plans and provide instant and personalized responses to patient inquiries.
4. Promoting a better work-life balance: AI can automate repetitive and mundane tasks, freeing employees to focus on more meaningful and high-value work and have more time for personal activities.

Additionally, one presenter noted that the utilization of AI presents substantial opportunities for proactive analysis to identify potential label updates that may be necessary.

Blockchain, a decentralized and distributed digital ledger technology, has emerged as a remarkable stride forward in PV because it facilitates secure and transparent transactions and record-keeping among trusted parties. The future of PV holds immense potential for substantial advancement by leveraging this technology, because it enables the collaboration of all stakeholders (including patients, regulators, investigators, and others) within a blockchain network. This integration enhances the efficiency and effectiveness of PV processes in both pre- and post-clinical stages in several ways.

In data integrity and traceability, for example, blockchain provides a tamper-proof and immutable ledger that records all PV-related data, ensuring its integrity and traceability. It can streamline the adverse event reporting process by enabling real-time data sharing and collaboration between healthcare providers, patients, researchers, and regulatory authorities to improve adverse event reporting.

Technology in the Science of PV Literature Surveillance

Technology plays a crucial role in the literature surveillance component of PV. Automation is known to enhance process excellence and accuracy (subject to human verification). One notable application is the conversion of E2B (electronic transmission of individual case safety reports) from R2 to R3 format typically performed by regulatory authorities, pharmaceutical companies, and other PV stakeholders. Automation enables seamless data integration, harmonization, and increased data quality; it also streamlines triaging processes, expediting the identification and prioritization of safety signals.

One speaker presented a regulatory intelligence database as an example of a step toward ensuring comprehensive coverage and access to relevant regulatory information. Such databases have emerged through the collective efforts of various companies, entrepreneurs, and professionals in the regulatory compliance and technology space, and the inclusion of journals in local literature monitoring serves as a valuable resource for tracking emerging safety concerns and regulatory updates.

The journal catalogs within this sample database span more than 6,000 publications which represent approximately 120 countries. This extensive collection of journals enriches the breadth and depth of literature surveillance efforts and helps PV professionals stay informed about the global drug safety landscape. The synergistic integration of automation and the regulatory intelligence database increases the PV professional’s ability to proactively identify and address potential medication safety risks.

Future development of PV will also greatly rely on the crucial role played by natural language processing (NLP), especially in literature monitoring how NLP search engines intelligently prioritize relevant results and present them prominently for easy identification and navigation, while displaying less relevant searches below. However, it is important to note that a primary challenge of automatic search tools lies in their limited capability to identify Individual Case Safety Reports (ICSRs) and offer a comprehensive end-to-end solution, from data collection to regulatory submission.

AI in PV Systems to Improve Patient Safety

As PV continues to evolve, its integration into other established systems and procedures to ensure the harmonization and coordination of various components for improved safety monitoring and risk management is essential. In this context, pharmaceutical companies are adopting the practice of integrating multiple technologies into a unified data link. This integration enables user-friendly data visualization and consumption and enhances the overall effectiveness of PV practices.

Promoting and encouraging patient self-reporting of cases is imperative, particularly in India, one speaker noted, where self-reporting is significantly lower compared to other countries. However, adoption of new tools and techniques to do so presents a significant challenge. While larger companies have successfully implemented AI-based technologies in their PV operations, smaller companies encounter cost constraints as an obstacle in adopting such technologies.

A knowledge graph framework can play a vital role in enabling connections between entities in various domains. By organizing information in a graphical structure, it becomes easier to identify relationships and uncover meaningful insights. However, one of the main challenges of this approach is that implementing a single solution may not always lead to the desired outcome. Given the complexity and diverse nature of data, a more nuanced and adaptable approach may be required to effectively leverage the potential of knowledge graphs. It is important to consider the specific context, data sources, and objectives to develop a comprehensive and robust solution that truly harnesses the power of the knowledge graph framework.

What Does the Technology-Rich Future Hold for Pharmacovigilance Professionals?

Technology has found its rightful, undisputable place in pharmacovigilance more than ever before. Boundaries between organizations providing core pharmacovigilance services and those providing pharmacovigilance technology solutions have started to disappear. Today’s PV experts need to possess a strong understanding of the technologies emerging around them. Today’s technology experts must correspondingly understand the challenges of conducting PV activities in the always-evolving regulatory environment, and in response to the growing variety of cutting-edge medicinal products in the pipeline.

This might seem to imply that tomorrow’s PV workforce will be smaller. But, in our opinion, one can just as easily argue the contrary: Continuous, appropriate, and robust training and upskilling will empower PV professionals to operate at more strategic and scientific levels of PV decision-making, and leave the rest to automation, algorithms, and robots.