This workshop focuses on the transformative role of digital pharmacovigilance in enhancing drug safety monitoring and risk assessment. Participants will learn about the integration of digital technologies such as big data analytics, and AI machine learning in detecting and reporting adverse drug reactions (ADRs). The workshop covers the limitations of traditional pharmacovigilance systems and the advantages offered by digital and AI tools, with reflections on quick detection and improved compliance. Attendees will develop problem-solving skills through hands-on experience while in group-based settings exploring regulatory digital pharmacovigilance frameworks and assessing challenges faced during implementation. Designed for healthcare professionals, regulatory authorities, and researchers, this workshop aims to capacitate participants in adopting innovative digital pharmacovigilance with AI empowered practices.
This workshop focuses on the transformative impact of artificial intelligence (AI) in pharmacovigilance, addressing the limitations of traditional manual processes that are often time- and resource-intensive. Participants will gain foundational knowledge of AI concepts, including machine learning (ML) and natural language processing (NLP), and learn how these technologies can enhance the identification, classification, and analysis of adverse drug reactions (ADRs). The workshop explores AI-driven tools for automating case processing and regulatory reporting while discussing associated ethical implications and risks. Aimed at healthcare professionals, regulators, and researchers with practical insights, the workshop aims to improve patient safety and regulatory compliance in pharmacovigilance through AI.
This workshop focuses on the applications of generative AI in pharmacovigilance, highlighting its potential to transform drug safety monitoring. Participants will learn about the fundamentals of generative AI, including large language models and deep learning, and how these technologies can enhance adverse event detection, automate case processing, and improve regulatory reporting. The workshop will also address challenges such as data privacy, ethical implications, and regulatory compliance associated with integrating AI in pharmacovigilance. Through case studies and best practices with interactive group-based discussions, healthcare professionals, regulatory authorities, and researchers will gain insights into effective strategies for implementing generative AI solutions to improve patient safety and operational efficiency.
This workshop aims to enhance participants' understanding of case management in pharmacovigilance utilising artificial intelligence (AI) methods and technologies. Focusing on the collection, assessment, and reporting of adverse drug reactions (ADRs), the workshop addresses the limitations of traditional methodologies that rely on manual pharmacovigilance processes. Participants will explore AI-driven solutions such as natural language processing and machine learning to automate case intake, streamline narrative analysis, and improve regulatory submissions. The workshop also discusses the challenges of AI implementation, including data quality and compliance with regulatory frameworks. Aimed at a wide audience, including healthcare professionals and regulatory bodies, this session will prepare attendees to effectively leverage AI to improve drug safety outcomes.
This workshop addresses the critical role of signal detection in pharmacovigilance, focusing on identifying potential safety issues related to medicinal products. Participants will learn how traditional methods may fall short due to the increasing complexity and volume of data from sources like electronic health records and social media. The session will introduce artificial intelligence (AI) techniques, including machine learning and natural language processing, which enhance the efficiency and accuracy of signal detection by automating data collection and analysis. Challenges related to data quality, algorithm transparency, and regulatory compliance are also discussed, capacitating attendees to implement AI solutions for improved patient safety in pharmacovigilance.
This workshop provides an in-depth examination of the regulatory landscape governing digital pharmacovigilance, focusing on guidelines from global agencies like the FDA, EMA, and WHO. Participants will explore the importance of data integrity, transparency, and AI governance while addressing challenges such as data privacy, cybersecurity, and algorithm validation. The workshop aims to capacitate attendees with knowledge on implementing digital tools in compliance with evolving regulations, ensuring safety and efficacy in drug monitoring. Participants will analyse the application of Good Pharmacovigilance Practices (GVP) principles and identify best practices for integrating technological advancements while maintaining regulatory compliance in pharmacovigilance workflows.
This workshop is focused on the integration of Digital Pharmacovigilance within healthcare practices, patient sociotechnical systems, pharmaceutical industries, and regulatory frameworks. It underscores the transformative potential of advanced digital technologies, including natural language processing, large language models, and AI-driven machine learning algorithms, in enhancing drug safety and accurately identifying adverse drug reactions. Participants will acquire insights into how such integration can facilitate patient-centred care and optimise pharmacovigilance workflows through comprehensive data analysis and real-time monitoring. This workshop employs reflective discussions informed by recent academic and industrial research, along with feedback from industry leaders, healthcare professionals, and regulatory experts. This workshop addresses practical emerging challenges, encourage the exchange of best practices, and develop collaborative strategies that are essential for advancing contemporary AI-driven Digital Pharmacovigilance practices within the evolving landscape of drug safety management.
This is an insightful workshop exploring the integration of data analytics in digital pharmacovigilance. As healthcare systems evolve, the increasing complexity and volume of real-world data from electronic health records, social media, and other sources necessitate innovative approaches to drug safety monitoring. This workshop introduces descriptive and predictive data analytics techniques, and AI machine learning algorithms for the early detection of adverse drug reactions. Participants will have the opportunity to learn in group-based settings structured and unstructured data integration, regulatory frameworks, and ethical considerations while collaborating to advance data-driven pharmacovigilance strategies. This work is ideal for healthcare professionals, researchers, and regulatory authorities committed to enhancing drug safety.
This workshop comprehensively addresses the modeling of both standard quality and functional requirements for digital pharmacovigilance systems through the application of non-functional requirements and use-case modeling, respectively. Participants will gain insights into ensuring effective detection of adverse drug reactions while maintaining data accuracy, informed risk management, enhanced interoperability, and compliance with global regulatory standards such as GDPR, FDA, and EMA. Furthermore, through applied group discussions, attendees will understand that misalignment between quality and functional objectives may result in inefficiencies, inaccurate ADR detection, data breaches, and regulatory non-compliance, thereby compromising patient safety. This understanding is essential for enhancing system reliability, data security, and user decision-making capabilities in well informed pharmacovigilance.
This workshop provides an in-depth analysis of advanced real-world case studies addressing the implementation and challenges associated with digital pharmacovigilance, including adverse drug reaction (ADR) signal detection and management. Participants will critically explore advanced methodologies and the contributions of advanced AI machine learning algorithms to enhancing drug safety monitoring while ensuring regulatory compliance with bodies such as the FDA and EMA, and WHO, alongside ethical considerations in data management. Participants will engage in group-based settings using advanced pharmacovigilance case studies to comprehend and reflect on the role of mobile health (mHealth) applications, social media, and patient-reported outcomes in improving ADR reporting and patient engagement.
This workshop focuses on the development of digital pharmacovigilance risk management plans using a process-driven approach, addressing the essential role of effective strategies in ensuring drug safety and regulatory compliance as digital technologies transform pharmacovigilance. Participants will explore and analyze risks associated with digital pharmacovigilance, including data security, data privacy, AI algorithm bias, interoperability (EHRs, mHealth), system reliability, inaccurate signal detection, regulatory compliance, change management, cost-effectiveness, and capacity development. Through a structured methodology, attendees will learn to develop and implement comprehensive risk management plans that effectively mitigate these challenges while promoting the ethical use of AI in pharmacovigilance. This workshop will examine global regulatory standards, including FDA, EMA, and WHO guidelines. It is designed for healthcare professionals, researchers, and regulatory bodies dedicated to optimizing digital pharmacovigilance practices.