About this Research Topic
Signal identification in disparate real-world data (RWD) sources is a burgeoning field that extends beyond traditional approaches that rely primarily on data from spontaneous reporting systems (SRSs). Historically, SRSs have been pivotal in identifying safety signals for marketed drugs and vaccines since the 1960s, particularly for rare and acute events. However, SRSs are not without limitations, such as underreporting and the absence of a population denominator for calculations of absolute risks. In response, there has been a growing interest in leveraging other RWD sources, including healthcare databases, digital tools, wearables, and mobile devices, for signal detection. Recent studies have explored the potential of the FDA Sentinel system, which integrates multiple healthcare databases, for signal identification. Additionally, the reliability of artificial intelligence (AI) in analyzing social media and search query records for signal detection is under investigation. Despite these advancements, there remains a significant gap in understanding the optimal use of these diverse RWD sources for effective signal identification.
This research topic aims to explore the potential of various RWD sources and methodologies for signal identification. The primary objectives include evaluating the effectiveness of different RWD types, such as healthcare data and social media, and assessing the performance of both traditional and digital methods, including AI, in signal detection. Key questions to be addressed include the sensitivity, specificity, and accuracy of these methods and tools in identifying signals.
To gather further insights in the realm of signal identification using diverse RWD sources, we welcome articles addressing, but not limited to, the following themes:
• Different types of RWDs are used, including healthcare data sources, social media, and others.
• The application of various approaches, both traditional and digital, such as AI, for signal detection.
• The performance metrics of different signal identification methods and tools focus on sensitivity, specificity, and accuracy.
Conflict of Interest statement:
Topic Editor Dr. Juhaeri Juhaeri is an employee of Sanofi and holds its shares.
Topic Editor Dr. Jeffrey Brown is a Chief Scientific Officer of TriNetX, LLC.
Topic Editor Prof. Sengwee Toh declares no competing interests with regard to the Research Topic subject.
This research topic aims to explore the potential of various RWD sources and methodologies for signal identification. The primary objectives include evaluating the effectiveness of different RWD types, such as healthcare data and social media, and assessing the performance of both traditional and digital methods, including AI, in signal detection. Key questions to be addressed include the sensitivity, specificity, and accuracy of these methods and tools in identifying signals.
To gather further insights in the realm of signal identification using diverse RWD sources, we welcome articles addressing, but not limited to, the following themes:
• Different types of RWDs are used, including healthcare data sources, social media, and others.
• The application of various approaches, both traditional and digital, such as AI, for signal detection.
• The performance metrics of different signal identification methods and tools focus on sensitivity, specificity, and accuracy.
Conflict of Interest statement:
Topic Editor Dr. Juhaeri Juhaeri is an employee of Sanofi and holds its shares.
Topic Editor Dr. Jeffrey Brown is a Chief Scientific Officer of TriNetX, LLC.
Topic Editor Prof. Sengwee Toh declares no competing interests with regard to the Research Topic subject.
Keywords: real-world data, Spontaneous reporting systems, signal detection, Signal identification, Pharmacovigilance, Performance, Methods
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
