The common misunderstanding of the scope of real-world data (RWD) is that it includes only health care data sources. Besides health care data, RWD also includes a wide variety of sources such as spontaneous reporting systems (SRSs), registries, and patient-reported outcomes, and, with advances in new technologies, it also includes digital tools/wearables/mobile devices.
Spontaneous reporting systems (SRSs) have been the main data sources of signal identification for most marketed drugs and vaccines since the 1960s. While SRSs have been shown to be useful for identifying rare and acute events, especially for newly marketed products, their limitations have also been well documented, including underreporting and the lack of a population denominator to calculate rates.
There have been efforts to use other RWD sources besides SRSs for signal detection. Healthcare databases have been used for signal evaluation for decades, but, despite their many strengths relative to SRSs, their use for signal identification has been limited. Recently there have been studies to expand the use of the FDA Sentinel system, which consists of several healthcare databases in the US, beyond signal evaluation, and a few articles on its use for signal identification have been published. There have also been studies to evaluate the reliability of artificial intelligence (AI) for signal identification using social media and query log data. Although much has been learned from the studies, more studies are needed to improve our understanding of how to use these RWD sources for signal identification.
We are looking for articles on signal identification - either reviews, commentaries, or original research projects - in the areas as follows:
• The use of different types of RWDs (such as healthcare data sources, social media, and others),
• The use of different approaches (traditional and digital methods such as AI) and tools,
• The performance of different methods and tools for signal identification (sensitivity, specificity, 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.
The common misunderstanding of the scope of real-world data (RWD) is that it includes only health care data sources. Besides health care data, RWD also includes a wide variety of sources such as spontaneous reporting systems (SRSs), registries, and patient-reported outcomes, and, with advances in new technologies, it also includes digital tools/wearables/mobile devices.
Spontaneous reporting systems (SRSs) have been the main data sources of signal identification for most marketed drugs and vaccines since the 1960s. While SRSs have been shown to be useful for identifying rare and acute events, especially for newly marketed products, their limitations have also been well documented, including underreporting and the lack of a population denominator to calculate rates.
There have been efforts to use other RWD sources besides SRSs for signal detection. Healthcare databases have been used for signal evaluation for decades, but, despite their many strengths relative to SRSs, their use for signal identification has been limited. Recently there have been studies to expand the use of the FDA Sentinel system, which consists of several healthcare databases in the US, beyond signal evaluation, and a few articles on its use for signal identification have been published. There have also been studies to evaluate the reliability of artificial intelligence (AI) for signal identification using social media and query log data. Although much has been learned from the studies, more studies are needed to improve our understanding of how to use these RWD sources for signal identification.
We are looking for articles on signal identification - either reviews, commentaries, or original research projects - in the areas as follows:
• The use of different types of RWDs (such as healthcare data sources, social media, and others),
• The use of different approaches (traditional and digital methods such as AI) and tools,
• The performance of different methods and tools for signal identification (sensitivity, specificity, 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.