About this Research Topic
As Real World Evidence (RWE) continues to transform human healthcare through rich sources like electronic health records and disease registries, its integration into veterinary medicine is burgeoning yet remains less defined and robust. The need for systematic and scalable approaches to gather and analyze Real World Data (RWD) from companion animals and livestock is increasingly evident. Despite initiatives making strides in this area, the field faces key challenges including limited data sources, variable data quality and fragmented epidemiological and economic efforts. This lack of comprehensive methodologies and definitive data sources hampers the ability to conduct extensive RWE studies within veterinary contexts, critical for advancing animal health care practices and policies.
This Research Topic aims to encapsulate the ongoing evolution and critical examination of RWD and RWE in the veterinary sector. It seeks to foster an understanding of existing practices and highlight innovative approaches that bridge gaps between human and animal health data utilization. By delving into the application of artificial intelligence for data mining, replication of human health methodologies, and exploring specific disease areas in veterinary medicine, this collection endeavors to spotlight the pivotal role that rationally used RWE can play in enhancing animal health diagnostics, treatment, and management practices broadly.
The scope of this Research Topic is articulated around several key areas of interest that tackle present challenges and future potential of RWE in veterinary medicine:
• In-depth analysis of veterinary data databases, including discussions on geographic, species-specific, and temporal variations.
• Exploration of data sources in veterinary medicine, pinpointing existing limitations and outlining potential enhancements.
• Studies exploring the application of artificial intelligence in veterinary data analysis and its implications.
• Critical evaluations of RWE studies in terms of relevance, reliability, and design tailored to specific species or diseases.
• Future directions for RWE in veterinary health, focusing on emerging technologies such as wearable devices, Patient Reported Outcome Measures, and economic assessment tools.
Contributions are encouraged that present original research, methodological reviews, and innovative studies that apply advanced data analytics to understand and navigate the complexities of animal health. By defining current limitations and highlighting strategic opportunities, this Research Topic aims to not only broaden the understanding of RWE in veterinary medicine but also to facilitate its practical application in improving care for animal populations worldwide.
Drs. Andrea Wright and Kennedy Mwacalimba are employed by Zoetis.
This Research Topic aims to encapsulate the ongoing evolution and critical examination of RWD and RWE in the veterinary sector. It seeks to foster an understanding of existing practices and highlight innovative approaches that bridge gaps between human and animal health data utilization. By delving into the application of artificial intelligence for data mining, replication of human health methodologies, and exploring specific disease areas in veterinary medicine, this collection endeavors to spotlight the pivotal role that rationally used RWE can play in enhancing animal health diagnostics, treatment, and management practices broadly.
The scope of this Research Topic is articulated around several key areas of interest that tackle present challenges and future potential of RWE in veterinary medicine:
• In-depth analysis of veterinary data databases, including discussions on geographic, species-specific, and temporal variations.
• Exploration of data sources in veterinary medicine, pinpointing existing limitations and outlining potential enhancements.
• Studies exploring the application of artificial intelligence in veterinary data analysis and its implications.
• Critical evaluations of RWE studies in terms of relevance, reliability, and design tailored to specific species or diseases.
• Future directions for RWE in veterinary health, focusing on emerging technologies such as wearable devices, Patient Reported Outcome Measures, and economic assessment tools.
Contributions are encouraged that present original research, methodological reviews, and innovative studies that apply advanced data analytics to understand and navigate the complexities of animal health. By defining current limitations and highlighting strategic opportunities, this Research Topic aims to not only broaden the understanding of RWE in veterinary medicine but also to facilitate its practical application in improving care for animal populations worldwide.
Drs. Andrea Wright and Kennedy Mwacalimba are employed by Zoetis.
Keywords: Real World Data (RWD), Real World Evidence (RWE), Artificial Intelligence, Veterinary and Animal Health Economics and Outcomes Research
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.
