There is a fundamental gap in our ability to monitor antimicrobial use (AMU) at the individual animal level in dairy cattle nationally. The continued existence of this gap hinders the development of data-driven antimicrobial stewardship and understanding of the relationships between AMU in dairy cattle and antimicrobial resistance—one of the most pressing One Health challenges we face today. AMU monitoring requires an approach to collecting and quantifying data on AMU. Also, most herds must participate in data sharing for an AMU monitoring system to succeed. However, farmers lack the incentive to participate in monitoring, the labor involved in data collection is prohibitive for already busy farmers, and they have concerns about the loss of privacy and business advantage through sharing their AMU data via a monitoring system. These major bottlenecks are impeding the establishment of AMU monitoring in dairy cattle in the US. Thus, there is an urgent need for a system approach to animal-level AMU monitoring in dairy cattle that provides private value to the participating farmer, automates laborious data collection tasks, protects farmers’ privacy, and advances One Health goals. Our long-term goal is to deploy a functional and efficient system for monitoring AMU in food animals. Thus, the overall objective of this application is to develop a system for monitoring AMU in dairy cattle that provides farmers with actionable clinical and business insights, automates data collection, and protects their proprietary information. The rationale that underlines the proposed research is that such an AMU monitoring system will incentivize dairy farmer participation and enable One Health to benefit from the national level AMU monitoring. This objective will be achieved by systematically building the three pillars of an effective AMU monitoring system: Data, Models, and People. Specifically, we will pursue the following specific aims: (1) Collect detailed, complete, and validated multi-year animal-level AMU data on dairy farms; (2) Develop a system approach to animal-level AMU monitoring in dairy cattle; and (3) Evaluate perceptions of farmers and veterinarians about AMU monitoring in dairy cattle. The AMU monitoring system developed in Aim 2 will have four innovative elements: (i) instant private clinical/business insights for the farmer to incentivize their participation in data collection and sharing, (ii) standardization and automation to ease the data collection burden on farmers, (iii) augmentation with synthetic data, and (iv) privatization techniques that give the farmer the governance over their AMU data while allowing peer learning, further incentivizing participation in AMU monitoring. The proposed research is significant because it is expected to enable scaling up monitoring animal-level AMU on dairy farms in the US with a system approach and technology that are tailored to the dairy farming industry and have translational value to other food animal sectors. In addition to technological innovations, the project will generate multi-year AMU data in dairy cattle and data about farmers' perceptions of AMU monitoring, which will be instrumental in developing a use-inspired AMU monitoring system.