AI-powered data tools to track and summarize antibiotic use in companion animals
Official title: Artificial Intelligence and Dashboard Tools to Collect, Anonymize, and Summarize Companion Animal Antimicrobial Use Data
Funded at Cornell University · NIH-11419461 · Data updated 2026-11-12
Aimed at building an easy, privacy-protective system to collect and summarize antibiotic use data from veterinary clinics.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cornell University NIH-funded |
| Lab location | 1 site (Ithaca, United States) |
| Project start | September 20, 2025 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $200,000 |
| NIH project number | 5U01FD008648-02 |
| Project ID | NIH-11419461 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable to patients; this grant focuses on veterinary practices and their data.
✕Less likely to help
Direct patient care patients are not the target; the work is about data systems for animal clinics.
What this research studies
This project will create a scalable data extraction and analytics pipeline that can pull AMU information from different veterinary electronic health records, anonymize it, and display it on a user-friendly dashboard. Using advanced natural language processing and machine learning, the team will develop an LLM-based extractor validated against gold-standard datasets. The platform will run on a secure, privacy-conscious platform so practices can participate without exposing sensitive information. Over the grant period, researchers will test generalizability across EHR systems and refine the dashboard to support long-term surveillance.
Why it matters
Potential benefit: If successful, clinics and researchers could monitor AMU patterns more quickly, leading to better stewardship of antibiotics in animals.
How similar studies have gone: Previous efforts were limited and manual; this project aims for a scalable, automated approach with some prior NLP/LLM groundwork in health data.
Where this research is happening
-
Cornell University
Ithaca, United States Active
Researchers
- Principal investigator: Goggs, Robert — Cornell University
- Study coordinator: Goggs, Robert
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.