Improving antibiotic use to fight resistant bacteria
Official title: Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria
Funded at Stanford University · NIH-10877936 · Data updated 2026-11-12
This study is working on a smart tool to help doctors choose the best antibiotics for people with urinary tract infections, aiming to reduce the use of unnecessary medications and fight against antibiotic resistance.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | July 1, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2024 |
| Award (this year) | $764,336 |
| NIH project number | 5R01AI178121-02 |
| Project ID | NIH-10877936 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals suffering from urinary tract infections who require antibiotic treatment.
✕Less likely to help
Patients with infections not related to urinary tract infections may not benefit from this research.
What this research studies
This research focuses on developing methods to enhance antibiotic prescribing practices to combat antibiotic-resistant infections, which currently lead to millions of illnesses and thousands of deaths annually. By utilizing machine learning, the project aims to create Personalized Antibiograms that can predict the most effective antibiotics for individual patients based on historical data. The study will specifically target urinary tract infections, a common condition that often results in unnecessary antibiotic prescriptions. The goal is to provide clinical decision support systems that help healthcare providers make better-informed choices about antibiotic use.
Why it matters
Potential benefit: If successful, this research could lead to more effective treatment of bacterial infections and a reduction in antibiotic resistance.
How similar studies have gone: Previous research has shown promise in using machine learning for personalized medicine, indicating potential success for this novel approach.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Chen, Jonathan H. — Stanford University
- Study coordinator: Chen, Jonathan H.
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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.