Using AI to improve early detection and treatment of sepsis in hospitals
Official title: AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for Sepsis
Funded at Clairyon, INC. · NIH-11060671 · Data updated 2026-11-12
This study is working on a smart computer program that helps doctors spot and treat sepsis faster by looking at patient health records, so they can make better decisions and help you or your loved ones get the care you need more quickly.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Clairyon, INC. NIH-funded |
| Lab location | 1 site (San Diego, United States) |
| Project start | July 7, 2023 |
| Project end | June 30, 2025 |
| Funding year | 2024 |
| Award (this year) | $1,000,000 |
| NIH project number | 4R42AI177108-02 |
| Project ID | NIH-11060671 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are hospitalized patients who are at risk of developing sepsis due to infections.
✕Less likely to help
Patients who are not hospitalized or those who do not have infections that could lead to sepsis may not benefit from this research.
What this research studies
This research focuses on developing an advanced artificial intelligence system designed to enhance the early recognition and treatment of sepsis, a life-threatening condition caused by the body's response to infection. By analyzing electronic health record (EHR) data, the AI aims to identify patients at risk of sepsis more accurately and quickly than current methods. The approach involves using machine learning to detect patterns in clinical data and improve decision-making for healthcare providers. The goal is to create a clinically validated tool that can support doctors in making timely and informed decisions regarding sepsis management.
Why it matters
Potential benefit: If successful, this research could significantly reduce sepsis-related deaths and improve patient outcomes through faster and more accurate diagnosis and treatment.
How similar studies have gone: Previous research has shown promise in using machine learning for sepsis prediction, but this approach aims to create a novel, clinically validated system.
Where this research is happening
-
Clairyon, INC.
San Diego, United States Active
Researchers
- Principal investigator: Josef, Christopher — Clairyon, INC.
- Study coordinator: Josef, Christopher
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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.