Using machine learning to improve emergency care triage
Official title: Improving safety and quality of emergency care using machine learning-based clinical decision support at triage
Funded at Kaiser Foundation Research Institute · NIH-10928186 · Data updated 2026-11-12
This study is working on using smart computer technology to help emergency rooms quickly figure out how urgent each patient's needs are, so everyone, including kids and those from different backgrounds, can get the right care when they need it most.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Kaiser Foundation Research Institute NIH-funded |
| Lab location | 1 site (Oakland, UNITED STATES) |
| Project start | September 30, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $394,156 |
| NIH project number | 5R01HS029179-02 |
| Project ID | NIH-10928186 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals seeking emergency care, particularly those who may be critically ill or require immediate attention.
✕Less likely to help
Patients who do not seek emergency care or those whose conditions are not addressed by the triage process may not benefit from this research.
What this research studies
This research aims to enhance the triage process in emergency departments by developing machine learning models that can better predict patient acuity and resource needs. Current triage systems often misclassify patients, leading to delays in care, especially for those who are critically ill. By analyzing over 6 million emergency department encounters, the team will refine models to prioritize patients more effectively, including considerations for pediatric patients and health equity. The goal is to ensure that patients receive timely and appropriate care based on their specific needs.
Why it matters
Potential benefit: If successful, this research could lead to safer and more efficient emergency care, reducing wait times and improving outcomes for patients.
How similar studies have gone: Previous studies have shown that machine learning models can outperform traditional triage systems, indicating a promising avenue for improving emergency care.
Where this research is happening
-
Kaiser Foundation Research Institute
Oakland, United States Active
Researchers
- Principal investigator: Sax, Dana — Kaiser Foundation Research Institute
- Study coordinator: Sax, Dana
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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.