Improving patient monitoring to prevent deterioration in hospitals using machine learning.
Official title: Optimization of monitoring, prediction and phenotyping of deterioration of inhospital patients using machine learning and multimodal real time data
Funded at Feinstein Institute for Medical Research · NIH-11092280 · Data updated 2026-11-12
This study is looking to improve how hospitals keep an eye on patients to catch any health issues early, using advanced technology and data from millions of patients to help doctors know who might need extra care, so they can focus on those who need it most while not bothering those who are doing well.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Feinstein Institute for Medical Research NIH-funded |
| Lab location | 1 site (Manhasset, United States) |
| Project start | August 16, 2023 |
| Project end | May 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $800,320 |
| NIH project number | 5R01NR020774-03 |
| Project ID | NIH-11092280 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are hospitalized adult patients who are at risk of deterioration during their stay.
✕Less likely to help
Patients who are not hospitalized or those who are stable and not at risk of deterioration may not benefit from this research.
What this research studies
This research focuses on enhancing the monitoring of hospitalized patients to prevent deterioration that may require intensive care. By utilizing a large dataset of electronic health records from 2.4 million patients, the study aims to develop machine learning models that can predict which patients are at risk of declining health. Continuous monitoring devices will be employed to gather real-time data, allowing for timely interventions based on individual patient needs. The goal is to optimize monitoring practices, reducing unnecessary checks for stable patients while ensuring high-risk patients receive adequate attention.
Why it matters
Potential benefit: If successful, this research could lead to improved patient outcomes by preventing serious health declines and reducing unnecessary interventions.
How similar studies have gone: Previous research has shown success with similar machine learning approaches in predicting patient deterioration and optimizing monitoring practices.
Where this research is happening
-
Feinstein Institute for Medical Research
Manhasset, United States Active
Researchers
- Principal investigator: Zanos, Theodoros — Feinstein Institute for Medical Research
- Study coordinator: Zanos, Theodoros
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.