Improving methods to identify extreme health outcomes in patient data
Improving statistical inference when interest focuses on the identification of extreme random effects in clustered data
This study is working on new ways to help doctors spot patients who might suddenly get worse or hospitals that aren't doing well, so they can provide better care and support, and it will also include easy-to-use software to make these tools accessible for healthcare teams.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project ID | NIH-10665751 on NIH RePORTER |
What this research studies
This research focuses on enhancing statistical models that predict extreme health outcomes for patients, such as rapid declines in health or poor hospital performance. By developing new methods to identify these outliers, the research aims to provide healthcare professionals with better tools for evaluating patient and hospital performance. The approach includes theoretical calculations, simulations, and real-world examples to ensure the methods are robust and effective. Additionally, user-friendly software will be created to help implement these new techniques in clinical settings.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients with complex health conditions who may experience rapid changes in their health status.
Not a fit: Patients with stable health conditions or those not part of clustered data analyses may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved identification of patients at risk for severe health declines, allowing for timely interventions.
How similar studies have performed: Previous research has shown that advanced statistical methods can significantly improve the identification of outlier health outcomes, suggesting that this approach has the potential for success.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Mcculloch, Charles E — University of California, San Francisco
- Study coordinator: Mcculloch, Charles E
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 below.