Predicting which older adults may return to the emergency room soon after discharge
Official title: GEMRA: Geriatric Emergency Medicine Risk Prediction Model for Return VisitAdmissions
Funded at Weill Medical Coll of Cornell Univ · NIH-11311862 · Data updated 2026-11-12
This project builds a computer tool to predict which older adults are likely to return to the emergency department and be admitted within 72 hours after discharge.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | April 1, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2026 |
| Award (this year) | $802,914 |
| NIH project number | 5R01AG076998-04 |
| Project ID | NIH-11311862 on NIH RePORTER |
Who could benefit from this research
✓Could help
Older adults who are discharged from the emergency department at one of the participating hospitals are the intended candidates for this work.
✕Less likely to help
People who are admitted on their first ED visit, treated at hospitals not participating in the project, or who are much younger than the study's geriatric focus may not see direct benefit from this tool.
What this research studies
If you are an older adult seen in the emergency department, the team will use data normally collected in your medical record to train a machine-learning model. They will combine records from five different hospitals so the tool works for different kinds of patients and EDs. The goal is to make a model that can run inside electronic health records as a decision-support aid for clinicians making discharge plans. Researchers will test and refine the model so it uses information already gathered during routine care.
Why it matters
Potential benefit: This could help clinicians spot high-risk older patients before discharge and reduce unsafe discharges, short-term returns, and related admissions.
How similar studies have gone: Existing geriatric ED risk tools have had limited predictive success, while machine-learning approaches have shown promise in other emergency-department predictions but are not yet proven for 72-hour return-with-admission in older adults.
Where this research is happening
-
Weill Medical Coll of Cornell Univ
New York, United States Active
Researchers
- Principal investigator: Zhang, Yiye — Weill Medical Coll of Cornell Univ
- Study coordinator: Zhang, Yiye
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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.