Using deep learning to improve heart failure care quality and reduce disparities.
Official title: Deep Learning-enhanced Evaluation of Quality of Care and Disparities Among Patients with Heart Failure in the Electronic Health Record
Funded at Yale University · NIH-10998367 · Data updated 2026-11-12
This study is looking at how to improve care for people with heart failure, especially for Black individuals who often face more challenges, by using advanced technology to better understand their health records and find ways to provide better support and treatment.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | August 1, 2024 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $36,958 |
| NIH project number | 1F30HL176149-01 |
| Project ID | NIH-10998367 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include Black individuals diagnosed with heart failure, as well as other patients experiencing disparities in cardiovascular care.
✕Less likely to help
Patients who do not have heart failure or those who are not part of racial or ethnic minority groups may not benefit directly from this research.
What this research studies
This research focuses on heart failure, a serious condition affecting millions of Americans, particularly Black individuals who face significantly higher rates of incidence and mortality. The project aims to develop advanced deep learning models to better evaluate the quality of care provided to heart failure patients. By analyzing electronic health records, the research seeks to identify and address disparities in care that contribute to poorer outcomes for minority populations. Patients may benefit from improved management strategies and tailored interventions that arise from this enhanced understanding of care quality.
Why it matters
Potential benefit: If successful, this research could lead to improved heart failure management and reduced health disparities for affected patients.
How similar studies have gone: Previous research has shown promise in using data science and machine learning to improve healthcare outcomes, suggesting that this approach could be effective in addressing disparities in heart failure care.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Adejumo, Philip O — Yale University
- Study coordinator: Adejumo, Philip O
Related research
Other NIH-funded projects on the same topics.
-
Personalized CRT planning using advanced heart imaging and AI
A scientist is testing a new AI-assisted imaging approach to help doctors choose the best CRT device (BIVP or LBBAP) for heart failure patients with specific conduction problems.
-
Predicting future heart failure with health records and wearables
This project uses medical records, clinic ECGs, and smartwatch heart-rate data to try to predict who may develop heart failure within the next year.
-
Using AI to Understand and Improve Treatment for a Type of Heart Failure
This project uses advanced computer methods to better understand different forms of heart failure with preserved ejection fraction, aiming to find the most effective treatments for each person.
-
Predicting when heart failure might occur using data from wearables and health records
This study is looking to help people at risk of heart failure by using smart technology and health records to predict when it might happen, giving doctors a chance to step in and help up to a year in advance.
-
Using deep learning to improve lung ultrasound assessments for heart failure in emergency patients
This study is looking at how to make it easier and faster for doctors in the Emergency Department to check for heart failure in patients by using smart technology to analyze lung ultrasound images, helping you get the care you need more quickly.
-
AI-supported heart failure diagnosis and management in Rwanda
Aiming to help people with suspected heart failure in Rwanda get faster, more accurate diagnoses and better care using AI-guided ultrasound and care protocols.
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.