# Deep Learning-enhanced Evaluation of Quality of Care and Disparities Among Patients with Heart Failure in the Electronic Health Record

> **NIH NIH F30** · YALE UNIVERSITY · 2024 · $36,958

## Abstract

PROJECT SUMMARY/ABSTRACT
Heart failure (HF) is a pervasive, high-risk, and expensive condition that affects over 6.2 million Americans,
many of whom endure an excessive burden of hospitalization and reduced life expectancy. This condition,
although widely prevalent, disproportionately affects Black individuals who experience a 20-fold higher
incidence rate and a 3-fold higher mortality rate in comparison to White individuals. As the population and
diversity of the United States continue to grow, there is an expected parallel increase in the number of HF
patients, particularly from racial and ethnic minority groups. The continuing disparity in HF outcomes among
Black individuals, despite advances in HF care, represents a significant challenge that needs urgent attention.
The primary concern remains the lack of validated methods to explore and address the underlying reasons for
these racial/ethnic disparities.
 Addressing the challenges, this grant proposal is dedicated to the development of robust models that
enhance the assessment and utilization of care-quality process measures in the treatment of HF. We propose
to develop and implement robust deep learning models to enhance the evaluation of care quality in HF
management. The main objective is to improve the outcome of patients with cardiovascular disease by using
deep learning to optimize care management and to identify and reduce systemic care differences in HF leading
to disparate care quality in minority populations. Aim 1: Automate the assessment of HF phenotypes to
evaluate the non-prescription of evidence-based therapies in majority and minority populations. The model
will use deep learning-based natural language processing (NLP) methods applied to clinical documentation to
determine individual HF subtypes and optimize treatment regimens. Aim 2: Automate the identification of
social determinants of health and biased language associated with minority cardiovascular care differences.
 This aim plans to train a deep learning NLP feature extraction model to identify social challenges and
biased language patterns, assessing how these features impact care quality in minority patient populations.
The outcome of this work will provide an invaluable foundation for advancing data-driven innovations in
cardiovascular medicine, promoting data-driven, individualized patient care. This project is anticipated to have
a substantial impact on how HF care for racially and ethnically diverse populations is measured and
conceptualized. The goal is to enhance the standardization of care and improvement in minority health
outcomes for diverse populations, thus helping to shape the future of clinical care for one of the most
common, high-risk, and high-cost conditions affecting the American population.

## Key facts

- **NIH application ID:** 10998367
- **Project number:** 1F30HL176149-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Philip O Adejumo
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $36,958
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998367

## Citation

> US National Institutes of Health, RePORTER application 10998367, Deep Learning-enhanced Evaluation of Quality of Care and Disparities Among Patients with Heart Failure in the Electronic Health Record (1F30HL176149-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10998367. Licensed CC0.

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