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.

PROEJCT NARRATIVE Heart failure (HF) is a high-risk and costly condition, disproportionately affecting Black individuals in America. Through this grant and its principal aims, we aim to develop and implement deep learning models to enhance the assessment of care quality in heart failure management by: (1) automating the assessment of heart failure phenotypes to evaluate and improve the prescription of evidence-based therapies across diverse populations (2) identifying and addressing social determinants of health and biased language in medical documentation that contribute to disparities in cardiovascular care. The anticipated findings from this work will have a substantial impact on how heart failure care for racially and ethnically diverse populations is measured and conceptualized, thus informing the future of clinical care for one of the most common, high-risk, and high-cost conditions affecting the American population.