Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure - 25/03/22

Doi : 10.1016/j.hfc.2021.11.001

Amber E. Johnson, MD, MS, MBA ^a, LaPrincess C. Brewer, MD, MPH ^b, Melvin R. Echols, MD ^c, Sula Mazimba, MD, MPH ^d, Rashmee U. Shah, MD, MS ^e, Khadijah Breathett, MD, MS ^f,^⁎
^a University of Pittsburgh School of Medicine, Heart and Vascular Institute, Veterans Affairs Pittsburgh Health System, 200 Lothrop Street, Pittsburgh, PA 15213, USA
^b Division of Preventive Cardiology, Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA
^c Division of Cardiovascular Medicine, Morehouse School of Medicine, 720 Westview Drive, Atlanta, GA 30310, USA
^d Division of Cardiovascular Medicine, Advanced Heart Failure and Transplant Center, University of Virginia, 2nd Floor, 1221 Lee Street, Charlottesville, VA 22903, USA
^e Division of Cardiovascular Medicine, University of Utah, 30 N 1900 E, Cardiology, 4A100, Salt Lake City, UT 84132, USA
^f Division of Cardiovascular Medicine, Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, PO Box 245046, Tucson, AZ 85724, USA

^∗Corresponding author.

Résumé

Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions.

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Keywords : Artificial intelligence, Machine learning, Health equity, Racial disparities, Risk prediction, Guideline-directed therapy, Health services research