Design and Architecture of a Generative-AI-Supported, Nonphysician-Delivered Model for GDMT Optimization in HFrEF

Abstract

Patients with heart failure with reduced ejection fraction require rapid initiation and uptitration of guideline-directed medical therapy (GDMT), which is resource-intensive. In a prospective, open-label pilot trial, we assessed the feasibility, acceptability, and safety of a generative artificial intelligence-powered virtual assistant (VA), with retrieval-augmented generation and expert prompt engineering, to optimize GDMT. Patients with new heart failure with reduced ejection fraction (n = 60) were randomized to VA-guided care, delivered by nonmedical staff at 2-weekly intervals or standard-of-care treatment delivered by doctors or nurses. At 12 weeks, patients in the VA arm had superior GDMT optimization across all medication classes, lower N-terminal pro-B-type natriuretic peptide, and fewer hospitalizations. Patient-reported acceptability, appropriateness and feasibility scores were high, with no safety disagreements between VA and clinician recommendations. Treatment by an artificial intelligence-powered VA, run by nonmedical staff, with minimal remote medical supervision, is acceptable to patients, and can safely and effectively optimize GDMT, representing a scalable strategy to optimize treatment and health care resource utilization.

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