Dynamic alignment of large language models for evidence-grounded heart failure decision support

Objectives: Large language models (LLMs) are increasingly studied for clinical decision support, but high-risk cardiology exposes persistent weaknesses in hallucination control, guideline adherence, and medication-safety reasoning. Heart failure with reduced ejection fraction (HFrEF) is a demanding test case because safe care requires structured guideline-directed therapy, comorbidity-aware monitoring, and reliable risk warnings. Methods: We developed a dynamic alignment framework using 1087 retrospective HFrEF cases from Affiliated Zhongshan Hospital of Dalian University. An open-source LLaMA-3.1 backbone was optimized through four sequential stages: continual pre-training for heart-failure domain adaptation, supervised fine-tuning for structured clinical responses, reinforcement policy optimization for safety-oriented alignment, and retrieval-augmented generation for guideline grounding. Models were assessed with dual-track clinical and linguistic metrics. Results: LLaMA-3.1 was the strongest supervised baseline, but supervised fine-tuning alone did not fully resolve guideline-adherence limitations. Staged alignment produced a measurable Alignment Tax: the final retrieval-grounded variant improved the Clinical Score from 0.716 to 0.864 and reached a Guideline Score of 0.881, while BLEU-4 decreased from 0.371 to 0.272. The decline in surface overlap coincided with stronger risk safety, stricter structure, and more guideline-directed outputs. Conclusions: Dynamic alignment shifted the model from linguistic mimicry toward clinically constrained HFrEF decision support. These findings suggest that staged optimization with policy alignment and retrieval grounding can improve evidence-based recommendations, while conventional language-overlap metrics may underestimate clinically safer generation.