Admission note quality affects clinical safety and research reliability, yet resident drafts often suffer from missing information, unclear logic, and other issues, requiring time-consuming manual review. Current large language models (LLMs) focus on text generation or formatting correction but rarely optimize clinical logic or mitigate hallucinations in specialized notes. We developed HQ-ANR (High-Quality Admission Note Refinement), a hyperthyroidism-specific quality-control model that integrates multi-agent collaboration with diagnosis-driven retrieval-augmented generation (RAG) to optimize the history of present illness (HPI) in admission notes. Built on a general-purpose LLM (Qwen3-32B), HQ-ANR anchors generation to a dual-source knowledge base (guidelines and expert notes) and employs specialized agents for symptoms, examinations, and treatments. In a blinded evaluation of 30 resident-drafted hyperthyroidism notes, HQ-ANR significantly improved median Physician Documentation Quality Instrument (PDQI-9) scores (from 39.00 to 42.00) and reduced the hallucination rate (positive factual hallucinations only) to 6.67%, compared to 26.67% for a general LLM with chain-of-thought (CoT) prompting. Efficiency analysis showed HQ-ANR reduced physician refinement time by an average of 112.9 s per note ( p < 0.001). These results demonstrate that augmenting a general LLM with external knowledge and a multi-agent framework can achieve reliable, in-depth optimization of clinical notes without expensive vertical training, offering a practical pathway toward intelligent, safe documentation quality control and reducing physician documentation burden.