Traditional manual review methods are plagued by issues such as being time-consuming, labor-intensive, and prone to inconsistent standards. To overcome these limitations, an intelligent medical record review assistant was developed in conjunction with the cardiac surgery department of a tertiary Grade A hospital, leveraging the advanced text comprehension and reasoning of large language models (LLMs). This assistant, built on the Dify platform, employs the divide-and-conquer strategy that structures the review into a three-stage visual workflow: text structuring, multi-dimensional parallel analysis, and results aggregation. During the parallel analysis stage, prompt engineering techniques, including Expert Mimicry and Chain-of-Thought, direct multiple LLM nodes to independently assess records across four key dimensions: medical terminology, content completeness, diagnostic rationale, and the appropriateness of the treatment plan. Experimental results demonstrate that LLMs outperform both traditional machine learning and deep learning methods in medical record reviewing, and that the divide-and-conquer strategy yields better outcomes than using a single LLM.