Medical documentation, including discharge notes, is crucial for ensuring patient care quality, continuity, and effective medical communication. However, the manual creation of these documents is not only time-consuming but also prone to inconsistencies and potential errors. The automation of this documentation process using artificial intelligence (AI) represents a promising area of innovation in healthcare. This study directly addresses the inefficiencies and inaccuracies in creating discharge notes manually, particularly for cardiac patients, by employing AI techniques, specifically large language model (LLM). Utilizing a substantial dataset from a cardiology center, encompassing wide-ranging medical records and physician assessments, our research evaluates the capability of LLM to enhance the documentation process. Among the various models assessed, Mistral-7B distinguished itself by accurately generating discharge notes that significantly improve both documentation efficiency and the continuity of care for patients. These notes underwent rigorous qualitative evaluation by medical expert, receiving high marks for their clinical relevance, completeness, readability, and contribution to informed decision-making and care planning. Coupled with quantitative analyses, these results confirm Mistral-7B's efficacy in distilling complex medical information into concise, coherent summaries. Overall, our findings illuminate the considerable promise of specialized LLM, such as Mistral-7B, in refining healthcare documentation workflows and advancing patient care. This study lays the groundwork for further integrating advanced AI technologies in healthcare, demonstrating their potential to revolutionize patient documentation and support better care outcomes.

翻译：医学文档（包括出院报告）对于保障患者护理质量、延续性及有效医疗沟通至关重要。然而，手动创建这些文档不仅耗时，且易出现不一致性和潜在错误。利用人工智能（AI）自动化该文档处理流程是医疗领域极具前景的创新方向。本研究直接针对手动创建出院报告（特别是针对心脏病患者）存在的低效与不准确问题，采用AI技术，尤其是大语言模型（LLM）进行解决。基于某心脏中心涵盖广泛医疗记录与医生评估的大量数据集，我们评估了LLM提升文档处理流程的能力。在评估的多种模型中，Mistral-7B脱颖而出，其准确生成的出院报告显著提高了文档处理效率及患者护理的延续性。这些报告经医学专家严格定性评估，在临床相关性、完整性、可读性及对知情决策和护理规划的贡献方面获得高度评价。结合定量分析，结果证实Mistral-7B在将复杂医学信息提炼为简洁连贯摘要方面具有显著效能。总体而言，我们的研究结果揭示了Mistral-7B等专用LLM在优化医疗文档工作流及推进患者护理方面具有巨大潜力。本研究为在医疗领域进一步整合先进AI技术奠定了基础，展示了其革新患者文档处理流程并支持更优护理结果的可能性。