Summarization of electronic health records (EHRs) can substantially minimize 'screen time' for both patients as well as medical personnel. In recent years summarization of EHRs have employed machine learning pipelines using state of the art neural models. However, these models have produced less than adequate results that are attributed to the difficulty of obtaining sufficient annotated data for training. Moreover, the requirement to consider the entire content of an EHR in summarization has resulted in poor performance due to the fact that attention mechanisms in modern large language models (LLMs) adds a quadratic complexity in terms of the size of the input. We propose here a method that mitigates these shortcomings by combining semantic search, retrieval augmented generation (RAG) and question-answering using the latest LLMs. In our approach summarization is the extraction of answers to specific questions that are deemed important by subject-matter experts (SMEs). Our approach is quite efficient; requires minimal to no training; does not suffer from the 'hallucination' problem of LLMs; and it ensures diversity, since the summary will not have repeated content but diverse answers to specific questions.

翻译：电子健康记录的摘要可大幅减少患者及医务人员双方的“屏幕时间”。近年来，电子健康记录的摘要工作已采用基于最先进神经模型的机器学习流水线。然而，这些模型产生的效果不尽如人意，原因在于获取足够标注数据进行训练存在困难。此外，在摘要过程中需要考虑电子健康记录的完整内容，导致性能不佳，因为现代大语言模型中的注意力机制会随着输入规模增加而引入二次复杂度。本文提出一种方法，通过结合语义搜索、检索增强生成以及基于最新大语言模型的问答技术，有效弥补上述不足。在我们的方法中，摘要被视为对领域专家认为重要的特定问题的答案提取过程。该方法效率极高：几乎无需或仅需极少量训练，不存在大语言模型的“幻觉”问题，且能确保多样性——摘要不会包含重复内容，而是呈现针对特定问题的多元化答案。