Abstract
Medical errors in clinical text pose significant risks to patient safety. The MEDIQA-CORR 2024 shared task focuses on detecting and correcting these errors across three subtasks: identifying the presence of an error, extracting the erroneous sentence, and generating a corrected sentence. In this paper, we present our approach that achieved top performance in all three subtasks. For the MS dataset, which contains subtle errors, we developed a retrieval-based system leveraging external medical question-answering datasets. For the UW dataset, reflecting more realistic clinical notes, we created a pipeline of modules to detect, localize, and correct errors. Both approaches utilized the DSPy framework for optimizing prompts and few-shot examples in large language model (LLM) based programs. Our results demonstrate the effectiveness of LLM based programs for medical error correction. However, our approach has limitations in addressing the full diversity of potential errors in medical documentation. We discuss the implications of our work and highlight future research directions to advance the robustness and applicability of medical error detection and correction systems.
Abstract (translated)
临床文本中的医疗错误对患者安全构成重大风险。MEDIQA-CORR 2024 共享任务的重点是在三个子任务中检测和纠正这些错误：发现错误的存在、提取错误的句子和生成修正的句子。在本文中，我们提出了我们在所有三个子任务中实现最出色表现的策略。对于包含微妙错误的 MS 数据集，我们开发了一个基于检索的外部医疗问题回答数据集的系统。对于反映更真实临床笔记的 UW 数据集，我们创建了一个模块来检测、定位和纠正错误。两种方法都利用了 DSPy 框架优化大型语言模型（LLM）程序中的提示和少样本示例。我们的结果证明了基于 LLM 的程序在医疗错误更正方面的有效性。然而，我们的方法在解决医疗记录中可能出现的各种错误方面存在局限性。我们讨论了我们工作的影响，并强调了未来研究的方向，以提高医疗错误检测和纠正系统的稳健性和适用性。
URL
https://arxiv.org/abs/2404.14544
