通过实时提取关键信息，让医生参与笔记生成，减少错误和冗余。
FactsR: A Safer Method for Producing High Quality Healthcare Documentation
- 实时提取临床关键信息，递归生成笔记
- 生成更准确且简洁的医疗记录
- 适合需要高安全性的临床辅助场景
目前众多医疗AI书写工具依赖大语言模型进行环境化文档生成，但大多采用单次或少量提示，在诊疗结束后生成笔记，缺乏推理能力。这导致笔记过长、幻觉增多、误解医生意图，且需医生手动校对，若因工作负荷和疲劳疏忽，可能危及患者安全。本文提出FactsR方法，实时提取诊疗过程中的关键临床信息（Facts），并递归利用这些信息生成最终病历。该方法通过将医生纳入笔记生成流程，提升记录准确性与简洁性，同时拓展了实时决策支持的新应用场景。
原文摘要 · Abstract (English)
There are now a multitude of AI-scribing solutions for healthcare promising the utilization of large language models for ambient documentation. However, these AI scribes still rely on one-shot, or few-shot prompts for generating notes after the consultation has ended, employing little to no reasoning. This risks long notes with an increase in hallucinations, misrepresentation of the intent of the clinician, and reliance on the proofreading of the clinician to catch errors. A dangerous combination for patient safety if vigilance is compromised by workload and fatigue. In this paper, we introduce a method for extracting salient clinical information in real-time alongside the healthcare consultation, denoted Facts, and use that information recursively to generate the final note. The FactsR method results in more accurate and concise notes by placing the clinician-in-the-loop of note generation, while opening up new use cases within real-time decision support.
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