提出DeepScore综合评分体系，量化评估AI生成病历质量。
DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation
- 构建多维度指标体系，融合准确性、完整性等维度
- 推出综合评分DeepScore，实现整体质量量化评估
- 适合医疗AI质检团队与临床系统开发者参考
医疗从业者正快速采用生成式AI进行临床文档撰写，显著节省时间并减轻压力。然而，评估AI生成文档的质量仍是一项复杂且持续的挑战。本文概述了DeepScribe在评估与管理病历质量方面的方法，重点关注多种指标及综合评分“DeepScore”，该评分是质量与准确性的总体指数。这些方法旨在通过问责机制和持续改进，提升患者病历记录的质量。
原文摘要 · Abstract (English)
Medical practitioners are rapidly adopting generative AI solutions for clinical documentation, leading to significant time savings and reduced stress. However, evaluating the quality of AI-generated documentation is a complex and ongoing challenge. This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality, focusing on various metrics and the composite "DeepScore", an overall index of quality and accuracy. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。