走向医疗计费自动化:门诊临床医生笔记分类的NLP
medRxiv : the preprint server for health sciences
|July 28, 2023
概括
自然语言处理 (NLP) 模型可以从临床笔记中预测门诊评估和管理 (E/M) 代码,准确度为74.8%. 然而,去识别这些笔记显著降低了NLP模型的性能.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床文档 临床文档
背景情况:
- 准确编码门诊评估和管理 (E/M) 服务对于医疗补偿和质量评估至关重要.
- 电子健康记录 (EHR) 包含丰富的临床笔记,可以被潜在地用于自动编码.
- 数据非识别对临床文本分析NLP模型性能的影响仍然是一个需要进一步研究的领域.
结论:
- 基于NLP的文档分类器有望从各种临床笔记中准确预测E/M LoS CPT代码.
- 临床注释分类的复杂性需要进一步研究.
- 取消识别过程可能会对NLP分类器的性能产生负面影响,突出了隐私和实用性之间的权衡,需要仔细考虑和进一步研究.
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