医学学生对模拟遭遇后自动化笔记反的感知
Saurabh K Bansal1, Manajyoti Yadav1, Jianing Zhou2
1Department of Internal Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA.
The clinical teacher
|November 18, 2025
概括
使用自然语言处理 (NLP) 的自动评分系统为医学学生患者笔记 (PN) 提供了形成性反的潜在工具. 虽然在视觉上有吸引力,但这种NLP反在质量上并没有超过传统方法.
科学领域:
- 医疗教育 技术 技术 医学教育
- 在医疗保健中的自然语言处理.
- 改善临床文档 改善临床文档
背景情况:
- 评分医学学生的患者笔记 (PNs) 是耗时的.
- 自然语言处理 (NLP) 为自动分级提供了一个潜在的解决方案.
- 这项研究探讨了基于NLP的自动反对PN的价值.
研究的目的:
- 部署和评估一个自动化的NLP系统,用于对医学学生PNS进行评分.
- 与传统方法相比,评估自动反的感知价值.
- 探索学生对反质量和有用性的看法.
主要方法:
- 一个NLP系统在经过标准化患者接触后,从三年级医学学生中评分PN.
- 针对"找到的项目"和"未找到的项目"创建了个性化反报告.
- 学生首先收到自动反或教师撰写的模型笔记反.
主要成果:
- 定性分析表明,自动反具有视觉吸引力,并有助于对文档进行比较.
- 学生发现自动反对提高文档化技能有帮助.
- 模型笔记被认为是值得信赖的,自动反的质量也没有超过它.
结论:
- 自动化的NLP系统显示了对笔记写作形成性反的潜力.
- 当前的自动化系统在质量上可能无法超过传统的反方法.
- 订单效应和小样本大小限制了概括性;软件偶尔会出现错误.
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