使用自然语言处理可视化叙述反在医学学生的表现仪表板中的叙述反.
Christina Maimone1, Brigid M Dolan2, Marianne M Green3
1C. Maimone is associate director of research data services, Northwestern University, Evanston, Illinois.
概括
用于医学学生反的自然语言处理 (NLP) 仪表板并没有显著提高能力审查效率. 然而,审查者发现预测元素是有用的,并且在能力决策中更有信心.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 临床能力委员会使用叙事反来评估学习者.
- 审查广泛的叙事反是耗时的.
- 自然语言处理 (NLP) 为高效的反分析提供了潜力.
研究的目的:
- 探索NLP生成的视觉仪表板的效率,用于审查预科生医学学生的叙事反.
- 在能力评估中评估视觉摘要和预测分析的有用性.
主要方法:
- 利用了来自西北大学费恩伯格医学院的学前实习能力审查数据 (2014-2021年).
- 与经验丰富的审稿人进行了用户需求分析,以告知仪表板设计.
- 开发了可视化仪表板,显示结果预测和比较叙事可视化.
- 调查了六名教师评审人员的仪表板实用性和对效率的影响.
主要成果:
- 评审者认为仪表板的预测组件是最有用的.
- 只有17%的审查者同意仪表板提高了流程效率.
- 50%的审稿人认为,由于视觉效果,他们对能力决策更有信心.
- 50%的评论员表示愿意在未来的评论中使用视觉摘要.
结论:
- 可视化和总结叙事反在综合评估系统中存在局限性.
- 未来的工作重点是优化仪表板,以满足审查员的需求.
- 大型语言模型的进步和外部协作是未来发展的关键.
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