在健康和医疗信息的可读性评估方法的开发:一个范围审查
Anunita Nattam1, Himaja Chintalapalli1, Hexuan Liu1
1University of Cincinnati, Cincinnati, Ohio, United States.
Studies in health technology and informatics
|August 8, 2025
概括
研究人员分析了健康信息可读性指标,发现了诸如语言特征,机器学习和自然语言处理等常见策略. 尽管取得了进展,但广泛采用仍然是一个挑战,强调了未来的机遇.
科学领域:
- 医疗信息学 医疗信息学
- 医学沟通 医学沟通
- 自然语言处理自然语言处理.
背景情况:
- 健康和医疗信息 (HMI) 的可读性对于患者的理解和遵守至关重要.
- 现有的HMI可读性措施需要进一步开发和验证.
- 需要进行系统审查,以确定HMI可读性测量开发中的共同策略.
研究的目的:
- 提取开发HMI可读性测量研究的特征.
- 探索HMI可读性测量开发中使用的共同主题和策略.
- 确定这个领域未来研究的差距和机会.
主要方法:
- 按照PRISMA指南,在四个数据库中进行系统的文献搜索.
- 数据分析包括统计总结,主题分析和思想网络.
- 从最初的1,129篇文章搜索中包含了14篇文章.
主要成果:
- 出现了四种主要的开发策略:语言特征 (10项研究),机器学习 (ML,6项研究),自然语言处理 (NLP,5项研究) 和人类注释 (5项研究).
- ML和NLP技术经常被使用,并通过专业和非专业人士的判断得到验证.
- 观察到最近的发展有所下降,大多数研究是在2006年至2015年之间发表的.
结论:
- 目前对HMI的可读性措施,尽管使用了ML和NLP等先进技术,但尚未得到广泛采用.
- 通过人工智能和大型语言模型的进步,有机会开发更可用的和可靠的HMI可读性测量方法.
- 未来的研究应该专注于解决采用差距和利用新兴的人工智能技术.
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