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Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study

机译:电子病历的可读性公式和用户感知难度:一项语料库研究

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Background: Electronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients’ comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations.Objective: The objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople’s perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas’ appropriateness and generalizability on predicting difficulty levels of highly complex technical documents.Methods: We collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople’s perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text.Results: The distributions of both readability formulas’ scores (P<.001) and laypeople’s perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople’s perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P<.001). Word usage in the 2 related genres still differed significantly (P<.001).Conclusions: Our findings suggested that the readability formulas’ predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers’ perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes.
机译:背景:电子健康记录(EHR)是用于开发应用程序以吸引患者并促进患者激活的丰富资源,因此具有增强以患者为中心的护理的强大潜力。研究表明,为患者提供他们自己的EHR注释可以提高他们对自己的临床状况和治疗方法的理解,从而改善医疗保健效果。但是,EHR注释中的技术性语言妨碍了患者的理解。许多研究已经使用诸如Flesch-Kincaid成绩等级(FKGL),Gobbledygook的简单度量(SMOG)和Gunning-Fog指数(GFI)等可读性公式评估了与健康相关的文本的难度。他们得出的结论是,这些材料的写作水平常常比一般推荐水平高。目的:我们的研究目的是探讨上述可读性公式与非专业人员在两种类型的文本上所感知的难度之间的关系:一般健康信息和EHR笔记。方法:我们收集了140篇有关糖尿病的Wikipedia文章和242篇带有《国际疾病分类》(第九修订版)的EHR注释,对公式的适用性和普遍性进行了预测。我们招募了15位Amazon Mechanical Turk(AMT)用户来评估文档的难度级别。测量了外行人的感知难度水平与可读性公式得分之间的相关性,并测试了它们之间的差异。我们还比较了两种类型的文字的用词方式和医学概念的影响。结果:两种类型的可读性公式得分(P <.001)和外行人士的看法(P = .002)的分布不同。可读性预测与外行人的看法之间的关联很弱。此外,尽管以不同的等级进行评分,但不同类别的文档仍被认为具有不同的难度(P <.001)。两种相关类型的单词用法仍存在显着差异(P <.001)。结论:我们的发现表明,可读性公式的预测与这两种文本类型的感知难度均不符。广泛使用的可读性公式彼此高度相关,但与读者的感知困难之间却没有显示出足够的相关性。因此,它们不适合评估EHR注释的可读性。

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