A multi-metric evaluation of readability in psychiatric discharge summaries.

IF 7.9 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Fuchen Li, Casey Overby Taylor, Ayah Zirikly
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引用次数: 0

Abstract

BACKGROUND: Readability in medical notes is critical for patient engagement and comprehension, especially in mental health, where readable documentation reduces stigmatizing language, promotes patient-friendly communication, and strengthens trust. The importance of readability has been amplified by the federal OpenNotes policy (mandated by the 21st Century Cures Act since April 2021), which requires providers to offer patients immediate electronic access to their clinical notes. This mandate makes it imperative that mental health documentation is both clinically useful and easily understood. METHODS: This study evaluated psychiatric discharge summaries from the MIMIC-IV dataset. Specifically, we compared patient-facing “Discharge Instructions” against provider-facing “Brief Hospital Course” sections to assess documentation practices. We analyzed 1,745 notes associated with four major psychiatric diagnoses (Major Depression, Bipolar Disorder, Schizophrenia, and Eating Disorders), with a paired subset of 880 notes containing both sections. Readability was assessed using a multidimensional framework of 12 metrics, spanning traditional grade-level formulas (e.g., SMOG, KFGL) and deep learning–based models (ClinicalBERT, MedReadMe). The internal correlation across these metrics was also evaluated. RESULTS: Discharge Instructions were significantly shorter, structurally simpler, and scored lower on difficulty indices compared to Brief Hospital Course sections. Additionally, complexity varied significantly by diagnosis: the Eating Disorder cohort uniquely displayed a “readability inversion,” having the simplest instructions but the most complex hospital course summaries. Furthermore, traditional statistical measures showed moderate to high internal consistency. While they demonstrated moderate correlation with MedReadMe, they showed weaker correlation with ClinicalBERT rankings, suggesting that deep learning models capture semantic dimensions of text difficulty distinct from surface-level syntactic complexity. CONCLUSION: Psychiatric documentation practices successfully simplify patient-facing text. However, the observed variability across diagnostic groups and the divergence between traditional and neural readability metrics highlight the necessity of using multipronged evaluation frameworks to accurately assess the accessibility and comprehension of clinical text in the OpenNotes era.

精神科出院摘要可读性的多指标评价。
背景:医疗记录的可读性对于患者的参与和理解至关重要,特别是在精神卫生领域,可读的记录减少了污名化的语言,促进了患者友好的沟通,并加强了信任。可读性的重要性被联邦开放笔记政策(自2021年4月起由21世纪治愈法案强制执行)所放大,该政策要求供应商为患者提供即时的电子访问其临床记录。这一任务要求精神卫生文件必须在临床上有用且易于理解。方法:本研究评估了来自MIMIC-IV数据集的精神科出院摘要。具体而言,我们比较了面向患者的“出院说明”和面向提供者的“简短医院课程”部分,以评估文件编制实践。我们分析了1745个与四种主要精神诊断(重度抑郁症、双相情感障碍、精神分裂症和饮食失调)相关的笔记,其中880个笔记包含了这两个部分。可读性使用12个指标的多维框架进行评估,涵盖传统的年级水平公式(例如,SMOG, KFGL)和基于深度学习的模型(ClinicalBERT, MedReadMe)。还评估了这些指标之间的内部相关性。结果:与《医院课程简介》章节相比,出院说明明显更短,结构更简单,难度指数得分更低。此外,复杂性因诊断而显著不同:进食障碍队列独特地显示出“可读性反转”,具有最简单的说明,但最复杂的医院病程摘要。此外,传统的统计测量显示中等到高度的内部一致性。虽然它们与MedReadMe表现出适度的相关性,但它们与ClinicalBERT排名的相关性较弱,这表明深度学习模型捕获了文本难度的语义维度,而不是表面层次的句法复杂性。结论:精神病学文献实践成功地简化了面向患者的文本。然而,观察到的诊断组之间的差异以及传统和神经可读性指标之间的差异突出了使用多管齐下的评估框架来准确评估OpenNotes时代临床文本的可访问性和理解性的必要性。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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