合作文档对以人为本的护理的影响:临床笔记文本分析。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
Victoria Stanhope, Nari Yoo, Elizabeth Matthews, Daniel Baslock, Yuanyuan Hu
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引用次数: 0

摘要

背景:协作记录(CD)是一种行为健康实践,涉及医疗服务提供者和消费者共同撰写门诊记录。尽管 CD 得到了广泛传播,但有关其有效性或对以人为本的护理(PCC)的影响的研究却十分有限。以人为本的护理(PCC)规划原则是一种以康复为基础的服务规划方法,它将以人为本的护理(PCC)具体化,可为在临床记录中衡量以人为本的护理提供参考:本研究旨在使用自然语言处理(NLP)的临床信息学方法,研究 CD 对门诊记录中以人为中心的影响。本研究采用基于字典的方法,对一家社区心理健康中心员工接受 CD 培训前后的门诊记录进行文本分析:本研究使用了一家社区心理健康中心的 10 名医疗服务提供者在接受 CD 培训前后 6 个月的就诊记录(n=1981)。使用语言调查和字数统计(LIWC)词典对所有笔记进行评估,该词典对 5000 多个语言和心理词汇进行了分类。通过 3 位领域专家的共识,选择了 12 个 LIWC 类别,并将其映射到 PCC 规划原则中。使用 LIWC-22 上下文分析器从笔记中提取与 LIWC 类别相对应的句子片段。然后,使用固定效应模型确定 CD 培训前后笔记的差异,同时考虑到提供者内部的嵌套:结果:通过情境化过程识别出的句子片段说明了访问笔记是如何展示 PCC 的。固定效应分析发现,在 CD 培训后,以人为本发生了显著的积极转变;在所选的 LIWC 类别中,有 6 个类别观察到了这种转变。具体来说,与成就相关的词语明显增加(β=.774,PConclusions:通过使用基于词典的方法,本研究确定了 CD 如何影响临床笔记中 PCC 原则的整合。虽然结果参差不齐,但研究结果凸显了 CD 在临床笔记中增强以人为本的潜在有效性。通过利用 NLP 技术,这项研究阐明了临床笔记叙事在评估行为健康护理质量方面的价值。这些发现强调了 NLP 在医疗质量保证方面的前景,并强调了改进算法以更准确地衡量 PCC 的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.

Background: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered care (PCC) has been limited. Principles of PCC planning, a recovery-based approach to service planning that operationalizes PCC, can inform the measurement of person-centeredness within clinical documentation.

Objective: This study aims to use the clinical informatics approach of natural language processing (NLP) to examine the impact of CD on person-centeredness in clinic visit notes. Using a dictionary-based approach, this study conducts a textual analysis of clinic notes from a community mental health center before and after staff were trained in CD.

Methods: This study used visit notes (n=1981) from 10 providers in a community mental health center 6 months before and after training in CD. LIWC-22 was used to assess all notes using the Linguistic Inquiry and Word Count (LIWC) dictionary, which categorizes over 5000 linguistic and psychological words. Twelve LIWC categories were selected and mapped onto PCC planning principles through the consensus of 3 domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modeling was used to identify differences in notes before and after CD training while accounting for nesting within the provider.

Results: Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated PCC. The fixed effects analysis found a significant positive shift toward person-centeredness; this was observed in 6 of the selected LIWC categories post CD. Specifically, there was a notable increase in words associated with achievement (β=.774, P<.001), power (β=.831, P<.001), money (β=.204, P<.001), physical health (β=.427, P=.03), while leisure words decreased (β=-.166, P=.002).

Conclusions: By using a dictionary-based approach, the study identified how CD might influence the integration of PCC principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of CD in enhancing person-centeredness in clinic notes. By leveraging NLP techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in health care settings and emphasize the need for refining algorithms to more accurately measure PCC.

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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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