以临床社会工作笔记为主题建模,探讨健康的社会决定因素。

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2024-01-14 eCollection Date: 2024-04-01 DOI:10.1093/jamiaopen/ooad112
Shenghuan Sun, Travis Zack, Christopher Y K Williams, Madhumita Sushil, Atul J Butte
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引用次数: 0

摘要

目的:关于健康的社会决定因素(SDoH)的现有研究主要集中于医生笔记和电子病历中的结构化数据。本研究认为,社会工作笔记是一个尚未开发的、潜在的丰富的 SDoH 信息来源。我们假设,社工的职责是改善社会和经济因素,与主要集中于医疗诊断和治疗的医生笔记相比,社工记录的临床笔记可能会成为 SDoH 数据的补充信息来源。我们的目的是利用词频分析和主题建模来识别大量社会工作笔记(包括门诊和住院咨询)中的流行术语和重要讨论主题:我们从加利福尼亚大学旧金山分校 181644 名患者的 0.95 万份临床社会工作笔记中检索了一个多样化、去标识化的语料库。我们进行了与 ICD-10 章节相关的词频分析,以确定笔记中的流行术语。然后,我们应用潜狄利克特分配(LDA)主题建模分析来描述该语料库并确定潜在的讨论主题,并根据笔记类型和疾病类别对其进一步分层:词频分析主要确定了与 ICD10 具体章节相关的医学术语,但也发现了一些微妙的 SDoH 术语。相比之下,LDA 主题建模分析提取了 11 个与健康风险社会决定因素明确相关的主题,如财务状况、虐待史、社会支持、死亡风险和心理健康。主题建模方法有效地展示了不同类型社会工作笔记之间以及不同类型疾病或病症患者之间的差异:讨论:我们的研究结果凸显了 LDA 主题建模在提取 SDoH 相关主题和捕捉社会工作笔记中的差异方面的有效性,显示了其为针对高危人群的干预措施提供信息的潜力:社会工作笔记提供了大量关于个人 SDoH 的独特而有价值的信息。这些笔记提供了一致且有意义的讨论主题,可以有效地分析和利用这些主题来改善患者护理,并为针对高危人群的干预措施提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Topic modeling on clinical social work notes for exploring social determinants of health factors.

Objective: Existing research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.

Materials and methods: We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.

Results: Word frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.

Discussion: Our findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.

Conclusion: Social work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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