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在电子健康记录中系统识别痴呆症患者的照护者:已知联系人与自然语言处理队列研究

Systematic Identification of Caregivers of Patients Living With Dementia in the Electronic Health Record: Known Contacts and Natural Language Processing Cohort Study.

作者信息

Martin Daniel, Lyons Jason, Powers J David, Daddato Andrea E, Boxer Rebecca S, Bayliss Elizabeth, Portz Jennifer Dickman

机构信息

Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO, United States.

Department of Internal Medicine, University of California Davis, Sacramento, CO, United States.

出版信息

J Med Internet Res. 2025 May 5;27:e63654. doi: 10.2196/63654.

Abstract

BACKGROUND

Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhancing care coordination, and advancing research and population health efforts in caregiving. Despite EHRs being effective in identifying patients through standardized data fields like demographics, laboratory results, medications, and diagnoses, identifying caregivers through the EHR is challenging in the absence of specific caregiver fields.

OBJECTIVE

Recognizing the complexity of identifying caregiving networks of people living with dementia, this study aims to systematically capture caregiver information by combining EHR structured fields, unstructured notes, and free text.

METHODS

Among a cohort of people living with dementia aged 60 years and older from Kaiser Permanente Colorado, caregiver names were identified by combining structured patient contact fields, that is, known contacts, with name-matching and natural language processing techniques of unstructured notes and patient portal messages containing caregiver terms.

RESULTS

Among the cohort of 789 people living with dementia, 95% (n=749) had at least 1 caregiver name listed in structured fields (mean 2.1 SD 1.1). Over 95% of the cohort had caregiver terms mentioned near a known contact name in unstructured encounter notes, with 35% having a full name match in unstructured patient portal messages. The natural language processing model identified 7556 "new" names in the unstructured EHR text containing caregiver terms among 99% of the cohort with high accuracy and reliability (F-score=.85; precision=.89; recall=.82). Overall, 87% of the cohort had a new name identified ≥2 near a caregiver term in their notes and portal messages.

CONCLUSIONS

Patterns in caregiver-related information were distributed across structured and unstructured EHR fields, emphasizing the importance of integrating both data sources for a comprehensive understanding of caregiving networks. A framework was developed to systematically identify potential caregivers across caregiving networks using structured and unstructured EHR data. This approach has the potential to improve health services for people living with dementia and their caregivers.

摘要

背景

在电子健康记录(EHR)中系统地识别照护者是提供以患者为中心的护理、加强护理协调以及推进护理研究和人群健康工作的关键步骤。尽管电子健康记录能够通过人口统计学、实验室检查结果、用药情况和诊断等标准化数据字段有效地识别患者,但在缺乏特定照护者字段的情况下,通过电子健康记录识别照护者具有挑战性。

目的

认识到识别痴呆症患者照护网络的复杂性,本研究旨在通过结合电子健康记录的结构化字段、非结构化笔记和自由文本,系统地获取照护者信息。

方法

在科罗拉多州凯撒医疗集团的60岁及以上痴呆症患者队列中,通过将结构化的患者联系字段(即已知联系人)与包含照护者术语的非结构化笔记和患者门户网站消息的名称匹配及自然语言处理技术相结合,识别照护者姓名。

结果

在789名痴呆症患者队列中,95%(n = 749)在结构化字段中列出了至少1个照护者姓名(平均2.1,标准差1.1)。超过95%的队列在非结构化的会诊记录中,照护者术语出现在已知联系人姓名附近,35%在非结构化的患者门户网站消息中有全名匹配。自然语言处理模型在99% 的队列中,从包含照护者术语的非结构化电子健康记录文本中识别出7556个“新”姓名,具有较高的准确性和可靠性(F值 = 0.85;精确率 = 0.89;召回率 = 0.82)。总体而言,87%的队列在其笔记和门户网站消息中,在照护者术语附近识别出≥2个新姓名。

结论

与照护者相关的信息模式分布在电子健康记录的结构化和非结构化字段中,强调了整合这两个数据源以全面了解照护网络的重要性。开发了一个框架,以使用结构化和非结构化电子健康记录数据系统地识别照护网络中的潜在照护者。这种方法有可能改善痴呆症患者及其照护者的健康服务。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d5a/12089870/1f7eb86ce804/jmir_v27i1e63654_fig1.jpg

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