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患者门户信息支持痴呆症患者友好型老年健康系统。

Patient portal messages to support an age-friendly health system for persons with dementia.

机构信息

Johns Hopkins University School of Nursing, Baltimore, Maryland, USA.

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

出版信息

J Am Geriatr Soc. 2024 Jul;72(7):2140-2147. doi: 10.1111/jgs.18841. Epub 2024 Feb 27.

Abstract

BACKGROUND

Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how message concerns relate to age-friendly issues.

METHODS

We conducted a two-part observational study. We first assessed the feasibility of automating care partner identification from patient portal messages by developing and testing a natural language processing (NLP) rule-based classification system from portal messages of 1973 unique patients 65 and older. Second, two independent reviewers manually coded a randomly selected sample of portal messages for 987 persons with dementia to identify the frequency of expressed needs from the 4M domains of an Age-Friendly Health System (medications, mentation, mobility, and what matters).

RESULTS

A total of 267 (13.53%) of 1973 messages sent from older adults' portal accounts were identified through manual coding as sent by a nonpatient author. The NLP model performance to identify nonpatient authors demonstrated an AUC of 0.90. Most messages sent from the accounts of persons with dementia contained content relevant to the 4Ms (60%, 601/987), with the breakdown as follows: medications-36% (357/987), mobility-10% (101/987), mentation-16% (153/987), and what matters (aligning care with specific health goals and care preferences)-21%, 207/987.

CONCLUSIONS

Patient portal messaging offers an avenue to identify care partners and meet the informational needs of persons with dementia and their care partners.

摘要

背景

患者门户安全消息传递可以支持对认知症友好的护理,但对于护理伙伴如何使用门户以及消息关注与认知症友好问题的关系知之甚少。

方法

我们进行了一项两部分的观察性研究。首先,我们通过从 1973 位 65 岁及以上的患者的门户消息中开发和测试基于自然语言处理(NLP)规则的分类系统,评估了从患者门户消息中自动识别护理伙伴的可行性。其次,两名独立审查员对 987 名痴呆症患者的门户消息进行了随机抽样,以识别 4M 领域(药物、思维、移动性和重要性)中表达的需求频率。

结果

通过手动编码,从老年人门户账户发送的 1973 条消息中,共有 267 条(13.53%)被识别为非患者作者发送的消息。识别非患者作者的 NLP 模型性能显示 AUC 为 0.90。从痴呆症患者账户发送的大多数消息都包含与 4M 相关的内容(60%,601/987),具体如下:药物-36%(357/987)、移动性-10%(101/987)、思维-16%(153/987)和重要性(将护理与特定健康目标和护理偏好保持一致)-21%,207/987。

结论

患者门户消息传递提供了一种识别护理伙伴并满足痴呆症患者及其护理伙伴信息需求的途径。

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