使用新的自然语言处理方法来检查关于护理诺姆居民痴呆症的老年友好沟通
Kimberly R Powell1, Mira Isnainy2, Suhwon Lee2
1Sinclair School of Nursing, University of Missouri, Columbia, Missouri, 65211, United States.
The Gerontologist
|February 16, 2026
概括
养老院文本中的培训和流动性讨论与可避免的居民转移有关. 这突出了沟通模式如何影响老年患者,包括痴呆症患者的护理结果.
科学领域:
- 老年医疗护理 医疗护理
- 医疗信息学 医疗信息学
- 养老院质量提升 养老院质量提升
背景情况:
- 年龄友好型卫生系统模型强调4M:重要的是什么,移动性,思维和药物.
- 有效的跨学科沟通对于改善患者的治疗结果至关重要,可能会减少可避免的护理家庭到医院的转移.
研究的目的:
- 调查在养老院卫生保健工作者短信中所代表的4M与可避免的居民转移之间的联系.
- 在患有和没有阿尔茨海默氏症和相关痴呆症 (ADRD) 的居民中探索这些关联.
主要方法:
- 利用了来自16个养老院的30 066条短信和3687个居民转移记录 (2016-2020) 的合并数据集.
- 使用自然语言处理 (NLP) 识别短信中的 4M 个术语.
- 应用了通用线性混合模型来分析通信中的4M与可避免的转移之间的关联,控制居民和设施因素.
主要成果:
- 诸如心肺复苏状态,晚期ADRD,养老院床位大小和位置等因素与可以避免的转移有关.
- 包含与思维和流动性相关的术语的短信与可避免的护理家庭到医院转移有显著的关联.
结论:
- 在非结构化通信数据中,NLP可以有效地识别年龄友好的护理元素.
- 在制定策略时,养老院应该考虑居民层面和设施层面的因素,以及沟通内容,以最大限度地减少可避免的转移,特别是对于患有ADRD的居民.
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