电子健康记录审计日志的可扩展和可扩展的逻辑数据模型用于时间数据挖掘 (RNteract):模型概念化和制定
Victoria L Tiase1, Katherine A Sward1,2, Julio C Facelli1
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
JMIR nursing
|June 24, 2024
概括
一个新的逻辑数据模型,RNteract,可以分析来自审计日志的护士电子健康记录 (EHR) 交互. 这种方法量化护理工作量,以帮助减少护士倦怠和提高患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 护理信息学 护理信息学
- 数据科学数据科学数据科学
背景情况:
- 增加的工作量,特别是电子健康记录 (EHR) 文档,是护士倦怠的主要驱动因素,对患者的安全和满意度产生负面影响.
- 传统的工作量分析方法往往是行政 (例如,护士与患者的比例) 或主观的快照 (例如,时间运动研究),未能捕捉到护理护理的动态性质.
- 检查EHR审计日志提供了一种可扩展和不引人注目的方法来量化护理工作量,前提是复杂的数据是结构化的,以便进行高级分析.
研究的目的:
- 使用EHR审计日志数据对分析护士-EHR交互的逻辑数据模型进行概念化.
- 促进时间机器学习 (ML) 模型的开发,以了解护理工作负载模式.
- 创建数据驱动干预措施的基础,旨在减轻护士倦怠.
主要方法:
- 进行了EHR审计日志的初步审查,以确定护理特定的数据点.
- 制定了一个逻辑数据模型,结合文献和时间生物医学数据模式的先前经验.
- 设计了模型,以可扩展和可扩展的方式描述护士-EHR交互,影响特征和工作负载结果.
主要成果:
- 介绍了RNteract,这是一个逻辑数据模型,由与护理工作负载相关的EHR审计日志数据构建.
- 从概念上证明了RNteract支持时间无监督ML和高级AI预测建模的能力.
- 突出了该模型在护士-EHR互动中发现复杂的时间模式的潜力.
结论:
- RNteract逻辑数据模型可以适应各种基于AI的系统,并且可以在不同的电子健康记录系统和医疗保健环境中进行概括.
- 定量分析护士-EHR互动的时间模式对于开发有针对性的干预措施至关重要.
- 这种方法为解决护士倦怠和改善护理文档工作量提供了一个基本步骤.
关键词:
在这里,我们可以看到AIAIAI.欧洲人权理事会 欧洲人权理事会ML ML 在 ML算法算法是一种算法.人工智能的人工智能是人工智能.燃烧症是什么?燃烧症是什么?燃烧症是什么?数据建模数据建模数据集数据集数据集.电子健康记录 电子健康记录机器学习是机器学习.护士护士护士是什么意思护理 护理 护理实用模型实用模型模型预测分析 预测分析预测模型是一个预测模型.专业的专业的专业的专业的专业.时间机器学习是时间机器学习.更多相关视频
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