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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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相关实验视频

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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预测预期的远程医疗使用:使用美国国家调查调查的CONTEST分数和机器学习模型的开发.

Richard C Wang1, Usha Sambamoorthi2

  • 1St. Mark's School of Texas, 10600 Preston Rd., Dallas, TX 75230, USA.

Healthcare (Basel, Switzerland)
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概括

一个名为CONTEST的新工具有助于识别可能停止使用远程医疗的患者. 关键因素包括方便性,技术问题,感知质量和推意愿. 这有助于在混合护理模式中保持远程医疗的使用.

关键词:
在HINTS HINTS中使用.公平的公平的公平.机器学习是机器学习.远程医疗服务是远程医疗服务.

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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科学领域:

  • 医疗保健服务研究 医疗服务研究
  • 数字健康数字健康
  • 医疗信息学 医疗信息学

背景情况:

  • 预期的远程医疗使用对于将远程医疗整合到混合护理模型中至关重要.
  • 现有有限的工具可以预测患者停止使用远程医疗服务.
  • 识别有风险的患者对于持续采用远程医疗至关重要.

研究的目的:

  • 确定影响患者继续使用远程医疗的意图的因素.
  • 开发和验证一个风险分层工具 (CONTEST),用于预测远程医疗的终止.
  • 将CONTEST的性能和公平性与机器学习 (ML) 模型进行比较.

主要方法:

  • 对2024年健康信息国家趋势调查7 (HINTS 7) 数据的回顾性分析.
  • 调查加权后勤回归用于开发CONTEST积分得分.
  • 机器学习模型 (XGBoost,随机森林,后勤回归) 使用AUROC,精度和回忆进行训练和评估.
  • 使用跨性别和种族/种族的群体和个人反事实指标进行公平性评估.

主要成果:

  • 近10%的远程医疗用户表示不愿继续未来使用.
  • 感知较低的便利性,技术问题,感知较低的质量和不愿推是关键因素.
  • 竞赛实现了强烈的歧视 (AUROC 0.876);XGBoost在ML模型中表现最好 (AUROC 0.902).
  • 基于ML的得分和模型显示了与CONTEST.相似的性能.
  • 在公平度指标中观察到性别和种族/种族之间存在差异,个人反事实率较低.

结论:

  • CONTEST评分和ML模型有效地分层了预期远程医疗使用率较低的风险.
  • 确保方便性,技术可靠性和感知质量对于持续的远程医疗参与至关重要.
  • 实施需要将预测工具与运营支持和持续的公平监测相结合,以解决差异.