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相关概念视频

Current Trends in Nursing II01:30

Current Trends in Nursing II

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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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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Current Trends in Nursing I01:28

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Current trends in nursing include:
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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从常规医院数据估计护士工作量使用预测模型:算法开发和验证.

Paul Meredith1,2, Christina Saville1,2, Chiara Dall'Ora1,2

  • 1School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Building 67, University Road, Southampton, S017 1BJ, United Kingdom, 44 23 8059 5903, 44 23 8059 8909.

JMIR medical informatics
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概括
此摘要是机器生成的。

一个算法可以使用常规数据估计护理工作量,减少护士的行政任务. 这种方法有助于实时监控护士人员需求,并提高效率.

关键词:
护理人员的护理工作人员.预测模型是一个预测模型.更安全的护理护理工具工作人员 工作人员工作负载的工作负载.

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科学领域:

  • 医疗保健管理的管理
  • 护理信息学 护理信息学
  • 预测分析是一种预测分析.

背景情况:

  • 护士人员管理是复杂的,受患者需求,敏度和依赖度波动的影响.
  • 实时监控护士人员充足程度对于安全有效的员工部署至关重要.
  • 目前用于工作量测量的患者分类系统 (PCS) 需要护理人员频繁的行政投入.

研究的目的:

  • 探索一个算法的潜力,以估计病房工作量.
  • 确定是否可以使用例行记录的数据来估计工作负载.

主要方法:

  • 利用英国一家医院的匿名入院记录和PCS评估 (2017年2月至2020年2月).
  • 开发了一个使用常规记录的行政数据和国家早期预警分数的预测模型.
  • 结果变量:病房工作量以每名患者的全职等价 (WTE) 护理人员来衡量.

主要成果:

  • 该预测模型在11592个病房评估的测试组中实现了0.078的平均绝对误差和4.9%的平均百分比误差.
  • 95%的预测在每名患者0.21 WTE范围内,正如布兰德-阿尔特曼图表所示.
  • 在一般病房使用一组有限的常规收集的变量时,观察到适度的准确性.

结论:

  • 从常规数据中自动化护士人员需求评估是可行的.
  • 这种方法可以减少护理人员的非临床行政开销.
  • 可以实现更好的实时监控护理人员人员压力.