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相关概念视频

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.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
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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,...
1.2K
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

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At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.3K
Pulse rhythm01:30

Pulse rhythm

842
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Traditional Level Of Health Care System01:26

Traditional Level Of Health Care System

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The levels of care describe the services provided in the healthcare system. Accordingly, there are six levels of the traditional healthcare system in the US: preventive, primary, secondary, tertiary, restorative, and continuing healthcare. A nurse must understand how the healthcare industry organizes and provides services within these levels of care.
The preventive healthcare service includes tests for screening. Preventive health care services include identifying and reducing disease risk...
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相关实验视频

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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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超越监督学习以普及医疗保健

Xiao Gu, Fani Deligianni, Jinpei Han

    IEEE reviews in biomedical engineering
    |July 20, 2023
    PubMed
    概括

    医疗保健中的机器学习面临着数据挑战. 本综述探讨了超越监督方法的高级学习策略,以改善现实世界普遍的医疗保健应用的模型概括.

    科学领域:

    • 计算健康信息学 计算健康信息学
    • 机器学习在医学中的应用
    • 为医疗保健提供传感技术.

    背景情况:

    • 医疗数据的稀缺性,质量和异质性限制了监督机器学习.
    • 监督学习依赖于统计学适配,这对于复杂的医疗保健数据来说是不够的.
    • 无处不在的医疗保健应用需要强大的和可泛化的计算模型.

    研究的目的:

    • 识别应用机器学习到普遍医疗保健中的挑战.
    • 审查克服完全监督学习局限性的学习策略.
    • 提出数据效率高和可信赖的医疗保健的未来方向.

    主要方法:

    • 对超越完全监督学习的当前趋势的调查.
    • 对七个关键学习策略的分析,以提高概括性.
    • 确定医疗信息学领域新兴的研究方向.

    主要成果:

    • 强调了监督学习中经验风险最小化的局限性.
    • 总结了七种不同的学习策略,以提高模型性能.
    • 通过先进的方法来解决医疗保健数据的关键挑战.

    结论:

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    • 先进的学习策略对于在普遍医疗保健中有效的机器学习至关重要.
    • 未来的研究应该关注数据的效率,可扩展性和可信度.
    • 利用多模式和多源传感数据对于未来的医疗保健AI至关重要.