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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.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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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,...
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Ethics and Bioethics01:22

Ethics and Bioethics

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Ethics is a philosophical study of moral actions. Ethics attempts to determine what is valuable for individuals and society. It examines the rational justification of moral judgments and analyzes what is morally just, fair, and right. Bioethics is a sub-discipline of applied ethics that analyzes the philosophical, social, and legal issues in life sciences and medicine. Ethical theories serve as a foundation for decision-making and represent the viewpoints from which people seek direction. They...
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Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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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...
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Patient-centered Care01:13

Patient-centered Care

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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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相关实验视频

Updated: Jun 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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走向透明的医疗保健:在可解释的人工智能中推进本地解释方法

Carlo Metta1, Andrea Beretta1, Roberto Pellungrini2

  • 1Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy.

Bioengineering (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

像LORE这样的本地可解释的人工智能 (XAI) 方法提高了医疗保健AI透明度,以更好地诊断和治疗疾病. 这些方法提供了关键的具体案例见解,改善了医生和患者对AI结果的理解.

关键词:
人工智能的人工智能是人工智能.可解释的人工智能机器学习是机器学习.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人工智能在医疗保健中越来越多地用于诊断,预后和个性化医疗.
  • 透明度和可解释性对于人工智能在临床环境中的信任和采用至关重要.
  • 当地可解释AI (XAI) 方法为AI模型预测提供了具体案例解释.

研究的目的:

  • 探索当地XAI方法在医疗保健中的应用和影响.
  • 强调可解释性在人工智能驱动的医疗决策中的重要性.
  • 审查当地XAI在医疗环境中的关键贡献.

主要方法:

  • 专注于局部基于规则的解释 (LORE) 作为一个特定的局部XAI技术.
  • 在医疗保健中对当地XAI进行现有文献和案例研究的审查.
  • 分析模型性能与可解释性之间的权衡.

主要成果:

  • 当地XAI方法,如LORE,为AI预测提供了细粒度,易于理解的见解.
  • 这些方法可以提高医生和患者对人工智能驱动的医疗结果的理解.
  • 当地XAI有助于改善临床决策,公平性和监管合规性.

结论:

  • 当地XAI对于医疗保健中透明和可靠的AI应用至关重要.
  • 实施本地XAI可以显著提高AI工具在医学中的实用性和接受性.
  • 对本地XAI的进一步研究对于在临床实践中推进AI至关重要.