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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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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Purpose of Health Records I01:11

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The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
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相关实验视频

Updated: Sep 13, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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加强人工智能研究的临床数据基础设施:数据管理架构的比较评估.

Richard Gebler1, Ines Reinecke2, Martin Sedlmayr1

  • 1Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.

Journal of medical Internet research
|August 1, 2025
PubMed
概括

选择正确的临床数据管理架构是人工智能研究和患者护理的关键. 数据仓库提供治理,数据湖提供灵活性,数据湖平衡两者,但需要专业知识.

关键词:
医疗保健中的大数据临床数据管理 临床数据管理数据架构评估数据架构评估数据湖 数据湖数据湖房数据湖房数据仓库数据仓库的数据仓库是什么?

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TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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科学领域:

  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学
  • 医疗保健中的人工智能

背景情况:

  • 临床数据的快速增长给医疗机构带来了挑战.
  • 传统的数据管理难以处理大型,多样化和动态的数据集.
  • 需要支持人工智能研究和改善患者护理的架构.

研究的目的:

  • 比较临床数据仓库,数据湖和数据湖仓库.
  • 使用FAIR原则和大数据5V分析架构.
  • 指南选择平衡数据治理和分析灵活性.

主要方法:

  • 开发了一个整合数据治理和技术性能的框架.
  • 进行了关于数据管理架构的快速文献审查.
  • 评估了可扩展性,实时处理,元数据和专业知识要求.

主要成果:

  • 数据仓库:强有力的治理,有限的可扩展性/实时处理.
  • 数据湖:灵活,可用于异质数据的可扩展,潜在的质量/元数据问题.
  • 数据湖房:结合优势,需要高专业知识和复杂的集成.

结论:

  • 最佳的架构取决于组织的需求,资源和目标.
  • 为未来的基础设施平衡治理,灵活性和可扩展性.
  • 需要进一步的研究来简化混合模型并改善临床标准的整合.