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Related Concept Videos

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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Nursing Clinical Information System01:27

Nursing Clinical Information System

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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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Current Trends in Nursing I01:28

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Current trends in nursing include:
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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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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Related Experiment Video

Updated: Apr 22, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Artificial Intelligence in Health Care: Clinical Opportunities, Validation Trends, and Implementation Challenges

Itrat Zehra, Shakir Ali, Seung Won Lee

    Journal for Healthcare Quality : Official Publication of the National Association for Healthcare Quality
    |April 20, 2026
    PubMed
    Summary

    Artificial intelligence (AI) in healthcare shows great promise for improving diagnostics and efficiency. However, challenges like regulatory hurdles and data quality must be addressed for successful implementation.

    Keywords:
    artificial intelligenceclinical decision supportclinical validationdeep learninghealth caremachine learningsystematic review

    Related Experiment Videos

    Last Updated: Apr 22, 2026

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
    05:33

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

    Published on: July 11, 2025

    1.5K

    Area of Science:

    • Medical Informatics
    • Health Services Research
    • Computer Science Applications in Medicine

    Background:

    • Artificial intelligence (AI) is transforming healthcare, driven by increased computing power, data storage, and electronic health record adoption.
    • AI applications are expanding across various clinical contexts, necessitating a review of recent advancements and challenges.

    Purpose of the Study:

    • To systematically review recent (2020-2025) artificial intelligence applications in clinical settings.
    • To evaluate the performance, implementation challenges, and validation methods of AI in healthcare.

    Main Methods:

    • Systematic review adhering to PRISMA 2020 guidelines.
    • Searched PubMed, IEEE Xplore, and Web of Science for studies published between January 2020 and September 2025.
    • Included original research with prospective or external validation of AI in clinical contexts.

    Main Results:

    • Fifteen studies met inclusion criteria, with a peak in publications in 2024.
    • Deep learning, particularly convolutional neural networks, dominated AI methods (60.0%), with applications in radiology (33.3%), oncology (20.0%), and cardiology (13.3%).
    • Median diagnostic performance (AUC) was 0.91. Key challenges included regulatory compliance (53.3%), algorithmic transparency (40.0%), and data quality (33.3%). External validation rates increased to 46.7%.

    Conclusions:

    • AI holds significant potential to enhance healthcare quality, accuracy, safety, efficiency, and equity.
    • Addressing implementation barriers such as regulatory uncertainty, transparency, data quality, and clinical integration is crucial for realizing AI's full potential in healthcare.