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Related Concept Videos

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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Ethical Standards II01:23

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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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Issues And Trends In Healthcare Delivery System01:29

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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.
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Legal Guidelines for Documentation01:06

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Nurses' Legal Responsibilities II01:23

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Establishing a secure, collaborative nurse-patient relationship is crucial for delivering high-quality care. This relationship, founded on trust, respect, and honesty, enhances the patient's comfort and willingness to share vital health information. For example, a nurse who listens actively and without judgment provides clear information about health conditions and treatment options and respects patient decisions, which builds a trusting relationship.
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Healthcare Agencies II01:17

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Related Experiment Video

Updated: Feb 24, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Empowering Collaborative Healthcare with Federated Learning: Navigating Security and Privacy Priorities.

Qianying Liao1, Davy Preuveneers1, Dimitri van Landuyt1,2

  • 1DistriNet, Department of Computer Science, KU Leuven, B-3001 Leuven, Belgium.

Studies in Health Technology and Informatics
|February 23, 2026
PubMed
Summary

Federated learning enables collaborative AI research on medicines using dispersed health data. This approach addresses data fragmentation and privacy concerns in smart health applications.

Keywords:
federated learninghealthcareprivacysecuritythreat modeling

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Data Privacy

Background:

  • Healthcare is increasingly decentralized, with data spread across devices and institutions.
  • This data fragmentation challenges AI-driven research for new treatments.
  • Privacy regulations (HIPAA, GDPR) restrict centralized data aggregation.

Purpose of the Study:

  • To explore federated learning as a solution for collaborative healthcare research.
  • To address challenges in leveraging decentralized health data for AI.
  • To discuss knowledge transfer and shared representations in smart health.

Main Methods:

  • Federated learning for collaborative model training across decentralized data sources.
  • Discussion of privacy-enhancing techniques.
  • Illustration of challenges in smart health applications.

Main Results:

  • Federated learning allows harnessing collective intelligence from diverse datasets.
  • Identified challenges in data fragmentation, security, and privacy.
  • Explored leveraging shared representations and knowledge transfer.

Conclusions:

  • Federated learning offers a promising approach for collaborative AI in healthcare.
  • Addressing data fragmentation, security, and privacy is crucial for its success.
  • Further research is needed to overcome intricacies of knowledge transfer in smart health.