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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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Purpose of Health Records I01:11

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Related Experiment Video

Updated: Jul 17, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.

Celia Alvarez-Romero1, Alicia Martínez-García1, A Anil Sinaci2

  • 1Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain.

Open Research Europe
|August 30, 2023
PubMed
Summary

The FAIR4Health project enhanced health data sharing using FAIR principles (Findable, Accessible, Interoperable, Reusable). Federated machine learning on FAIRified data demonstrated its potential for improving health outcomes and social care research.

Keywords:
FAIR principlesHL7 FHIRdata reusedata sharinghealth datahealth researchhealth research data managementmachine learning.open scienceprivacy-preserving computing

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Area of Science:

  • Health Informatics
  • Data Science
  • Bioinformatics

Background:

  • Health data sharing and reuse face ethical, legal, and technical challenges.
  • The FAIR principles (Findable, Accessible, Interoperable, Reusable) offer a framework to address these barriers.
  • Publicly funded health research initiatives generate valuable data that could be better utilized.

Purpose of the Study:

  • To promote and apply FAIR principles to health research data.
  • To demonstrate the feasibility of the FAIR4Health solution through case studies.
  • To establish an EU-wide strategy for FAIR data in health research.

Main Methods:

  • Developed a FAIRification workflow to transform raw health data and metadata into FAIR data and metadata.
  • Conducted two pathfinder case studies utilizing federated machine learning algorithms.
  • Applied the FAIR4Health solution to datasets from five health research organizations.

Main Results:

  • Federated machine learning algorithms were successfully executed on FAIRified datasets.
  • The FAIR4Health solution demonstrated significant potential impact on health outcomes and social care research.
  • Promoted FAIRified data sharing and reuse within the European Union Health Research community.

Conclusions:

  • The FAIR4Health project successfully facilitated the application of FAIR principles in health research.
  • The developed solution is feasible and shows promise for advancing health outcomes and social care research.
  • Laid the groundwork for a roadmap for health research institutions to adopt FAIR data practices.