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Related Concept Videos

Ethical Standards I01:25

Ethical Standards I

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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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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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An institutional framework to support ethical fair and equitable artificial intelligence augmented care.

Steven Dykstra1,2, Matthew MacDonald1,2, Rhys Beaudry1,2

  • 1Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

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|February 5, 2025
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A new framework, PULSE, enables ethical, patient-guided access to multi-domain health data for developing artificial intelligence-augmented clinical decision support (AI-CDS). This approach ensures fair and equitable AI-CDS models by incorporating social determinants of health.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Scalable institutional frameworks for multi-domain health data access are needed for AI-augmented clinical decision support (AI-CDS).
  • Existing systems often lack patient-guided, ethically governed ecosystems for data utilization.

Purpose of the Study:

  • To present the PULSE framework, an integrative and ethically governed ecosystem for patient-guided use of multi-domain health data.
  • To support the development and surveillance of AI-CDS models through routine patient engagement and data abstraction.
  • To emphasize the contextualization and evaluation of AI-CDS models for fair and equitable use by incorporating social determinants of health and patient-reported outcomes.

Main Methods:

  • Stakeholder engagement and infrastructure development for routine patient consent-guided data abstraction.
  • Pre-processing and cloud migration of multi-domain health data for AI-CDS model development.
  • Routine collection of social determinants of health and patient self-reported health status.

Main Results:

  • Inaugural feasibility demonstrated with over 30,000 consecutively engaged patients.
  • Development of a validated architecture for AI-CDS model development and surveillance.
  • Successful integration of patient-guided data abstraction and ethical governance.

Conclusions:

  • The PULSE framework provides a scalable, ethically governed ecosystem for multi-domain health data utilization in AI-CDS.
  • The framework facilitates the development of fair and equitable AI-CDS models by incorporating patient context and social determinants of health.
  • PULSE is translatable to various disease domains and suitable for large-scale tertiary care institutions.