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Related Concept Videos

Ethical Standards I01:25

Ethical Standards I

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.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
Data Validation01:03

Data Validation

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.
Nursing assessment guides are generally based on holistic models rather than medical...
Standards of Care II01:19

Standards of Care II

Nurses bear specific legal responsibilities under several federal statutes, including:
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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...
Purpose of Health Records II01:19

Purpose of Health Records II

Health records serve various essential purposes in the healthcare system. Here are some key purposes:
Ethics in Research01:56

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.

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From Data Stewardship to Model Stewardship: Extending Governance Frameworks for AI-Era Health Data Use.

Leon Rozenblit1,2, Steven Labkoff3,2, Charles Safran4,2

  • 1Q.E.D. Institute, 164 Linden St.A-1, New Haven, US.

Journal of Medical Internet Research
|May 12, 2026
PubMed
Summary

Existing data governance frameworks are inadequate for training artificial intelligence (AI) models using electronic health record data. The study proposes extending data stewardship to include model stewardship for ethical AI development.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence Ethics
  • Data Governance

Background:

  • Electronic health record (EHR) data is increasingly used for artificial intelligence (AI) development.
  • Current governance frameworks for secondary data use are insufficient for AI model training.
  • AI model training generates persistent artifacts encoding clinical patterns.

Purpose of the Study:

  • To highlight ethical challenges in using EHR data for AI.
  • To propose an extended governance framework for AI model training.
  • To address the limitations of existing data stewardship models.

Main Methods:

  • Review of existing data stewardship frameworks.
  • Analysis of AI model training requirements.
  • Development of a model stewardship concept.

Main Results:

  • Existing governance frameworks fail to address unique aspects of AI model training.
  • AI models create generalizable knowledge from local clinical data.
  • A new model stewardship approach is necessary.

Conclusions:

  • Ethical AI development requires governance beyond data stewardship.
  • Model stewardship is crucial for managing AI artifacts.
  • Extended frameworks are needed for responsible EHR data utilization in AI.