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Living Textbook of
Pragmatic Clinical Trials

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Rethinking Clinical Trials

A Living Textbook of Pragmatic Clinical Trials

  • Design
    • What is a Pragmatic Clinical Trial?
    • Decentralized Pragmatic Clinical Trials
    • Developing a Compelling Grant Application
    • Experimental Designs and Randomization Schemes
    • Endpoints and Outcomes
    • Analysis Plan
    • Using Electronic Health Record Data
    • Building Partnerships and Teams to Ensure a Successful Trial
    • Intervention Delivery and Complexity
    • Patient Engagement
  • Data, Tools & Conduct
    • Assessing Feasibility
    • Acquiring Real-World Data
    • Assessing Fitness-for-Use of Real-World Data
    • Study Startup
    • Participant Recruitment
    • Monitoring Intervention Fidelity and Adaptations
    • Patient-Reported Outcomes
    • Clinical Decision Support
    • Mobile Health
    • Electronic Health Records–Based Phenotyping
    • Navigating the Unknown
  • Dissemination & Implementation
    • Data Sharing and Embedded Research
    • Dissemination Approaches for Different Audiences
    • Implementation
    • End-of-Trial Decision-Making
  • Ethics & Regulatory
    • Privacy Considerations
    • Identifying Those Engaged in Research
    • Collateral Findings
    • Consent, Disclosure, and Non-Disclosure
    • Data and Safety Monitoring
    • Ethical Considerations of Data Sharing in Pragmatic Clinical Trials
    • Ethics for AI and ML
    • IRB Responsibilities and Procedures

Data Procurement and Consent

CHAPTER SECTIONS

Ethics for Artificial Intelligence and Machine Learning in Pragmatic Clinical Trials


Section 3

Data Procurement and Consent

Expand Contributors

Vasiliki N. Rahimzadeh, PhD
Kaitlyn Jaffe, PhD
Kayte Spector-Bagdady, JD, MBE

Contributing Editor

Elizabeth McCamic, MA

Consent requirements and methods for data procurement can differ substantially depending on the type of data collected or used in a digital PCT. Research studies funded by the federal government that collect, use, or share health-related data where individuals can be readily identified are subject to the Common Rule and require consent from participants or an IRB-approved waiver of informed consent if:

  • The research involves no more than minimal risk to the subjects;
  • The research could not practicably be carried out without the requested waiver or alteration;
  • If the research involves using identifiable private information or identifiable biospecimens, the research could not practicably be carried out without using such information or biospecimens in an identifiable format;
  • The waiver or alteration will not adversely affect the rights and welfare of the subjects; and
  • Whenever appropriate, the subjects or legally authorized representatives will be provided with additional pertinent information after participation.

For further discussion see the Waivers and Alterations section of the Consent, Waivers of Consent, and Regulatory Notification chapter.

These allowances result in variable consent requirements for the same data used for AI/ML-enabled projects. Some data used for AI/ML projects stem from digital PCTs where participants fully consented to the primary research as well as other future uses. Other data are generated from low-risk PCTs that receive an IRB waiver of informed consent and do not obtain participant consent.  Of note, some studies conducted with a waiver of informed consent include notification about the study, the effectiveness of which is determined by the type of notification used. But the end result is when a waiver of informed consent is used, patients may misunderstand that their data have been used to train AI/ML systems, or receive a general notice that their data has been used in research when registering with the hospital for clinical care.

Investigator tip: To the extent feasible, investigators could adopt creative notification strategies for patients to learn about what AI/ML research and development activities are taking place at PCT sites where the research has an IRB waiver of consent. Investigators could also encourage transparency about data usage at the hospital or institutional level (see examples in Mechanisms of Notification).

Previous Section Next Section

SECTIONS

CHAPTER SECTIONS

sections

  1. Introduction
  2. Institutional Review Board Approval
  3. Data Procurement and Consent
  4. Training Data Generation
  5. Conclusion


Version History

Published November 7, 2023

current section :

Data Procurement and Consent

  1. Introduction
  2. Institutional Review Board Approval
  3. Data Procurement and Consent
  4. Training Data Generation
  5. Conclusion

Citation:

Rahimzadeh V, Jaffe K, Spector-Bagdady K. Ethics for Artificial Intelligence and Machine Learning in Pragmatic Clinical Trials: Data Procurement and Consent. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials. Bethesda, MD: NIH Pragmatic Trials Collaboratory. Available at: https://rethinkingclinicaltrials.org/chapters/ethics-and-regulatory/ethics-and-equity-for-ai-and-ml/data-procurement-and-consent/. Updated December 3, 2025. DOI: 10.28929/233.

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