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IRB
Institutional Review Boards, or IRBs, play an essential role in protecting clinical trial participants. Recent changes in the regulatory landscape have created new challenges and opportunities, so we’ve partnered with the UC IRB Directors to test new approaches that can make IRB approval quicker and more efficient.
Leadership
Eric Mah
UC BRAID IRB Project Director
Contact
David Grady
Program Manager, UC BRAID
david.grady@ucbraid.org
415-514-8281
Goals
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Increase capacity for multisite studies
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Establish infrastructure and processes to facilitate use of single IRB review
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Assess current processes, determine potential causes of variability and delay, and develop methods for improvement
Goals
CARE-Q: Quality Assurance for Your IRB
UC BRAID supports CARE-Q, the Consortium of Applied Research Ethics Quality. This alliance between University of California and Stanford University resulted in a simple and cost-effective program for assuring quality and certification of human subjects protections programs. It will soon be available across the nation.
Multisite Study
A Multisite Study of Performance Drivers among Institutional
Review Boards
ABSTRACT
The time required to obtain Institutional Review Board (IRB) approval is a frequent subject of efforts to reduce unnecessary delays in initiating clinical trials. This study was conducted by and for IRB directors to better understand factors affecting approval times as a first step in developing a quality improvement framework.
807 IRB-approved clinical trials from 5 University of California campuses were analyzed to identify operational and clinical trial characteristics influencing IRB approval times.
Reducing unnecessary delays in obtaining IRB approval will require a quality improvement framework that considers operational and study characteristics as well as the larger institutional regulatory environment.
UCSD Uses a Systems Approach to Assess and Improve IRB Performance
An article in the Journal of Clinical and Translational Science details how over 8,000 IRB, coverage analysis, and contract applications were analyzed using machine learning to quantify the interdependencies within the regulatory environment, including the IRB.
Staffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.
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