Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Feb 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

IRB Process Improvements: A Machine Learning Analysis.

Kimberly Shoenbill1, Yiqiang Song1, Nichelle L Cobb2

  • 1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.

Journal of Clinical and Translational Science
|October 31, 2017
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A computational phenotype for pediatric asthma exacerbations requiring hospitalization using electronic health record data.

JAMIA open·2026
Same author

Why do intensive care units in countries without board-certified clinical informatics specialists risk falling behind in the Artificial Intelligence era?

Critical care science·2026
Same author

Journal of Bone and Mineral Research (JBMR) 40th anniversary celebration: the second decade (part 1).

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026
Same author

Screening and Counseling for Unhealthy Alcohol Use in Primary Care Practices.

JAMA network open·2026
Same author

Journal of Bone and Mineral Research (JBMR) 40th anniversary celebration: the beginning and first decade.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026
Same author

Continuous Glucose Monitors Among Adults With Type 2 Diabetes Mellitus in the Primary Care Setting: Qualitative Study Informed by Technology Acceptance Model and Health Belief Model.

JMIR diabetes·2025
Same journal

Flexible outlier detection in multicenter clinical trials.

Journal of clinical and translational science·2026
Same journal

Research on the road: Partnering with community emergency medical services to expand access to clinical trials.

Journal of clinical and translational science·2026
Same journal

Mapping the landscape of dissemination and implementation science across the CTSA consortium: A multi-domain environmental website scan.

Journal of clinical and translational science·2026
Same journal

Are you still engaged? Leveraging the LMS to support engagement in asynchronous courses.

Journal of clinical and translational science·2026
Same journal

Prediction of α-synuclein seed amplification assay positivity in remotely followed <i>LRRK2</i> G2019S carriers using a validated data-driven model.

Journal of clinical and translational science·2026
Same journal

Embracing complexity: An opportunity for developmental evaluation.

Journal of clinical and translational science·2026
See all related articles
This summary is machine-generated.

Identifying predictors of delays in institutional review board (IRB) reviews, such as review type and staff involvement, can improve clinical research efficiency. Process changes based on these findings enhance IRB operations.

Area of Science:

  • Clinical Research Administration
  • Health Services Research

Background:

  • Clinical research is vital but often faces lengthy and costly review processes.
  • Institutional Review Board (IRB) approval is a common requirement for human subjects research.
  • Optimizing IRB review is crucial for accelerating research timelines and reducing costs.

Purpose of the Study:

  • To identify key predictors influencing delays or accelerations in the IRB review process.
  • To leverage identified predictors for implementing process improvements in IRB operations.
  • To enhance the efficiency, transparency, consistency, and communication of IRB reviews.

Main Methods:

  • Analysis of protocol submission timelines to correlate protocol or IRB characteristics with processing times.
  • Utilized single variable analysis and machine learning to pinpoint significant predictors of IRB processing duration.
Keywords:
Ethics CommitteesIRBInstitutional Review Boardmachine learningprocess improvement

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

Related Experiment Videos

Last Updated: Feb 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
  • Implemented workflow and staffing modifications based on initial findings and re-evaluated the analysis.

    • Main Results:

    • Identified specific predictors of IRB review delays, including the type of review required.
    • Determined that protocols under Veteran's Administration purview and specific IRB staff assignments were associated with processing times.
    • Statistical and machine learning methods effectively identified factors impacting IRB protocol review duration.

    Conclusions:

    • Successfully identified predictors of IRB protocol review delays using advanced analytical methods.
    • Process improvements implemented in two IRBs resulted in demonstrably increased protocol review efficiency.
    • Ongoing workflow and system enhancements aim to achieve improved IRB efficiency, consistency, transparency, and communication.