IRB Process Improvements: A Machine Learning Analysis

Journal of Clinical and Translational Science
Kimberly ShoenbillEneida A Mendonca

Abstract

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Applicatio...Continue Reading

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Citations

Apr 9, 2019·Evolutionary Bioinformatics Online·Reka Howard, Diego Jarquin
Aug 8, 2018·Journal of Clinical and Translational Science·John FontanesiGary S Firestein

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