IRB Process Improvements: A Machine Learning Analysis

Abstract

ObjectiveClinical 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.MethodsWe 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.ResultsOur 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.ConclusionsWe have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Systems approach to assessing and improving local human research Institutional Review Board performance

“…The highly skewed right-sided tail is composed of statistically significant "outliers" identified using Tukey one-sided test (p = 0.0001). Further analyzing outliers creates an opportunity to identify variations affecting performance that might benefit from a change in the workflow [28][29][30][31]. Outliers were independently identified for 2 key phases of the approval process: (1) administrative review by staff; and (2) IRB committee review (see below).…”

Bolstering the complex study start-up process at NCI cancer centers using technology

“…The second aspect was limited staffing, which could be addressed by automating processes within that system that were historically performed manually. It is shown that the ‘human factor’ plays a large role in IRB processing time so automation will help to reduce staff impact [ 9 ].…”

Ethics Reviews in Socially Engaged Arts Research

“…Literature shows that scholars understand the ethical requirements for studies involving human participants and consider them as highly relevant. Still, they emphasize that the process is usually too long and restrictive (Shoenbill et al, 2017). Since institutional regulations frequently change, researchers involved in socially engaged arts-related projects and other researchers often experience difficulties through this process (Dingwall, 2008(Dingwall, , 2012Emmerich, 2013;Librett & Perrone, 2010;Lombardo, 2017).…”