From paper to screen: regulatory and operational considerations for modernizing the informed consent process.

Electronic platforms provide an opportunity to improve the informed consent (IC) process by permitting elements shown to increase research participant understanding and satisfaction, such as graphics, self-pacing, meaningful engagement, and access to additional information on demand. However, including these elements can pose operational and regulatory challenges for study teams and institutional review boards (IRBs) responsible for the ethical conduct and oversight of research. We examined the experience of two study teams at Alzheimer's Disease Research Centers who chose to move from a paper-based IC process to an electronic informed consent (eIC) process to highlight some of these complexities and explore how IRBs and study teams can navigate them. Here, we identify the key regulations that should be considered when developing and using an eIC process as well as some of the operational considerations eIC presents related to IRB review and how they can be addressed.

IRB Process Improvements: A Machine Learning Analysis.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: We 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.