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

Area of Science:

• Health Services Research
• Regulatory Science
• Clinical Trial Operations

Background:

• Human subject research involves complex regulatory oversight.
• Institutional Review Boards (IRBs), Medicare coverage analysis, and contract negotiations are critical but often interdependent processes.
• Optimizing these regulatory workflows is essential for timely research initiation.

Purpose of the Study:

• To quantify interdependencies within the regulatory environment for human subject research.
• To identify factors influencing the timelines of IRB, coverage analysis, and contract negotiation processes.
• To explore the utility of predictive analytics for improving regulatory efficiency.

Main Methods:

• Analysis of over 8000 IRB, coverage analysis, and contract applications (2013-2016).
• Application of traditional and machine learning analytics for quality improvement.
• Development of a predictive algorithm to identify workflow outliers.

Main Results:

• Staffing ratios, study funding, number of arms, and ancillary reviews significantly impacted research timelines.
• A predictive algorithm successfully identified outliers requiring distinct workflows.
• Enhanced communication, integrated functions, and education improved the regulatory approval process.

Conclusions:

• A systems approach is necessary to understand and improve interdependencies between regulatory units.
• Predictive analytics can be a valuable tool for optimizing human subject research regulatory processes.
• Improving inter-unit communication and integration is key to efficient research authorization.