 This paper examined the interdependence between three different departments in order to better understand how they can work together more efficiently. The authors used traditional and machine learning analytics to analyze over 8,000 applications submitted between 2013 to 2016. They found that staffing ratios, study characteristics, source of funding, and number and type of ancillary reviews all had an impact on the timeline it took to get approval. Additionally, they developed a predictive algorithm which could identify outliers in the workflow. By understanding these interdependences, the authors were able to make improvements to the workflow, such as integrating common functions, improving communication between departments, and providing educational outreach. This paper provides valuable insight into how different departments can work together more effectively and efficiently. This article was authored by John Fontenegi, Antoni Maget, Jennifer J. Ford, and others.