Clinical Supply Study Complexity and Risk Assessment

Pfizer GCS evaluates clinical trial complexity to optimally allocate limited GCS resources across hundreds of trials each year. Our team was tasked with identifying opportunities for improved quantitative analysis for GCS complexity scoring. We leveraged existing data to propose a new complexity model and updated dashboard visualizations to deliver precise metrics to better inform GCS decision making. To address gaps in historical data for manually entered responses to study complexity questions and eliminate expensive manual entry processes in the future, we proposed and prototyped several automated data collection techniques for complexity scoring machine learning models that could be implemented at GCS. 

Faculty Advisor:

• Lynn Andrea Stein

Team Members:

• Dre Hilton
• Evelyn Kessler
• Berwin Lan
• Miles Mezaki
• Trevor Zou

Project Poster