Filer Evaluation prioritization using risk-based decision Machine Learning approach
Description
The FDA's Office of Information Operations (OIO) has an opportunity to enhance its evaluation capabilities across over 4,000 filers in the current FDA inventory by implementing a systematic, data-driven approach to risk assessment and prioritization. By developing standardized evaluation processes and integrated analytical tools, OIO can optimize resource allocation, improve consistency in risk identification, and strengthen the FDA's capacity to effectively protect public health through targeted regulatory oversight.
Detailed example
The ML-based model provides a complete list of filers with all the relevant information along with their relative risk scores for FDA staff to conduct evaluation of the filers.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
OIO is responsible for filer evaluations. There are over 4,000 filers in the current FDA inventory and this ML-based risk scoring approach to identify high-risk filers reduces the burden of sorting through the information manually and provides a standard process for conducting evaluations for the staff. An interactive dashboard has been developed that displays model outputs in various forms for staff use.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Import operations data including but not limited to filer evaluation history, corrections to transmitted data, database lookup failures, filer table record creation dates, PREDICT scores