UMB P.I.s and CP Co-PI: Daniel Mullins, PhD and Philip Resnik, PhD
FDA Collaborator: Christine Lee, Pharm D, PhD

Project Description and Goals

To achieve this goal, we will leverage data science and social science, imparting natural language processing and machine learning techniques to analyze text and supplementing with social science methods. The project is a collaboration between the FDA’s Social and Behavioral Sciences Working Group (SBSWG), FDA Office of Minority Health and Health Equity (OMHHE), and the University of Maryland (M-CERSI).This collaboration between FDA and M-CERSI will identify categories of discussion in language based unstructured sources (e.g., FDA docket comments, FDA Advisory committee transcripts, focus groups etc.) to gain insight into relevant perspectives on One-Health issues and to assist in the development of FDA One-Health messaging, educational and communication campaigns.

The computational methods used in this project aim to be scalable and generalizable for widespread use. They will aid FDA subject matter experts in effectively gaining insight into consumer perceptions, attitudes, beliefs, and perceptions of the One Health Initiative as it pertains to pandemic response. The result will be the technological advancement of methods for gathering unstructured patient and consumer input. By way of example, the scope of this research gathers public comments in response to a variety of FDA publicly available data (e.g., federal register notices, guidance documents published during the COVID-19 pandemic, etc.). Advancing traditional manual extraction methods allows us to magnify the use of meaningful data to support regulatory decision making and to create well informed educational messaging and communication efforts.

A final component of this research will be focused not only on message development, but on design and dissemination of messaging, and on testing message effectiveness. Results of this research will be submitted to peer review for publication and disseminated via designated platforms such as the UMD CERSI PATIENTS Program’s Facebook and LinkedIn page, newsletters, and the FDA website.