I-Corps: Translation Potential of an Automated Dynamic Antibiogram

Award ID: 2528698

This I-Corps project focuses on the development of an automated system that provides healthcare professionals with up-to-date information about which antibiotics are effective against specific bacteria. Antimicrobial resistance is a major public health problem worldwide, in part due to misuse and overuse of antibiotics. Current methods for tracking bacterial resistance patterns involve static reports that are updated infrequently, sometimes only once per year, leading to outdated guidance for clinicians. This technology allows healthcare providers across various settings including hospitals, clinics, and long-term care facilities to access continuously updated resistance data. The solution helps providers select the most appropriate antibiotics for patient treatment, potentially reducing mortality rates, shortening hospital stays, and lowering healthcare costs. By improving antibiotic selection, the technology contributes to national health objectives related to combating antibiotic resistance. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a dynamic susceptibility reporting system that transforms laboratory microbiology data into actionable clinical intelligence. The system integrates with existing laboratory information systems and leverages modern web development frameworks to process microbiological data in real-time. Unlike traditional static reports, the technology allows for customized filtering by variables such as patient location, specimen type, and time period, creating tailored guidance for specific clinical scenarios. The system architecture accommodates varying institutional data structures and reporting requirements, enhancing its potential for widespread adoption. Technical innovations include automated data processing that eliminates the labor-intensive manual compilation required for traditional reports and real-time analysis capabilities that reflect current resistance trends rather than historical patterns. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman