  The rapid emergence of antibiotic-resistant bacteria is recognized as a
global healthcare crisis, undermining the efficacy of life-saving antibiotics.
This crisis is driven by the improper and overuse of antibiotics, which
escalates bacterial resistance. In response, this study explores the use of
clinical decision support systems, enhanced through the integration of
electronic health records (EHRs), to improve antibiotic stewardship. However,
EHR systems present numerous data-level challenges, complicating the effective
synthesis and utilization of data. In this work, we transform EHR data into a
serialized textual representation and employ pretrained foundation models to
demonstrate how this enhanced feature representation can aid in antibiotic
susceptibility predictions. Our results suggest that this text representation,
combined with foundation models, provides a valuable tool to increase
interpretability and support antibiotic stewardship efforts.
