Enhancing Antibiotic Prescribing in Urgent Care by Leveraging Large Language Models for Optimized Clinical Decision Support
International Research Journal of Modernization in Engineering Technology and Science · 2024
This study investigates the integration of large language models into clinical decision support systems to enhance antibiotic prescribing practices in urgent care settings.Antibiotic resistance poses a significant global health threat, emphasizing the critical need for judicious antibiotic use.Leveraging the capabilities of large language models, particularly their natural language processing and machine learning abilities, this research aims to optimize clinical decision-making regarding antibiotic prescriptions.By analyzing vast amounts of patient data, these models can assist healthcare providers in real-time, offering tailored suggestions and predicting patient outcomes based on comprehensive linguistic and medical information.This paper examines the potential of these models to minimize unnecessary antibiotic prescriptions, mitigate antibiotic resistance, and improve patient care quality in urgent care settings.Moreover, it addresses ethical considerations, implementation challenges, and prospects for widespread adoption, highlighting the transformative impact of integrating large language models into clinical decision support for optimized antibiotic prescribing practices.