The healthcare sector is undergoing rapid transformation with the integration of Artificial Intelligence (AI), particularly Large Language Models (LLMs). These models, with their exceptional capabilities in natural language processing and generation, offer innovative solutions to address challenges in managing clinical workflows. Traditional Clinical Decision Support Systems often lack flexibility and struggle with real-time data integration, highlighting the need for more advanced approaches. This work proposes a clinical chatbot that leverages LLMs within a multi-agent framework to automate the prescription of medical exams and efficiently retrieve clinical data from patient records. Unlike diagnostic systems, the chatbot focuses on automating repetitive workflows, reducing clinician workload, and enhancing operational efficiency. The multi-agent architecture enables dynamic collaboration among specialized agents, while LLMs process natural language inputs, construct Fast Healthcare Interoperability Resources (FHIR) and Structured Query Language (SQL) queries, and generate actionable responses in real-time. This research underscores the transformative potential of LLMs, demonstrating their ability to streamline clinical workflows and improve resource management in healthcare environments.