<![CDATA[Healthcare services require fast and accurate access to operational information such as doctor schedules, drug availability, patient registration procedures, and outpatient administration. Conventional information systems often require users to manually search through menus, which can reduce efficiency and slow service processes. This study aims to implement a Retrieval-Augmented Generation (RAG)-based chatbot integrated into an outpatient information system at Bina Usada Clinic. The system was developed using the Waterfall method and implemented through the Laravel framework with MySQL as the primary database. Internal clinic data were processed as a knowledge base using text chunking, vector embeddings, and semantic retrieval to enable context-aware responses. The chatbot also applied role-based guardrails to ensure secure access between clinic staff and patients. System functionality was evaluated using Black Box Testing, while chatbot performance was assessed through comfort and utility dimensions involving 25 respondents consisting of clinic staff and patients. The results showed that all system functions operated successfully with a 100% validity rate. In addition, the chatbot obtained an average score of 88.24%, indicating a high level of user acceptance and usefulness. The implementation of the RAG chatbot improved information accessibility, reduced manual search time, and supported digital transformation in outpatient healthcare services. These findings indicate that integrating chatbot technology into healthcare information systems can enhance operational efficiency and user experience.]]>
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