Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers

• URL: http://arxiv.org/abs/2502.15722v1
• Date: Tue, 28 Jan 2025 02:18:26 GMT
• Title: Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers
• Authors: Axum AI, :, J. Owoyemi, S. Abubakar, A. Owoyemi, T. O. Togunwa, F. C. Madubuko, S. Oyatoye, Z. Oyetolu, K. Akyea, A. O. Mohammed, A. Adebakin,
• Abstract summary: "Drug Insights," an open-source Retrieval-Augmented Generation (RAG), is designed to streamline medication lookup for healthcare workers in Africa.<n>By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, the system delivers accurate, context-specific responses with minimal hallucination.<n>Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, healthcare professionals in Africa often rely on manual and time-consuming processes to retrieve drug information, exacerbated by limited access to pharmacists due to brain drain and healthcare disparities. This paper presents "Drug Insights," an open-source Retrieval-Augmented Generation (RAG) chatbot designed to streamline medication lookup for healthcare workers in Africa. By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, including Pinecone databases and GPT models, the system delivers accurate, context-specific responses with minimal hallucination. The chatbot integrates prompt engineering and S-BERT evaluation to optimize retrieval and response generation. Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access while highlighting areas for enhancement, such as UI/UX refinement and extended corpus integration.

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