Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913706560978944 |
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| author | AI, Axum : Owoyemi, J. Abubakar, S. Owoyemi, A. Togunwa, T. O. Madubuko, F. C. Oyatoye, S. Oyetolu, Z. Akyea, K. Mohammed, A. O. Adebakin, A. |
| author_facet | AI, Axum : Owoyemi, J. Abubakar, S. Owoyemi, A. Togunwa, T. O. Madubuko, F. C. Oyatoye, S. Oyetolu, Z. Akyea, K. Mohammed, A. O. Adebakin, A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers AI, Axum : Owoyemi, J. Abubakar, S. Owoyemi, A. Togunwa, T. O. Madubuko, F. C. Oyatoye, S. Oyetolu, Z. Akyea, K. Mohammed, A. O. Adebakin, A. Information Retrieval Human-Computer Interaction 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. |
| title | Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers |
| topic | Information Retrieval Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.15722 |