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

TL;DR

This paper introduces 'Drug Insights,' an open-source RAG chatbot that leverages Nigerian pharmaceutical data and AI to provide accurate, context-specific medication information for African healthcare workers, aiming to improve patient safety and decision-making.

Contribution

The paper presents a novel open-source RAG framework tailored for African healthcare, integrating local pharmaceutical data with advanced AI to enhance medication information retrieval.

Findings

System delivers accurate, context-specific drug information

Pharmacist feedback indicates improved medication lookup efficiency

Preliminary tests show potential for broader healthcare impact

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.