AI-Driven Electronic Health Records System for Enhancing Patient Data Management and Diagnostic Support in Egypt
Arwa Alorbany, Mariam Sheta, Ahmed Hagag, Mohamed Elshaarawy, Youssef Elharty, Ahmed Fares
TL;DR
This work tackles fragmented and uneven EHR adoption in Egypt by proposing an AI-enabled EHR prototype built on a microservices architecture with polyglot persistence. It integrates Llama3-OpenBioLLM-70B for medical history summarization and chatbot functionality, Vision Transformer-based X-ray pneumonia classification, and RBAC via Auth0, deployed on Azure AKS. The study combines a physician/patient survey with six Agile development cycles to deliver secure data governance, real-time AI-assisted workflows, and scalable infrastructure tailored to Egyptian regulations. The results indicate strong AI-enabled summarization with high semantic fidelity, actionable clinician support, and acceptable performance under load, highlighting the system’s potential to improve diagnostic support, workflow efficiency, and data-driven decision-making in Egypt and similar contexts.
Abstract
Digital healthcare infrastructure is crucial for global medical service delivery. Egypt faces EHR adoption barriers: only 314 hospitals had such systems as of Oct 2024. This limits data management and decision-making. This project introduces an EHR system for Egypt's Universal Health Insurance and healthcare ecosystem. It simplifies data management by centralizing medical histories with a scalable micro-services architecture and polyglot persistence for real-time access and provider communication. Clinical workflows are enhanced via patient examination and history tracking. The system uses the Llama3-OpenBioLLM-70B model to generate summaries of medical histories, provide chatbot features, and generate AI-based medical reports, enabling efficient searches during consultations. A Vision Transformer (ViT) aids in pneumonia classification. Evaluations show the AI excels in capturing details (high recall) but needs improvement in concise narratives. With optimization (retrieval-augmented generation, local data fine-tuning, interoperability protocols), this AI-driven EHR could enhance diagnostic support, decision-making, and healthcare delivery in Egypt.