AI-Driven Electronic Health Records System for Enhancing Patient Data Management and Diagnostic Support in Egypt

TL;DR

This paper presents an AI-enhanced electronic health records system tailored for Egypt, integrating scalable architecture, AI models for summaries and diagnostics, aiming to improve healthcare data management and clinical decision-making.

Contribution

It introduces a novel AI-driven EHR system with integrated Llama3-OpenBioLLM and Vision Transformer models, tailored for Egypt's healthcare infrastructure, enhancing data accessibility and diagnostic support.

Findings

High recall in medical history summaries

Effective pneumonia classification with ViT

Potential for improved diagnostic accuracy

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.