Modernising Healthcare with AWS

Overview

UMCH Technology modernized its healthcare application infrastructure by migrating to AWS, optimizing cloud costs, and enhancing AI-driven chatbot and health summary capabilities. This migration ensures compliance with data sovereignty regulations while boosting system reliability and improving patient interaction.

Challenge

UMCH faced challenges in modernizing its healthcare application infrastructure to meet data sovereignty requirements for sensitive health data, while also needing to optimize cloud costs. The existing system lacked the performance and scalability needed to support AI-driven features like patient-facing chatbots and health summaries.

Solution

The project optimized UMCH's cloud infrastructure by migrating core applications to AWS and enhancing AI capabilities. The solution focused on performance, cost efficiency, and data sovereignty compliance, while improving chatbot and health summary functionality.

Key Features

• Cloud Migration & Cost Optimization: Migrated core applications to AWS Malaysia, leveraging Graviton-based EC2 instances and Aurora Serverless to reduce infrastructure costs.
• AI Workload Migration: Moved AI workloads to Amazon SageMaker for improved processing power and scalability, supporting AI-driven chatbot and health summary functions.
• Large Language Models (LLM) Evaluation: Evaluated nine LLMs to optimize AI response times and accuracy for patient-facing chatbot interactions, ensuring efficient performance.
• High Availability & Reliability: Implemented a Multi-AZ architecture to ensure 99.9% uptime, enhancing system reliability for mission-critical healthcare applications.
• Data Sovereignty Compliance: Ensured data residency in AWS Malaysia, adhering to local data protection regulations while safeguarding sensitive health data.

Result

The project successfully optimized UMCH's cloud infrastructure, resulting in significant cost savings. System availability reached 99.6% uptime, and most AI models performed as expected, with a few models exceeding the 5-second response time limit. Core applications and AI-driven chatbot functionalities were fully migrated and validated, ensuring smooth operations post-migration.

• Cost Reduction and Efficiency: By migrating to AWS Graviton-based EC2 instances and Aurora Serverless, UMCH achieves significant cloud cost savings, optimizing their IT budget for other strategic priorities.
• Enhanced Operational Continuity: With a Multi-AZ architecture ensuring 99.9% uptime, UMCH minimizes downtime for critical applications, guaranteeing seamless service availability for healthcare providers and patients.
• Improved Patient Experience and AI-Driven Insights: The migration to Amazon SageMaker enhances the performance of patient-facing chatbots and health summaries, leading to quicker, more accurate responses and improved patient engagement.