Cloud-Based Monitoring Platform Migration for Medical Imaging Equipment

The client manages a vast network of diagnostic imaging devices globally, which are interconnected through a centralized monitoring platform. They face challenges in scaling their data processing capabilities, achieving real-time analytics, and improving system reliability and maintenance efficiency. The existing on-premise infrastructure limits their ability to handle increasing data volumes and demands for timely insights.

A large medical device manufacturer with extensive installation of diagnostic imaging equipment, seeking scalable data monitoring and analytics solutions.

• Enhance scalability of the device monitoring platform by migrating to a cloud environment.
• Implement near real-time data analytics and visualization to improve device maintenance and fault detection.
• Establish a robust data pipeline transforming raw device data into actionable insights.
• Support an agile development approach and foster a data-driven culture within the organization.
• Optimize data processing performance and reduce system downtime through advanced analytics capabilities.

• Data ingestion module capable of capturing raw device data from multiple sources.
• ETL pipelines designed to transform raw data into structured data products suitable for analysis.
• Interactive dashboards for real-time visualization of device status, error rates, and maintenance metrics.
• Analytics engine supporting anomaly detection, pattern recognition, and fault prediction.
• Self-service BI platform providing customizable reports, data pre-aggregation, and event notifications.
• Support for integrating data processing tools such as Spark and Databricks, optimized for high-speed data handling.
• Configuration of reliability monitors including dashboards to track device health and maintenance costs.

• Device data sources via secure APIs or streaming protocols.
• Analytics and data processing modules such as Databricks or Spark clusters.
• Business Intelligence tools for reporting and visualization.
• Event notification systems for alerts and proactive maintenance signaling.

• Scalability to handle increasing data volumes from thousands of devices with minimal latency.
• Data processing throughput supporting near real-time analytics with minimal delay.
• High system availability (targeting 99.9% uptime) to ensure uninterrupted monitoring.
• Robust data security and compliance, including anonymization where necessary.
• Flexible architecture supporting iterative development and deployment of new features.

The migration to a cloud-based monitoring platform aims to significantly increase data processing capacity, enabling near real-time analytics that can reduce device downtime, accelerate fault detection, and optimize maintenance workflows. Expected outcomes include improved operational efficiency, reduced maintenance costs, and enhanced device reliability, contributing to better patient care and increased compliance with industry standards.