Cloud Migration and Real-Time Analytics Platform Development for Medical Device Monitoring

The client operates hundreds of thousands of medical imaging devices worldwide that are linked to a centralized monitoring platform. The existing on-premises system faces limitations in scalability, data processing speed, and real-time analytics capabilities, hindering proactive maintenance and efficient resource management. There is a pressing need to migrate the monitoring solution to a robust cloud architecture to support growth, improve data processing latency, and enable advanced analytics for device health and performance optimization.

A large healthcare technology company with a broad portfolio of medical imaging devices, seeking to modernize its device monitoring infrastructure through scalable cloud solutions and enhanced analytics capabilities.

• Design and implement a scalable cloud architecture to support real-time monitoring and analytics of medical imaging devices.
• Migrate existing ETL pipelines and business logic to cloud-native solutions ensuring minimal disruption and data consistency.
• Establish a self-service analytics platform with interactive dashboards for device usage, errors, and performance trends.
• Enhance data processing speed to enable near real-time analytics, improving device issue detection and response times.
• Implement advanced data integrity and duplicate removal methods to handle diverse data types efficiently.
• Support ongoing agile development practices to accelerate deployment and facilitate continuous improvements.

• Unified cloud architecture incorporating scalable data ingestion and processing pipelines.
• Migration of existing business logic for device data analysis into cloud-enabled ETL workflows.
• Configuration of interactive BI dashboards for device usage, error diagnostics, and maintenance planning.
• Implementation of a self-service data platform supporting pre-aggregation, fast reporting, and integration with existing enterprise systems.
• Development of specialized modules such as pattern detection engines to reduce device diagnosis time and improve resource resource allocation.
• Enhanced data lakehouse platform leveraging open, cloud-native data processing tools with optimized duplicate removal and streaming capabilities.

• Integration with device data sources and management systems for continuous data collection.
• Connectivity to enterprise BI tools and reporting platforms for analytics delivery.
• Messaging and event-based systems for real-time data updates and alerts.

• System scalability to accommodate increasing numbers of connected devices and data volume.
• Near real-time data processing with minimal latency to enable timely insights.
• High availability and reliability to ensure continuous monitoring and analytics.
• Data security and compliance measures aligned with healthcare data standards.
• Flexible architecture supporting iterative development and agile deployment cycles.

The migration and platform development are expected to significantly enhance the platform's scalability and processing speed, enabling near real-time analytics that improve device issue detection and maintenance response times. Overall, the client aims to reduce device downtime, optimize maintenance costs, and support growth by providing robust, flexible data solutions that fuel data-driven decision making.