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Development of digital health platform
Customer challenge
Enable legacy imaging devices with advanced capabilities such as AI/ML, data aggregation and analytics
Aggregate data from various sources including point-of-care medical devices
Zero impact on existing devices and systems in a hospital
Near real-time analysis and outcomes
Scope
Develop a platform for imaging devices with feature-rich capabilities
Future roadmap for the platform with product release alignments
Support data protocols
Enable platform to support different deployment models based on product needs
Support product team with queries and possible defects
Solution
A cloud native edge compute environment with GPUs to run clinical applications
Customizable, purpose-built, modular software platform based on cloud-native technology (Kubernetes)
Deployment of healthcare solutions to aggregate, correlate, analyze, and process data from connected medical devices (hardware & software)
An orchestrator to create and execute algorithm pipelines
On-prem, data center, and cloud deployment
Features
Healthcare-specific services – DICOM services, HL7
Imaging modality apps – visualization, collaboration, scanner configuration management, etc
Gateway – a data aggregator for point of devices
Algorithm orchestrator – to run AI models and algorithms
Cloud connectivity
Remote serviceability
Benefits
Reduced time for the development of healthcare applications leveraging platform capabilities
Readily available capabilities to realize functionalities without infrastructure concerns
Results
Clinical workflow
Improved imaging workflow with AI/ML algorithms in near real-time
Data aggregation for remote patient monitoring
Access to patient data from multiple sources
Digital Health Platform
Microservices-based architecture
Multiple deployment models – On-prem | data center | cloud
Scalable services to run on small to very large computing environments
Enabling third-party application development