Motivation

Internet of things (IoT) medical devices transmit data automatically into the cloud for remote monitoring and data collection. Data captured can be used to perform realtime data analytics to monitor patient health by tracking patient disease and therapies, as well as device health by monitoring the performance of the devices, tracking device fault/alarm trends and even root cause analyses using the data collected. The data can also be used to develop AI models to make predictions on patient outcome, identify optimum parameters and even make recommendations for personalized treatments.

Solution

Internet connectivity is provided to the non-networked medical device via a network enabled gateway tablet and data from the device is transmitted by Azure IoT Hub, ensuring data security and protection with encryption, certificates and unique connection strings.

Microservices deployed in the cloud back-end for processing of device data, longitudinal patient reported outcomes and data analytics and orchestrated by Azure Kubernetes Service. These microservices can automatically scale up and down based on load. Future microservices, such as AI-based clinical decision support developed using the data collected, can also be deployed separately by taking advantage of the modular architecture of microservices.

The IoT captured data from the field is integrated, analyzed, organized and served by Azure Synapse Analytics for real-time analytics, dashboarding and reporting, and AI.

As an example, a dashboard populated with made-up IoT data serves to demonstrate the real-time status monitoring of over 500 medical devices installed in hospitals around the United States. The real-time information aids in ensuring that each deployed device is operating optimally, reliability and remote support provided when needed.

Furthermore, role-based acess control and dynamic masking can be implemented to ensure patient privacy and comply with privacy laws.