Remote Diabetes Management with Azure IOT for FHIR


Harness real-time device data and longitudinal health records to triage patient risk for diabetes

According to the CDC, over 100 million adults are living with diabetes or pre-diabetes in the United States. Given the complex approach required to help lower A1C levels and prevent or reduce diabetes complications, managing this group has posed healthcare professionals with immense challenges. KenSci’s solution provides out of box AI capabilities to risk stratify high risk population and predict time to:

  1. Pre diabetes to diabetes progression
  2. Uncontrolled Diabetes
  3. Diabetes related complication like Chronic Kidney Diseases
  4. Higher utilization and hospitalization

The KenSci AI driven Remote Diabetes Management solution, powered by Azure IoT Connector for FHIR helps care teams remotely manage their at-risk diabetes population using real-time device data  from continuous glucose monitors, activity tracking devices combined with longitudinal health record information.  KenSci’s machine learning driven risk stratification and recommendation system automates the complexity of prioritizing at risk patients and surfaces the right intervention to fill gaps in Diabetes care in real time. 

The out of box solution integrates seamlessly with existing care Management tools, patient engagement tools and acute care systems in hospitals and healthcare ecosystems and can be utilized by device manufacturers as they design new products.

The deployment of this solution involves 5 key stages:

  1. Reference Architecture - Standardized infrastructure for enterprise Data and Cloud workloads with FHIR service
  2. Data on-boarded on Azure - Seamless data onboarding and ingestion pipeline to bring in data at scale from devices to FHIR service 
  3. Data transformation - Map device data with patients and prepare for analytics and Machine Learning
  4. Analytics Dashboard - Embedded analytics in KenSci's analytics portal, uncovering key insights to aid in patient monitoring
  5. ML-based insights - Extend to include predictive risk stratification models for patient population leveraging data collected by devices, EMR, Claims, labs etc.