Smart Diabetes Tracker
IoT-Based App For People With Diabetes
US-based forerunner in Healthtech device monitoring.
Request
Result
Technologies
- Swift
- Kotlin
- MQTT.js
- AWS
- Firebase
- AWS IoT Core
- OpenTelemetry
Result
Results
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Uninterrupted data transmission
FDA-compliant IoT devices are equipped with failover mechanisms using Bluetooth and WLAN. They ensure continuous automatic data transfer even in case of network disruptions, thus reducing the risk of errors.
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Faster onboarding with a simplified interface
The app's user-friendly interface helped users adapt to connected medical wearable devices faster, reducing onboarding time by 30%. This allowed users to start managing their condition efficiently right away.
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Scalable system architecture
The robust system architecture supports up to 1,000 simultaneous device connections without experiencing latency issues. App scalability ensures the room for the future growth and increasing user demands, maintaining performance under high loads.
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Strengthened market position
By seamlessly integrating the app with familiar devices, the solution demonstrated a 7-point NPS growth and reduced user churn by 15%, solidifying the client's position as a leader in a highly competitive healthtech market.
Challenge
A major manufacturer of medical devices needed to supplement its product line for people living with diabetes (PWDs) which included continuous glucose monitors, insulin pumps, and smart glucose meters. The need to focus on this direction arose from two key factors.
The first is the incidence of the disease. According to the CDC, over 38 million people have diabetes. 1 in 5 don't know they have it which opens up a large field for diagnostic and research work.
Secondly, the medical wearable device manufacturing market is a highly competitive environment where innovative solutions are needed.
To stay competitive and meet the growing demand for comprehensive diabetes management, the customer needed an app to complement their medical devices. This app would offer users a seamless, integrated solution for monitoring, tracking, and managing their condition more effectively.
Solution
Regulatory compliance and security
The app protects sensitive medical data by meeting FDA, HIPAA, and GDPR standards. It uses secure multi-factor authentication, token-based access, and end-to-end encryption (TLS) for all data transmissions. Regular software and device firmware updates, penetration testing, and vulnerability assessments ensure robust security and compliance.
Automated alerts and maintenance
The app provides real-time notifications to users and their families (provided they have access) about critical health events, such as blood sugar spikes, and alerts them about device issues or required maintenance.
Seamless device integration
The app seamlessly pairs with various IoT devices, including glucose meters, insulin pumps, and fitness trackers, creating a cohesive and user-friendly ecosystem. It ensures reliable real-time communication between devices, the cloud, and users, using MQTT for fast data exchange, failover mechanisms for uninterrupted connectivity, and edge computing for local processing.
ML-driven personalization
The app leverages ML algorithms to analyze user data and deliver tailored recommendations for diet, exercise, and medication adjustments, supporting users in achieving their health goals. Its ML capabilities also provide proactive alerts and predictive insights, enhancing overall health management.
Comprehensive API design
The API served as a critical interface for communication between wearable devices, the app, and the cloud system. It facilitated secure data transfer, synchronized real-time metrics, and ensured compatibility with continuous glucose monitors, smart insulin pens, and blood pressure monitors within the ecosystem.
Cloud synchronization
The app uses a secure, scalable cloud platform to synchronize data automatically, enabling real-time access to health information across devices. Its robust architecture supports high data volumes, ensuring the app can handle high volumes of data while maintaining low latency.
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