DeepMind

The Challenge: Addressing Key Difficulties in Hypertension Management

Effective management of hypertension at scale is complicated by several persistent factors. Patients often receive generic treatment plans that do not adapt to their individual biometrics or behaviors. This can lead to insufficient monitoring, where real-time data on vitals and lifestyle is not used to inform care decisions, and low adherence, as patients may lose motivation. Furthermore, relevant health information is often siloed across different fitness apps, wellness trackers, and medical devices. The objective was to create a unified system to address these issues and test whether a personalized, AI-guided approach could produce a measurable decrease in hypertension.

The Solution: A Unified Digital Platform for Patient Care

To support the clinical trial, Triveous developed a comprehensive digital platform consisting of a patient-facing mobile app and a robust backend. While the core machine learning models were provided by the Google DeepMind team, our role was to build the system that would deploy these models in a reliable and engaging patient experience.

• Triveous's Contribution: The Technical Foundation
  
  
  • Mobile & Backend Design and Development: We built the Android application and the scalable backend infrastructure from concept to deployment. The platform was designed to ensure data integrity and high availability, which are critical requirements for a clinical study.
  • Multi-Source Data Integration: We integrated health data from a variety of sources, including Google Fit, Headspace, HealthifyMe, Garmin devices, and Omron BP monitors, to create a holistic view of each patient's health profile.
  • AI Workflow and Alert Logic: Working closely with DeepMind’s researchers, we co-developed the workflows and alert logic. Our backend processed and synced mobile data to trigger personalized notifications, reminders, and health recommendations based on the AI models' outputs.
  • Patient Engagement and Adherence: The front-end was designed with the patient experience as a priority. In partnership with designers and behavioral psychologists, we developed a dynamic task engine with reminders and gamified elements to encourage users to log their vitals, track their progress, and stay active in their care plan.

A Multidisciplinary Collaboration

This project was built on a foundation of close collaboration. The Triveous engineering team worked directly with respected cardiac doctors, clinical researchers, behavioral psychologists, and Google DeepMind's machine learning engineers. This multidisciplinary approach ensured the final product was technically sound, clinically relevant, and user-focused.

Impact: Supporting a Data-Driven Clinical Trial

The platform Triveous built provided the essential digital infrastructure for this clinical trial, enabling the study to collect data and measure its primary outcomes.

• Improved Patient Engagement: By providing relevant, real-time nudges and a clear way to track habits, the application was designed to improve patient adherence to their care plans.
• High-Quality, Research-Ready Data: The system delivered structured and continuous patient data, creating a valuable dataset for hypertension research and for validating the study's findings.
• Demonstrating the Value of AI in Healthcare: The project served as a practical example of how AI, supported by solid engineering, can be applied to chronic condition management.

By connecting advanced research with the real-world patient experience, Triveous helped build a tool that contributes to the growing body of work on improving chronic care through digital health solutions.

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