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Development of a Scalable AI-Powered Health Assistant for Real-Time Personalized Wellness Insights

codedistrict.com

Health & Fitness

Information technology

Medical

Key Challenges Identified

Users struggled to interpret complex health reports independently, leading to reliance on external sources and fragmented experiences. Integration of wearable and video-based assessment data was inefficient, while the platform lacked scalable infrastructure to support real-time queries and secure health data processing. Ensuring access to credible, personalized health insights remained a critical need.

About the Client

A digital health platform leveraging AI and NASA-licensed technology for virtual health assessments and personalized wellness recommendations.

Project Goals and Objectives

Enable real-time, context-aware health guidance through an AI assistant
Automate integration and interpretation of multi-source health data
Establish a scalable cloud infrastructure for secure, low-latency performance
Deliver evidence-based, personalized health recommendations
Enhance user engagement and retention through interactive experiences

Core Functional Requirements

Real-time health data analysis from wearables and video assessments
Context-aware multistep conversational interface for health queries
Integration with HIPAA-compliant data storage and processing
Dynamic risk assessments for metabolic, cardiovascular, and weight management
Secure user authentication with role-based access control

Preferred Technologies

Node.js

Python

AWS Lambda

AWS Cognito

OpenAI APIs

Required Integrations

Wearable health devices (e.g., Fitbit, Apple Watch)
Video-based assessment systems
Medical knowledge databases (e.g., PubMed, CDC)

Key Non-Functional Requirements

Scalable architecture for on-demand AI response handling
Sub-200ms latency for real-time interactions
HIPAA-compliant data encryption and access controls
High-availability deployment with AWS Lambda
Multi-factor authentication for user data security

Expected Business Impact

Anticipated outcomes include a 3x increase in user engagement, 60% faster health data interpretation, 40% improvement in user retention, and 30% reduction in onboarding time. The solution will enable 2x faster health assessments and a 4x increase in personalized recommendations, driving long-term user loyalty and platform scalability.