Business Problem

• The client, being an independent Healthcare Services provider, onboards patients onto long term Medical Health plans, managing their wellbeing across life stages. Analysing patients historical data, for patient profiling, building personalized health plans and diagnosis for early treatment, was a time consuming activity as it required deep analysis of long-term patient data, which included structured Electronic Health Records (EHR), unstructured doctor notes, voice transcriptions, and medical imaging. As it was labour-intensive & error-prone, it often led to incorrect diagnoses or ineffective treatment plans.
• 
  Patient onboarding times were as high as 3 – 4 months, which was a major roadblock towards scaling their services & also resulted in revenue loss, due to ltd no of patients being treated.

About Atria

A $250 M, NY based,  independent Healthcare Services provider, Atria is a budding leader in healthcare Services, with an extensive network of expert doctors and medical officers serving thousands of patients globally.

Solution

The solution integrates an AI-powered multi-agent framework that efficiently processes decades of patient history to assist healthcare providers in real-time. This framework acts as a virtual medical assistant, enhancing decision-making by automating complex data analysis.

Key components of our approach include:

Automated Patient Profiling

Processes 20–30 years of patient history within seconds, creating comprehensive patient summaries.

Predictive Trend Analysis

Identifies patterns, disease progression, and potential risk factors based on historical data.

AI-Assisted Diagnosis & Treatment Recommendations

Leverages advanced machine learning models to achieve a 99% accuracy rate in diagnoses and generate personalized treatment plans.

Architecture

• Authentication & Security:
• Okta: Secure user authentication and access control.
• Cloud Security & SNS: Ensures compliance with healthcare data security regulations and real-time alerts.
• Data Processing & Storage:
• AWS Lambda: Enables serverless, real-time data processing.
• Snowflake: Acts as a unified repository for structured and unstructured healthcare data.
• NebulaGraph: Constructs a knowledge graph to uncover hidden correlations between medical events.
• Weaviate: Facilitates fast semantic search for relevant patient data retrieval.
• AI-Powered Insights:
• AWS Bedrock: Utilizes Retrieval-Augmented Generation (RAG) with generative AI models (Claude v3, Llama 2) for accurate medical insights.
• Logic Layers: Implements intelligent business logic to support auditing and decision workflows.
• Deployment & Integration:
• FastAPI & Docker: Ensure a scalable and flexible system architecture.
• API Gateway: Manages API requests and optimizes data flow.
• Google Docs Integration: Streamlines report generation and sharing for seamless physician collaboration.