Business Problem

• Difficulty in accessing and analyzing clinical findings quickly.
• Limited ability to detect diagnosis trends and predict emerging health patterns.
• Inefficiencies in tracking patient longitudinal journeys across multiple clinical interactions.
• Manual effort required for data analysis, impacting timely decision-making.
• Lack of an intuitive, user-friendly interface for healthcare professionals to query data.

Overview

Max Healthcare, a leader in quality healthcare services, is committed to leveraging technology for enhanced patient care and operational efficiency. As part of their digital transformation journey, they partnered with goML to develop a Generative AI-powered Copilot. This AI-driven solution enables healthcare professionals to intuitively access clinical findings, analyze diagnosis trends, and track longitudinal patient journeys, ensuring informed decision-making and improved patient outcomes.

Solution

goML proposed and developed a secure, scalable, and AI-powered platform tailored to Max Healthcare’s needs. The solution seamlessly integrates with Max Healthcare’s AWS infrastructure, ensuring efficient data ingestion, processing, and compliance with healthcare regulations.

Platform building
The platform is built using Python for the backend, .  

Search Clinical Findings
AI-driven querying of clinical records for specific metrics and contextual insights, including Cohort clinical analysis and Medication Impact analysis.

Intuitive Querying with Streamlit
A user-friendly interface allowing healthcare professionals to retrieve and visualize data seamlessly.  

LLM Used
This solution is powered by Claude 3.5 on AWS Bedrock for AI-driven querying and analysis.

Analyze Diagnosis Trends
Identification of diagnosis patterns across demographics and timeframes, offering data-driven recommendations.

Serving
AWS Athena serves as the database for efficient storage and retrieval of clinical records, while Streamlit provides a lightweight and intuitive UI for natural language-based querying and visualization.

Patient Longitudinal Journey Tracking
Mapping of clinical events, vitals, and diagnoses over time to enable proactive healthcare interventions.

Architecture

• Data Processing Layer
      • Data Source
           ⚬ Patient Data ingestion from external sources.
      • Processing Pipeline
           ⚬ Ingestion & Preprocessing of data.
           ⚬ Feature Computation.
           ⚬ Data Quality & Validation.
           ⚬ Reference Framework.
           ⚬ Anonymization & Masking.
           ⚬ Data stored in Data Lake.
      • Data Storage
  Warehouse storage in Amazon S3.

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• Intel Layer
      • Data Processing & Transformation
           ⚬ ETL processing.
           ⚬ Data stored in structured format.
      • AI/ML Model Processing
           ⚬ Model execution using multiple ML instances.
           ⚬ Integration with GoML Fine-tuned AI/ML models.
      • Data Security Layer
           ⚬ Data Anonymization & Protection.
           ⚬ Secure access control.
      • Governance & Compliance
           ⚬ Data security policies.
           ⚬ Compliance monitoring.

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• Consumption Layer
      • Data Retrieval & Query Processing
           ⚬ EC2 for query execution.
           ⚬ Query responses processed and sent to users.
      • User Interaction
           ⚬ End-user query handling.
           ⚬ Response visualization.