Generative AI-Powered Copilot for Max Healthcare

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. 

About Max Healthcare

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.  

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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.  

1.

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

4.

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

7.

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

2.

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

5.

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

3.

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. 

6.

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. 
  • 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. 
  • 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. 
Generative AI-Powered Copilot for Max Healthcare
Outcomes

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Enhanced Clinical Insights – Faster and more accurate analysis of patient data improved diagnosis and treatment planning. 

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Operational Efficiency – Reduced manual effort and streamlined access to critical patient information, increasing productivity. 

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Improved Decision-Making – Real-time insights empowered clinicians with data-driven decision support, enhancing patient care.