Knowledge Graph Ecosystem

 

Knowledge Graph Ecosystem connecting disparate healthcare data sources and stakeholders, centered around the Equitus KGNN (Knowledge Graph Neural Network) for intelligent data integration and analysis.

__________________________________________________________________________

Component/Stakeholder	Data Sources/Storage	Data Integration/Processing	Centerpiece/Knowledge Graph	Interconnections & Output
Mobile Nurse/Doctor 🧑‍⚕️	Mobile Device Data (EHR/EMR excerpts, real-time vitals, location data)	AWS (IoT, Lambda for event processing)	Equitus KGNN (Central Hub)	Real-time patient insights, treatment plans, immediate billing data capture.
Hospital/Home 🏥/🏠	Clinical Systems ($\mathbf{SQL}$ Databases), PACS (Medical Images), Patient Monitoring Systems	Databricks (Spark for large-scale data transformation and analytics)	Equitus KGNN (Central Hub)	Holistic patient view, resource allocation, optimized care pathways.
Billing Programs 🧾	Claims Data, Payer Information, Procedure Codes, $\mathbf{SQL}$ Databases	Snowflake (Cloud Data Warehouse for structured/semi-structured data), AWS (S3 for secure storage)	Equitus KGNN (Central Hub)	Accurate billing, fraud detection, compliance reporting.
Main Company 🏢	$\mathbf{SQL}$ Databases (Legacy data), HR, Finance, Research Data	$\mathbf{AWS}$ Data Scalers (Kinesis, DynamoDB for high-throughput/low-latency)	Equitus KGNN (Central Hub)	Operational dashboards, strategic planning, cross-functional reporting.

---

System Flow and Roles

This ecosystem is designed to pool information from operational systems into a central, structured knowledge representation:

1. Data Ingestion: Data from SQL databases, mobile apps, and other systems is fed into the cloud environment (AWS, Snowflake).

2. Data Transformation: Databricks handles complex cleaning, transformation, and preparation of raw data, often converting it into a format suitable for graph modeling.

3. Knowledge Graph Creation: The pre-processed data flows into the Equitus KGNN at the center. The KGNN builds and continuously updates the Knowledge Graph, establishing relationships (edges) between entities (nodes) like Patient, Treatment, Doctor, Insurance Claim, and Location.

4. Security, Compliance, and Auditing: This is an overlay function integrated across all components, particularly AWS and Snowflake, and monitored by the Equitus KGNN.

   • Security: AWS security features (IAM, VPC) and Snowflake data encryption ensure data protection.

   • Compliance: The KGNN can model regulatory rules (e.g., HIPAA) as part of the graph, allowing it to automatically audit data access and usage against those rules.

   • Auditing: All data flows and access requests are logged and analyzed by the AWS and Snowflake services, providing a clear trail for compliance checks.

---

Key Technology Roles

• Equitus KGNN (Center): The core intelligence. It uses graph-based AI to derive complex insights, predict outcomes (e.g., patient deterioration, billing errors), and provide context-aware information to all stakeholders.

• AWS Data Scalers: Provide the necessary infrastructure for scaling data ingestion (Kinesis), storage (S3), and real-time processing (Lambda, DynamoDB).

• Snowflake: Acts as a scalable, secure Cloud Data Warehouse, ideal for consolidating structured and semi-structured data from various sources before it is modeled into the graph.

• Databricks: Used for high-performance ETL (Extract, Transform, Load) and advanced analytics on large datasets, preparing them for the Equitus KGNN.

• SQL Databases: Represent the source-of-truth for many legacy and operational systems (e.g., patient EHR/EMR systems, basic billing ledgers).