Case Study

Proprietary Data to FHIR Service for Consumers

Transforming proprietary medical data formats into the standard HL7 FHIR format at scale, ensuring high performance and reliability for seamless healthcare data interoperability.

Industry

Time Frame

6 Months

Services We offer

FHIR Compliance

Scalable Conversion

Caching

Cost Evaluation

About the client

The client, a prominent leader in healthcare technology, specializes in delivering innovative solutions for patient care and monitoring around glucose.

Problem Statement

The client needed a scalable solution to transform proprietary medical data formats into the standard HL7 FHIR format.

The challenge was to process millions of records per minute through both single and bulk data ingestion. This required evaluating the performance, cost, and stability of the FHIR store while designing a Kubernetes-based solution with robust queue buffering and transformation capabilities.

And as a POC, store the patient resouces into OPENEMR using pub sub mechanism.

Approach

Our approach centered around designing a scalable architecture capable of handling high volumes of data efficiently. Key considerations included:

Supporting multiple FHIR resources for broader compatibility.
Implementing an API gateway to streamline client registration and expose APIs securely.
Adding a caching layer to minimize redundant processing and reduce operational costs.
Leveraging Kubernetes for auto-scaling Kafka and transformation services to meet dynamic workload demands.

The Solution

FHIR Transformation Engine: Developed a robust service to transform proprietary medical data into FHIR format, ensuring compliance with healthcare interoperability standards.
Scalable Data Ingestion: Designed a system to process millions of records per minute, handling both single and bulk data ingestion seamlessly.
API Gateway: Built an API gateway for secure client registration and data access, providing a simplified interface for interacting with the FHIR services.
Caching Layer: Introduced caching to reduce processing costs by avoiding duplication of resources.
Auto-Scaling Infrastructure: Leveraged Kubernetes for dynamic scaling of Kafka and transformation services, ensuring uninterrupted performance during peak loads.

Value Delivered

1%

Savings on GCP FHIR Store due to Caching

1%

Compatible with FHIR HL7 for Interoperability.

Get Detailed Case Study

Proprietary Data to FHIR Service for Consumers

Transform proprietary medical data into FHIR format with a scalable, secure, and efficient service. Features include dynamic scaling, API gateway, caching, and bulk data ingestion for seamless healthcare interoperability.

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