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Reduced data processing time by ~30% with integrated data flow approach into FHIR Server


Client Background

The client is one of the market leaders in independent testing and laboratory services for genomics, discovery pharmacology, forensics, advanced material sciences


  • The client receives data from multiple stakeholders like sampling centers, labs, employers in various formats, and it is challenging to manage the data
  • Loading bulk data into the FHIR server is a challenge and is a blocker for real-time analysis
  • The client faced a dilemma in choosing the best-suited FHIR server for their interoperability needs
  • Lab reports and diagnosis data needed to be handled securely, along with sample tracking


  • Implemented scalable backend solution using azure functions and azure service bus topic for translating the excel file data into FHIR format
  • Implemented auto-scale azure functions based on need and load. Azure Front Door global load balancer is implemented based on region
  • Uploaded all members of a batch in a single POST call to FHIR. Current Implementation is at the member level
  • Utilized Azure FHIR server to push the data for further consumption by downstream applications
  • Created APIs authentication framework and API gateway to manage multiple requests inflow
  • Developed a template that can help in gathering required information as per the FHIR standards
  • Utilized Fire Hose and Fire Hydrant to process all requests securely

Tools & Technologies

Azure SQL Server, Azure BLOB, Azure Functions, Azure API Management, Azure Event Grid, Azure Front Door, .NET Core, Azure FHIR, Angular

Key benefits

  • Client onboarding and data processing time reduced by close to 30% with integrated data flow approach into FHIR Server
  • New API creation and management are now easy and achievable in significantly lesser time
  • Flexible architecture design helps continuous improvement and related implementations
  • Manual intervention and operational difficulties in converting the file data is completely avoided using automated process using Azure functions
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