Bridging HL7 to FHIR: Why Mirth Connect and FastAPI are Better than Pure Python
Why I Put Mirth Connect in Front of FastAPI Instead of Parsing HL7 in Python
Summary
The author details the architecture for converting legacy HL7 v2.5 messages into modern FHIR R4 resources. Initially attempting a pure Python solution, the process encountered significant challenges related to MLLP handling, complex HL7 parsing, and message routing, leading to the adoption of a two-service structure using Mirth Connect and FastAPI.
Details
This article discusses the architectural challenges and solutions for building a pipeline that converts legacy hospital data (such as HL7 v2.5 messages: ADT, ORM, ORU) into FHIR R4 resources. The author initially attempted to handle everything in Python alone, but faced three major issues. First, MLLP (Minimum Lower Layer Protocol), used by hospital systems over TCP sockets, requires custom connection management and ACK/NACK handling, unlike standard HTTP. Second, HL7 parsing is complex; simple string splitting fails due to component separators, repeating fields, and encoding characters. Third, message routing based on type must be built from scratch. To solve these issues, the pipeline adopts a two-layer structure: Mirth Connect handles the legacy protocol layer (MLLP reception, HL7 parsing, field extraction), while FastAPI/Python manages the FHIR transformation and validation layer (JSON conversion, R4 mapping). This separation of concerns proves effective for bridging legacy hospital systems with modern healthcare APIs.
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