Problem
Laboratory analyzers produced HL7 messages, but downstream systems needed structured JSON to ingest data reliably. Onboarding new devices was slow and error-prone due to differences in device formats and configuration steps.
Constraints
- Service must be stateless to scale and recover cleanly on Cloud Run.
- Traffic reliability matters (medical data workflows).
- Messages needed validation and predictable JSON output.
Solution
I built a proxy service using Fastify that accepts HL7 input, parses and validates it, transforms it into a consistent JSON schema, and exposes endpoints for downstream ingestion. The service was packaged as a Docker container and deployed to GCP Cloud Run.
Architecture
Key decisions
- Stateless design: no local file storage and no in-memory “state” required for correct processing, so Cloud Run can scale instances safely.
- Containerized deployment: consistent runtime + easy rollbacks with image versions.
- Validation layer: reject malformed messages early to protect downstream systems.
- Observability mindset: structured logs to troubleshoot parsing and integration issues.
Impact
- Reduced new equipment onboarding time by ~50%.
- Improved operational reliability by standardizing output format and handling edge cases consistently.
What I learned
- Designing for stateless compute (Cloud Run) and scaling characteristics.
- Building robust parsers + defensive validation for “messy” real-world inputs.
- Deploying containerized services with predictable releases and rollbacks.