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How to scale your FHIR backend performance by 3X
Healthcare runs on data - but legacy FHIR backends buckle under scale. Here's how 4medica, a leader in healthcare data management, modernized its infrastructure with Aidbox FHIR server, slashing resource usage while unlocking real-time analytics and seamless STU3 → R4 migration. The Stack: Aidbox FHIR CDR (replacing a legacy backend) Google Cloud + Kubernetes (scalable deployment) SQL on FHIR (for analytics without ETL hell) Custom migration scripts (STU3 → R4 in 4 weeks!) Results: 3X better performance (lower CPU/memory usage) 50% infra cost reduction (12GB RAM vs. 32GB previously) Billion-record readiness (scalable for analytics & real-time apps) Key Takeaways for Devs: FHIR migrations are painful—but worth it. 4medica’s custom bulk export script saved months of work. Aidbox’s batch import made R4 migration frictionless. SQL > REST for analytics. Native SQL on FHIR let them query data without middleware. Cloud-native FHIR pays off. K8s + Aidbox = auto-scaling for surges in clinical data.
Healthcare runs on data - but legacy FHIR backends buckle under scale. Here's how 4medica, a leader in healthcare data management, modernized its infrastructure with Aidbox FHIR server, slashing resource usage while unlocking real-time analytics and seamless STU3 → R4 migration.
The Stack:
- Aidbox FHIR CDR (replacing a legacy backend)
- Google Cloud + Kubernetes (scalable deployment)
- SQL on FHIR (for analytics without ETL hell)
- Custom migration scripts (STU3 → R4 in 4 weeks!)
Results:
- 3X better performance (lower CPU/memory usage)
- 50% infra cost reduction (12GB RAM vs. 32GB previously)
- Billion-record readiness (scalable for analytics & real-time apps)
Key Takeaways for Devs:
- FHIR migrations are painful—but worth it.
- 4medica’s custom bulk export script saved months of work.
- Aidbox’s batch import made R4 migration frictionless.
- SQL > REST for analytics.
- Native SQL on FHIR let them query data without middleware.
- Cloud-native FHIR pays off.
- K8s + Aidbox = auto-scaling for surges in clinical data.
