Events with FHIR: a Simplified Way to Unify Health Data
summary
A company unifies siloed patient records by publishing every data change as event streams using schemas derived from FHIR profiles but simplified to required fields, enums, and provenance, then transforming them into full FHIR resources for indexing with Kafka into Elasticsearch. Validation uses Avro/protobuf schemas and a central registry, updates use business identifiers and ordered events, and the system accepts eventual consistency while offering domain services for read‑after‑write guarantees. Simplified schemas speed adoption and improve data quality.
AI-written summary · about
chapters (10)
- 0:00Intro
- 0:35The Problem: Silos
- 1:52Solution: Overview
- 2:35Solution: Benefits
- 5:01Solution: Drawbacks
- 6:46State with Events
- 8:26Event Schemas
- 9:58Simplified Schema: Example
- 11:17Simplified Schemas
- 12:34External Data
related talks
36mTransforming HL7 version 2 to FHIR
20mRevolutionize Health Apps: Leverage Legacy for Massive Scale
16mThe story of FHIR data exchange and storage combined
38mScaling FHIR with microservices
19mTurning data into better healthcare: data for hemophilia
20mFHIR for scientific data: implementing the FAIR principles
43mEnglish introduction to FHIR
23mThe Smart4Health project