Every FHIR server ships as "R4-conformant" and "US Core"-ready. Production distinguishes the ones that ship from the ones that struggle across seven concrete capabilities. 1. Batch/transaction Bundle handling. FHIR Bundle transactions require atomic all-or-nothing writes with placeholder URN resolution. Servers that quietly downgrade to per-resource writes break downstream […]
7 Practical FHIR Data Uses That Ship in 2026 Health IT
FHIR data flowing through EHRs supports a lot more than the aspirational "unified patient record" narrative suggests. Seven specific use cases show up in production 2026 deployments. 1. Population health analytics via bulk export. Bulk Data IG $export feeds Spark/dbt/Databricks pipelines for quality reporting and risk stratification. This […]
Workflow Engines for FHIR: When SubscriptionTopic Isn't Enough
FHIR Subscription delivers events on resource changes; a workflow engine composes those events into multi-step processes. The two are complementary, not overlapping, and picking the right primitive matters for maintainability. When Subscription alone is enough. - Single-step reaction: new Observation → run one function → done. - Notification-only […]
FHIR API Adoption: 7 Design Choices That Prevent Rework
Standing up a FHIR API sounds like picking a server and turning on endpoints. Seven design choices made upfront prevent 6-12 months of downstream rework. 1. Version strategy: R4 with R5 preparation. US Core, CMS-0057, and most Da Vinci IGs reference R4. Ship R4 endpoints, but structure the […]