Analyzing FHIR Data Using ML and Analytical Tools
summary
Explains why FHIR needs flattening for analytics and lists concrete pitfalls: deeply nested arrays, choice types, modifier extensions that change meaning, recursion, invariants and varied reference formats, all of which break naive joins. Shows two solution patterns: self‑service dashboards using Power BI connector to Azure API for FHIR, and cohort exports using group/$export with anonymization that write NDJSON to Azure Storage. Exports feed Azure Synapse, Azure ML or Databricks and an open source FHIR Analytics Pipelines generates ADF ETL to CDM and Synapse.
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chapters (8)
- 0:00Introduction
- 1:00FHIR Data
- 4:16SelfService Analytics
- 6:04CaseBased Analytics
- 9:05File Analytics Pipeline
- 9:55Summary
- 10:20Resources
- 10:40Contact Details
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