EHR de-identification and FHIR standardization demo
Watch a multi-agent system built with SUPERWISE de-identify healthcare data and convert it into FHIR bundles, in a short healthcare demo.
What's in this video
- The demo follows healthcare data through a multi-agent workflow.
- One step de-identifies the example electronic health record.
- Another converts the result into standardized FHIR bundles.
Frequently asked questions
Topics from this video and why governed AI matters.
- How does the EHR de-identification system work?
- A schema-identifying agent scans structure and field names (no values) to flag PII and suggest tweaks like stripping dates to years. A standardization agent takes clean data and converts it into valid FHIR bundles. The process is automated, safe, and auditable.
- Why use two agents for EHR de-identification?
- One agent handles schema compliance and PII mapping; the other handles transformation. Separating them keeps de-identification and standardization auditable and makes it easier to enforce HIPAA safe harbor without losing clinical value.
- What does HIPAA safe harbor de-identification require?
- HIPAA Safe Harbor requires removing or generalizing 18 identifier categories, including names, geographic data smaller than state, dates (except year), phone, email, SSN, medical record numbers, and similar. The SUPERWISE workflow enforces that at the field level and produces interoperable FHIR bundles ready for analytics or AI pipelines.
- Why is governed AI essential for EHR and FHIR pipelines?
- EHR data is messy and full of PHI. Automating de-identification and FHIR conversion with guardrails and auditability ensures you stay compliant and that downstream systems only see safe, standardized data.
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