ModelRefs / EHR Data Extraction — Architecture Blueprint

EHR Data Extraction — Architecture Blueprint

Production architecture blueprint for EHR Data Extraction: components, deployment patterns, cost & latency, failure modes, evaluation and governance, with sources and review dates.

Overview

This is the implementation view of EHR Data Extraction: the components it requires, where it can run, what it costs in latency and spend, how it fails, and what you must measure before putting it in front of users.

5 components to assemble, 6 documented failure modes, high implementation complexity. Every statement below comes from the canonical workflow record with its sources and review date; where the evidence does not settle a question, the page says so rather than filling the gap.

What this workflow takes in and produces

Takes in

  • authorized clinical notes
  • document and encounter metadata
  • approved field schema
  • terminology and interoperability mappings
  • access and consent context

Produces

  • candidate structured fields
  • note-span provenance
  • missing and ambiguous field flags
  • validation queues

Applied to

  • clinical-note field extraction support
  • source-linked registry preparation
  • human-validated data normalization

Components you need to assemble

A working implementation needs 5 distinct components. Each is a build-or-buy decision in its own right.

  • secure clinical document access
  • OCR where required
  • schema and terminology validation
  • FHIR-compatible mapping
  • qualified validation workflow

Implementation complexity: high. This describes the integration and evaluation effort, not the difficulty of any single component.

Deployment patterns

Deployment options recorded for this workflow: managed-api, hybrid.

Topologies it has been recorded against: serverless-api, managed-container, hybrid-private-cloud. Each changes the data-residency, scaling and cost profile, so confirm the one you need against current provider documentation.

Cost and latency

  • Long notes, OCR, terminology normalization, provenance storage, validation, and correction dominate cost.
  • Measure cost per validated field and downstream correction risk rather than documents per minute.

How this workflow fails

Observed failure modes for this class of workflow. Design a check for each one before shipping, not after.

  • wrong field or value
  • negation or temporality error
  • missing or ambiguous fact
  • lost note provenance
  • schema or terminology mismatch
  • PHI exposure

Risk areas the evidence covers

  • field precision and recall
  • negation and temporality
  • schema adherence
  • source traceability
  • missing-data handling
  • privacy and security

Proving it works before you ship

Evaluation readiness: Partial — Field accuracy, schema, provenance, missingness, ambiguity, note-type, and reviewer measures are defined; deployment-specific notes, fields, and thresholds remain required.

Worked evaluation case: Human-validated clinical-note extraction

Extract approved candidate fields from authorized notes with note-span provenance, explicit unknowns, and qualified validation before downstream use.

What to measure

  • field-level precision and recall
  • negation, temporality, and unit accuracy
  • schema and terminology adherence
  • source-span and document provenance
  • validator correction, missingness, and review time

Governance and data handling

  • Apply approved minimum-necessary access, role, purpose, retention, security, audit, and disclosure controls for clinical data.
  • Keep extracted fields provisional until qualified validation; no clinical interpretation or chart write-back should occur by default.

Implementation notes

  • Store document, note, section, span, author, encounter, timestamp, transformation, model, prompt, validator, and correction provenance for every candidate field.
  • Evaluate note types, templates, abbreviations, copy-forward, negation, temporality, OCR, missingness, ambiguity, and cross-document conflict separately.

What this blueprint does not establish

  • Interoperability and provenance standards do not establish extraction accuracy, clinical meaning, or fitness for a downstream decision.
  • This workflow does not autonomously interpret clinical facts, update the legal health record, diagnose, or recommend treatment.

Source coverage: Partial — HL7 FHIR defines interoperable resources and provenance foundations; HHS defines safeguards for electronic protected health information. Neither validates extraction accuracy or authorizes chart use.

Sources reviewed 2026-07-02. Revalidate note distributions, schemas, FHIR profiles, terminologies, PHI controls, model behavior, and validator policy after every material change.

Sources

Candidate models and benchmarks

Candidate models with published references, the providers behind them, and the benchmarks whose task shape bears on this workflow are on the EHR Data Extraction workflow reference. This blueprint covers implementation; that page covers selection.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to EHR Data Extraction — Architecture Blueprint.