ModelRefs / EHR Data Extraction — Canonical Workflow

EHR Data Extraction — Canonical Workflow

EHR Data Extraction: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.

Overview

Prepare schema-validated candidate fields from authorized clinical notes with note-span provenance, ambiguity flags, and qualified validation before any chart or downstream clinical use. Healthcare deployment requires organization- and jurisdiction-specific review of privacy, security, access, retention, and applicable regulatory obligations. Outputs remain provisional and require qualified human review; no clinical autonomy or compliance claim is implied. Validate PHI flows, source traceability, model and prompt versions, reviewer actions, rollback, and incident handling before clinical use.

Implementation profile

Categoryllms
Implementation maturityenterprise
Evidence statuspartial
Primary use casesextraction
Deployment optionsmanaged-api, hybrid
Architecturesserverless-api, managed-container, hybrid-private-cloud

Candidate models with published references

Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.

Benchmarks relevant to this workflow

miracl, mkqa, mldr, swe-bench, aider-polyglot, gpqa, aime-2025, tau-bench, browsecomp-long-context, longfact-concepts, terminal-bench, mmmu, mmlu-pro, livecodebench.

Relevance is a coverage signal from the canonical registry. Each benchmark only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.

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 — Canonical Workflow.