EHR Data Extraction
The patient's story, scattered across systems and scans — extracted into data that care and research can use.
EHR data extraction is the structuring of clinical information from electronic health records — a task that exists because "electronic" never meant "structured." Inside every EHR lives a mix: coded fields (problems, medications, labs) alongside free-text notes where the clinical substance concentrates, plus a shadow archive of scanned documents — faxed referrals, outside records, signed consents, old paper charts — attached as images the EHR stores but cannot read. Extraction reaches all three layers: normalizing the coded data, applying clinical NLP to the narratives, and running document AI over the scanned layer that traditional health-IT integration ignores.
The technical stack is healthcare-specific at every level. Clinical language processing handles the notes — abbreviations, negation, temporality, and mapping to standard vocabularies (SNOMED, ICD-10, RxNorm, LOINC) so extracted facts interoperate across systems and feed FHIR-shaped exchanges. Document AI handles the scanned layer: classification sorting the referral from the insurance card from the outside lab report, OCR robust to fax quality, and extraction schemas per document type. Cross-source reconciliation then faces medicine's version of entity resolution: the same medication appearing in three notes at two doses, the outside record contradicting the internal problem list — with conflicts surfaced for clinical review rather than machine-resolved, because the discrepancy itself is clinically meaningful.
The applications drive different accuracy postures: care-facing extraction (surfacing the outside allergy note, completing the medication history) errs toward recall with clinician verification; billing and quality-measure abstraction demands defensible precision; research and registry population runs at corpus scale under IRB and de-identification regimes. Uniformly, PHI governance shapes the architecture — processing within covered infrastructure, minimum-necessary access, audit logging — and provenance is clinical safety: every extracted fact pointing back to its source note or scanned page, because a clinician acting on extracted data needs to be one click from the original words.
The medicine is in the narrative — mining the free-text notes where clinicians actually record what happened.
The chart read for what the registry, the measure, or the study needs — abstraction as clinical data work.
The hospital stay, condensed to a few pages — extracted into the data that drives follow-up care, coding, and claims.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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