openmed

Maps laboratory and clinical observation names extracted by OpenMed to LOINC codes using the public Regenstrief LOINC and FHIR terminology APIs. Use when the user wants to code lab tests, vital signs, or observations to LOINC, resolve a test name plus specimen and method to the correct LOINC part-model code, attach UCUM units, or build a US Core Laboratory Result Observation. Trigger keywords: LOINC, lab coding, observation code, UCUM units, specimen, method, US Core lab, FHIR Observation, lab result mapping, panel vs analyte. Pairs after OpenMed NER: consume Disease/Chemical/lab-name entities from openmed.analyze_text and map each measurement to a LOINC code. LOINC is free to use under the Regenstrief license (registration/terms-of-use, no fee); UMLS/SNOMED stay user-supplied and out-of-process.

Load OpenMed clinical/biomedical NER models from the Hugging Face Hub or a local path and reuse them efficiently across calls. Use when the user wants to load an OpenMed model, control the model cache, run fully offline after a one-time download, reuse a ModelLoader to avoid reloading, set a cache_dir or device, or pick between a registry key, a full Hugging Face id, and a local directory. Pairs with choosing-openmed-models (pick the model) and extracting-clinical-entities (run it).

Links entities extracted by OpenMed to UMLS Metathesaurus CUIs using the USER'S OWN UTS API key, with nothing from the Metathesaurus bundled or cached. Use when the user wants to normalize concepts across vocabularies to a single CUI, resolve synonyms via the UMLS, filter by semantic type, or cross-walk between SNOMED CT, ICD-10, RxNorm and MeSH through their shared CUI. Trigger keywords: UMLS, CUI, Metathesaurus, UTS API key, semantic type, TUI, MetaMap, QuickUMLS, concept normalization, cross-vocabulary. Pairs after OpenMed NER: consume Disease/Pharmaceutical/Chemical/Anatomy entities from openmed.analyze_text and resolve each span to a CUI out-of-process. UMLS is license-restricted — the Metathesaurus is NEVER bundled; every call uses the user's UTS account.

Turn scanned faxes, images, and CSV/CDA exports into clean text ready for OpenMed de-identification and NER, fully on-device. Use when the user has clinical documents (image scans, photographed/faxed notes, tabular CSV/TSV exports, C-CDA XML) and needs OCR or structured intake before openmed.deidentify and openmed.analyze_text, asks about openmed.multimodal, OCR engines (Tesseract / PaddleOCR), tabular redaction, or layout and reading order. Covers the verified ocr() and redact_document() entry points and the ExtractedDocument contract. Pairs before deidentifying-clinical-text and extracting-clinical-entities.

Replace detected PHI with realistic, type-matched fake values in OpenMed so clinical notes stay readable and parseable instead of full of [REDACTED] markers. Use when the user wants surrogate names, MRNs, addresses, or dates rather than opaque masks, needs consistent fake identities across a document, must keep notes natural for downstream NLP, or wants to register a custom surrogate generator or provider. Covers deidentify(method="replace", consistent=True, seed=..., locale=...), register_label_generator, register_clinical_provider, and Anonymizer/AnonymizerConfig. Pairs with OpenMed deidentifying-clinical-text and configuring-privacy-policies.

Generates synthetic but realistic patient records (FHIR R4 bundles, C-CDA documents, CSV) with MITRE Synthea for development, CI fixtures, demos, and leakage-gate test sets — zero real PHI. Use when you need safe, shareable test data for an OpenMed pipeline, reproducible fixtures for tests, or a held-out set for de-identification leakage gates, instead of touching real clinical data. Synthea output feeds the FHIR/C-CDA ingestion skills and openmed.eval. Trigger keywords: Synthea, synthetic data, fake patients, test fixtures, demo data, FHIR bundle generator, synthetic EHR, no PHI.

Add a CI gate that fails the build when an OpenMed de-identification model's recall on a held-out PHI set drops below threshold or any critical identifier leaks. Use when the user wants a pytest test or CLI step that exits nonzero on de-id regression, wants to wire OpenMed's leakage-first release gates into GitHub Actions / CI, needs a recall floor plus zero-leakage assertion against a synthetic held-out set, or wants to block merges that weaken de-identification. Trigger on "CI gate", "fail the build", "regression test", "de-id recall threshold", "block the merge", "exit nonzero", or "leakage check in CI" for OpenMed.

Fetches and pages FHIR R4 resources (Patient, DocumentReference, DiagnosticReport, Observation, Condition) from a FHIR REST server, decodes base64 attachments, and extracts clinical narrative for OpenMed. Use before OpenMed processing when pulling charts from an EHR FHIR API (Epic, Cerner/Oracle, HAPI, or any US Core server) and you need the note text de-identified and analyzed, then results rejoined by patient. Hand narrative to openmed.deidentify and openmed.analyze_text; openmed.interop.fhir_operations implements a $de-identify operation over Bundles. Trigger keywords: FHIR, R4, US Core, DocumentReference, DiagnosticReport, Bundle, _revinclude, presentedForm, base64, EHR API.

Extracts social determinants of health (SDOH) — housing instability, food insecurity, unemployment, transportation barriers, social isolation, financial strain — from clinical narrative and maps the spans to ICD-10-CM Z-codes (Z55–Z65). Use after running OpenMed NER when the user wants SDOH surfacing, Z-code suggestion, health-equity analytics, or to recover SDOH that is documented in free text but not coded. Pairs with OpenMed analyze_text output. Standards: ICD-10-CM Z55–Z65, Gravity Project value sets, n2c2 2022 SDOH track. Trigger keywords: SDOH, social determinants, Z-codes, housing, food insecurity, health equity, Gravity Project.

Detect PHI/PII spans in clinical text with OpenMed's extract_pii without altering the text. Use when the user wants to find names, dates, MRNs, phone numbers, addresses, SSNs, or other identifiers and get their offsets and labels (not redact them), inspect what would be removed before de-identifying, route spans to a custom redactor, normalize labels to a canonical taxonomy, or filter by confidence and language. Covers extract_pii, the PIIEntity fields, CANONICAL_LABELS / normalize_label, and how it differs from deidentify. Pairs before reidentifying-text and deidentifying-clinical-text.

Detects and extracts tabular laboratory panels from PDFs, scans, and images into structured rows ready for OpenMed and FHIR. Use when the user has a CBC, CMP, lipid panel, or other lab report as a scanned image / PDF / spreadsheet and needs the test name, value, unit, reference range, and abnormal flag as clean rows. Trigger keywords: lab table extraction, lab panel, OCR labs, table detection, layout analysis, header detection, reference range column, abnormal flag column, LOINC, UCUM, CBC, CMP, structured labs. Pairs before OpenMed: OCR/parse the table on-device (openmed.multimodal.ocr.ocr, read_table), de-identify embedded PHI with openmed.deidentify, then hand structured rows to LOINC/UCUM mapping and openmed.clinical lab flagging. Image/CSV/TSV intake is supported; PDF/DOCX raise UnsupportedDocumentError — render those to images or text first.

Reads DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and embedded report text, and flags PHI carried in header tags. Use before OpenMed processing when ingesting imaging data (CT/MR/CR/US, radiology SR) and you need the report narrative de-identified and analyzed, plus a list of header tags that must be scrubbed. Hand SR/report text to openmed.deidentify and openmed.analyze_text; use pydicom to read tags. Trigger keywords: DICOM, pydicom, DICOM-SR, structured report, PatientName, study metadata, PACS, radiology report, PS3.

Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyze_text. Use when the user wants to extract diseases, drugs, anatomy, genes, or other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence, group entities, toggle sentence detection, or save spans to JSONL; or wants the openmed analyze CLI. Pairs with loading-openmed-models and choosing-openmed-models, and runs after deidentifying-clinical-text in a privacy-first pipeline.

Convert OpenMed NER output (entities from openmed.analyze_text) into FHIR R4 resources — Condition, MedicationStatement, Observation — using OpenMed's built-in FHIR R4 export helpers in openmed.clinical.exporters. Covers the verified CodeableConcept builder (coding, codeable_concept, system_uri), deterministic fullUrl references, and OperationOutcome reporting. Use after running OpenMed NER when the user wants standards-conformant FHIR JSON, mentions FHIR, Condition/Observation/MedicationStatement, CodeableConcept, RxNorm/LOINC/ICD-10/SNOMED coding, or interoperability with an EHR. Pairs after extracting-clinical-entities; feeds assembling-fhir-bundles and validating-us-core.

Kick off and harvest a FHIR Bulk Data $export (system-, group-, or patient-level) and stream the resulting NDJSON into a batch OpenMed de-identification + NER pipeline at cohort scale. Covers the async kickoff (Prefer respond-async) -> poll Content-Location -> download NDJSON flow, the Bulk Data Access IG, _type/_since filters, and feeding DocumentReference/DiagnosticReport notes into openmed.deidentify in batch. Use when the user needs population-scale note extraction from an EHR or data warehouse to feed OpenMed, mentions bulk export, $export, NDJSON, Flat FHIR, or cohort de-identification. Pairs before the OpenMed de-id/NER pipeline.

Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or QUARANTINED, enforce direct-identifier recall floors, require zero critical leakage, fit calibration thresholds, or produce a signed gate report. Trigger on "release gate", "leakage", "is this model safe to ship", "G1a", "G3", "quarantine", "recall floor", or "calibration thresholds" in an OpenMed de-id context.

Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP CDM v5.4 clinical tables (condition_occurrence, drug_exposure, measurement) for OHDSI/ATLAS analytics. Use when the user wants to load NLP-derived facts into an OMOP database, build an OHDSI ETL from clinical notes, populate condition_occurrence or drug_exposure from text, or standardize note-derived findings to OMOP standard concepts. Covers the source-to-standard concept mapping pattern, required vs optional CDM fields, type concepts for NLP-derived rows, and the user-supplied OHDSI vocabulary (CONCEPT/CONCEPT_RELATIONSHIP). Consumes coded OpenMed analyze_text output (after SNOMED/RxNorm/LOINC linking) and produces OMOP-conformant rows.

Add a logging and telemetry guard that scrubs or blocks PHI from logs, traces, and error reports around an OpenMed deployment. Use when the user wants a Python logging.Filter that redacts protected health information before records are emitted, wants to keep PHI out of OpenTelemetry spans or error trackers, needs structured no-PHI log fields, or is worried that logs and stack traces are leaking patient data. Trigger on "scrub logs", "redact PHI from logs", "no-PHI logging", "logging filter", "telemetry redaction", "logs leaking patient data", or "OpenTelemetry redaction" in an OpenMed deployment.

Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds Ratio, run Empirical Bayes (EBGM/EB05) or Information Component shrinkage, or screen a drug for over-reported reactions. Trigger keywords: disproportionality, signal detection, PRR, ROR, EBGM, EB05, IC, BCPNN, MGPS, 2x2 table, signal of disproportionate reporting, SDR, OpenFDA, FAERS. Pairs adjacent to OpenMed: aggregate de-identified, coded cases (from reporting-adverse-events) then query the public OpenFDA /drug/event count API to build the contingency table. Reaction terms are MedDRA PTs (licensed, user-supplied).

Run OpenMed's Model Context Protocol (MCP) server so coding agents (Claude Code, Codex) and chat clients can call clinical NER, PII extraction, and de-identification as tools, on-device. Use when the user wants to add OpenMed to an agent's MCP config, expose de-id/NER as MCP tools, run an MCP server over stdio or Streamable HTTP, give Claude/Codex access to OpenMed, or containerize the MCP server. Covers the mcp extra, create_mcp_server, the 7 tools (openmed_analyze_text, openmed_extract_pii, openmed_deidentify, openmed_list_models, openmed_list_pii_languages, openmed_loaded_models, openmed_unload_model), the resources and prompts, stdio vs streamable-http transports, ServiceRuntime env config, and MCP client config snippets.

De-identify non-English clinical text on-device with OpenMed by passing lang= and locale= to deidentify(). Use when the user has Spanish, German, French, Italian, Portuguese, Dutch, Hindi, Telugu, Arabic, Japanese, or Turkish medical notes, needs locale-aware fake surrogates, must handle language-specific national IDs (DNI, NIR, Steuer-ID, codice fiscale, BSN, CPF, TCKN, Aadhaar), or asks which languages OpenMed PII supports. Covers SUPPORTED_LANGUAGES, get_pii_models_by_language, get_patterns_for_language, LANG_TO_LOCALE, and accent normalization. Pairs with OpenMed deidentifying-clinical-text and generating-synthetic-surrogates.

Remove, mask, or replace PHI/PII in clinical free text on-device with OpenMed's deidentify(). Use when the user needs to de-identify medical notes, strip patient identifiers, redact PHI before sharing or analysis, anonymize discharge summaries, or pick a de-id method (mask vs remove vs replace vs hash vs shift_dates). Covers confidence_threshold for safety, consistent+seed for stable surrogates, keep_mapping for reversible de-id, policy= profiles, and the DeidentificationResult fields. Pairs with OpenMed extract_pii (detect spans), reidentify (restore), configuring-privacy-policies, and auditing-deidentification-runs.

Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build concept sets, or augment code-based criteria with text features. Trigger keywords: phenotype, cohort definition, OHDSI, ATLAS, CIRCE, OMOP CDM, concept set, PheKB, Phenotype Library, eMERGE, computable phenotype. Pairs adjacent to OpenMed: NLP features from openmed.analyze_text augment code-based phenotypes for entities that are poorly captured by structured codes. OMOP CDM and OHDSI tools are open source; restricted vocabularies (SNOMED, CPT) are user-supplied.

Select and customize OpenMed's seven bundled privacy policy profiles for de-identification, and build custom surrogate generators. Use when the user asks which policy fits HIPAA Safe Harbor vs Expert Determination vs GDPR vs PIPEDA vs a research limited dataset vs strict no-leak, wants to pass policy= to deidentify(), needs to keep quasi-identifiers for research, or must register a custom MRN/name/address surrogate provider. Covers the profile-to-use-case map, AnonymizerConfig/Anonymizer for fine control, and register_clinical_provider / register_label_generator. Pairs with OpenMed deidentifying-clinical-text and generating-synthetic-surrogates.

Compute electronic clinical quality measures (eCQMs) over structured data using CQL/QDM logic, lifting note-derived numerator and exclusion facts from OpenMed to improve measure capture. Use when the user wants to compute an eCQM, evaluate a CMS/ECQI quality measure, improve numerator capture from clinical notes, build CQL/QDM measure logic, or close documentation gaps that structured codes miss. Covers eCQM structure (IPP/denominator/numerator/exclusions), CQL v1.5 and QDM v5.6, MADiE authoring, and mapping OpenMed entities to QDM data elements. Consumes OpenMed analyze_text facts (coded via the linking skills) to supplement structured EHR data; does not replace certified measure engines.