openmed

Structures adverse-event mentions that OpenMed extracts into FAERS / ICH E2B(R3) reportable fields — suspect drug, reaction (MedDRA PT), seriousness criteria, and outcome. Use when the user needs to build an individual case safety report (ICSR), populate a FAERS submission, map a narrative to E2B(R3) data elements, classify seriousness (death, life-threatening, hospitalization, disability, congenital anomaly), or assign reaction outcomes. Trigger keywords: adverse event, ADR, ICSR, FAERS, E2B, E2B(R3), suspect drug, seriousness, MedDRA, reaction outcome, pharmacovigilance case. Pairs after OpenMed NER: consume Pharmaceutical/Chemical and Disease entities from openmed.analyze_text. MedDRA is licensed and user-supplied — never bundled. De-identify the narrative with openmed.deidentify before any external submission.

Assign negation, temporality, and uncertainty (the ConText axes) to clinical entities extracted by OpenMed, so "denies chest pain" is not counted as chest pain and "history of MI" is not counted as an active MI. Use after NER when the user needs assertion status, negation detection, family-history / hypothetical / historical flags, or ConText/NegEx-style classification before grounding entities to FHIR or a problem list. Covers openmed.clinical.resolve_negation / resolve_temporality / resolve_uncertainty / resolve_span_context / assert_context_axes, ClinicalAssertion, and the AFFIRMED/NEGATED, RECENT/HISTORICAL/HYPOTHETICAL, CERTAIN/UNCERTAIN constants. Pairs after extracting-clinical-entities.

Run expert-determination-style quasi-identifier risk scoring (k-anonymity, l-diversity) plus OpenMed's empirical re-identification attack on a de-identified dataset, then document residual risk in a defensible memo. Use when the user needs HIPAA Expert Determination (45 CFR 164.514(b)(1)) support, asks whether a dataset is safe to release, worries about singling-out via age/ZIP/dates, or wants a statistical "very small risk" determination. Covers identifying quasi-identifiers, computing k-anonymity / l-diversity, running openmed.eval.attacks.reid (run_reid_attack / run_reid_benchmark) as the adversarial attack, and writing the risk memo. Pairs after deidentifying-clinical-text and auditing-deid-leakage.

Run OpenMed models fully on-device with the MLX (Apple Silicon), CoreML (iOS/macOS), or ONNX/WebGPU (cross-platform/browser) backends, including convert-quantize-run workflows. Use when the user wants to deploy OpenMed at the edge, run NER/de-id on Apple Silicon, target iPhone/iPad/Mac, export to ONNX or WebGPU, quantize a clinical model to int8/4-bit, run with no network, or pick between MLX/CoreML/ONNX. Covers the mlx/coreml/onnx extras, the convert() functions and python -m convert CLIs, quantization, loading a local MLX artifact through analyze_text, OpenMedMLXLanguageModel/generate_text, and the on-device-only PHI guarantee (nothing leaves the host).

Extract arbitrary, custom entity types from clinical or biomedical text with no fine-tuning using OpenMed's GLiNER / GLiNER2 zero-shot support. Use when the user wants to define their own labels on the fly (e.g. Drug, Symptom, Device, Procedure), has no labelled data or a label set not covered by a fine-tuned model, or asks about openmed zero deps/index/infer, the gliner extra, or GLiNER. Pairs adjacent to extracting-clinical-entities (use that for high-accuracy fixed-schema NER) and loading-openmed-models.

Scaffold a SMART-on-FHIR app (SMART App Launch v2 — EHR launch and standalone launch, OAuth2 PKCE, scopes, token handling, fhirContext) so an OpenMed-powered tool can run inside Epic or Cerner/Oracle Health. Covers the .well-known/smart-configuration discovery, authorize/token sequence, scopes like patient/DocumentReference.rs and launch/patient, and fetching clinical notes the app then de-identifies and runs NER on locally with OpenMed. Use when the user wants to embed OpenMed inside an EHR, mentions SMART on FHIR, OAuth2 launch, scopes, Epic/Cerner app, or clinician-facing FHIR app. Pairs adjacent.

Searches ClinicalTrials.gov for studies by condition, intervention, and recruitment status using the modern v2 REST API with cursor (pageToken) pagination. Use when the user wants to find trials for a diagnosis or drug, screen patients against open studies, build a trial-matching feature, or pull a trial corpus for analysis. Trigger keywords: clinical trial, ClinicalTrials.gov, NCT number, trial search, recruiting studies, eligibility, query.cond, query.intr, pageToken, v2 API. Pairs adjacent to OpenMed: take Disease/Pharmaceutical entities from openmed.analyze_text and turn them into query.cond / query.intr filters; the returned eligibility text feeds parsing-trial-eligibility. ClinicalTrials.gov API v2 is fully public — no API key, no license.

Split a clinical note into canonical sections (Chief Complaint, HPI, PMH, Medications, Allergies, Assessment & Plan, etc.) before running OpenMed NER or de-identification, so section context sharpens downstream precision. Use when the user has a free-text note or discharge summary and wants section-aware processing, header detection, mapping headers to LOINC document-section codes, or per-section NER/de-id. Covers heuristic header detection, normalization to canonical section labels, LOINC/SecTag framing, and why a finding in PMH is historical while the same finding in A&P is active. Hand-off: feed each sectioned chunk into openmed.analyze_text / openmed.deidentify. Pairs before extracting-clinical-entities.

Stand up OpenMed's FastAPI REST service for clinical NER, PII extraction, and de-identification, with health checks, model keep-alive/unload, optional dynamic batching, and no-PHI logging. Use when the user wants to serve OpenMed over HTTP, deploy a de-id/NER REST API, run an inference endpoint for clinical text, add a /analyze or /pii/deidentify route, or containerize OpenMed as a service. Covers the service extra, launching create_app with uvicorn, the real endpoints (/health, /analyze, /pii/extract, /pii/deidentify, /models/loaded, /models/unload), request/response shapes, ServiceRuntime env-var configuration, and self-hosted auth/CORS/TLS notes.

Collect a bounded set of de-identification policy decisions and write a deterministic, reviewable DEID-POLICY.md from the versioned local template. Use when a project needs explicit jurisdiction, recall floor, surrogate strategy, model policy, audit location, and human approval before privacy work begins.

Apply consistent per-patient date shifting in OpenMed that preserves intervals between events while satisfying HIPAA Safe Harbor's date rule. Use when the user needs to de-identify dates but keep temporal structure for research, shift all dates by the same offset per patient, preserve days-between-events for survival or longitudinal analysis, cap ages over 89, or strip everything but the year. Covers deidentify(method="shift_dates", date_shift_days=..., keep_year=...) and per-patient reproducible offsets via consistent=True, seed=.... Pairs with OpenMed deidentifying-clinical-text and auditing-safe-harbor-checklist.

Converts free-text radiology narratives into structured findings and impression — with measurements, laterality, anatomy, and follow-up recommendations — after OpenMed NER. Use when the user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique, comparison, findings, impression), lesion measurements and laterality captured, BI-RADS / Lung-RADS assessment categories pulled, or incidental findings and recommended follow-up tracked. Trigger keywords: radiology report, findings, impression, RadLex, DICOM-SR, BI-RADS, Lung-RADS, ACR, laterality, measurement, nodule, incidental finding, follow-up, structured reporting. Pairs after OpenMed: run openmed.analyze_text on the report (Anatomy/Disease/measurement entities), then assemble structured findings. De-identify the report first. Decision-support only — not a diagnostic medical device.

Produces structured, citation-anchored summaries of clinical notes — one-liner, hospital course, and problem-oriented views — where every claim cites a source span so nothing is hallucinated. Use after de-identifying notes when the user wants a discharge summary draft, handoff/SBAR, problem list, or chart-abstraction summary. De-identify FIRST with openmed.deidentify, then anchor summary claims to entity spans from openmed.analyze_text. Trigger keywords: summarize note, discharge summary, hospital course, problem-oriented, one-liner, SOAP, SBAR, handoff, chart abstraction.

Validate FHIR R4 resources and Bundles against US Core / USCDI profiles with the official HL7 FHIR validator before submitting to an EHR. Covers running validator_cli.jar (or the public validator.fhir.org), declaring meta.profile, must-support elements, common conformance gaps (missing code/category/status), and turning validator output into a FHIR OperationOutcome. Use after exporting-to-fhir / assembling-fhir-bundles to check OpenMed-produced FHIR for US Core conformance, when the user mentions US Core, USCDI, must-support, profile validation, or Epic/Cerner ingestion requirements. Pairs after.