Research
De-identification for research datasets
500k clinical records de-identified at >99% precision/recall with audit log per record. IRB-compatible.
↓ 9mo backlog → 19hr runtime
We build John Snow Labs clinical NLP pipelines that de-identify, extract, and structure healthcare data at scale, turning free-text records into HIPAA-safe, analytics-ready clinical insight.
Free-text records de-identified and structured into analytics-ready data, safely.
JSL de-identification achieves >99% precision and recall on PHI across clinical note types. Every de-identified record has an audit log. The output meets HIPAA Safe Harbour de-identification standards and is suitable for research partnerships, AI training pipelines and population health analytics.
JSL NER models extract: diseases, medications, dosages, procedures, anatomical sites, clinical findings, temporal expressions - linked to SNOMED CT, RxNorm, LOINC and ICD-10. Free text becomes queryable structured data. Your analytics team can finally query what's in the notes.
ICD-10 and CPT code suggestions with confidence scores and supporting text extracts from the clinical note. Coders review and confirm - they don't code from scratch. On complex multi-code records, this typically doubles coder throughput and improves accuracy on missed secondary diagnoses.
General NLP misreads clinical language; John Snow Labs is built for medical context.
Whoever owns research or coding data: we deliver accuracy on real clinical text.
De-identification, entity extraction, and ICD/CPT coding running live.
500k clinical records de-identified at >99% precision/recall with audit log per record. IRB-compatible.
Clinical notes → ICD-10/CPT suggestions with supporting text. Coders review.
NER over clinical notes → structured disease, medication, procedure entities linked to SNOMED/RxNorm.
Clinical notes → adverse drug event extraction with causality and severity. Population-scale surveillance.
NLP over clinical notes → HEDIS/PQRS quality measure evidence extraction. Automated quality scoring.
Identify patients with conditions buried in free text not just ICD codes.
De-identified, entity-annotated clinical text for fine-tuning clinical language models.
Identify where PHI exists across your unstructured data estate for compliance and data mapping.
Systematic extraction of clinical findings from published literature + EHR data for research.
Sample 1,000 clinical notes across your note types. Benchmark JSL de-identification and NER on your actual text. Accuracy report on your sample before we propose the full pipeline.
De-identification pipeline. Clinical NER with SNOMED/RxNorm/LOINC linking. ICD/CPT coding assistance (if in scope). Audit log per record.
Spark cluster configuration for your record volume. EHR integration (HL7 FHIR, Epic, Cerner, or batch export). Backlog run at scale. Integration with your data lake.
Runbooks, pipeline maintenance docs, re-run procedures, accuracy monitoring. Optional 3-month SLA.
The John Snow Labs, pipeline, and validation stack tuned for clinical accuracy.
Live pipelines turning free-text records into coded, analytics-ready data.
Deep teams with industry context - not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
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John Snow Labs NLP processes diverse formats, including physician notes, pathology reports, discharge summaries, and clinical trial documents, reliably.
Yes, it has certified PHI de-identification models that remove personal identifiers while preserving data utility for research and analytics.
Models are fine-tuned with domain-specific context, leveraging negation detection and disambiguation algorithms to clarify shorthand effectively.
Fullestop offers fine-tuning on client-specific data, integration with custom workflows, and tailored dashboards for actionable insights.
Yes, its fast processing allows extraction of timely insights from incoming clinical text to assist immediate point-of-care decisions.
