Build patient registries without coding and populate them 10x faster than with manual workflows. Every field arrives already coded, with its source document, supporting text, and confidence score attached. Full governance with human-in-the-loop review. Runs inside your environment on OMOP CDM.
enterprise customers across health systems, pharma, payers, and government
peer-reviewed papers and patents on clinical NLP and medical LLMs
downloads of our open-source libraries and medical models
public case studies of real-world implementations
This study presents a hybrid automation framework designed to solve the chronic delays in cancer registry reporting, where manual data abstraction typically lags by up to two years. By combining healthcare-specific Small Language Models (SLMs), medical NLP, and a deterministic reasoning layer, the system automates the filtering of non-reportable cases and the extraction of complex clinical data. This approach allows the platform to adhere to thousands of pages of strict SEER and AJCC guidelines while maintaining full data provenance and security within a private cloud environment.
The results show a transformative impact on efficiency, reducing the time required for case abstraction from 120 minutes to less than 2 minutes – a 98% reduction in human effort. Beyond speed, the framework achieved regulatory-grade accuracy and high concordance with expert-validated standards. By shifting the role of oncology specialists from manual data entry to high-level verification, this architecture provides a scalable, secure blueprint for generating high-quality Real-World Evidence (RWE) in oncology.
Accuracy on real-world data, above human expert abstraction
Reduction in human effort at regulatory-grade concordance
Per case, for human review;
down from 120 minutes
On-premises, private cloud on AWS, Azure, or GCP, Databricks and Snowflake, and air-gapped environments. No external API calls, so no clinical data leaves your control.
All registry data lands in OMOP CDM v5.4, with FHIR and HL7 ingestion and an OMOP-to-FHIR converter. Exports go out as NAACCR XML or JSON, CDC and state formats, JSON, and CSV.
HIPAA and GDPR compliant, with multimodal de-identification and full provenance on every extracted fact: source document, timestamp, confidence score, and transformation lineage, which is what FDA real-world evidence guidance asks for.
A pre-built registry template runs against historical cases in days. Most custom registries reach production in 6-8 weeks. Licensing is fixed-cost, with no per-token fees.
Bring a sample of already-abstracted cases. We’ll compare the platform’s output field by field against your validated data, so you can judge it on your own results.
No. Registrars stay the approvers. The platform removes chart hunting and transcription so their time goes to complex cases, conflicting evidence, and quality validation. Every case still passes through human review before submission, and every correction is logged and feeds back into model quality.
Run a pilot on a sample of your own already-abstracted cases and compare field by field. The version diff view puts the AI extraction next to your registrar's validated abstraction, so you can see exactly where the model agrees, where it diverges, and on which fields. Dual abstraction with inter-rater tracking is available for ongoing QA.
Every value traces to a source document, a specific text span, a confidence score, and a timestamp. Registrar edits record the original value, the correction, who made it, and when. That trail satisfies CoC, state registry, and SEER audit requirements without extra documentation work.
Guideline logic lives in registry-specific agents as deterministic decision graphs, separate from the extraction models. Revisions are updated in that layer and applied consistently going forward, instead of retraining every registrar on a new edition at the same time.
Yes. Custom registries take any ontology you define in plain language against any OMOP-scoped cohort: disease-specific research cohorts, device and implant tracking, quality improvement programs, or outcomes studies. Most reach production in 6–8 weeks. Human-in-the-loop validation for custom registries is in development and will follow the same field-level review workflow.
No. The models run inside your infrastructure, on-premises or in your private cloud, including air-gapped deployments. There are no external API dependencies and no data movement.
Demos
