AI-powered Cancer Registry Case Automation

Case Automation

Automate Casefinding and Abstraction - Faster, Smarter, and Aligned with Your Team

- Streamline your registry workflows
- Clear your backlog
- Pre-populate abstracts
- Integrates directly with EMRs

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Step 1: Ingestion

We pull in pathology reports, CCDs, HL7 messages, and other relevant documentation directly from your EMR

How This Benefits You
-> Seamlessly consolidates multiple file types (EMR extracts, PDFs, HL7, CSV) into a single pipeline-> Reduces registrar time spent on manual data collection and formatting-> Minimizes errors by standardizing inputs before review-> HIPAA-compliant infrastructure ensures data security from the start

Step 2: Case Triage

A machine learning model triages cases by flagging those that are reportable, saving your team time upfront.

How This Benefits You
-> Automatically flags reportable vs. non-reportable cases, reducing wasted registrar review time-> Prioritizes high-value cases (e.g., likely reportable cancers) so registrars focus where it matters most-> Accelerates backlog clearance by filtering out noise-> Creates an auditable trail for compliance and validation

Step 3: Abstract Pre-Fill

An LLM generates a pre-populated draft for each abstract, including key elements like primary site, histology, behavior, and reportability rationale. ODS-Cs can review, adjust, and finalize the abstract within our secure interface.

How This Benefits You
-> Provides ODS-Cs with pre-populated fields (diagnosis, histology, staging) to cut abstraction time in half-> Reduces risk of missed or miscoded fields by cross-referencing structured data with rules-> Ensures faster, more accurate reporting to CoC / SEER / NPCR-> Improves ODS-C satisfaction by turning tedious data entry into quick validation