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Clinical Operations · Diagnostics

Pathology Report Structuring Agent

Converts free-text pathology reports into structured findings for oncology pathways and tumor boards.

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The work it takes off your plate

Pathology reports are rich in clinical detail but often free-text, making it hard to feed oncology pathways, tumor boards, registries, and decision support. Manual abstraction of diagnosis, grade, stage elements, biomarkers, and margins is slow and inconsistent. Downstream teams re-read reports to find key fields, delaying treatment planning. The Pathology Report Structuring Agent converts free-text pathology reports into structured findings so oncology pathways and tumor boards can act on complete, computable data.

How it works

The agent ingests final pathology reports, extracts and normalizes key diagnostic and biomarker fields using NLP and ontology mapping, validates required elements for cancer pathways, and publishes structured outputs to EHR oncology modules, tumor board packets, and registries.

  1. 1Ingest Pathology ReportsReport text is organized and ready for structured extraction.
  2. 2Extract Structured FindingsCore oncology data elements are available as structured fields.
  3. 3Normalize and ValidateStructured data is standards-aligned and quality-checked.
  4. 4Publish for Pathways and Tumor BoardsOncology teams receive computable pathology data without manual abstraction.

  1. Step 1: Ingest Pathology Reports

    • Retrieve final and amended surgical pathology and cytology reports
    • Identify report sections (diagnosis, microscopic, synoptic, IHC/molecular)
    • Link reports to specimen, encounter, and oncology episode

    Result: Report text is organized and ready for structured extraction.

  2. Step 2: Extract Structured Findings

    • Extract primary diagnosis, histology, grade, and laterality
    • Capture margins, lymph nodes, pTNM elements, and specimen adequacy
    • Pull biomarkers and molecular results (e.g., ER/PR/HER2, EGFR, MSI)

    Result: Core oncology data elements are available as structured fields.

  3. Step 3: Normalize and Validate

    • Map terms to SNOMED, ICD-O, and CAP synoptic standards
    • Validate completeness against cancer pathway and registry requirements
    • Flag ambiguities or missing elements for pathologist review

    Result: Structured data is standards-aligned and quality-checked.

  4. Step 4: Publish for Pathways and Tumor Boards

    • Write structured findings to EHR oncology and discrete data fields
    • Assemble tumor board summaries with key pathology highlights
    • Feed cancer registry and clinical trial matching workflows

    Result: Oncology teams receive computable pathology data without manual abstraction.

Why use it

  • Pathway Readiness. Turns narrative pathology into discrete inputs for oncology protocols.
  • Faster Tumor Boards. Auto-prepares key findings for multidisciplinary review.
  • Registry Accuracy. Improves completeness of cancer registry abstraction.
  • Reduced Manual Chart Review. Saves coordinator and oncologist time.
  • Interoperable Data. Maps to standard terminologies for analytics and research.