Pathology report intelligence: 90% faster abstraction at a leading Pharmaceutical Company

Pathology reports are central to clinical decisions, yet they rarely arrive in a form that is easy to use. At one leading pharmaceutical company, reports came in from hospitals in dozens of different formats, with no consistent structure. Clinicians had little choice but to spend a lot of time reading each one in full to find and interpret the relevant findings.

Reading each report took time that clinicians could instead spend on higher-value work. The organization saw this as a chance to modernize how critical findings were interpreted by building even greater consistency into a process where consistency matters most. With speed and reliability as the goal, the case for a modernized approach was clear.

Working together, we built and deployed into production an AI system that automatically extracts, normalizes, and relates clinical entities from unstructured pathology reports. The system reached full deployment within four months, reducing the time needed to extract and structure clinical findings by more than 90 percent.

The challenge: Navigating unstructured clinical data

Pathology reports are among the most information-dense documents in clinical workflows. They carry biomarker results, measurement values, diagnostic conclusions and test references often structured differently across health institutions and individual pathologists. 

For a leading pharmaceutical company operating at scale, this heterogeneity created tangible operational friction. Extracting structured information required reading each document from end-to-end, and the findings were subject to interpretation.

This slowed down the downstream analysis. It created a bottleneck at precisely the point where clinical decisions depend on accurate and fast access to information.

Why existing approaches failed to scale

Rule-based extraction alone couldn’t account for the breadth of linguistic variation across pathology reports. Keyword matching missed context-dependent entities and failed on ambiguous or abbreviated terminology. The existing tooling system was slow and offered limited ability for clinicians to efficiently verify extracted data against the original report. 

How we helped: Automated pathology report intelligence

We designed and deployed an end-to-end pathology report intelligence system that automatically extracts clinical entities, normalizes their values, identifies relationships between them, and presents the results through a structured visual interface. 

The system handles the full document lifecycle: ingestion of pathology reports in varying formats, text extraction via OCR, entity recognition, relation extraction, value normalization, and delivery of structured output through an abstraction API consumed by the client's interface layer.

Key capabilities

Entity extraction and normalization

The system identifies clinical entities including biomarkers, diagnostic results, measurement values and associated test references. Extracted values get normalized to a consistent standard regardless of the source format, enabling reliable downstream analysis and consistent UI rendering.

Relation extraction

The system maps relationships between entities, including pairwise and multi-entity relations. This allows the structured output to reflect how the findings relate to one another within the clinical context of the report. 

Visual abstraction interface

Extracted entities are surfaced in a color-coded overlay in the source PDF and text view, allowing clinicians to verify findings directly against the original document. A dedicated “compare” view enables side-by-side review of abstraction outputs and the architecture is designed to support future model comparison workflows. 

API-first architecture

The abstraction layer is exposed as an API decoupling the extraction logic from the interface and enabling the client’s team to surface results across multiple downstream systems.

Impact: Faster abstraction, more time for clinicians 

The abstraction service delivers results through a single API call in approximately 10 seconds. This represents a reduction of over 90% in retrieval time versus the baseline task. Where clinicians once waited nearly three minutes before they could begin reviewing findings, results are now available in seconds. Faster abstraction returns time to clinicians and lets them focus on interpretation and decision-making rather than waiting on tooling.

The system reached full production deployment within four months of project initiation. The architecture’s design supports faster exposure of model changes and in future iterations direct comparison between model versions within the “compare” interface.