Advanced Natural Language Processing (NLP) enabled the transformation of unstructured EHRs into structured, research-ready data.

4,482 patient records were enriched using NLP, complementing the 655 structured entries and significantly expanding the dataset.

364 variables analyzed per patient, including diagnosis, treatments, staging, comorbidities, and genetic alterations.

Federated OMOP-CDM model ensured interoperability across four hospitals in Spain.

Thyroid cancer is the most prevalent endocrine malignancy, representing 3.1% of global cancer cases. In Spain, national data is limited due to the lack of population-based registries, creating barriers for research and public health planning.

The NATI study (NATural language in ThyroId cancer), aimed to provide an updated, real-world view of how thyroid cancer is diagnosed and managed in clinical practice by analyzing patient data from 4 Spanish hospitals.

Obtaining a comprehensive picture of thyroid cancer in Spain required access to large volumes of clinical information, much of which is buried in unstructured formats within EHRs. Manual data extraction is time-consuming and resource-intensive, making it unfeasible at scale.

The NATI study aimed to overcome this challenge by using AI and NLP technologies to automate data capture and generate a more complete dataset to support thyroid cancer research.

IOMED implemented its AI-powered Data Space Platform to support individual patient characterization through the automated extraction of real-world data from electronic health records (EHRs). Conducted across four hospitals in Spain, IOMED leveraged Natural Language Processing (NLP) to transform unstructured clinical narratives into high-quality, research-ready data.

This approach significantly enhanced data completeness and depth, revealing genetic alterations and demonstrating the scalability of NLP for large-scale oncology research. By unlocking valuable clinical content hidden in free-text medical records, IOMED’s technology played a key role in enabling a more accurate and granular view of thyroid cancer care across participating institutions.