Axtria Unveils GenAI-Powered LUCCID to Streamline Unstructured Clinical Data Extraction

By integrating LUCCID into clinical workflows, Axtria aims to enhance the data management capabilities of life sciences organizations and accelerate trial timelines.

US-based data analytics firm Axtria Inc. has announced the launch of LUCCID (LLM-based Unstructured Clinical Concept Identification), a generative AI-powered solution aimed at transforming the way clinical researchers process unstructured data from electronic health record (EHR) systems.

The LUCCID platform is designed to extract and convert clinical concepts from unstructured data sources into structured formats for use in clinical trial systems. 

According to Axtria, nearly one-third of clinical trial budgets are allocated to data abstraction, source validation, and monitoring—areas that LUCCID aims to streamline.

“LUCCID identifies and extracts clinical concepts from unstructured sources and transforms them into structured data for downstream trial systems,” said Luke Dunlap, Principal and Head of Clinical Solutions at Axtria. 

“Automating this process can dramatically reduce the time and cost involved in inputting and validating patient study data.”

Addressing Key Bottlenecks in Clinical Trials

While FHIR-based EHR-to-EDC systems are used to manage structured data, an estimated 80% of clinical trial data originates from unstructured sources. 

LUCCID addresses this challenge by automating the traditionally manual and error-prone task of unstructured data abstraction.

The solution uses large language models (LLMs) trained on clinical data and supported by dedicated biomedical reasoning modules to ensure accuracy and efficiency. 

By integrating LUCCID into clinical workflows, Axtria aims to enhance the data management capabilities of life sciences organizations and accelerate trial timelines.

Axtria plans to present LUCCID’s research and performance data at upcoming industry conferences, including ISPOR and SCDM. 

The company continues to support life sciences clients by leveraging data science and cloud software to improve therapeutic decision-making and patient outcomes.

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