Developed by Fresh Gravity, the Clinical Study Build Automation toolkit leverages Natural Language Processing and Machine Learning

capabilities to effectively generate the study build components. The tool can generate the design of the eCRFs specifications and data

validation checks directly from the clinical protocol, using standard CDASH and therapeutic area libraries.

The main aim of the tool is:

To reduce database startup time (DB go-live and First Patient In) by reusing standard eCRFs, edit checks and mappings from previous studies
The effective management and use of library components
To improve the quality of the build process

Key Benefits:

Automate the process of creating eCRF from the protocol document (PDF format). Using advanced NLP techniques, it

extracts the required paragraphs from the protocol document and compares the text with the standard library maintained

within the tool to identify the eCRF and the questions to be captured in the study

• Utilize machine learning modules to predict the eCRF design and required questions. The resultant output can lead to at least

70-80% reduction in manual efforts

• Predict the edit check rules (data validation rules) to be programmed in the EDC (Electronic Data Capture) system

• Depict the SDTM (Study Data Tabulation Model) mapping for the generated eCRFs in the tool

Fresh Gravity - Clinical Study Build Automation

autor Fresh Gravity, Inc.