Machine Learning Application in Clinical Data Management

This case study outlines the implementation of Saama's Smart Data Quality (SDQ) solution, which utilizes machine learning to enhance clinical data management processes. The document describes how a leading pharmaceutical company faced challenges during the COVID-19 vaccine trial, particularly in reconciling discrepancies in their electronic data capture (EDC) system. The SDQ solution was chosen to automate these tasks, enabling the company to automatically detect discrepancies, identify their causes, and pre-generate query text while maintaining human oversight. The results demonstrated significant improvements, including a reduction of 20,000 hours in data management efforts and an acceleration of the data reconciliation process from 25.4 days to just 1.7 days. Additionally, the SDQ processed over 105 million data points and 750,000 free text sentences, contributing to enhanced data quality and patient safety. The case study emphasizes the transformative impact of AI and machine learning technologies in clinical trials.