Enhancing Data Integrity through Data Governance in Clinical Trials: A Computational Approach

Abstract

This paper presents a novel computational approach to enhance data integrity in the context of clinical trials through effective data governance strategies. Clinical trials are pivotal in the development of new medical treatments and therapies, and ensuring the accuracy and reliability of the data collected is of utmost importance. In this study, we propose a framework that leverages advanced informatics and computer science techniques, including AI/ML algorithms, to implement data governance practices that minimize errors and discrepancies in clinical trial data. Additionally, we explore the concept of Data Integrity as a Code (DIaC) and its application in this context. Through the integration of DIaC principles, our approach not only detects data anomalies but also proactively prevents them, ultimately leading to more trustworthy and robust clinical trial outcomes.