Data Integrity and Quality in Clinical Trials

Abstract

Maintaining high standards of data integrity and quality is paramount for generating reliable and accurate results in clinical trials. This paper examines the critical aspects of data integrity and quality management, emphasizing the role of robust data management practices in ensuring credible trial outcomes. We explore key topics, including regulatory standards for data quality, data governance frameworks, and practical quality control measures. Furthermore, we discuss the potential of advanced technologies like artificial intelligence to enhance data quality and streamline data management processes. The paper also addresses the challenges in maintaining data integrity in clinical trials and proposes strategies for overcoming these obstacles. Finally, we examine future trends in clinical data quality management, highlighting the evolving landscape of data integrity in clinical research