REVOLUTIONIZING CLINICAL TRIAL DATA QUALITY THROUGH INTELLIGENT QUERY MANAGEMENT

Syeda Khadija Tul Kubra*, Digambar Sopan Kumbhar, Divyank Rajendra Patil, Mansi Balkrishna Patil and Venkata Shivani Duggu

ABSTRACT

Clinical trials are the cornerstone of medical research, providing essential data for evaluating the safety and efficacy of new treatments. However, issues related to data quality frequently compromise the integrity of these trials, leading to inaccurate results and delayed approvals. This review examines the role of intelligent query management (IQM) systems in addressing these challenges. IQMs utilize advanced algorithms and machine learning to automate the identification and resolution of data discrepancies in real-time, significantly improving data accuracy. By proactively flagging anomalies, these systems facilitate quicker query resolutions, reducing the burden on clinical trial teams and allowing them to concentrate on critical research tasks. Additionally, the user-friendly interfaces of modern IQMs promote ease of use and encourage broader adoption among clinical staff. The integration of IQMs also strengthens regulatory compliance by providing comprehensive documentation and tracking capabilities, which are vital during audits. Through a series of case studies, this review highlights real-world applications where IQMs have successfully improved data quality and streamlined trial processes. Ultimately, the findings suggest that the implementation of intelligent query management systems not only enhances the reliability of clinical trial outcomes but also accelerates the overall research timeline. As the clinical research landscape continues to evolve, embracing IQMs will be crucial for advancing medical science and improving patient outcomes.

Keywords: Clinical Trials, Data Quality, Intelligent Query Management, Automation, Regulatory Compliance.