Clinical Data Management in Pharmaceutical Research: Digital Transformation, Data Integrity, and Future Trends

Abstract

Clinical trial is intended to find answers to the research question by means of generating data for proving or disproving a hypothesis. The quality of data generated plays an important role in the outcome of the study. CDM is the process of collection, cleaning, and management of subject data in compliance with regulatory standards. The primary objective of CDM processes is to provide high-quality data by keeping the number of errors and missing data as low as possible and gather maximum data for analysis. To meet better data quality best practices are adopted to ensure that data are complete, reliable, and processed correctly. Commonly used CDM tools are ORACLE CLINICAL, CLINTRIAL, MACRO, RAVE, and e-Clinical Suite. These CDM tools ensure the audit trail and help in the management of discrepancies. These software tools are expensive and need sophisticated Information Technology infrastructure to function. As a Pharmaceutical trial is designed to answer the research question, thereby CDM process will be eventually designed to deliver an error-free, valid, and statistically sound database. In regulatory submission studies, maintaining an audit trail of data management activities is of paramount importance. According to the roles and responsibilities, multiple user IDs can be created with access limitation to data entry, medical coding, database designing, or quality check. During a regulatory audit, the auditors can verify the discrepancy management process; the changes made and can confirm that no unauthorized or false changes were made. Developments on the technological front has positively impacted the CDM process and systems, thereby leading to encouraging results on speed and quality of data being generated.