Clinical data management (CDM) is an important step in clinical research, generating high-quality, reliable, and statistically sound data from clinical trials. This helps to dramatically reduce the time from drug development to marketing. CDM’s team members are actively involved in all phases of a clinical trial from inception to completion. They must have adequate process knowledge to help maintain the quality standards of the CDM process.
Commonly used CDM tools are ORACLE CLINICAL, CLINTRIAL, MACRO, RAVE, and eClinical Suite. In terms of functionality, these software tools are somewhat similar, and there is no significant advantage for one system over the other.
Clinical data management systems are widely used software for managing data generated in clinical trials. The purpose of this study is to review the technical characteristics of clinical trial data management systems.
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 this objective, best practices are adopted to ensure that data are complete, reliable, and processed correctly.
Many of the software tools available for data management are called Clinical Data Management Systems (CDMS). In multi-center trials, CDMS has become essential for handling vast amounts of data. Most CDMSs used by pharmaceutical companies are commercial, but a few open source tools are also available.
Clinical Data Interchange Standards Consortium (CDISC), a multidisciplinary non-profit organization, has developed standards to support acquisition, exchange, submission, and archival of clinical research data and metadata. Metadata is the data of the data entered. This includes data about the individual who made the entry or a change in the clinical data, the date and time of entry/change and details of the changes that have been made.