Clinical data management (CDM) is an important segment of clinical research, focused on generating high-quality, accurate, reliable, and reliable data for clinical studies that can be easily analyzed by the biostatic. Test data serve as the basis for the entire drug development cycle. The challenges associated with the CDM often involve skills-based practices that demand time and work. These steps must be performed with extreme precision so as not to violate legal requirements. In clinical studies, the patient safety approach is critical. Therefore, CDM activities must be conducted in a way that does not allow for fraudulent, duplicate, redundant, and distorted results.
Well-managed data facilitates the support of therapeutic and/or prophylactic outcomes for a specific condition or disease. If research data is not organized and managed properly, this can lead to delays in regulatory approval, which can take a long time to market a promising substance.
At HCL, we provide quick solutions to identify gaps in CDM procedures, as well as technology integration for mobile devices and other similar research devices. HCL experts are working to improve the CDM portfolio to seek the highest quality data, enabling robust procedures. Appropriate application of simple predictive analysis techniques and timely confirmatory action in procedural gaps can help reduce drug development time and study budget.
This article discusses an internal pilot study as a proof of concept.