Clinical Data Management: What Are The Key Challenges And How To Navigate Them?

One of the biggest challenges clinical data management faces is the sheer amount of data that needs to be processed. With more and more patient data becoming available, it can be difficult for CDM systems to keep up. In addition, many CDM systems are not user-friendly or interactive, making it difficult for users to get the most out of them.

What challenges do clinical data management systems currently face?

Clinical Trial Complexity

The modern clinical trial design requires real-time data modelling and simulation to provide reliable information that supports faster decision making and reduces development time, costs, and late-stage research failures. Nowadays, many clinical trials are considered adaptive, meaning that they can change as the trial progresses and that incoming data is used to determine next steps. In such a scenario, if a patient does not react to a drug, it may be decided to change the drug or dosage.

Some therapeutic areas and scenarios like immuno-oncology, multi-arm trials also add new levels of complexity to clinical trials.

The future of clinical data management lies in the ability to adapt to these changes and needs. In order to be truly effective, a CDM system must be able to handle large amounts of data, be user-friendly and utilise artificial intelligence to automate tedious manual tasks.