In the realm of clinical trials, clinical data management takes center stage, ensuring the smooth flow of information. Explore the world of clinical trial data management systems (CDMS) and uncover how they shape the future of clinical trials. Join us in discussing the challenges faced, from wrangling complex data to maintaining data integrity, all while envisioning a future where CDMS revolutionizes clinical trials
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 modeling 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 the 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, and 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 utilize artificial intelligence to automate tedious manual tasks.
Mid Study Changes
Clinical Data Management is a complex process. It involves multiple stakeholders, from investigators to sponsors and CROs. This can make CDM challenging, especially when it comes to the mid-study changes (MSCs).
Mid-study changes are amendments to protocols or study data management plans (SDMPs).
Mid-study changes can be due to any or all of these reasons:
- Change in inclusion/exclusion criteria
- Change in dosage/frequency of drug administration
- Exclusion/inclusion of new patient subpopulations
- Inclusion/exclusion of new therapeutic agents/devices
- Change in primary outcome measure (PRO) or secondary outcome measures (SO).
A study by Tufts says that approximately 70% of its respondents say unplanned mid-study changes are the primary reason for the trial delay. Planned changes can be even more challenging as they require extensive planning before going live so that they don’t interfere with ongoing trials or other projects.
The changes needed during the study are a major challenge for CDM. Planned and unplanned mid-study changes are significant reasons for the trial delays. So a system that supports faster mid-study changes and which is very easy to use and faster to go live is the need of the hour.
The CDM system should be able to handle all the required changes in a single place instead of going through multiple systems to make changes
Is The Role of Clinical Data Managers Changing?
Clinical data management has come a long way in the last few decades. What once started out as a small department within a clinical research organization has now become a critical and highly specialized function. In the past, clinical data managers were responsible for data entry and cleaning.
In the late 90s, the role of the CDM began to change as electronic data capture (EDC) became more prevalent. The CDM was responsible for configuring the EDC system and creating and managing data queries.
Today, clinical data managers are responsible for developing and implementing data management plans, ensuring data accuracy and completeness, and ensuring data security.
