Clinical Data Management (CDM) and Why it Matters

Clinical trials are a long and meticulous process. The arduous process naturally involves a heap of accumulated data that are as important as they are numerous. In recent years, Clinical Data Management (or CDM) has emerged as an increasingly prioritized phase of clinical trials. In our latest Q&A blog, Aparna Priyadarshi, Associate Principal here at TheMathCompany explains exactly what CDM is and why it matters for clinical research today.


1. For the uninitiated, could you explain what clinical data management (CDM) means and why it is important?
Clinical Data Management (CDM) could be explained as the stringent set of processes and protocols followed to ensure that data acquired from external sources such as third-party data organizations, healthcare providers, laboratories etc. is available for focused clinical research. CDM is concerned with how data is collected, cleaned, stratified, standardized within a very stringent regulated environment. It provides high quality and statistically sound data both for and from clinical trials, resulting in significant reduction of the lengthy process between drug development and approval for marketing while simultaneously ensuring data security and governance.


2. Could you give some examples as to how CDM has been helping clinical trials?
Clinical research requires high-quality data that is reliable, ethically collected, and securely managed. CDM processes are crucial for clinical trials as they minimize errors, maximize data accuracy and provide stratified and standardized clinical data, supported through semantic and secured data exchange. It also enables quality audits and assessments at regular intervals of the Clinical Trials through Case Report Form (CRF) designing and annotation, database design, data entry, validation, discrepancy management, medical coding, and data extraction.


3. Apart from existing regulations and quality assurance standards, what other practices are key to proper CDM?
Clinical Trials are highly regulated by FDA 21CFR part 11, Good Clinical Practices (GCP) and WHO recommended Global Competency Frameworks (GCF). Considering that this is a highly regulated environment, checks and balances are required at each step across Data Collection, Data Integration, Data Validation, and dissemination of the results. CDM ensures that processes and protocols are compliant to periodic changes to the compliance and regulations in clinical data management. Intrinsic design, processes and protocols such as access control, logging, audit trails, and constant quality checks has to be applied for the external data acquired, database design and management.


4. How did the COVID-19 pandemic impact the way clinical data management is done?
The Covid-19 pandemic brought about changes in clinical trials across the world. People self-isolating and in quarantine, combined with the need to ensure the safety and security of on-site staff meant increased inaccessibility of participants and trial personnel. Disruptions in the supply chain processes also affected material availability and disrupted timelines. Finally, protocols and practices for ethical and accurate outreach, information dissemination and intervention had to be updated to support the pandemic scenario and the post-pandemic world today.


5. What would you say are some of the biggest challenges in clinical data management today?
CDM is core to any clinical research as any inaccuracies in the data lead to clinical trials having to repeat certain processes, completely shutting down, or remarkably excessive costs in both monetary value and time-to-market a possible treatment to the people. Clinical trials are complex, and their progress is based on results at different stages of the study. Hence, mid-study changes such as changes in cohort, medication potency or consumption timeline, lead to setbacks for a study in progress as it will require a change in protocols with the new data sets. Also, there are many patients who are dependent on clinical trials and managing these patients when the results are not positive, leads to having to think of new and ethical ways to get new patients enrolled into other trials.


6. Do you think AI and Analytics hold the key to helping ease the burden of an ever-increasing volume of data in CDM?
Clinical research has always been driven by statistical analysis and research. Clinical data is evidence-based, and analytics has a very big potential to be able to provide a longitudinal view of impact of therapies / treatment, impact of environment to rate of recovery, to site a few. Much of how AI and analytics can help CDM will depend on new innovations and how they are applied by the right people.


To learn more on how AI and Analytics are shaping industries, follow us on our social channels to never miss a post.